Influenza A viruses dual and multiple infections with other ...

Influenza A viruses dual and multiple infections

with other respiratory viruses and risk of

hospitalization and mortality

A thesis submitted to The University of Manchester

for the degree of Doctor of Philosophy in Medicine

in the Faculty of Medical and Human Sciences

2014

Edward Anthony Chilongo GOKA

School of Medicine

1

Table of contents

List of tables ................................................................................................................................................ 7

List of figures ............................................................................................................................................. 11

List of appendices ..................................................................................................................................... 14

List of Abbreviations ................................................................................................................................. 15

Abstract ..................................................................................................................................................... 20

Declaration ................................................................................................................................................ 21

Copyright statement ................................................................................................................................. 22

Acknowledgements .................................................................................................................................. 23

Dedication ................................................................................................................................................. 24

Rationale for submitting the thesis in alternative format ........................................................................ 25

Outline of the thesis ................................................................................................................................. 26

Part I: Introduction ................................................................................................................................... 28

1.1. Introduction .............................................................................................................................. 29

1.1.1. Epidemiology and significance of influenza and other respiratory viruses ...................... 29

1.1.2. Factors associated with severity of respiratory virus infections ...................................... 29

1.1.3. Hypothesis ........................................................................................................................ 34

1.1.4. Objectives ......................................................................................................................... 35

1.1.5. Research questions and study aims .................................................................................. 35

1.1.6. Significance of the study ................................................................................................... 35

1.1.7. Methodology..................................................................................................................... 37

1.1.7.1. Study design and setting ................................................................................................... 37

1.1.7.2. Analysis of electronic data ................................................................................................ 37

1.1.7.3. Variables and information extracted from the MMPL database ...................................... 38

1.1.7.4. Inclusion and exclusion criteria ........................................................................................ 38

1.1.7.5. Respiratory viruses detection assays ................................................................................ 38

1.1.7.6. Testing for coronaviruses and human bocavirus .............................................................. 40

1.1.7.7. Primers, templates and probes for hCoV, hBoV ............................................................... 41

1.1.7.8. Determination of analytical sensitivity and reproducibility of RT-PCR protocols ............ 41

1.1.7.9. PCR for identification of coronaviruses and bocavirus in samples ................................... 42

1.1.7.10. Strategies in systematic reviews ....................................................................................... 42

1.1.7.11. Statistical analysis ............................................................................................................. 43

1.1.7.12. Ethical considerations ....................................................................................................... 45

Part II: The nature of respiratory virus infections..................................................................................... 46

2.1. The nature of respiratory virus disease Part A: Clinical characteristics of respiratory virus

infections, vaccines and treatments ..................................................................................................... 47

2.1.1. Reproductive number and mode of transmission ............................................................ 47

2.1.2. Signs and symptoms ......................................................................................................... 47

2

2.1.3. Available vaccines and research on vaccines for other respiratory viruses ..................... 48

2.1.4. Available treatments and research on treatments for other respiratory viruses ............ 49

2.2. The nature of respiratory virus disease Part B: Incidence of viral respiratory infections ....... 51

2.2.1. Incidence of respiratory virus infections globally ............................................................. 51

2.2.1.1. Incidence as reported by community based studies ........................................................ 51

2.2.1.2. Incidence as reported by hospital based studies .............................................................. 52

2.2.1.3. Prevalence of different respiratory virus infections ......................................................... 53

2.2.2. Rates of severe disease and incidence of hospitalization and mortality associated with

respiratory virus infections ............................................................................................................... 58

2.2.2.1. The rates of severe respiratory disease ............................................................................ 58

2.2.2.2. The incidence of hospitalization and mortality ................................................................ 58

2.2.2.3. The burden of hospitalization and mortality .................................................................... 59

2.2.2.4. Waves and relative virulence of influenza virus pandemics and epidemics .................... 59

2.2.3. Risk pyramid for respiratory virus infections .................................................................... 61

2.2.4. Seasonality of viral respiratory infections ........................................................................ 67

2.2.5. Social-economic factors and incidence of viral respiratory infections ............................. 70

2.3. The nature of respiratory virus disease Part C: The incidence of acute respiratory virus

infections and influenza A viruses associated hospitalizations in North West England, 2007 – 2012 . 74

2.3.1. Abstract ..................................................................................................................................... 75

2.3.2. Introduction .............................................................................................................................. 76

2.3.3. Methodology ............................................................................................................................ 77

2.3.3.1. Setting, data source and laboratory methods .................................................................. 77

2.3.3.2. Calculation of incidence and other statistical analyses .................................................... 77

2.3.3.3. Ethical and research and development approval ............................................................. 78

2.3.4. Results ....................................................................................................................................... 78

2.3.4.1. Profile of patients with ARIs and the source population .................................................. 78

2.3.4.2. Incidence of hospitalizations associated with ARI ............................................................ 80

2.3.4.3. Age-specific average annual incidence of hospitalization associated with the pandemic

and seasonal influenza A viruses .......................................................................................................... 83

2.3.5. Discussion and conclusion ........................................................................................................ 85

2.3.6. Supplementary material ........................................................................................................... 88

2.4. The nature of respiratory virus disease Part D: Virology of respiratory virus infections ......... 89

2.4.1. Virology of influenza viruses ..................................................................................................... 90

2.4.1.1. Host range of influenza viruses ......................................................................................... 91

2.4.2. Virology of respiratory syncytial virus (RSV), human metapneumovirus (hMPV) and human

parainfluenza virus (hPIV) ..................................................................................................................... 93

2.4.3. Virology of coronaviruses (CoV) ............................................................................................... 95

2.4.3.1. Host range of coronaviruses ............................................................................................. 96

2.4.4. Virology of the rhinoviruses ...................................................................................................... 99

3

2.4.5. Virology of adenovirus and human bocavirus (AdV and hBoV) .............................................. 100

2.5. The nature of respiratory virus infections Part E: Genetic mutations associated with

pathogenicity of pandemic influenza A(H1N1)pdm09 virus: A review .............................................. 103

2.5.1. Abstract ........................................................................................................................... 104

2.5.2. Introduction .................................................................................................................... 105

2.5.2.1. The virulence genes of influenza A viruses ..................................................................... 105

2.5.2.2. The HA genes’ virulence related functional sites ........................................................... 105

2.5.2.3. The PB2 genes’ virulence related functional sites .......................................................... 106

2.5.2.4. The NS1 genes’ virulence related functional sites .......................................................... 107

2.5.2.5. Rationale for conducting the review .............................................................................. 107

2.5.2.6. Aims and objectives of the review .................................................................................. 108

2.5.3. Methodology................................................................................................................... 109

2.5.3.1. Protocol for the review ................................................................................................... 109

2.5.3.2. Search strategy ............................................................................................................... 109

2.5.3.3. Study assessment tool and study selection criteria........................................................ 110

2.5.3.4. Assessment of bias in the studies ................................................................................... 110

2.5.3.5. Data extraction and statistical analysis........................................................................... 111

2.5.4. Results ............................................................................................................................. 111

2.5.4.1. Characteristics of the included studies ........................................................................... 111

2.5.4.2. Possible sources of bias in the included studies ............................................................. 112

2.5.4.3. Association between mutations in influenza A(H1N1)pdm09 and severe disease ........ 116

2.5.4.3.1. HA-D222G mutation and severity ................................................................................... 116

2.5.4.3.2. Mutation HA-D222E and D222N and disease outcome.................................................. 116

2.5.4.4. Association between PB2-E627K mutation and severe disease ..................................... 120

2.5.4.5. NS1-T123V and other mutations and disease severity ................................................... 121

2.5.5. Discussion and conclusion .............................................................................................. 123

2.5.6. Supplementary material ................................................................................................. 127

2.5.6.1. The cause of influenza pandemics .................................................................................. 136

Part III: Review of available literature on patterns of co-infections and association between co-

infections and disease severity ............................................................................................................... 139

3.1. Co-infections; patterns and severity Part A: The patterns of co-infection between influenza A

and other respiratory viruses and its effect on viral load and interferon production: A systematic

review and meta-analysis ................................................................................................................... 139

3.1.1. Abstract ........................................................................................................................... 140

3.1.2. Introduction .................................................................................................................... 142

3.1.2.1. Rationale for conducting this review .............................................................................. 143

3.1.2.2. Aims and objectives of the systematic review and meta-analysis ................................. 143

3.1.3. Methodology................................................................................................................... 144

3.1.3.1. Review protocol .............................................................................................................. 144

4

3.1.3.2. Search strategy ............................................................................................................... 144

3.1.3.3. Assessment of study quality and selection process ....................................................... 145

3.1.3.4. Exclusion and inclusion criteria ....................................................................................... 145

3.1.3.5. Assessment of bias .......................................................................................................... 146

3.1.3.6. Data extraction and statistical analysis........................................................................... 146

3.1.4. Results ............................................................................................................................. 147

3.1.4.1. Characteristics of the included studies ........................................................................... 147

3.1.4.2. Co-infection patterns between influenza A and other respiratory viruses .................... 150

3.1.4.3. Association between co-infection with viral load and interferon production ............... 150



3.2. Co-infections patterns and severity Part B: Single and multiple respiratory virus infections

and severity of respiratory disease: A systematic review and meta-analysis .................................... 162

3.2.1. Abstract ................................................................................................................................... 163

3.2.2. Introduction ............................................................................................................................ 164

3.2.2.1. Rationale for conducting the review .............................................................................. 164

3.2.2.2. Objectives and aims of the review and meta-analysis ................................................... 165

3.2.3. Methodology .......................................................................................................................... 165

3.2.3.1. Review protocol .............................................................................................................. 165

3.2.3.2. Literature search ............................................................................................................. 166

3.2.3.3. Study quality assessment and selection criteria ............................................................. 167

3.2.3.4. Assessment of bias .......................................................................................................... 167

3.2.3.5. Data extraction from the studies .................................................................................... 169

3.2.3.6. Statistical analysis ........................................................................................................... 169

3.2.4. Results ..................................................................................................................................... 170

3.2.4.1. Characteristics of the studies included in this review .................................................... 170

3.2.4.2. Factors associated with positivity and co-infection rates............................................... 170

3.2.4.3. Assessment of bias in the studies ................................................................................... 173

3.2.4.3.1. Study design, year of study and viral genetics ................................................................ 173

3.2.4.3.2. Publication bias ............................................................................................................... 173

3.2.4.4. Co-infection and risk of hospitalization to a general ward ............................................. 174

3.2.4.5. Co-infection and risk of admission to intensive care unit (ICU) ..................................... 176

3.2.4.6. Co-infection and risk of bronchiolitis .............................................................................. 178

3.2.4.7. Co-infection and risk of pneumonia ............................................................................... 178

3.2.4.8. Influenza A virus single and mixed infections and disease severity ............................... 181

3.2.5. Discussion and conclusion ...................................................................................................... 183

3.2.6. Supplementary material ......................................................................................................... 187

Part IV: Co-infections and risk of hospitalization and mortality in NW England 2007 – 2012 ............... 192

5

4.1. Co-infections and risk of hospitalization and mortality Part A: Single, dual and multiple

respiratory virus infections and risk of hospitalization and mortality ................................................ 192

4.1.1. Abstract ........................................................................................................................... 194

4.1.2. Introduction .................................................................................................................... 195

4.1.3. Methodology................................................................................................................... 196

4.1.3.1. Study design and setting ................................................................................................. 196

4.1.3.2. Virus identification, inclusion and exclusion criteria ...................................................... 196


4.1.3.4. Ethical and research and development approval ........................................................... 197

4.1.4. Results ............................................................................................................................. 197

4.1.4.1. Respiratory virus infections ............................................................................................ 197

4.1.4.2. Demographic and other factors associated with single or multiple infections .............. 198

4.1.4.3. Single and multiple infections and risk of hospitalization to a general ward, or admission

to an intensive care unit (ICU) and death ....................................................................................... 198

4.1.5. Discussion and Conclusion .............................................................................................. 200


4.2. Co-infections and risk of hospitalization and mortality Part B: Influenza A viruses dual and

multiple infections with other respiratory viruses and risk of hospitalization and mortality ............ 213

4.2.1. Abstract ........................................................................................................................... 214

4.2.2. Introduction .................................................................................................................... 215

4.2.3. Methodology................................................................................................................... 215

4.2.3.1. Study Design and setting ................................................................................................ 215


4.2.4. Results ............................................................................................................................. 217

4.2.4.1. Identification of influenza and respiratory viral infections Jan 2007 – June 2011 ......... 217

4.2.4.2. Characteristics of included and excluded patients ......................................................... 218

4.2.4.3. Respiratory viruses’ positivity rates and subjects demographics ................................... 218

4.2.4.4. Respiratory viral infections yield by type of sample ....................................................... 222

4.2.4.5. Seasonal distribution of respiratory viral infections ....................................................... 222

4.2.4.6. Flu A(H1N1)pdm09 and seasonal Flu A, pattern of co-infections and patients

demographics ................................................................................................................................. 222

4.2.4.7. Risk of hospitalization, admission to ICU and death associated with influenza

A(H1N1)pdm09 co-infections ......................................................................................................... 225

4.2.4.8. Risk of hospitalization, admission to ICU and death associated with seasonal influenza A

virus co-infection ............................................................................................................................ 226


4.2.6. Supplementary tables ........................................................................................................... 231

4.3. Co-infections and risk of hospitalization and mortality Part C: Influenza A viruses co-infection

with human coronavirus and bocavirus and risk of hospitalization: Use of SYBR Green and TaqMan

RT-PCR assays for virus identification ................................................................................................. 233

6

4.3.1. Abstract ........................................................................................................................... 235

4.3.2. Introduction .................................................................................................................... 236

4.3.3. Methodology................................................................................................................... 237

4.3.3.1. Clinical samples and setting ............................................................................................ 237

4.3.3.2. Primers, templates and probes for hCoV, hBoV ............................................................. 237

4.3.3.3. Determination of analytical sensitivity and reproducibility of RT-PCR protocols .......... 239

4.3.3.4. PCR for identification of coronaviruses and bocavirus in samples ................................. 240

4.3.3.5. Confirmation of positive samples using MGB probes .................................................... 241

4.3.3.6. Polyacrylamide gel (PAGE) electrophoresis .................................................................... 241


4.3.3.8. Ethics ............................................................................................................................... 242

4.3.4. Results ............................................................................................................................. 242

4.3.4.1. Efficiency, analytical sensitivity and reproducibility of the hCoV and hBoV PCRs.......... 242

4.3.4.2. Respiratory virus infections in samples that were positive for influenza A viruses ....... 244

4.3.4.3. Demographic characteristics, seasonality of infections and disease outcome .............. 245



4.3.6.1. Optimization of primers and annealing temperature .................................................... 249

4.3.6.2. Standard curve experiments for hCoV using RNA from 229E and pEX-A vector ............ 250

4.3.6.3. RNA extraction from the live hCoV 229E virus ............................................................... 250

4.3.6.4. RNA Synthesis and associated protocols ........................................................................ 252

4.3.6.5. Purification of PCR Products ........................................................................................... 254

Part V: Discussion and conclusion .......................................................................................................... 265

5.1. Discussion and conclusion ...................................................................................................... 266

5.1.1. Age, gender and epidemiology of respiratory viruses .................................................... 266

5.1.2. Burden of co-infection and associated hospitalizations and mortality .......................... 267

5.1.2.1. Influenza A viruses co-infections and disease outcome ................................................. 267

5.1.2.2. Co-infections among respiratory viruses, in general, and severity ................................ 268

5.1.3. Possible interaction between RV and influenza A viruses .............................................. 270

5.1.4. Seasonality and epidemiology of respiratory viruses ..................................................... 271

5.1.5. Limitations of this study .................................................................................................. 272

5.1.5.1. Limitations in the systematic review and meta-analysis papers .................................... 273

5.1.5.2. Limitations inherent in the four primary studies ............................................................ 274

5.1.5.3. Possible selection and diagnostic bias in the primary studies ........................................ 277

5.1.6. Summary of limitations in the study design and recommendations for future designs 278

5.1.7. Summary of the major findings and recommendations ................................................. 279

List of appendices ................................................................................................................................... 327

7

List of tables

Page

Part I

Table 1.1 Study questions and list of studies designed to answer them 37

Table 1.2 Details of the type of information that was extracted from the HPA

respiratory database

41

Part II

Table 2.1 Incidence of respiratory virus infections: population based family

studies and hospital based studies

55

Table 2.2 Proportion of patients with viral respiratory infection that seek medical

attention or develop acute respiratory tract infection

63

Table 2.3 Hospitalization and mortality associated with respiratory virus

infections

64

Table 2.4 Mortality associated with different influenza virus strains 65

Table 2.5 Socio-economic status (SES) and risk of respiratory virus infection 73

Table 2.6 Demographics of patients and that of the NW England general

population

80

Table 2.7 Age-specific average annual incidence of ARI hospitalizations per

100,000 population in NW England 2007-2012

82

Table 2.8 Age-specific average annual incidence of hospitalization for pandemic

and seasonal influenza A viruses

85

Table 2.3S1 Number of ILI hospitalizations and age-specific mid-year population

figures for NW England 2007-2012

89

Table 2.9 The known coronaviruses 99

Table 2.10 Characteristics of the studies included in the analysis of the effect of

HA, NS1 and PB2 mutations on virulence of pandemic influenza

A(H1N1)pdm09 viruses

115

Table 2.5S1 Summary map of influenza A(H1N1)-HA gene antigenic sites by study 128

Table 2.5S2 Mutations on the H1-HA antigenic sites in representative strains 1918-

2009

130

Table 2.5S3 Assessment of bias in studies included on the review: Mutations in

FluApdm09 and severity

132

Table 2.5S4 Search history on EMBASE for review number 1: Mutations

associated with severity of pandemic influenza A(H1N1)pdm09

viruses: A systematic review and meta-analysis

136

8

List of tables continued

Page

Table 2.11 Evidence on the origin of the pandemic influenza A(H1N1)pdm09

virus genomes 1918-2009

139

Part III

Table 3.1 Inclusion criteria for studies in this systematic review and meta-

analysis of patterns of co-infections between influenza and other

respiratory viruses

148

Table 3.2 Characteristics of the studies included in the meta-analysis of the

patterns of co-infections with influenza A viruses

150

Table 3.3 Influenza A virus co-infection and viral load/interferon production 156

Table 3.4 Summary of pooled proportions of co-infections 157

Table 3.1S1 Assessment of bias in included studies – patterns of co-infections

review

160

Table 3.1S2 Search history on MEDLINE for review number 2: Co-infection

patterns between influenza and other respiratory viruses

162

Table 3.5 Inclusion criteria for studies in this systematic review and meta-

analysis on respiratory viruses single and multiple infection and

severity

165

Table 3.6 Characteristics of studies included in the review on association

between single and multiple infections and severity of respiratory

disease

173

Table 3.2S1 Number of co-infections for different respiratory viruses in included

studies

188

Table 3.2S2 Study quality assessment criteria – bias and other study characteristics 189

Table 3.2S3 Search history on Web of Science for review number 3: Single and

multiple respiratory virus infections and severity

190

Part IV

Table 4.1A Patterns of single and multiple infections between respiratory viruses 204

Table 4.1B The likelihood and burden of co-infection for specific viruses

Table 4.2 Demographic and other characteristics of single and multiple

respiratory virus infections in NW England, 2007 – 2012

205

Table 4.3 Risk of hospitalization to a general ward in single and multiple

respiratory virus infections

207

Table 4.4 Risk of admission to ICU/death in single and multiple respiratory

virus infections

208

9


Page

Table 4.1S1A Respiratory viruses’ positivity rates by age group NW England, Jan 2007

– Jun 2012

209

Table 4.1S1B Age distribution of patients that were positive for any respiratory virus

infection NW England Jan 2007 – Jun 2012

210

Table 4.1S2A Number of respiratory virus infections identified in each season NW

England 2007 - 2012

211

Table 4.1S2B Proportion of each respiratory virus identified in each season NW

England 2007 - 2012

212

Table 4.1S2B Number of hospitalisations and deaths by virus type 213

Table 4.5 Demographic characteristics of patients positive for any respiratory virus 222

Table 4.6 Influenza A(H1N1)pdm09 co-infections, demographics and risk of

hospitalization, admission to ICU and death

227

Table 4.7 Other influenza A viruses co-infections, demographics and risk of

hospitalization, admission to ICU and death

228

Table 4.2S1 Odds ratios for influenza A(H1N1)pdm09 co-infections and risk of

hospitalization, and ICU or death

232

Table 4.2S2 Odds ratios for other influenza A viruses co-infections and risk of

hospitalization, admission to ICU/death

233

Table 4.8 Sequences of coronavirus used in the design of pan-coronavirus primers

(downloaded from GenBank on 18th December, 2011)

239

Table 4.9 Number of co-infection between influenza A viruses and human

coronavirus, human bocavirus and other respiratory viruses in NW

England between June 2011 and June 2012

245

Table 4.10 Demographic and other characteristics of patients that were positive for

influenza A virus infection in NW England between June 2011 and June

2012

246

Table 4.11 Disease severity in single influenza A virus infections vs. in co-infection

with hCoV, hBoV and in multiple infection with other respiratory viruses

247

Table 4.12 Details of primer optimization experiment set up 250

Table 4.3S1 Number of RNA copies calculation for hCoV – T7 synthesized PEX-A

vector

264

Table 4.3S2 Number of copies of hBoV PEX-A-COBOC plasmid in serial dilutions 265

10


Part V Page

Table 5.1 Population Attributable Fraction Percent (PAF% or PAR%) associated

with influenza A virus co-infections

269

Table 5.2 Summary of research questions and findings 273

Table 5.3 Prevalence of confounders associated with respiratory virus infections

(UK)

276

Table 5.4 Number of samples tested for influenza A viruses that were also tested

for other respiratory viruses

278

11

List of figures

Page

Part I

Figure 1.1 Schematic of the thesis outline 28

Figure 1.2 Factors associated with severity of respiratory virus infections 32

Figure 1.3 Schematic of the project design 40

Figure 1.4 The search words used in systematic reviews of this study 45

Part II

Figure 2.1 Mean number of respiratory illnesses (and 95% confidence intervals)

experienced per year by age and sex, 1976 – 81 Tecumseh Michigan,

USA

56

Figure 2.2 Age-specific prevalence of cross-reactive antibodies from baseline

pandemic sera

56

Figure 2.3A Prevalence of respiratory virus infections as reported by different studies

globally

57

Figure 2.3B Age and patient group – specific prevalence of respiratory viruses as

reported by different studies globally

58

Figure 2.4 Age distribution of deaths associated with influenza A pandemics and

interpandemics in the United States, 1918 – 1995

66

Figure 2.5 Ratio of confirmed H1N1pdm deaths to hospitalizations for selected

countries

66

Figure 2.6 Risk pyramid for respiratory virus infections 67

Figure 2.7 RCGP weekly ILI rate per 100,000 in England and Wales from various

seasons since 1999/00

69

Figure 2.8 Total number of respiratory syncytial virus (RSV) cases identified

throughout several years of observation in six North American cities

70

Figure 2.9 Weekly incidence of ARI hospitalizations in NW England 2007-2012 83

Figure 2.10 Diagram of an influenza A virus virion 91

Figure 2.11 Ecology of influenza viruses and interspecies transmission 93

Figure 2.12 Genomic maps of Pneumovirinae 95

Figure 2.13 Genomic structure of parainfluenza viruses 96

Figure 2.14 Genomes of coronaviruses 98

Figure 2.15 The genome map of human rhinoviruses 100

Figure 2.16 Genome organisation of hAdV-C2, hAdV-F40, and hAdV-D53 102

Figure 2.17 Map of the hBoV genome 103

12

List of figures continued

Page

Figure 2.18 Number of studies identified, excluded and included in the review on

genetic mutations in pandemic influenzas A(H1N1)pdm09 and severity

111

Figure 2.19 Mutation HA-D222G and risk of severe disease and mortality 118

Figure 2.20 Mutation HA-D222E and risk of severe disease and mortality 119

Figure 2.21 Mutation HA-D222N and risk of severe disease and mortality 120

Figure 2.22 Mutation PB2-E627K and risk of severe disease and mortality 121

Figure 2.23 Mutation NS1-T123V and others and severe disease and mortality 123

Figure 2.5S1 Sites under differential selection between isolates from seasonal human

and the pandemic 2009 clusters

134

Figure 2.5S2 Updated evolutionary dynamics of positively selected sites on the HA1

domain of human influenza A/H3N2

135

Figure 2.5S3 Genetic Relationships among Human and Relevant Swine Influenza

Viruses, 1918–2009

138

Part III

Figure 3.1 Number of studies identified, excluded and included in the review on

patterns of co-infection between influenza and other respiratory viruses

149

Figure 3.2 Influenza A/RSV co-infection patterns by patient group and age 152

Figure 3.3 Influenza A/hBoV co-infection patterns by patient group and age 153

Figure 3.4 Influenza A/RV co-infection patterns by patient group and age 153

Figure 3.5 Influenza A/hCoV co-infection patterns by patient group and age 154

Figure 3.6 Influenza A/hMPV co-infection patterns by patient group and age 154

Figure 3.7 Influenza A/AdV co-infection patterns by patient group and age 155

Figure 3.8 Influenza A/hPIV co-infection patterns by patient group and age 156

Figure 3.9 Number of studies that were identified, included and excluded in the

review on respiratory virus co-infections and disease severity

172

Figure 3.10 Funnel plot of observed odds ratios to check publication bias 175

Figure 3.11 Respiratory virus co-infections and risk of admission to a general ward 176

Figure 3.12 Respiratory virus co-infections and risk of admission to an intensive

care unit

178

Figure 3.13 Respiratory virus co-infections and risk of bronchiolitis 180

13

List of figures continued

Page

Figure 3.14 Respiratory virus co-infections and risk of pneumonia 181

Figure 3.15 Influenza A viruses single and multiple infections and disease severity 183

Part IV

Figure 4.1 Schematic diagram of tests conducted, results, included and excluded

patients

220

Figure 4.2 Respiratory viruses positivity rate by age group 221

Figure 4.3 Seasonal distribution of respiratory viral infections Jan 2007 – Jun 2011 224

Figure 4.4 Co-infections patterns, influenza A viruses vs. other respiratory viruses 225

Figure 4.5 A section of the consensus sequence (showing the region identical in all

coronaviruses, derived from consensus sequences for 15 coronaviruses)

that was used to design pan-coronavirus primers

240

Figure 4.6 Polyacrylamide gel electrophoresis of hCoV PCR products 244

Figure 4.3S1 Ct values for the hCoV RT-PCR at different annealing temperatures 256

Figure 4.3S2 Strength of the fluorescence signal at different annealing temperatures

(hCoV RT-PCR)

257

Figure 4.3S3 Melting curves for hCoV 229E, pEX-A vector RNA and the negative

control wells at annealing temperature of 57oC

258

Figure 4.3S4 Amplification plots for the coronavirus RT-PCR standard curve

experiment

259

Figure 4.3S5 Standard curve for the hCoV RT-PCR 260

Figure 4.3S6 Amplification plot for probe confirmation experiment for a few hCoV

positive patient samples.

261

Figure 4.3S7 PEX-A vector map 262

Part V

Figure 5.1 Weekly activity of the pandemic Influenza A(H1N1)pdm09 and

rhinovirus in North West England between November 2008 and March

2010

272

Figure 5.2 Hospitalization to death ratio for patients 277

14

List of appendices

Page

Appendix I

List of papers included in this study

328

Appendix II

ICD-10 major diagnosis codes used to determine the number of

medically attended influenza like illnesses

329

Appendix III

Number of specific respiratory virus infections identified by

different studies globally

330

Appendix IV

Accession numbers for the coronavirus sequences that were

downloaded from the GenBank and used to design the pan-

coronavirus primers

332

Appendix V

The consensus sequence that was used to BLAST hCoV primer pair

334

15

List of Abbreviations

2-5OAS 2', 5' - oligoadenylate synthetase

AdV Adenovirus

AIDS acquired immunodeficiency syndrome

ALRI acute lower respiratory tract infection

ARI acute respiratory tract infection

BatCoV-61 bat coronavirus 61

BatCoV-HKU2 bat coronavirus HKU2

BatSARS-CoV bat SARS coronavirus

BCoV bovine coronavirus

CCoV canine coronavirus

CI confidence interval (95%)

COPD chronic obstructive pulmonary disease

CPSF cleavage and polyadenylation specificity factor

CTL cytotoxic lymphocytes

DALYS disability life adjusted years

DCoV duck coronavirus

DNA deoxyribonucleic acid

E envelope gene

ECoV equine coronavirus

eIF4G1 eukaryotic translation initiation factor 4 gamma 1

F fusion gene

Flu A influenza A viruses

Flu A(H1N1)pdm09 pandemic influenza A(H1N1)pdm09 virus

Flu B influenza B virus

G glycoprotein gene

GCoV goose coronavirus

GISRS Global influenza surveillance and response network

GP general practitioner

GW general ward

HA haemagglutinin gene

HA haemagglutinin

hBoV human bocavirus

16

List of abbreviations continued

hCoV human coronavirus

hCoV-229E human coronavirus 229E

hCoV-HKU1 human coronavirus HKU1

hCoV-NL63 human coronavirus NL63

hCoV-OC43 human coronavirus OC43

HE haemagglutinin esterase gene

HEF haemagglutinin-esterase-fusion

HES Hospital Episodes Statistics

HEV haemagglutinating encephalomyelitis virus

Hib Haemophilus influenzae type b

HIV human immunodeficiency virus

hMPV human metapneumovirus

HN haemagglutinin neuraminidase gene

HPA Health Protection Agency (now Public Health England)

hPIV1-3/4 human parainfluenzavirus types 1 to 3 or 1 to 4

HSWIC Health and Social Welfare Information Services

IBV infectious bronchitis virus

ICD-10 the International Conference for the Tenth Revision of the International

Classification of Diseases

ICD-9 the International Conference for the Ninth Revision of the International

Classification of Diseases

ICU intensive care unit

IFN-α interferon-alpha

IFN-β interferon-beta

IL-6/18 interleukin-6 or 18

ILI influenza like illness

INF-1 type 1 interferons

IRAS integrated research approval system

L large polymerase

LRTI(s) lower respiratory tract infections

M1/2 matrix protein 1 or 2

MAD-5 melanoma differentiation associated gene 5

MAVS mitochondrial antiviral signaling protein

17


MERS Middle East respiratory syndrome coronavirus

MGB dihydrocyclopyrroloindole tripeptide minor groove binder

MHC-I/II major histocompatibility complex class I or II

MHV mouse hepatitis virus

MMPL Manchester Microbiology Partnership Laboratory

MMU Medical Microbiology Unit

mRNA messenger ribonucleoprotein

N nucleocapsid

NA neuraminidase gene

NCBI National Centre for Biotechnology Information

NHS National Health Service

NP nucleoprotein

NS1 nonstructural protein 1

NW England North West England

ONS Office of National Statistics

OR odds ratio

ORF 1/2 open reading frame 1 or 2

P phosphoprotein

PA polymerase gene

PABP I/II polyadenine binding protein I or II

PAF(%) population attributable fraction (percent)

PB1/2 polymerase basic pritein 1 or 2

PCoV puffinosis coronavirus

PCoV pigeon coronavirus

PEDV porcine epidemic diarrhoea virus

PFP-1/2/3 purified fusion protein 1 or 2 or 3

PH power of hydrogen

PhCoV pheasant coronavirus

PI3k/Akt phosphatidylinositol 3-kinase

PKR protein kinase R

PRCoV porcine respiratory coronavirus

PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses

R&D research and development

18


R0 basic reproductive number

RCGP Royal College of Physicians General Practice

RCoV rat coronavirus

RD risk difference

RdRp RNA-dependent RNA polymerase

REC Research Ethics Committee

RIG-I retinoic acid-inducible gene 1

RNA ribonucleic acid

RNPs ribonucleoproteins

RR risk ratio

RSV respiratory syncytial virus

RTI respiratory tract infection

RT-PCR real-time polymerase chain reaction

RT-PCR reverse transcriptase polymerase chain reaction

RV rhinovirus

RVI respiratory virus infection

S spike protein

SARS-CoV severe acute respiratory syndrome coronavirus

SDAV sialodacryoadenitis virus

SES socio-economic status

SH small hydrophobic gene

SiRNAs small interfering ribonucleic acids

SO-IAV swine-origin (H1N1) influenza A viruses

SP Streptococcus pneumoniae

ssDNA double-stranded deoxyribonucleic acid

ssRNA single-stranded ribonucleic acid

STAT1 /2 signal transducers and activator of transcription factor 1 or 2

STROBE strengthening the reporting of observational studies

TAMRA tetramethylrhodamine

TCoV turkey coronavirus

TGEV transmissible gastroenteritis virus

TLR toll like receptor

TNF tumor necrosis factor

19


TRIM25 tripartite motif-containing 25

UK United Kingdom

USA United States of America

UTRs untranslated regions

VNPA nasopharyngeal aspirates

VNT nose and throat swabs

VP 1/2 viral protein 1 or 2

VSW swabs taken from the mouth and nose area

VTS throat swabs

WHO World Health Organisation

20

Abstract

Introduction: Epidemiological studies have indicated that 5-38% of influenza like illnesses

(ILI) develop into severe disease due to, among others, factors such as; underlying chronic

diseases, age, pregnancy, and viral mutations. There are suggestions that dual or multiple virus

infections may affect disease severity. This study investigated the association between co-

infection between influenza A viruses and other respiratory viruses and disease severity.

Methodology: Datum for samples from North West England tested between January 2007 and

June 2012 was analysed for patterns of co-infection between influenza A viruses and ten

respiratory viruses. Risk of hospitalization to a general ward ICU or death in single versus

mixed infections was assessed using multiple logistic regression models.

Results: One or more viruses were identified in 37.8% (11,715/30,975) of samples, of which

10.4% (1,214) were mixed infections and 89.6% (10,501) were single infections. Among

patients with influenza A(H1N1)pdm09, co-infections occurred in 4.7% (137⁄2,879) vs. 6.5%

(59⁄902) in those with seasonal influenza A virus infection. In general, patients with mixed

respiratory virus infections had a higher risk of admission to a general ward (OR: 1.43, 95% CI:

1.2 – 1.7, p = <0.0001) than those with a single infection. Co-infection between seasonal

influenza A viruses and influenza B virus was associated with a significant increase in the risk

of admission to ICU/ death (OR: 22.0, 95% CI: 2.21 – 219.8 p = 0.008). RSV/seasonal

influenza A viruses co-infection also associated with increased risk but this was not statistically

significant. For the pandemic influenza A(H1N1)pdm09 virus, RSV and AdV co-infection

increased risk of hospitalization to a general ward, whereas Flu B increased risk of admission to

ICU/ death, but none of these were statistically significant. Considering only single infections,

RSV and hPIV1-3 increased risk of admission to a general ward (OR: 1.49, 95% CI: 1.28 –

1.73, p = <0.0001 and OR: 1.34, 95% CI: 1.003 – 1.8, p = 0.05) and admission to ICU/ death

(OR: 1.5, 95% CI: 1.20 – 2.0, p = <0.0001 and OR: 1.60, 95% CI: 1.02 – 2.40, p = 0.04).

Conclusion: Co-infection is a significant predictor of disease outcome; there is insufficient

public health data on this subject as not all samples sent for investigation of respiratory virus

infection are tested for all respiratory viruses. Integration of testing for respiratory viruses’ co-

infections into routine clinical practice and R&D on integrated drugs and vaccines for influenza

A&B, RSV, and AdV, and development of multi-target diagnostic tests is encouraged.

21

Declaration

I hereby declare that no portion of the work referred to in the thesis has been submitted in

support of an application for another degree or qualification of this or any other university or

other institute of learning.

22

Copyright statement

1. The author of this thesis (including any appendices and/or schedules to this thesis) owns

certain copyright or related rights in it (the “Copyright”) and s/he has given The

University of Manchester certain rights to use such Copyright, including for

administrative purposes.

2. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy,

may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as

amended) and regulations issued under it or, where appropriate, in accordance with

licensing agreements which the University has from time to time.

3. The ownership of certain Copyright, patents, designs, trademarks and other intellectual

property (the “Intellectual Property”) and any reproductions of copyright works in the

thesis, for example graphs and tables (“Reproductions”), which may be described in this

thesis, may not be owned by the author and may be owned by third parties. Such

Intellectual Property and Reproductions cannot and must not be made available for use

without the prior written permission of the owner(s) of the relevant Intellectual Property

and/or Reproductions.

4. Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectual Property and/or

Reproductions described in it may take place is available in the University IP Policy

(see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=487) in any relevant

Thesis restriction declarations deposited in the University Library, The University

Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations) and

in The University’s policy on Presentation of Theses.

23

Acknowledgements

My dues go to my supervisors Professor Pamela Vallely and Professor Paul Klapper and my

advisor Dr Kenneth Mutton for their excellent guidance throughout the process of this study.

Dr David Baxter for his guidance during the first year of my study, Dr Andrew Turner for

approving that I use the Health Protection Agency (HPA) respiratory dataset and nucleic acid

samples tested for respiratory viruses at the Manchester Microbiology Partnership Laboratory

(MMPL) to conduct this study. Professor Christopher Roberts for the statistical support. I

acknowledge the University of Manchester for sponsoring the PhD, the Manchester Academic

Health Science Centre, and the Central Manchester University Hospitals NHS Foundation Trust

and staff for their support in the research. Dr Keith Paver, Mr. Alan Lord, Mr. Mark Hasselhold

for assistance with data extraction and mining of the samples.

I am also indebted to MS Yvonne Satchell, Mr. David Dennington and Dr Carol Yates for

assistance and guidance with laboratory experiments, Ms Audrey Coke and Peter Yates for

administrative support on laboratory matters. The officers in the research offices at the

University and the Central Manchester Universities NHS Trust; Ms Lynne Macrae and MS

Sophia Lockwood for their support with the ethical and R&D approvals. All members of the

NRES Committee Greater Manchester West for the ethical permission to carry out this study.

The North Manchester Management and administration of the Institute of Inflammation and

Repair Dr Catherine O’Neill, MS Christine Burns and all the other line managers for their

immeasurable support during my stay at the University.

Lastly I would like to thank all my colleagues in the MVU, i.e. fellow students and the post-

doctoral students for their love and friendliness while we were working together in our

laboratory. I also acknowledge my family in Malawi and in Taiwan, for the moral and material

support throughout the four years of my study. My dues also go to too many other people who

have supported me in one way or another during my studies. You are all thanked for the same.

24

Dedication

This thesis is dedicated to my daughter Ms Jia-Yi (Ntchayi) Goka.

25

Rationale for submitting the thesis in alternative format

Respiratory virus infections have been regarded as single virus infections. Although some

published studies have investigated the epidemiology and clinical significance of respiratory

virus dual and multiple infections and disease severity, there has been no previous attempt to

review the available evidence. Writing this thesis in the alternative format enables the

presentation of primary and review articles on the significance of co-infections on respiratory

disease outcome in publishable peer reviewed papers format. There is need for scientific

knowledge to help guide policy on multi-targeted testing, integrated vaccines, and combined

treatment of viral respiratory infections. Details of the papers included in this thesis and the

journals they were submitted to are presented in Appendix I.

26

Outline of the thesis

The thesis begins with an introduction chapter which briefly outlines the background for the

research question, the aim and objectives of the study, the justification for conducting this

investigation, the significance of its findings, and the methodology that was employed to

investigate the question.

Part 2 describes the nature of respiratory virus infections in five parts: Section A gives the

clinical signs and symptoms of respiratory disease, available treatments and vaccines. Section

B explores the incidence of respiratory virus infections globally and the factors that affect

incidence rates e.g. season and socio-economic factors. Section C drills down to the local

situation with a primary paper on the incidence of respiratory virus infections in North West

England between January 2007 and June 2012. Section D and E describes the genetics of

respiratory viruses investigated in this research and a review on the mutations in the pandemic

influenza A(H1N1)pdm09 virus and their relationship with severity, as reported by different

studies.

Part 3 is a review of published literature on the epidemiological significance of viral co-

infections on disease outcome. Section A discusses the observed patterns of co-infections

between influenza A and other respiratory viruses and its associated innate and virological

responses (interferon production and viral load). Section B reviews published literature on the

association between co-infections and risk of hospitalization to a general ward, admission to an

intensive care unit (ICU), bronchiolitis and pneumonia.

Part 4 presents the roles of co-infections on risk of hospitalization and mortality in North West

England between 2007 and 2012. Section A describes the role of respiratory viruses dual and

multiple infections on risk of hospitalization and mortality. Section B discusses influenza A

viruses co-infection with other respiratory viruses and risk of hospitalization and mortality; and

Section C presents the association between influenza A viruses co-infection with human

coronaviruses and human bocaviruses and disease severity. It also describes the SYBR Green

and TaqMan RT-PCR assays that were used to test for hCoV and hBoV.

Part 5 gives a discussion of the results and conclusions drawn from the study followed by a list

of all the references and appendices. Figure 1.1 gives a schematic of the thesis outline.

27

Figure 1.1: Schematic of the thesis outline

� Discussion,

conclusions &

recommendations

for future

research

Part I

Rationale & Methods

Part II

Nature of respiratory virus infections

Part III

Co-infections & viral virulence/severity

a review of literature

Part IV

Co-infections & risk of hospitalization &

mortality NW England

Part V

Discussion

& Conclusions

� Introduction

o Background

o Rationale &

justification

o Objectives

� Methodology

� Incidence of

respiratory viruses

globally

� Incidence of

respiratory viruses

in NW England

(2007 – 2012)

� Virology of

respiratory infections

� Patterns of co-

infections with

influenza A viruses

& cytokine release,

& viral load

� Co-infections &

hospitalization,

death, bronchiolitis

& pneumonia,

a review

� Respiratory virus single &

mixed infections & risk of

hospitalization & mortality NW

England

� Influenza A co-infection with

other respiratory viruses &

risk of hospitalization &

mortality, NW England

� Influenza A viruses

co-infection with hCoV, hBoV

& risk of hospitalization -

mortality, NW England

� Clinical picture of

virus respiratory

infections, available

vaccines and drugs

28

Part I: Introduction

Synopsis

This section gives the rationale for the investigation, and summarizes the methodology that was

employed to conduct the research.

29

1.1. Introduction

1.1.1. Epidemiology and significance of influenza and other respiratory viruses

Influenza virus and other acute respiratory tract infections (ARIs) cause considerable mortality and

morbidity worldwide. The 2009 World Health Organization (WHO) report on acute respiratory

infections estimated that, in each winter season, between 10% and 20% of populations worldwide

suffer from influenza (1) and that there are at least 2.2 to 3.9 million deaths from acute respiratory

tract infections (ARIs) throughout the world each year (2;3). A review published in 2013 by Walker

et al., (4) reported that between 2010 and 2011 the incidence of influenza was 982 per 1,000

children ≤5 years old (CI: 414–2699), with 7% of influenza cases developing into severe disease.

The incidence of influenza associated deaths was estimated at 137 per 1,000 children (CI: 38–163).

The authors also indicated that, during this period, there were 120 million episodes of pneumonia

(or acute lower respiratory infections - ALRI), 14 million of which developed into severe disease,

and 10.9% of the pneumonia cases were due to influenza virus. Another study on epidemiology of

pneumonia in 2010 [Rudan et al., (5)], identified RSV and influenza as the most important causes of

pneumonia with RSV being present in 29.0% of all pneumonia episodes and influenza in 17% (5).

In their earlier review Rudan et al., (6) estimated that, in each year, there are approximately 150.7

million new cases of acute lower respiratory tract infection (ALRIs) in children 0-4 years in

developing countries, of which 7 to 13% end up being hospitalized. In the United Kingdom, a study

on general practitioners (GP) consultations and hospitalization for influenza in England and Wales

estimated that influenza causes about 779,000 to 1,164,000 general practice consultations, 19,000 to

31,200 hospital admissions and 18,500 to 24,800 deaths annually (7). Whereas in the United States

of America (USA), Molinari et al., (8), study of 2007 reported that influenza is responsible for 20-

40 million outpatient visits, 330,000 hospitalizations and 30,000 deaths annually.

1.1.2. Factors associated with severity of respiratory virus infections

A number of factors may influence the severity of respiratory viral infections including: the age and

gender of the individual; pregnancy; presence or absence of underlying chronic disorders e.g.

asthma, chronic respiratory disease such as chronic obstructive pulmonary disease (COPD),

diabetes, ischaemic heart disease, chronic renal disease, chronic heart disease, chronic liver disease,

chronic neurological disease, atopic dermatitis (9-13); bacterial co-infection e.g. co-infection with

Streptococcus pneumoniae (SP) and Haemophilus influenzae type b (Hib) (14;15);

immunodeficiency e.g. human immunodeficiency virus/ acquired immunodeficiency syndrome

30

(HIV/AIDS); obesity; (9-12); host genetics and mutations in human gene e.g. Downs syndrome

(13;16;17); the virus gene constellation (18-20); vaccination status; previous exposure to certain

viruses (21); prematurity; low birthweight, malnutrition e.g. vitamin D deficiency (13;22;23);

low/vulnerable social-economic status (10), and active or passive smoking (13;23). Figure 1.2 is a

schematic of the factors associated with severity of respiratory viruses.

Patient characteristics like; age (young and old age), gender (being male), patients underlying

conditions (e.g. chronic lung diseases such as chronic obstructive pulmonary disease (COPD),

asthma, congenital heart disease), immunodeficiency, obesity, and being pregnant would precipitate

or offer favourable conditions for respiratory viruses to infect patients. A report published by the

United Kingdom’s (UK’s) Health Protection Agency (HPA), on the epidemiology of the 2009

pandemic influenza A(H1N1)pdm09 virus, indicated that people aged between 6 months and <65

years, with an underlying condition were 10 times more likely to be hospitalized and 18 times more

likely to die compared to people of the same age without an underlying condition (12). A similar

result was observed by the WHO influenza programme analyzing 9,700 hospitalized patients and

2,500 deaths from pandemic influenza A(H1N1)pdm09 from 19 countries, which occurred between

April 2009 and January 2010 (10). Patients aged ≤5 and between 5–14 years old had the highest per

capita risk of hospitalization (relative risk ratio – RR = 3.3 and 3.2) compared to the general

population, and the risk of death was highest among those aged 50–64 and >65 (RR = 1.5 and 1.6).

Further, patients with chronic conditions were more likely to be hospitalized, admitted to ICU or die

from influenza A(H1N1)pdm09 (median 31.1%, 52.3% and 61.8% respectively). Obesity increased

the risk of ICU admission 36 fold (RR = 36.3). A study conducted in China reported that obesity

increased the likelihood of experiencing severe manifestations (odds ratio OR: 35.61), in addition

young/old age and chronic conditions increased this risk 21-fold (OR: 21.4 and 9.79 respectively)

(11). The 2010 HPA report also observed that pregnancy increased risk of hospitalization 5-fold

(rate ratio – RR = 5.1) and mortality 7-fold (RR = 7.1 respectively), whereas immunodeficiency

increased risk of hospitalization 18-fold (RR = 18.5) and mortality 56-fold (RR = 56.0) (12). A

2013 meta-analysis by Jackson et al., (23) indicated that male gender significantly increased risk of

ALRI one and a half times (pooled odds ratio 1.5).

31

Figure 1.2: Factors associated with severity of respiratory virus infections

Notes: Fifteen factors affecting severity of respiratory virus infections have been reviewed here, this study investigates the role of respiratory virus co-infections.

Respiratory

virus severity

Gender (male gender)

HPA 2010 & 2011, Avarez et al., 2013, van Kerkhove/ et

al., 2013, Jackson et al., 2013

Age (being >65 year old and

young ≤5 years old)

Ren et al., 2013, van Kerkhovel et al., 2013, Alvarez et al., 2013, HPA

2010 & 2011

Host genetics (some mutations e.g. 874A

allele of the IFN-ᵞ gene or Downs syndrome)

Tregoning et al., 2010,

Alvarez et al., 2013, Ali et al., 2013, Gao et al., 1999,

Jameson et al., 1999

Immune status (e.g. HIV, or hormone

or genetically induced)

HPA 2010 & 2011, van Kerkhovel et al., 2013

Pregnancy

Ren et al., 2013, van Kerkhovel et al.,

2013, Alvarez et al., 2013

Virus genetic mutations

(e.g. PB2 E627K in influenza)

Van HN et al., 2009, Tumpey et al., 2007, Martnell et al., 2002,

Geis et al., 2002

Vaccination and prior repiratory virus

infections

Wlodarzyk et al., 2013, Foster at al., 1992, Ohmit et al., 1995

Obesity

HPA 2010 & 2011, Ren et al.,2013,

van Kerkhovel et al., 2013

Co-morbidities e.g. asthma, ischaemic

heart disease

van Kerkhovel et al., 2013, Alvarez et al., 2013,

Punpanich et al., 2012, Ruuskanes et al., 2011,

Ren et al., 2013

Respiratory viruses co-infections

Alvarez et al., 2013, Drews et al., 1997, Semple et al., 2005, Aberle et al., 2005, Esper et al., 2011

Bacterial and other pathogens co-

infections

Ruuskanen et al., 2011, Panpanich et al., 2012

Social-economic status (being of disadvantaged

society)

Alvarez et al., 2013, van Kerkhovel et al., 2013,

Punpanich and Chotpitayasunondh 2012

Prematurity and low birthweight

Welliver et al., 2003, Jackson et al., 2013,

Grant et al., 2011

Malnutrition (e.g. vitamin D

deficiency, poor breastfeeding

Grant et al., 2010,

Jackson et al., 2013

Passive or active smoking

Jackson et al., 2013, Noah et al., 2012, Jones et al., 2011, Sigurdsson et al.,

2012, vanarske et l., 2006, Gualano et al., 2008

32

It has been suggested that the increased severity associated with age could be because old age

leads to deterioration of dendritic cells, making them unable to produce type 1 interferons during

virus infection (24;25), whereas young children have an immature immune system lending them

susceptible to infections (26;27). On the other hand, gender may predispose individuals to

susceptible genes (28), or sex specific hormones e.g. testosterone select men to poor immunity

phenotype (29;30); some chronic conditions e.g. COPD facilitate infection through the removal

of immune barriers, i.e. epithelial cell layer leading to impaired production of mucus and

surfactants, anti-viral mediators, and some interfere with innate responses to virus infection, e.g.

production of inflammatory cytokines (31).

The severity of respiratory viruses may also be affected by the host or viral genetics (32;33). A

review by Tregoning et al., (17) found that an individual’s genetic trait is directly related to their

interferon response, antigen presentation, B-cell development, T-helper cell differentiation,

CD28, apoptosis and protein ubiquitination. In viruses, studies have indicated that the

haemagglutinin (HA), neuraminidase (NA), non-structural protein (NS1) and polymerase basic

protein 2 (PB2) are associated with severity of influenza viruses due to their interaction with

sialic acids, the virus receptors on host cells, antagonists of the interferon pathways, and in

determining the viruses host specificity (18-20;34-37). The NS1 protein in respiratory syncytial

virus (RSV) has also been found to perform similar immune regulatory functions as the influenza

A virus NS1 protein (38-40). Similarly bocavirus’ NS1 protein causes cell damage and facilitates

virus replication by among others activating the phosphatidylinositol 3-kinase (PI3K)/Akt

pathway (41;42). The molecules performing related functions in other viruses are the E protein in

adenovirus (AdV) (43), and the spike protein in human coronavirus (hCoV) (44-46). Whereas

the human metapneumovirus (hMPV) uses the P gene and parainfluenza viruses (hPIV1-4) the P

accessory proteins (C, D and V) to promote viral replication by inhibiting interferon production

through degradation of signal transducers and activator of transcription factor 1 or 2 (STAT1

and/or STAT2) (47-50). Recently, Walker et al., (51) have shown that rhinovirus 16 (RV16) 2A

and 3C proteases, cleave cellular protein and uses them to produce viral proteins. Similar

mechanisms were reported in RV14 by Gustin and Sarnow (52).

A 2011 meta-analysis by Grant et al., (53) identified the three child nutrition factors i.e. low

birthweight, zinc deficiency, and insufficient breast feeding as being associated with an

increased risk of developing lower respiratory tract disease (LRTI). The role of nutrition on

LRTI was also confirmed in the 2013 review by Jackson et al., (23). Further, Jackson et al., (23)

33

found lack of exclusive breastfeeding and passive smoking as risk factors. Children whose

parents were smokers or living with adults who smoke were almost 3 times more likely to have

severe respiratory disease than those without (OR: 2.5 and 2.7 respectively). Similar results were

reported by a systematic review and meta-analysis by Jones et al., (54). Active cigarette smoking

has been linked to an increased incidence and severity of respiratory virus infections (55-57), and

such link has also been demonstrated in mice (58). There is overwhelming evidence on the role

of bacterial and other co-infections in aggravating respiratory virus infection. A review by

Punpanich and Chotpitayasunondh (15) published in 2012 reported that up to 43% of influenza

A(H1N1)pdm09 virus associated paediatric deaths had bacterial co-infection. A well conducted

review by Ruuskanen et al., (59) found that up to a third of children with respiratory virus

infections have evidence of viral-bacterial co-infection. Evidence from animal studies, as

reviewed by Peltola and McCullers (60) indicated that the destruction of respiratory epithelium

by viruses may increase bacterial adhesion leading to more pneumonia, bronchiolitis and other

severe outcomes. Similar observations were made by an earlier review by Bakaletz et al., (61).

Some studies have suggested that individuals with low socio-economic status are more likely to

have more respiratory virus infections (62;63) and more severe outcome (10;15). Presumably

because they are more likely to live in an overcrowded household, have poor nutrition leading to

lower immunity have high prevalence of risky behaviours like smoking or may lack information

about vaccination and other types of medical care and hence access medical services to a lesser

extent. However, other studies have found no such link (64-67). Further, prematurity and low

birthweight have been identified as risk factors to severe respiratory disease (22;23;53). For

example, in their study, Jackson et al (23) found that underweight children were five times more

likely to develop ALRI.

Regarding host immunity, studies by Gao et al., (68) and Lu et al (69) with two H5N1 viruses i.e.

the A/HK/483/97 (H5N1), and the A/HK/486/97 (H5N1), isolated from fatal and mild human

cases respectively, showed that the former was highly pathogenic in inbred mice. However, cell

culture and ferret models used by (70) showed that both isolates escaped the antiviral effects of

type 1 interferons (IFN-1) e.g. TNF-α, and gave comparable clinical signs, whereas a separate

study by (71) showed that some cytotoxic lymphocytes (CTLs) were cross-reactive against both

A/HK/156/97 (H5N1) and A/HK/483/97 (H5N1), suggesting the role of host immunostatus or

immunosuppressive diseases. Vaccination is known to reduce the risk of hospitalization and

development of complications among the elderly and individuals with underlying medical

34

conditions (72;73). Cell-mediated immune response to previous exposure to certain viruses leads

to cytopathic effect following infection with another virus whose T cell epitopes cross-react with

the previous infection (21), and that such immune response could also be induced by vaccination

(74).

There have been suggestions, in recent literature, that respiratory virus co-infections affect

disease severity with some studies suggesting that dual and multiple infections increased severity

of respiratory disease (75-77), while others have found no association (78;79), and yet others

found that dual or multiple infection may actually be protective (80-82). Although literature has

shed some light on the patterns of respiratory virus co-infections, most of the studies, such as

Richard et al., (76), Semple et al., (83), Stenfaska et al., (84), Semple, 2005 695 /id}, Calvo et

al., (85), Ali et al., (86), have dwelled on co-infections between RSV and other respiratory

viruses, or bocaviruses and other respiratory viruses [e.g.Schidren et al., (87) and Zheqian et al.,

(88)] therefore the epidemiology or patterns of influenza A viruses co-infections i.e which

viruses most commonly co-infect with influenza A virus in large numbers (Flu A+RSV?

FluA+AdV? or Flu A+PIV1-3? etc), is not known. Further, the association between specific

types of co-infections with influenza A viruses and severity of respiratory disease is not well

understood. This is mainly because most studies have conducted crude analysis. For example, in

the 2011 study by Martin et al., (82), and 2013 studies by Bicer et al., (89) and Peci et al., (90),

only crude analysis was applied to present the association between single and multiple

respiratory infections and risk of admission to a general ward or intensive care unit. In addition,

most of the studies on respiratory virus co-infections [e.g. Rhedin et al., (91), Bicer et al., (89),

Richard et al., (76)] have recruited children ≤5 years old, therefore the impact of respiratory virus

infections in all age groups in not known. In addition, very few studies [Singleton et al., (92),

Libster et al., (93), Echenique et al., (94) and Rhedin et al., (91)] have investigated the effect of

influenza A and other respiratory viruses co-infection on disease outcome. Therefore the

relationship between specific co-infections between influenza and other respiratory viruses and

severity of disease i.e. risk of hospitalization to a general ward (GW), admission to the intensive

care unit (ICU) and death is still unclear. This study intends to generate knowledge to fill these

gaps.

1.1.3. Hypothesis

Severity of influenza A viruses disease will be influenced by certain types of co-infections with

respiratory viral pathogens.

35

1.1.4. Objectives

This study aimed to identify the association between influenza A and respiratory viral infections

dual or multiple infections and severity of influenza disease. The following were the specific

objectives:

1. To investigates the patterns of co-infections between influenza A and other respiratory

virus infections.

2. To determine whether infection with influenza A viruses alone has a different disease

outcome, i.e. risk of hospitalization, admission to a general ward, ICU and death,

compared to co-infections with respiratory viruses.

3. To identify whether the risk of hospitalization, admission to a general ward, ICU and

death in the pandemic influenza A (H1N1)pdm09 co-infections differed from that in

seasonal influenza A viruses.

1.1.5. Research questions and study aims

Table 1.1 gives a summary of the research questions and the studies or experiments that were

conducted to answer these questions. The research questions included:

1. What is the incidence of influenza and other respiratory virus infections, and their associated

hospitalization and mortality globally, in UK and NW England?

2. Did mutations known to increase virus severity occur in influenza A viruses during the study period?

3. Which respiratory viruses most commonly co-infect with influenza A viruses?

4. What is the disease outcome in single and multiple respiratory virus infections in general?

5. Which respiratory viral co-infections mostly enhance influenza A virus severe outcome?

6. Does hCoV and hBoV co-infect with influenza A virus in substantial numbers, if yes, is the disease in

such co-infection severe or mild?

1.1.6. Significance of the study

The WHO and other researchers have called for an integrated approach to the development of

therapeutics and vaccines for RSV, the hPIVs, hMPV, and influenza viruses (95-98). This study

will provide data on the clinical benefit of multiple disease screening in respiratory viral diseases;

it will also provide data for justifying virus combinations to be prioritized in research and

development of combined treatment, integrated vaccines and multi-target diagnostic tests.

36

Table 1.1: Study questions and list of studies designed to answer them

Research question

Aims

Study to address the question

Methodology applied

What is the incidence of influenza and other respiratory virus infections and associated

hospitalization and mortality globally, in UK and NW England?

To give an overview of the epidemiology of respiratory virus infections among all age groups. To describe the disease burden caused by respiratory viruses hence their importance to public health.

1. Conducted a narrative review of literature, in the introduction section, on incidence of respiratory viruses globally and factors associated with the same.

Searched for this evidence from previously published studies.

2. Conducted a primary study on the incidence of respiratory viruses’ hospitalization in the UK over the study period (2007 – 2012).

Used respiratory data from the Health Protection Agency at MMPL and data on hospitalizations due to acute respiratory virus infections in NW England, over the study period generated using ICD-10 codes.

Did mutations that are known to increase virus severity occur in influenza A viruses during the

study period?

To document the available evidence on the genetic mutations in influenza HA, PB2, NS1 genes observed in circulating viruses over the study period.

Conducted a systematic narrative review on the mutations on influenza virus HA, PB2 and NS1 gene and their association with severe outcome.

Searched the literature.

Which respiratory viruses most likely co-infect with influenza A

viruses?

To help generate knowledge on the respiratory viruses more likely to co-infect with influenza A at levels of public health significance.

Conducted a systematic review and meta-analysis on the patterns of respiratory virus co-infections with influenza A viruses.

Literature search.

What is the disease outcome in single and multiple respiratory

virus infections in general?

To establish whether disease outcome in single respiratory virus infections differed by virus type. To use this information as a baseline for comparing disease outcome when the viruses occur as mixed infections. To generate crude odds ratios for co-infections to be used to compare with odds ratios in stratified analysis.

Conducted a primary study on the association between single and multiple respiratory virus infections and disease outcome.

Used respiratory data from the Health Protection Agency at MMPL of samples that were received and tested for respiratory virus infections at MMPL between 2007 and 2012.

What is the disease outcome in single and multiple influenza A

virus infections?

To understand the effect of co-infections with influenza A viruses and their association with disease outcome.

Conducted a primary study on the associations between single influenza A virus and specific co-infections and disease outcome

Used respiratory data from the Health Protection Agency at MMPL of samples that were received and tested for respiratory virus infections at MMPL between 2007 and 2012.

Does hCoV and hBoV co-infect with influenza A virus in

substantial numbers, if yes, is the disease in such co-infection

severe or mild?

To investigate the importance of hCoV and hBoV as concomitant infections with influenza A viruses (the study is necessitated by the fact that over the study period, hCoV and hBoV were not routinely tested at MMPL).

Conducted a primary study on the patterns of co-infections between influenza A and other respiratory virus infections and their association with severe disease.

Designed primers for hCoV and identified previously published primers for hBoV. Designed and optimized a real-time polymerase chain reaction protocol, and tested 2017 samples that were positive for influenza A virus between 2011 and 2012.

37

1.1.7. Methodology

1.1.7.1. Study design and setting

This is a cross-sectional study that retrospectively analysed data of patient’s samples that were

sent to the Manchester Microbiology Partnership Laboratory (MMPL), by hospitals, medical

centres, and surgeries located in North West England, in the United Kingdom. The study

attempted to describe the epidemiology of twelve respiratory virus infections; pandemic influenza

A(H1N1)pdm09 virus (Flu Apdm09), seasonal influenza A viruses (Flu A), influenza B virus (Flu

B), RSV, RV, AdV, hMPV, hPIV1-3, hCoV and hBoV and characterise the association of their

co-infection with hospitalization to a general ward, admission to ICU or death.

The MMPL is based at the Central Manchester University Hospitals, National Health Service

(NHS) Foundation Trust and serves as a regional reference laboratory for North West England,

catering for a population of 6.9 million people (99). In addition to analysis of electronic laboratory

data, the study also tested samples that were positive for influenza A virus for the presence of

coronavirus and human bocavirus. The real-time polymerase chain reaction (RT-PCR) tests for

coronaviruses and human bocavirus were conducted in the Medical Microbiology Unit (MMU)

Laboratories in Stopford building of the University of Manchester medical school. For calculation

of incidence of respiratory virus infections, bespoke data on hospitalizations due to influenza like

illnesses (by age groups and week/season/year) in in the North West of England between January

2007 and June 2012 were obtained from the Hospital Episodes Statistic (HES) from the Health

and Social Welfare Information Centre – HSCIC (100). Population data for the North West

England was downloaded from the UK’s, Office of National Statistics – ONS (101). A schematic

of the project template summarizing the study design is given in Figure 1.3.

1.1.7.2. Analysis of electronic data

An interrogation of the MMPL respiratory database was conducted in two phases. In the first

phase, datum of samples that were sent to the MMPL for viral detection between 1st January 2007

and 23rd June 2011 was analysed. In the second phase, datum for samples submitted between 24th

June 2011 and 30th June 2012 was analysed. The analysis investigated the epidemiology of

respiratory virus infections (including the incidence of hospitalization, the patterns of co-infection

among them, and the association between co-infection between influenza A viruses and other

respiratory virus infections and the severity of influenza disease (hospitalization to a general ward,

admission to ICU and mortality).

38

1.1.7.3. Variables and information extracted from the MMPL database

Information collected from the MMPL electronic respiratory database included: patients age (in

years and no date of birth was collected), date sample was collected, date received, the medical

facility which submitted the sample and the location within the facility (secondary location), type

of sample that was submitted, type of tests that were requested, tests that were done and the results

of the same. Table 1.2 gives the details of the datum that was extracted from the dataset. A

selection from the J00 to J100 international classification of diseases codes, tenth revision (ICD-

10) as listed in Appendix II was used to extract the number of hospital visits, hospitalizations, or

deaths associated with influenza like illnesses (ILI), in North West England, during the study

period. The codes serve as respiratory classifiers – capturing patients with an influenza like event

as a major diagnosis in the Hospital Episodes database.

1.1.7.4. Inclusion and exclusion criteria

All records with a positive PCR for influenza A virus or patients who were hospitalized or were an

outpatient, and who had samples sent to the laboratory between 1st January 2007 – and 30th June

2012 for screening of respiratory viral infections, were eligible for inclusion. All entries with

incomplete data (where outcome datum was omitted or where the test result was missing either

because the PCR was inhibited or was not performed), were excluded.

1.1.7.5. Respiratory viruses detection assays

Samples submitted to the MMPL by attending physicians for respiratory virus identification

included: nose and throat swabs (VNT), throat swabs (VTS), nasopharyngeal aspirates (VNPA),

and other types of swabs taken from the mouth and nose area (VSW). Samples were submitted

using standard virus transport medium. Nucleic acids were extracted using the Qiagen total

nucleic acid extraction kit on a Qiagen Biorobot MDX (Qiagen, Crawley, UK). Duplex assays of

well-characterised “in-house” RT-PCR assays (unpublished) were used for the identification of

Flu A/B, RSV, RV, AdV, hMPV, and triplex assays for hPIV1-3. In addition, A duplex Assay

published by the Health Protection Agency (the HPA (H1)v - RT-PCR assay (102)) was used for

the identification of the pandemic influenza A(H1N1)psm09. For ribonucleic acid (RNA) viruses,

reverse transcription was accomplished using the Invitrogen Superscript III platinum one-step RT-

PCR kit (Invitrogen, Paisley, UK). All the PCRs were run on the ABI7500 real-time

39

Jan, 2007

Figure 1.3: Schematic of the project design

June 2012

Analysis of electronic dataset

� Influenza A viruses (Seas Flu A & Flu A(H1N1)pdm09)

� Influenza B virus (Flu B)

� Respiratory syncytial virus (RSV)

� Rhinovirus (RV)

� Adenovirus (AdV)

� Human metapneumovirus (hMPV)

� Human parainfluenza viruses 1-3 (hPIV 1-3)

Retrospective

June, 2011

Testing of samples that were positive for

influenza A viruses for:

� Human coronavirus (hCoV)

� Human bocavirus (hBoV)

(PCR tests done at Stopford

building, UNIMA)

Analysed using STATA Version 11.0

40

Table 1.2: Details of type the of information that was extracted from the HPA respiratory

database

Category

Data

Field 1

a) Age (years), gender (or sex)

b) Computer generated patient id (not same with real patient id) for anonymity

Field 2 a) Date sample was collected (mm/dd/yyyy)

b) Date sample was received at Manchester Medical Microbiology Partnership Laboratory at the

Manchester Royal Infirmary Hospital (mm/dd/yyyy)

Field 3 a) Sample location

b) Whether patient was admitted due to influenza disease or was seen as outpatient (code used)

c) If was admitted which ward (code used for each hospitals and ward)

d) details of sample secondary location (code used if the senders of the sample were sending it on

behalf of another medical facility)

e) Post code of the primary or secondary location

Field 4 a) The type of sample that was collected and sent for testing e.g. throat swab, nasal-pharyngeal

aspirates, sputum.

b) Tests requested by the doctors who submitted the samples and results PCR test. The tests

requested and carried out on the samples included: Flu A in general, Flu A(H1N1)pdm09, Flu B,

RSV, RV, AdV, hMPV and painfluenza virus types 1 to 3

c) Influenza PCR results, respiratory syncytial virus PCR results, rhinovirus PCR results, adenovirus

PCR results, human metapneumovirus PCR results, human parainfluenza viruses 1 to 3 PRC

results.

PCR instrument (Applied Biosystems, Warrington, UK). Positive amplification was determined

using TaqMan amplicon-specific probes quenched with tetramethylrhodamine (TAMRA) and

dihydrocyclopyrroloindole tripeptide minor groove binder (MGB) (Applied Biosystems,

Warrington, UK).

1.1.7.6. Testing for coronaviruses and human bocavirus

During the study period, hCoV and hBoV were not routinely tested at the MMPL. Therefore this

study tested 217 samples that were positive for influenza A viruses between 24th June 2011 and

30th June 2012 for presence of hCoV and hBoV using a one-step RT-PCR run on the StepOne™

& StepOnePlus™ Real-Time PCR Systems (Applied Biosystems, Warrington, UK). For hCoV,

new primers and template were designed and optimized.

41

1.1.7.7. Primers, templates and probes for hCoV, hBoV

Primers for hCoV were designed using the BLAST program [GenBank, Bethesda MD, 20894

USA]. Complete genomic sequences for 15 coronavirus were downloaded from the GenBank.

The retrieved sequences were aligned with BioEdit Sequence alignment Editor version 7.1.3.0

(103) and, where more than one sequence for each subtype was available, a consensus sequence

for each subtype generated. All the consensus sequences were then aligned to generate one

consensus for all consensuses which was then blasted on Primer Blast on the GenBank website,

limiting the search to nucleotide collection (nt) database and organism Coronaviridae. One pair

of primers targeting the region of the replicase open reading frame 1b (ORF1b) gene responsible

for the transcription of the non-structural protein 15 (nsp15; XendoU/NendoU) uridylate specific

endonuclease, which is highly conserved among all coronaviruses (104;105) was selected. For

the hBoV, the NS1 gene primers and template, designed by Qu et al., (106) were used. Details of

the sequences of the templates and primers are given in the methodology section of the paper on

hCoV and hBoV co-infection with influenza A viruses in the latter section of this thesis.

1.1.7.8. Determination of analytical sensitivity and reproducibility of RT-PCR protocols

A series of experiments were conducted to determine the analytical sensitivity and

reproducibility of the PCRs’, and to optimize the primer and probe concentrations (details of the

series of experiments that were conducted to determine the analytical sensitivity of the PCRs are

given in the paper). Briefly for hCoV, both a live coronavirus 229E (Public Health England

Culture Collection, Salisbury UK) and RNA transcribed from the templates (or amplicon

sequences) inserted into a bacterial plasmid pEX-A vector (Eurofins MWG Operon, Ebersberg

Germany) were used. For RNA synthesis the pEX-A vector was linearized using BAMHI

restriction enzyme (New England BioLabs, Ipswich USA) and RNA transcribed using the T7

polymerase enzyme using the T7 High Yield RNA synthesis Kit (New England BioLabs,

Ipswich USA). To determine the analytical sensitivity, standard curve experiments were

conducted in duplicates using serial dilutions of the transcribed RNA and RNA extracted from

the live human coronavirus 229E. The number of viral copies was calculated using the absolute

quantification method published by Brankatschk et al., (107) (an expanded discussion of the

analytical sensitivity experiments is given as an appendix to the paper on this subject). It would

have been good to also test the analytical sensitivity using serial RNA extracted from clinical

samples of all the known human coronaviruses. However, due to budgetary constraints, this was

not carried out and should be noted as one of the limitations of this study. The primer and probe

42

concentrations were also optimized using 20µM, 10µM, 7.50µM, and 5µM serial dilutions of

forward and reverse primer and 10µM, 6.60µM, 3.3µM, 0.37µM probe concentrations set up in

MicroAmp Fast Reaction PCR tubes and mounted on a 48 well plate. For the hBoV, similar

experiments were conducted, however, no RNA was transcribed, instead, the plasmid inserted

amplicon was used as a positive control.

1.1.7.9. PCR for identification of coronaviruses and bocavirus in samples

The RT-PCR experiments were conducted using the Power SYBR Green RNA – to – CT 1 –

Step Kit (Applied Biosystems, Warrington, UK). For identification of coronaviruses, the Power

SYBR Green RNA-to-CT 1-Step Kit (Applied Biosystems) was used in a one-step RT-PCR. The

reaction mixture comprised: Ten microliters (10µl) of Power SYBR Green RT-PCR Master Mix

(2X), 2.5µl forward and reverse primer (concentration; twenty micromolar 20µM), 1µl of

Arrayscript RT Enzyme mix (125X), 2µl of RNAse free water and 3µl of RNA template in

positive control well or sample in test wells. For identification of bocavirus the Power SYBR

Green PCR Master Mix (Applied Biosystems) was used. Details of the PCR protocols such as

cycling conditions are presented in the methodology section of the paper for this subject. For

both PCRs, positive amplification was determined using TaqMan amplicon-specific probes

quenched with MGB (Applied Biosystems, Warrington, UK).

1.1.7.10. Strategies in systematic reviews

Three systematic reviews and meta-analyses were conducted in this study. Individual study

quality was assessed using the STROBE tools (108) whereas the PRISMA statement for

reporting systematic reviews and meta-analysis (109) was followed to write the reviews (details

of the study assessment, selection criteria, and data extraction are provided in the methodology

sections of the reviews). Regarding the search strategies employed, briefly the searches were

conducted on the electronic databases; MEDLINE (Ovid) and EMBASE (Ovid) (which contains

papers published from May, 1946 to date), and WEB of Science (which contains studies

published from 1945 to date); Websites of health organisations e.g. the World Health

Organisation (WHO), United Kingdom’s Health Protection Agency (HPA; now Public Health

England), United States of Americas Centre for Disease Control (CDC), the World Influenza

Network Centre, and reference lists of well conducted published studies were also searched. A

summary of the search words, subject headings, mapping or text words used is provided in

Figure 1.4

43

1.1.7.11. Statistical analysis

Descriptive statistics were computed on the distribution of respiratory virus infections by age,

gender, season, year, and disease outcome (admission to general ward, admission to ICU and

death). The relationship between respiratory virus infections with any of these dichotomous

variables was first assessed using 2 x 2 tables, statistical differences were examined using the

Pearson’s Chi square (�²) statistic if each cell had >5 observations, and the Fisher’s exact test

for small samples.

The weekly age-specific number of hospitalizations associated with ARIs was used to calculate

incidence of hospitalizations in a Poisson model. Incidence of ARI hospitalizations was

calculated by dividing the age-specific weekly number of ARIs hospitalizations by the age-

specific mid-year population figures for North West England for the years 2007 – 2011, and the

2011 census figures for 2011/2012 season, and rounded up for every 100,000. Cumulative

annual hospitalization rates were calculated by summing up the weekly incidence rates. The

numbers of ARI hospitalizations were calculated using major complaint [respiratory classifiers (J

codes) of the International Conference for the Ninth Revision of the International Classification

of Diseases (ICD10].

For the incidence of FluApdm09 and SeasFlu A associated hospitalizations, the expected number

of hospitalizations was used. The expected number was calculated by dividing the weekly age-

specific number of hospitalizations with positive PCR identification of the viruses by the total

number of hospitalizations with ILI, multiplied by the age-specific total number of ARI

hospitalizations obtained using the ICD-10 J codes. Incidence was calculated by diving the

expected number of hospitalizations associated with each virus by the age-specific mid-year

population figures for North West England for the years 2007 – 2011, and the 2011 census

figures for 2011/2012 season, and rounded up for every 100,000 using a Poisson model.

44

Figure 1.4: The search words used in systematic reviews of this study

Notes: on electronic databases, words in each box were combined using ‘or’, search outcome from words in box 1 and

2B were also combined using ‘or’ whereas words in boxes 1, 2, 3 and 4 were combined using ‘and’. The examples of

outputs of searches from MEDLINE, EMBASE and WEB of SCIENCE are provided in appendixes to the review

papers.

1. Virus names ( strategy used for all reviews

Orthomyxoviridae, orthomyxoviridae infections, orthomyxovirus, influenza

human, influenza A virus, influenza A virus H1N1 subtype, 2009 H1N1

influenza, influenza A(H1N1)pdm09, influenza A virus H3N2 subtype.

2A. Virus genes/mutations (used in review 1)

influenza virus haemagglutinin, HA gene,

polymerase basic protein 2, PB2, non-structural

protein 1, NS1, evolution, molecular evolution,

mutation, genetic mutation, virus mutation, genetic

evolution, antigenicity.

2B. Virus names & infection patterns (used in review 2 &

3)

Viruses, virus, virus diseases, virus infection, respiratory

tract infections, respirovirus, respirovirus infections, lower

respiratory tract infection(s), upper respiratory tract

infection(s), rhinovirus, human rhinovirus, rhinovirus

infection, adenovirus, adenovirus infection(s), respiratory

syncytial virus(es), respiratory syncytial virus infection(s),

metapneumovirus, metapneumovirus human, parainfluenza

virus 1 human, parainfluenza virus 2 human, parainfluenza

virus 3 human, bocavirus, bocavirus infection, coronavirus,

coronavirus infection, co-infection(s),

4. Outcome of interest (used in all 3 reviews)

severity, severe disease, mild disease, pathogenicity,

virulence, virus virulence, prognosis, pathogenicity,

virus pneumonia, bronchiolitis, viral bronchiolitis,

hospital, hospitalisation, hospitalization, hospital care,

hospital admission, admission, patient admission,

length of stay, intensive care, critical care, intensive

care unit, ICU admission, fatality, mortality, death.

3. Outcome of interest (used in reviews 2 & 3)

mixed infection, dual infection(s), multiple infection(s).

.

45

Association between respiratory virus single and multiple infection with disease outcome was

evaluated; first using simple logistic regression models; and then using multiple logistic

regression models, controlling for age, sex, season, and an age and co-infection interaction

factor. The significance of the covariates was assessed using the likelihood-ratio test. All the

results were presented as odds ratios (OR) with 95% confidence intervals (CIs) with significance

level of p = 0.05. All analyses were done using the STATA software, version 11.0.

(STATACorp, Texas 77845, USA). In the systematic review, odds ratios were calculated using

the Comprehensive Meta-Analysis software – version 2 (BIOSTAT, Englewood, NJ 07631

USA) and results summarized using forest plots.

1.1.7.12. Ethical considerations

Preliminary permission to use the MMPL respiratory dataset was obtained from the Manchester

Health Protection Agency Research and Development (R&D) Committee. Ethical approval was

obtained from National Health Service – Research Ethics Committee (NHS-REC) through the

integrated research approval system (IRAS) (approval reference number 11 ⁄NW⁄ 0698). Further

ethical approval was obtained from the University of Manchester R&D governance office and a

PANMAN research approval form was completed. NHS R&D approval was obtained through

the NHS-CPS procedure (approval reference number R01835). All electronic datum obtained

from the MMPL respiratory database was anonymized by a third person and the original dataset

was delinked from the dataset given to the researcher; and no patient names or other sensitive

patient’s data were included. The electronic data were stored in encrypted USB and backups

were stored on the secure University of Manchester servers. Procedures of the Data Protection

Act 1998 were followed to avoid disclosure of confidential information through email and other

electronic routes. Results from this research have been published in different places; some

papers have been published in peer reviewed journals, others have been published as conference

abstracts as summarized in the prefixes to each section/chapter/paper and in Appendix I. All

publications have also been uploaded on eScholar of the University of Manchester John Rylands

Library. Project outcomes were also presented and discussed at Faculty of Medical and Human

Sciences quarterly postgraduate showcase meetings and the Medical Microbiology Unit monthly

research meetings.

46

Part II: The nature of respiratory virus infections

Synopsis

This section is an extension of the introduction. It describes the mode of spread, reproductive

number and duration of influenza like illnesses; reviews developments in the vaccination and

treatment protocols; describes the incidence of respiratory virus infections (including the

variations by seasonal, demographic, and social factors); describes the incidence and burden of

hospitalization and death associated with respiratory virus infections including mortality trends

in the past pandemics comparing it to the trend observed in the 2009 influenza A(H1N1)

pandemic, and concludes with a description of their genetics, and a review of the observed

virulence conferring mutations in the pandemic influenza A(H1N1)pdm09 virus.

47

2.1. The nature of respiratory virus disease Part A: Clinical characteristics of

respiratory virus infections, vaccines and treatments

2.1.1. Reproductive number and mode of transmission

The basic reproductive number (R0) for microorganisms range from 2 to 18; measles 13-14,

rubella 6 – 7, chicken pox 7 – 8, HIV 2 – 5, pertussis 16 – 18, mumps 7 - 14 (110). Respiratory

viruses spread through aerosols, droplets on hands and other fomites and are highly contagious.

Modelling studies have put the basic reproductive number (R0) [i.e. the expected number of

infectious hosts that one infectious patient would produce during his/her period of infectiousness,

in a population that is completely susceptible (110)] of the 1918-19, 1957-58 and 1968-69

influenza A virus pandemics at 2 to 3, 1.8 and 1.24 respectively (111-113). Whereas Glass et al.,

(114) and a review by Boelle et al., (115) put the R0 for the 2009 pandemic influenza virus at 1.0

to 2.0 and 1.2 to 2.3 with median value of 1.5 respectively. Conversely, in their review, Cintron-

Arias et al., (116) put the R0 of seasonal influenza viruses between 1.30 and 4.0. Although there

are some differences between studies, generally, the R0’s of other respiratory virus infections

closely resemble those of influenza viruses. In their study, Weber et al., (117), reported an R0 for

RSV ranging from 1.2 to 2.1. On the other hand, reproductive numbers of 2.6 - 2.7 were reported

for the SARS coronavirus by (118;119).

2.1.2. Signs and symptoms

After infection with a respiratory virus, signs and symptoms appear within 48 hours (range 24-96

hours), these mainly being fever, rhinorrhoea, myalgia, anorexia and transient depression,

followed by cough and sore throat. These symptoms normally last for three days before

subsiding usually by the 6th day (120;121). Most respiratory virus illnesses occur only as upper

respiratory tract infections, however, in 5% to 38%, respiratory virus infection may persist and

develop into acute lower respiratory tract infections (ALRI) with more severe symptoms such as,

worsening cough, tachypnoea, dyspnoea, bronchiolitis, bronchitis, croup, wheezing,

hyperinflation, myocarditis, febrile convulsions, myositis and complications in the form of

pneumonia or a toxic shock syndrome (14;17;122-125). In children of ≤5 years old, respiratory

syncytial virus infections are characterised by wheezing (126-128), whereas parainfluenza virus

infections are more associated with croup (17). On the other hand, the influenza B clinical

picture is similar to that of influenza A viruses, but influenza C and bocaviruses are

characterized by mild illness (87;129;130) although some studies have indicated that bocavirus

may cause significant ALRI (87;88).

48

While it is generally accepted that influenza like illnesses (ILI) are of short duration lasting for 4

to 7 days with the highest duration extending up to 14 days, evidence from studies among

immunocompromised individuals suggest that these individuals might harbour the viruses for

longer periods. Prolonged shedding of 1 year to one and a half years have previously been

reported by Weinstock et al., (131), Baz et al., (132), Ison et al., (133), McMinn et al., (134),

Rocha et al., (135) , Okomo-Adhiambo et al., (136), and Giannella et al., (137).

Specifically, Rocha et al., (135) reported influenza virus shedding by a child with severe

combined immunodeficiency disease who developed a 10 month long chronic infection with

naturally acquired influenza A virus, H1N1 subtype. The virus specimens were isolated from

nasal secretions taken at about monthly intervals over a 10 month period. Okomo-Adhiambo et

al., (136) reported an almost similar event for the 2009 pandemic influenza A(H1N1)pdm09

virus in a 2.5 year old child who shed the virus for 5 months before he finally died. Whereas

Giannella et al., (137) performed a prospective study of 64 adult patients (>16 years) of which

some were immunocompromised and others not. The patients were consecutively admitted with

laboratory confirmed influenza A(H1N1) virus infection between September and December,

2009. They reported that 16 of the 64 patients had prolonged viral shedding (range 7-28 days), of

which 6 had shed the virus for up to 14 days while 43 patients did not have prolonged viral

shedding. When classified by immunostatus, 11(68.8%) of the 16 patients who had prolonged

viral shedding were immunocompromised compared to 16 (33.3%) among immunocompetent

individuals who had a short duration of virus shedding and this difference was statistically

significant (OR: 5.15 95% CI: 1.2-22.2, p = 0.03) indicating that immunostatus was an important

risk factor for prolonged viral shedding.

2.1.3. Available vaccines and research on vaccines for other respiratory viruses

Infection with most respiratory viruses, triggers a variety of host immune responses, such as the

production of virus neutralizing antibodies by B-cells and the activation of virus-specific CD8+

cytotoxic T-lymphocytes (138;139). Antibodies against most respiratory surface proteins e.g.

HA-specific immunoglobulins, including IgM, IgA and IgG, appear within 2 weeks of virus

inoculation (130;140-145). The specificity of the antibody response to RNA virus infections in

humans is limited and varies considerably from individual to individual. The serum antibody

response is subtype specific, i.e. infection by one subtype confers little or no protective immunity

to other subtypes (141-145), or antibodies within the same strain or its variant may cross-react, a

phenomenon called "original antigenic sin" (140). This is possible because RNA viruses reassort

their genes and evade cell-mediated immunity. For example substitutions in influenza virus CTL

epitopes abrogate binding of immunogenic peptides to major histocompatibility complex class I

49

(MHC-I) molecules, binding of neutralizing antibodies, and diminish T-cell receptor interaction

(146). However, for the deoxyribonucleic acid (DNA) viruses i.e. adenoviruses and human

bocavirus, CD4-T cells offer long term immunity (147-151).

Because of the ever changing nature of RNA respiratory viruses, annual vaccination is used to

control the spread of influenza virus. Each year’s vaccine is designed based on a prediction of

the strains that are more likely to be in circulation in the next winter seasons (152). Currently,

vaccines for influenza viruses are in two forms; the trivalent inactivated vaccine and the live,

attenuated influenza vaccine (153). The former can be used for any person aged ≥6 months;

whereas the live vaccine can only be used in individuals aged 2–49 years and are considered not

safe in pregnant women and infants (153). Adenovirus based influenza virus vaccines offer new

hope for cost-effective, easy to use influenza vaccine, with long-lasting humoral and cellular

protective immunity (154-156).

For other respiratory viruses, a number of vaccine formulations e.g. for RSV, hMPV, hPIV and

SARS coronavirus are being investigated (95;96;157-159). The subunit vaccines focusing on the

first, second, and third generation purified fusion protein (PFP-1, PFP-2 and PFP-3) of RSV

were found to induce short lived immunity, however the live attenuated RSV, hPIV-3 and hMPV

vaccines look promising (96;159-163). Despite rhinoviruses being among the most common

aetiologic agent for respiratory virus illness, there is currently no vaccine(s) being developed

against rhinoviruses, as it is an almost impossible task, owing to the milliard serotypes of the

virus (164). For bocaviruses, as the clinical significance of bocaviruses is still under review, no

vaccine is currently under development (87).

2.1.4. Available treatments and research on treatments for other respiratory viruses

Among influenza viruses antiviral drugs, the matrix protein 2 (M2) channel blockers, amantadine

and rimantadine, and the neuraminidase inhibitors, oseltamivir, zanamivir and paramivir, are

used for treatment and chemoprophylaxis of influenza virus infections - oseltamivir and

zanamivir for both influenza A and B viruses, and amantadine and rimantadine for influenza A

viruses (165-168). Amantadine and rimantadine were introduced in 1966 and are effective when

treatment is begun within 48 hours of symptom onset (166). On the other hand, oseltamivir and

zanamivir were licenced in the 1990s (167). Antiviral prophylaxis and chemotherapy against

influenza is important however a major current concern is the emergence of resistance to one or

more of the four licenced antiviral agents since 2005 (165;169-175). As influenza A viruses

carry structurally distinct NAs, different mutations on the NA are responsible for the viruses

resistance to neuraminidase inhibitors (176). H1N1 and H5N1 viruses drug resistant mutations

50

have largely been marked by the H274Y mutation (177;178); H3N2 viruses the E119V and

R292K mutations (179) and influenza B viruses the R152K mutation (167). Whilst the

mutations L26F, V27A, A30T, and S31N have been reported to be responsible for resistance to

M2 channel blockers (169;180).

Treatment options for RSV are limited, only aerosolized ribavirin is licenced, however, the drug

is not widely used in treatment particularly because of the difficulties surrounding its

administration (157;181-184). Other RSV treatments include; corticosteroids (hydrocortisone,

dexamethasone and prednisone), salbutamol, ipratropium bromide, vitamin A, and interferon’s.

However, all these have not been found to be effective (157;185-187). Research on the antibody

palivizumab, for prophylactic treatment i.e. passive immunization, is promising

(157;186;188;189). In addition, a number of small molecules including; antibodies, fusion

inhibitors, attachment inhibitors, transcription inhibitors, antisense oligonucleotides, SiRNAs

and natural products or botanical, are being investigated (190).

No specific antiviral is presently licenced for treatment of hPIVs, however, a number of

chemical agents, indicated for paramoxyviruses are under review. For example: small synthetic

peptides targeting the F protein (191;192); inhibitors of virus protein and nucleic acid synthesis

e.g. puromycin (193); plant extracts (194); glucocorticoid (195;196); benzothiazole derivative;

1,2,4-thiazol-2 dicyanamide, carbocyclic-3-deezaadenosine; and calcium elenolate (47;197).

Treatments for RV have been reviewed by Anzueto (198) and include pleconaril which inhibits

the uncoating of the viral capsid and has been associated with reduction in severity and duration

of viral shedding in clinical trials. Other promising drug candidates include protein synthesis

inhibitors like AG7088 (199;200). As for the DNA respiratory virus (AdV), case reports and

clinical studies have reported the use of cidofovir, ribavirin, ganciclovir, and vidarabine for the

treatment of adenovirus infections; however, no clinical trial has been conducted yet (201;202).

The World Health Organisation made a call on the need for the pursuance of an integrated

approach to developing therapeutics and vaccines for RSV, hMPV, the hPIVs, and influenza

viruses i.e. for research to: “Identify gaps in knowledge and tools needed to develop effective

interventions; articulate a research agenda reflecting public health research priorities to

address these needs; and increase research efforts to develop new preventive and treatment

options” (98). Similar calls have previously been made by the scientific community (95-97).

This research aims at generating knowledge on the role of co-infections in disease severity so as

to aid in making a case for integrated drug, integrated vaccine and multi-target diagnostic tests

development.

51

2.2. The nature of respiratory virus disease Part B: Incidence of viral respiratory

infections

2.2.1. Incidence of respiratory virus infections globally

Studies have identified; influenza virus A/B, RSV, RV, AdV, hMPV, hCoV, hBoV and hPIV1-

4 as causers of viral respiratory infections (203-206). The World Health Organisation estimates

that in each winter season, in the temperate regions, and during rainy and cold seasons, in the

tropics, between 10-20% of populations suffer from influenza, and 22–40% of populations

present with symptoms of viral respiratory infections (1).

2.2.1.1. Incidence as reported by community based studies

Table 2.1 summarizes the results of community based studies on incidence of respiratory

viruses conducted in the 1920s, 60s, 70s, 80s and more recently. In Tecumseh USA, over 1,000

families (or 10% of the population) were followed up; from 1965-1971 in first phase, and from

1976-1981 in the second. Twenty two point one percent (22.1%) of the population suffered

from respiratory illness in each year. Incidence was greatly associated with age, infections

being more common in children < 5 years old - Figure 2.1 (204); age-specific annual attack

rates; (31.3%; 293.1 per 1,000 person years) among children 0-4 years old; 21.2% (116.1/1,000

person years) in 5-19 year olds; 18.6% (70.9/1,000 person years) in 20-39 year olds; and 15.8%

(26.7 per 1,000 person years) among those aged ≥ 40 years. Similar observations were reported

by other community based studies with incidence rates ranging between 28.9 and 6,200 per

1,000 population; including a USA study on families, civil servants, military and university

students (64;207), the Baltimore study (208), the New York Virus Watch (209), the Cleveland

family study (210), the Holland Family study (211) and a community based study in

Bangladesh (212) - Table 2.1. However some recent studies reported higher rates among

children 5-19 years old, for example, in a study of the epidemiology of influenza like illnesses

in the Amazon Basin (Peru), incidence also varied with age; 19.6% (122.9 per 1,000 person

years) among children 0-4 years old, 62.7/1,000 in 5-15 year olds, 24.5 in 15-29, 25.4 in 30-44,

31.4 in 45-59, and 29.8 per 1,000 person years in those aged ≥60 years (66). A recent meta-

analysis indicated that the pandemic influenza A(H1N1) virus infections peaked in the 5-19

year olds - Figure 2.2 as indicated by post-pandemic serological investigations (213).

52

2.2.1.2. Incidence as reported by hospital based studies

The incidence rates reported by hospital based studies tend to be lower than those reported by

community based studies – Table 2.1. This is because not all individuals suffering from

respiratory virus infections occurring in the community seek medical attention. It is also

because, community based surveys, which involve households with school going children, tend

to have higher incidence rates since ILI attack rates are much higher in school going children

(204;214).

In general, incidence reports from hospital based studies also indicate that the incidence of

respiratory virus infections is higher in children ≤5 years old. In the United States of America,

Fowlkes et al., (215) and Glezen et al., (216) used data from the MacGregor Medical

Association prepaid health care plan, sentinel surveillance schemes in Houston USA, and

outpatient practices in six states, respectively. Fowlkes et al., (215) reported an incidence of

20.0 (95% CI: 19.7 – 20.4) per 1,000 population for a six months period, whereas Glezen et al.,

(216) reported an incidence of 120 per 1,000 persons/year with preschool children having the

greatest burden, 270 per 1,000 persons during the 1981-1982 influenza season and 290 per

1,000 persons during the 1982-1983 season. In Europe, Hak et al., (217) analysed two years of

computerized data (May, 2000 and April, 2002) from 90 general practices in the Netherlands.

Overall upper respiratory virus infections had an incidence of 144 per 1,000 person years, and

just like in the in the Tecumseh and the Peruvian studies (66;204), the incidence was higher

among children 0-4 years than in other age groups (IR: 392 vs. 80 per 1,000 person years

respectively).

In the UK, data from the Royal College of Physicians General Practice weekly report database

(RCGP) indicated that between 1966 and 2006, respiratory virus infections peaked only during

4 winter seasons; 6.51 per 1,000 population per annum in 1969/70, 0.749 per 1,000 in 1971/72,

3.676 per 1,000 in 1972/73 and 4.103 per 1,000 during the 1975/76. After 1975/76, respiratory

virus activity was comparatively low for the next 13 years and peaked again during the 1989/90

season (3.03 per 1,000 population per annum) (218). During the recent influenza A virus

pandemic period, UK HPA reports indicated that during the 2009/2010 season, the highest

weekly incidence rate in England and Wales was 1.563 per 1,000 population, declining to 1.202

in the 2010/2011 winter season and 0.193 during the 2011/12 winter season with Scotland and

Northern Ireland reporting slightly lower but almost similar weekly rates – data not shown

(9;219). As for annual incidence rates, the RCGP reported an annual ILI incidence of 11.5 per

53

1,000 population in 2011 and annual rates of 6.8 – 14.0 per 1,000 population prior to the

pandemic, i.e. between 2001 and 2007, (220;221).

2.2.1.3. Prevalence of different respiratory virus infections

A number of reviews have investigated the relative importance of specific respiratory viruses as

causes of viral respiratory disease in adults and children globally. These studies have identified,

in order of their importance: RSV, RV, Flu A, AdV and hPIV1-4 as the most predominant

respiratory viruses (5;17;222-229).

To further understand which respiratory virus is the important cause of respiratory disease,

findings of 82 epidemiologic studies (total sample size 127,135), from all over the world, on

the prevalence of respiratory viruses, were summarized and the results are presented in Figures

2.2A and 2.2B (details of the studies that were used to compile these figures are presented in

Appendix III). In crude analysis, RSV was the most predominant virus followed by RV, and

influenza A i.e. half of the studies found 18.1% of patients with respiratory virus infection had

RSV (25th and 75th percentiles of 7.0 and 28.0% respectively), followed by RV (16.5%), hBoV

(6.9%), Flu A (5.9%), hMPV (5.0%), hPIV1-4 (4.9), AdV (4.0%), hCoV (3.1%), and Flu B

(1.6%) (Figure 2.2A). When the studies are stratified by patients age and whether the study

recruited only hospitalized or both hospitalized and outpatients, RV and influenza A viruses

were the leading causes of illness among adults whether they were hospitalized or not, while

RSV remained the highest cause of respiratory disease among hospitalized under-five children

(Figure 2.2B).

The finding here on the importance of hBoV is in agreement with a review by Schildgen et al.,

(87), which reported bocavirus as being commonly found co-infecting with other respiratory

viruses. Notwithstanding the importance of RSV, Flu A, RV, AdV and hPIV1-4 as

predominant causers of viral respiratory infections, it is however difficult to draw conclusions

on the epidemiology of hCoV as most of the studies reviewed here and by other researchers

(17;222-228), did not test for hCoVs. Since respiratory virus activity varies with season and

year, differences in study findings might also be due to the differences in the season or year the

studies were conducted.

54

Table 2.1: Incidence of respiratory virus infections: population based family studies and

hospital based studies

Type of study

Study name

& Sample size

Period

of study

No./1,000

per annum

Population based family studies

1 Forshey et al., (66) Iquitos, Peru

1,570 families (10,341 persons)

2008 - 2009 46.7

2 Monto and Sullivan (204) Tecumseh family study

1,000 persons (10% pp)

1965 – 1971

1976 - 1981

267 – 2,131

3 Fox et al. (209) New York Virus Watch

178 families (761 persons)

1961 - 1965 3,800

4 Badger et al., (210) Cleveland family study 1948 - 1950 6,200

5 Frost and Gover (207) Students, medics, soldiers,

civil servants, families ≤7,050

1923 - 1926 1,727 – 3,231

6 Van Loghem (211) Holland family study, 1623

families, 6933 individuals

1925 - 1926 4,280

7 van Volkenburgh and Frost (208) Baltimore family study

114 families (968 persons)

1928 - 1930 3,070 - 3,180

8 Townsend and Sydenstricker (64) Students, medics, soldiers,

civil servants, 775 families

(2,598 persons)

1923 - 1924 2,009

9 Azziz-Baumgartener et al., (212) Bangladesh community/hospital

based study 282 persons from 32

families

2008 - 2010 100 – 170

Hospital based studies

1 Glezen et al., (216) HMO & Sentinel data Houston, USA 1981 - 1983 120 - 290

2 Fowlkes et al., (215) Surveillance from 6 states in USA 2009 - 2010 19.7 – 20.4 Ϯ

3 Hak et al., (217) General practice database,

Netherlands

2000 - 2002 144

4 Paget et al., (230) European Paediatric Influenza

Analysis (EPIA) project

2002 - 2008 5.81 – 177.5

5 Ajayi-Obe et al., (231) Royal London Hospital, UK,

prospective descriptive study

2002 - 2004 7.3 – 82.8

6 RCGP (220) General practice surveillance data 2001 - 2007 6.8 – 14.0

7 RCGP (221) General practice surveillance data 2011 8.5 – 29.3

8 Feikin et al., (232) Hospital surveillance Kenya 2007 -2010 71 – 177

Notes: Ϯ incidence is cumulative incidence for 10 months. Annual cumulative incidence = weekly incidence rate *

52 weeks. It should be noted that study design e.g. whether families were selected for having a school going child or

for having a new born baby could affect the outcome. To enable comparison, where studies reported rates in units

other than 1,000, they were converted to 1,000 population.

55

Figure 2.1: Mean number of respiratory illnesses (and 95 % confidence intervals)

experienced per year by age and sex, 1976-81 Tecumseh Michigan, USA. Reproduced with

permission; Figure 1, Monto and Sullivan (204), Cambridge University Press.

Figure 2.2: Age-specific (A) prevalence of cross-reactive antibodies from baseline pre-

pandemic sera, (B) cumulative incidence of H1N1pdm infection using pre- and post-

pandemic sera and (C) H1N1pdm seroprevalence from post-pandemic sera. Legend: Point

estimates indicates pooled estimate and lines represent relevant 95% CI. Each line represents unadjusted age-

specific results from individual studies. Reprint of Figure 4, Van Kerkhove et al., (213), reprinted with

permission from John Wiley & Sons Ltd, Copyright Licence number 3234481364474.

56

Per

cen

tag

e

02

04

06

08

01

00

FluA FluB RSV RV AdV MPV CoV BoV PIV1_4

Virus type

Figure 2.3A: Prevalence of respiratory viruses as reported by different studies globally. Notes: Box plot summarizing the percentages of each virus from the total number of identified respiratory virus

infections, reported by different studies. The horizontal lines inside the box give the median (half the of the studies

reported such a proportion), the bottom and top lines of the boxes give the 25th and 75th percentile of all

proportions reported for the virus and the dots are outliers. Flu A - influenza A virus, Flu B - influenza B virus,

RSV - respiratory syncytial virus, RV - rhinovirus, AdV - adenovirus, hMPV - human metapneumovirus, hPIV1-4

– human parainfluenza virus types 1 to 4. Details of the studies that were used to plot the boxplots are summarized

in Appendix III. The appendix also contains the relative frequency (%) of each respiratory virus among all ILI,

reported by each study.

57

Per

cen

tag

e

02

04

06

08

01

00

Hospitalized In and outpatients

<5 yrs All Ages <5 yrs All ages

FluA FluB RSV RV AdV MPV CoV BoV PIV1_4

Patients type and age group

Figure 2.3B: Age and patient group - specific prevalence of respiratory viruses as

reported by different studies globally. Notes: Box plot summarizing the percentages of each virus

among a given patients group (e.g. under 5 hospitalized children). In each block, the order of the virus type from

left to right is the same as in the legend. The horizontal lines inside the box give the median (half the of the studies

reported such a proportion), the bottom and top lines of the boxes give the 25th and 75th percentile of all

proportions reported for the virus and the dots are outliers. Flu A - influenza A virus, Flu B - influenza B virus,

RSV - respiratory syncytial virus, RV - rhinovirus, AdV - adenovirus, hMPV - human metapneumovirus, hPIV1-4

– human parainfluenza virus types 1 to 4. Details of the studies that were used to plot the boxplots are summarized

in Appendix III. The appendix also contains the relative frequency (%) of each respiratory virus among all ILI,

reported by each study.

58

2.2.2. Rates of severe disease and incidence of hospitalization and mortality associated

with respiratory virus infections

2.2.2.1. The rates of severe respiratory disease

Evidence from epidemiological studies indicates that 5 - 38% of all ILI episodes would develop

into acute respiratory tract infections (8;209;226;233-236) and 5 - 25% would seek medical

consultation (204;212;226;237;238) and 0.01 – to 5% would die (6;8;226) - (Table 2.2).

Consequently, respiratory virus infections impart a substantial burden on the health care

system, with between 3.0 - 15 % of all medical consultations being due to ILI (215;239), and

up to 40.0% of all paediatric emergency department visits being due to ILI (231;240).

2.2.2.2. The incidence of hospitalization and mortality

The rates of hospitalization and mortality due to respiratory viruses has its highest toll in ≤ 5

years old children, ≥ 65 years old adults and those with high-risk conditions. Approximately

5.4 - 15% of all acute respiratory viral conditions, or all patients with a positive diagnosis of

respiratory virus are hospitalized (6;241-243), and between 1 % and 5% of those hospitalized

with ARI, or those with acute respiratory infections die (6;226;243;243-248). Table 2.3

summarizes studies, from all over the world, that estimated population based incidence rates of

hospitalization and mortality from respiratory virus infections. Hospitalization rates range from

200 to ≥1,000 per 100,000 population for persons aged ≥65 years, compared to 200 to 500 per

100,000 population for children 5 to 14 years of age, and 80 to 400 per 100,000 for persons 15-

64 years of age. The rates among middle aged (15-64 year olds) also varies with risk

conditions; with rate of 250 to 650 per 100,000 population in those with high-risk conditions

e.g. diabetes, and 20 to 80 per 100,000 in those without.

In their studies, Schanzer et al., (249) and Schanzer et al., (250) reported that, over the period

1994 -2000, in Canada, influenza-associated hospitalizations ranged from 18-200 per 100,000

population. The largest burden was in infants 6 to 11 months of age (200 per 100,000), almost

equivalent to the rate of 270–340 per 100,000 for adults 65 to 69 years old, compared to a rate

of 65/100, 000 among persons aged 20 years or older. In the UK, Nicholson et al., (251)

assessed children presenting to the Leicester Royal Infirmary Children’s Hospital (which serves

1 million inhabitants, including 68,000 children ≤71 months old), from October 2001 to June

2002. They reported that the rates of admission for influenza A, RSV and hMPV were 144

(95% CI: 117–175), 517 (95% CI: 465–574), and 126 (95% CI: 101–156) per 100,000

respectively.

59

2.2.2.3. The burden of hospitalization and mortality

Data from the World Health Organisation indicate that there are at least 3.9 million deaths from

ARI throughout the world each year and that ARIs are responsible for a loss of 94.0 million

(6.3% of total) disability-adjusted life-years (DALYs), making them rank first among causes of

disability-adjusted life-years (2). In the USA, Molinari et al., (8) reported influenza is

responsible for 20-40 million outpatient visits, 330,000, hospitalizations and 30,000 deaths

annually, almost similar findings were reported for influenza, RSV and parainfluenza virus type

3 by earlier studies (238;252-259). In Germany, a 2 year multicentre study of children aged <3

years by Forster et al., (260) reported that between 1999 and 2001, a total of 682,128 children <

3 years old were seen as outpatients by a doctor for LRTIs of which 69,847 were hospitalized

giving an annual hospitalization rate for LRTIs of 2,941 per 100,000 children.

Rudan and colleagues, estimated that in developing countries, the median incidence of lower

respiratory tract infections (LRTI) was 44 per 100 child-years, equal to approximately 150.7

million new cases each year and 11 – 20 million (7 to 13% of all ARI cases) would be

hospitalized (6). Reviews on the burden on influenza and RSV indicated that in 2008,

influenza caused 90 million infections globally and 21 million of these developed into lower

respiratory infections with 28,000–111,500 deaths. RSV caused 33.8 million ALRIs of which

3.4 million (22% of all ALRI cases) required hospital admission, and an estimated 66,000 to

199,000 of the children died (227;228). In the United Kingdom, Pitman et al., (7) estimated that

between 1990 - 2004 there were, in each year, around 779,000 to 1,164,000 influenza

associated general practice consultations, 19,000 to 31,200 hospital admissions and 18,500 to

24,800 deaths.

2.2.2.4. Waves and relative virulence of influenza virus pandemics and epidemics

In general, the influenza pandemics which have occurred in the past centuries, have shown

marked variation in severity, ranging from a mild pandemic in 1968/69, to a severe pandemic

in 1918/19 (261;262). In each of the five pandemics of the 20th century, there were multiple

waves of infection following the emergence of the virus. In the 1918 pandemic, the first wave

occurred in spring 1918 in the USA. The second wave began in August 1918 and the third

wave appeared in spring 1919 (263;264). It is estimated that in total the pandemic claimed over

50 million lives, and had an excessive death rate of 598/100,000 persons per year

(262;265;266) - Table 2.4.

The 1957–1958 pandemic originated in Yunan Province, China in February 1957 and second

wave was in early 1958 (261). The two waves were similar in severity in many countries but

60

the second had caused larger number of deaths in some (261;266;267). The estimated

worldwide death toll was around 1 million persons, while its excessive death rate was attributed

to around 40.6/100,000 persons per year – Table 2.4.

Similarly, the 1968–1969 pandemic began in China in July 1968, spreading rapidly to the

remainder of Southeast Asia and Australia and Europe by September 1968. It at first caused

mild illnesses until the winter season of 1969–1970 (268-270). An analysis of the genes of the

pandemic virus indicated that the pandemic was caused by PB1 and HA obtained from avian

sources (Figure 2.21). However because the NA segment was similar to earlier viruses,

antibodies to this glycoprotein conferred partial protection against clinical disease and probably

this was the reason for its relative mildness (271) – mortality rate of 16.9 per 100,000

population - Table 2.4. Estimates of victims of the 1969 pandemic show a range of 1-3 million

fatalities, of which over 30,000 were from the United Kingdom (272).

The 1977-78 pandemic was a return of the virus which had been circulating in the 1918 - 1950s

(273). Outbreaks of influenza were recorded in three Chinese provinces from end of May,

1977. Further outbreaks were recorded in China, Taiwan, the Philippines, Siberia, Singapore

and Russia in November, spreading to North America, the UK and a number of central

European countries and Japan in February 1978. By March, epidemics were reported in North

America, Scandinavian countries, Europe and the Middle East, reaching Australia, New

Zealand, Central America, South America in June. However, compared to the 1918/1919

pandemic, it caused mild deaths (261) – mortality rate of 21.0 per 100,000 population – Table

2.4.

As for the pandemic influenza A(H1N1)pdm09 virus, the first confirmed cases were from

California and Mexico reported by CDC on 17 April 2009. Later WHO confirmed the Mexico

and California cases to be the same novel virus, an outbreak notice was issued a week later on

24 April 2009 and a global pandemic alert which was then upgraded to level 6 on 11 June 2009

(274). The virus then spread to all other parts of the world (over 214 countries). Reports on the

first wave indicated that the majority of cases were mild influenza-like illnesses, with few cases

having high fever and some patients having gastrointestinal symptoms including diarrhoea

(275). The second wave was from October 2009 to January 2010. In the UK, by this time, the

virus had caused a total of 29,228 infections: with 20,595 occurring in England, 6,600 in

Scotland, 1,369 in Northern Ireland, and 664 in Wales with 440 deaths (276). The 3rd wave

was from December 2010 to February, 2011 (277). According to the 18th February 2011 WHO

weekly epidemiological update, by this time, the virus severity had increased with severe and

fatal cases reported in the UK and the Middle East (278). A study by Dawood et al., (279)

61

estimated that between April, 2009 and August, 2010 there were 18,500 laboratory confirmed

cases of the pandemic virus and between 151,700 and 575,400 deaths globally, representing a

rate of 1.7 to 7.8 per 100,000 population.

A general observation to make is that hospitalization and mortality rates in the past pandemics

varied with age. Simonsen et al.,(255) compared the age distribution of influenza-related deaths

in the United States during 3 influenza pandemics (1918 -1999, 1957 – 1969 and 1968 – 1969).

They observed that in each pandemic, hospitalization and mortality was high among young

people ≤65 years of age, but for all the 3 pandemics, the trend has been that this group

accounted for decreasingly smaller proportions of deaths during the decades that follow each

pandemic (Figure 2.4). The authors suggest that individuals infected by the pandemic virus

acquired immunity to the virus, the so called “original antigenic sin’’ (140), and that there is a

tendency for selective acquisition of protection against fatal illness among younger persons. A

recent review on the patterns of hospitalization and mortality of the pandemic influenza

A(H1N1)pdm09 indicated that, among the 19 countries included in the analysis, the median age

of hospitalization, admission to ICU and death were 7 - 38 years, 28 - 49.5 years and 30 - 56

years respectively, but the highest per capita death rate was in the elderly ≥65 old – Figure 2.5

(10). Similarly, a recent review by Wang et al., (280) on pandemic influenza A(H1N1)pdm09

fatality risk, has also reported an age dependent mortality risk. If the observations by Simonsen

et al., (255), Van Kerkhove et al., (10) and Wong et al., (280) are to repeat in the coming

decades, it could be expected that the future seasonal strains of the pandemic influenza

A(H1N1)pdm09 virus would cause more deaths in new born children and the very elderly.

2.2.3. Risk pyramid for respiratory virus infections

From the evidence reviewed above, it can be summarized that in each winter season, around

20% of the population suffer from respiratory virus infections, 5 to 25% of these would seek

medical attention, 5 to 15% of those that are medical attended would be hospitalized and

around 5% of those hospitalized will die. Figure 2.6 is a risk pyramid giving a schematic of

risk/burden of respiratory virus infections. The estimates here are only averages, and incidence

rates of infection, hospitalization, or death are likely to vary with virus type (including co-

infections), patients’ health status, age, and other factors.

62

Table 2.2: Proportion of patients with viral respiratory infection that seek medical

attention or develop acute respiratory tract infection

Study name

Viruses studied

Age of

participants

% episodes

seeking

medical

attention

% episodes

develops

ARIs

Children (ARIs)

1 Wright et al., (234) LRTI < 1 year 2.8 –32.9

2 Koch et al., (281) ARIs < 2 years 59.3

3 Zaman et al., (282) ARIs < 5 years 4.4 Ϯ

4 Bourgeois et al., (283) ARIs < 5 years 46.0

5 Broor et al., (284) ARIs < 5 years 19.5

Children (specific viruses)

6 Nicholson et al., (251) Flu A/B, RSV & hMPV < 6 years 34.1

7 Fisher et al., (233) Flu, RSV & hPIV < 5 years 10.8 – 37.8

8 Monto et al., (285) Flu A/B & RV < 14 years 29.6 – 54.6

9 Lee et al., (238) RSV & hPIV < 5 years 2.0 – 44.8 1.0 – 22.4

10 Neuzil et al., (286) Flu A < 5 years 9.5 – 20.0 9.0

All ages groups (ARIs)

11 Monto & Sullivan (204) ARIs All ages 25.4

12 Azziz-Baumgartner et al., (212) ILI All ages 16.0 – 36.0

13 Banatvala et al., (287) ARIs All ages 11.4

14 Fox et al., (209) ILI All ages 8.0 – 13.0

15 Glezen et al., (216) ARI All ages 1.1 – 29.3

16 Barker & Mullooly (247) ARI, Flu A/B >14 years 13.4 – 20.4 9.1 – 12.0

All ages groups (specific viruses)

17 Monto et al., (236) Flu A/B All ages 12.0 – 71.4

18 Knott et al., (235) hPIV1-3 All ages 18.0 – 48.0

Elderly (ARIs)

19 Nicholson et al., (237) ARIs > 60 years 40.2 65.0

Notes: Low incidence could be due to poor compliance, Ϯ = low percentage due to strict definition of ARI. The %

ILI that developed acute disease or were medically attended was calculated by diving the reported number of ARI or

ALRI by total number of ILI.

63

Table 2.3: Hospitalization and mortality associated with respiratory virus infections

No

Study name

Period

Viruses studied

Study design & country

Hospitalized No/100,000

Deaths No/100,000

Children

1 Forster at al., (260) 1999 - 2001 LRTIs, Flu, RSV, PIV

Hospital based surveillance PRIDE study, Germany

LRTI 2,941 (2,843 – 3,042)

NA

2 Mullooly et al., (288) 1968 - 1973 ILI Hospital based database, Kaiser-Permanente Medical care database, USA

180– 250 NA

3 Schanzer et al., (249) 1994 - 2000 ILI, Flu A/B, RSV, hPIV

HMDB Morbidity Database, Canada

Flu 18 – 200 RSV 130 – 2,000 hPIV 160

NA

4 Nokes et al., (289) 2002 – 2007 LRTI, RSV Hospital based, Kenya LRTI 1,912 – 5,528 RSV 293 – 1,107

NA

5 Iwane et al., (290) 2000 - 2001 ARIs, Flu A/B, RSV, hPIV

Hospitalized children linked to specific population, USA

Flu A/B 60 RSV 350 hPIV 120

NA

6 Chiu et al., (291) 2003 - 2006 ARIs, RSV, AdV, hPIV

Hospital admission database, Honk-Kong

Flu A/B 41.4 – 62.8 RSV 101.5 – 131.8 AdV 18.5 – 61.5 hPIV 13.8 – 32.5

NA

7 Izurieta et al., (258) 1992 - 1997 ARIs Health care organisations databases, USA

Low risk 11 – 193* High risk 16 – 231*

NA

8 Nicholson et al., (251) 2001-2002 ARI, Flu A/B RSV, hMPV

Hospital based prospective surveillance, UK

RSV 517 Flu A/B 144 hMPV 126

NA

9 Stockman et al., (292) 1997 - 2006 LRTI, RSV Hospital discharge records, USA

LRTI 2,790 RSV 670

NA

All ages/ Adults

10 Barker & Mullooly (247) 1968 - 1973 ARIs, Flu A/B

Hospital based database, Kaiser-Permanente Medical care database, USA

266 - 365 18 - 33

11 Glezen et al., (216) 1981 - 1983 ARIs Hospital & health care firm databases, USA

114 - 127 5.0 - 7.2

12 Karstaedt et al., (293) 1997 - 1999 Flu A/B RSV

Hospital based survey, RSA Flu A/B

82.2 – 220.9 Ɨ RSV 2.6 – 110.5

13 Wong et al., (294) 1996 - 2000 ARI Flu A/B

Hospital based database, Hong Kong

ARI 6.0 – 266.0 Ɨ

NA

14 Glezen (295) 1974 - 1981 ARIs Sentinel surveillance Houston & Harris, USA

ARIs 168-233 14.1

15 Azziz-Baumgartner et al., (296) 2002 - 2009 Flu A/B Hospital based database, Argentina

20 - 60 Ϯ Ɨ 6 - 21 Ϯ Ɨ

16 Feikin et al., (232) 2007 - 2009 ILI Flu A/B

Prospective hospital based surveillance, Kenya

ILI 699.9 Flu A/B 56.2

NA

17 Schanzer et al., (250) 1994 – 2000 ILI, Flu A/B, RSV, hPIV

HMDB Morbidity Database, Canada

Flu 270 – 340 RSV 30 – 110 hPIV 60 – 90

NA

Notes: Rates are annual rates per 100,000 population or person year/months. NA = data not provided, Ϯ – person

year * - person months, Ɨ – Excess number hospitalizations or deaths.

64

Table 2.4: Mortality associated with different influenza virus strains

Notes: Mortality data include deaths associated with all influenza A and B viruses combined. Many of these data

have been calculated with the use of differing methods and may not be strictly comparable. The 1934, 1951, and

1997 data span 2 years. Reprint of Figure 1; Morens et al., (266), reprinted with permission from

Massachusetts Medical Society.

65

Figure 2.4: Age distribution of deaths associated with 3 influenza A pandemics and

interpandemic seasons in United States, 1918–1995. Data from tables 1–3 are plotted graphically for

influenza A (H1N1), A (H2N2), and A (H3N2). For influenza A (H1N1) seasons, only data point for 1986–1987

season was included, which was the only season when excess mortality was attributed solely to A (H1N1) viruses.

Data points since 1968 are based on our analysis of pneumonia and influenza (P&I) mortality data. Reprint of

Figure 1; Simonsen et al., (255), by permission of Oxford University Press, Copyright Licence number

3234490313266.

Figure 2.5: Ratio of confirmed H1N1pdm deaths to hospitalizations for selected countries. Notes: Counties included: Spain, Singapore, China, Hong Kong, Canada, the Netherlands, Thailand, Chile,

Germany, Japan, USA, New Zealand. Bars represent maximum country ratio. Reprint of Figure 2; Van Kerkhove

et al., (10), reprinted with permission from the National Academy of Sciences, U.S.A

66

4. Deaths

5%

3. Hospitalizations

5 - 15%

2. Medically attended

5 - 25%

1. Incidence of RVIs

in the population

10 - 20%

Figure 2.6: Risk pyramid for respiratory virus infections. Notes: The proportions represent only

mean proportions as some studies reported, for example, higher rates of hospitalizations or deaths among the

elderly >65 years old or among those with high risk conditions e.g. immunocompromised individuals, or due to

other factors. In a previous incidence estimation study, Molinari et al., (8) used age specific attack rates of

between 6.6 – 20.3%, and 10.6 – 51.2% in high risk individuals, ILI associated hospital visit 31.3 – 62.0% and

62.5 – 91.0% in high risk individuals, hospitalization rates of 0.06 – 4.2% and death risks of 0.004 to 1.2%.

Much higher rates of acute respiratory disease, hospitalizations and deaths, than estimated in this figure, were

reported for RSV in the review by Crowcroft et al., (226).

67

2.2.4. Seasonality of viral respiratory infections

Globally, there is a clear seasonal variation in the activity of respiratory virus infections peaking in

winters in temperate regions and in rainy seasons in the tropics. Specifically, epidemiological

studies in Europe, Africa, South America, Asia, and Australia have reported consistent seasonal

variations in influenza and RSV infections, while in temperate regions e.g. the UK; RSV and

influenza A viruses peak in the winter season, in November-February, which corresponds to

March-August in the southern hemisphere (224;297-300). However while some respiratory

viruses have a predictable seasonal pattern, some researchers: Pitman et al., (7), Jansen et al.,

(301), Razanajatovo et al., (302), HPA (9) and Mak et al., (303), have reported that rhinovirus and

adenovirus infections occur year round.

In the United Kingdom, surveillance reports of influenza and other respiratory viruses over the

2010/2011 and 2011/2012 seasons (9;219) indicated that respiratory virus activity peaked in

weeks 48 to 6. The only exception was in 2009 when there was a heightened influenza like

activity in summer, weeks 26-36 – Figure 2.7 (9), coinciding with the WHO declaration of a world

pandemic of the influenza A(H1N1)pdm09. A review of the epidemiology of influenza and other

respiratory viruses in Africa (222), indicated predominance of influenza and RSV in the cooler

winter months of June to August, in southern Africa (Zambia, South Africa, and Madagascar), a

phenomenon similar to that in North America and Europe. However, seasonality was less

pronounced in the countries nearer to the equator such as Senegal. This correlated more with

constancy of temperature; in Senegal, temperature is relatively constant (at 27–32°C), whereas

rainfall varies substantially with heavy rains between July and September and relatively low in

November - May, yet respiratory virus circulation is continuous throughout these months.

Previous reviews of epidemiology of RSV by Weber et al., (223), Welliver (299) and Shek et al.,

(224) reported similar findings.

In a review of the relationship between meteorological conditions and RSV epidemiology, Yusuf

et al., (300) adds further explanation to the picture observed in the tropics. Respiratory virus

activity varies as we move from the equator towards the poles with continuous activity in places

where temperatures are very constant and rainfall is generally heavy (in the equatorial region up to

latitude 19.2o–25.8o North or south of the equator, e.g. Mexico City, Hong Kong, Dhaka in

Bangladesh, Riyadh in Saudi Arabia and Miami in USA). Beyond this point, i.e. latitudes 29.0o –

42.6o north or south, e.g. Jacksonville, Delhi, Kuwait City, Tucson, Santiago, Albuquerque,

68

Buffalo, UK, Europe, China, Japan respiratory viruses have a bimodal activity, a much

pronounced peak of incidence in winters (November to February in the Northern Hemisphere) and

a smaller one in summers. At much higher latitudes 49.5o –60.5 o north or south e.g. Winnipeg,

Oslo, Bethel, Alaska, Russia and the Scandinavia, where severely cold weather prolongs

throughout the year, there tend to be continuous respiratory virus activity throughout the year -

Figure 2.8. These variations have been linked to ambient temperatures, humidity, ultraviolet B

radiation, immunological changes, and human behaviour (staying indoors, or going outdoors);

warmer temperature and high humidity may offer greater stability of respiratory viruses in

aerosols, ultraviolet B radiation may inactivate the virus in the environment, cooler climates may

possibly increase virus stability, or cause cooling of the nasal passages with concomitant decrease

in respiratory defense, or cause crowding of susceptible individuals indoors (224;299;300).

Figure 2.7: RCGP weekly ILI rate per 100,000 in England and Wales from various seasons

since 1999/00. Reprint of Figure 2, page 15; PHE (9), “© Crown copyright. Reproduced with permission of

Public Health England”.

69

Figure 2.8: Total number of respiratory syncytial virus (RSV) cases identified throughout

several years of observation in six North American cities. Notes: The horizontal axis represents weeks

1–52 for five cities, and months 1–12 for Bethel, AK (only monthly RSV totals were available from Bethel). The

vertical axis displays the number of RSV cases identified during each year of surveillance at each site. Each circle

represents the total number of RSV cases identified in each week (or, in the case of Bethel, each month) for each year

surveyed. Reprint of Figure 1, Yusuf et al., (300), reprinted with permission from Cambridge University Press.

70

2.2.5. Social-economic factors and incidence of viral respiratory infections

The socio-economic status (SES) hypothesis proposes that individuals with low socio-economic

status will have increased risk of respiratory virus infections because they are more likely to have

more siblings in one household leading to overcrowding, an environmental condition conducive to

transmission of respiratory infections. The poor and less educated are also more likely to live

under poor environmental sanitation and poor hygienic practices; or are more likely to live under

stressful life conditions and poor nutrition which lower their immune system. They are presumed

to do less exercise or have high prevalence of risky behaviours like smoking; or may lack

information about vaccination and other types of medical care and hence access medical services

to a lesser extent than their richer counterparts (62;63).

However, evidence from a review of studies that investigated the association between socio-

economic factors and risk of infection, or hospitalization or mortality from respiratory virus

infections (Table 2.5) is not completely in agreement with these assertions. While some studies

have found association between socio-economic factors and risk of respiratory virus infections,

others did not. Specifically 11 of the 28 included studies found an association between low socio-

economic status and risk of respiratory virus infections (Table 2.5), whereas 3 did not (304-306);

seven (7) of the 28 included studies found an association between household crowding (i.e. having

large family or so many people living in the same house), and an increased risk of respiratory

virus infection hospitalization or mortality (210;307-312), whereas 4 studies did not (64-67); and 5

out of 28 studies i.e. Lenzi et al., (313), Launes et al., (314), Mayoral et al., (310), Sutmoller and

Maia (307) reported an association between education, especially maternal education and increase

risk of viral respiratory infection, yet two other studies (67;306) indicated educational status was

not important.

Probably the greatest agreement among studies is on use of public transport (306;315), and school

attendance (210;309;316-318) and risk of respiratory virus infections. Unsurprisingly,

communities with material deprivation (lack of social services) were found to have increased risk

(319;320). As for social deprivation, Charland et al., (319) suggests that having a leaner social

network could in fact be protective as they found decreasing healthcare utilization rates for

influenza with increasing social deprivation and suggested that this could be because people who

are socially deprived make fewer social contacts thus have fewer exposure opportunities to acquire

respiratory virus infections.

71

As for the gender distribution of infections, there has been conflicting evidence where some

studies found incidence is higher among females, especially among middle aged women

(204;209;210) – Figure 2.1, and others observed that respiratory virus infections have a slight

male preponderance especially in children (64;208), the same finding has been reported by recent

studies in Malaysia, Hong Kong, Taiwan and the USA (321-324).

Based on the inconsistencies in studies by (210;307-312) and (64-67), there is no evidence that

socio-econmic status increases risk of respiratory virus infection or severity. A possible source of

the contradictions in the findings on the association with some of the social-economic factors and

risk of respiratory virus infections could be confounded by environmental factors, such as school

attendance, involvement in an occupation that involves interaction with people and use of public

transport. For example, in Margolis et al., (312) study, the association between socio-economic

status and risk of respiratory virus infections was reduced after controlling for environmental

factors such as day care attendance. However some of the socio factors e.g. age and gender, have

been attributed to biomedical processes which influence susceptibility (24-26;28-30;325).

72

Table 2.5: Socio-economic status (SES) and risk of respiratory virus infection

Study name Country Study design

Major finding

1 Margolis et al., (312) USA Community based follow up of children from birth 1,241 (1986-88)

Low socio-economic status & household crowding increased risk of infection but risk reduced after controlling for environmental factors e.g. daycare attendance

2 Ballard et al., (311) Kenya Community based follow up of 106 children (weekly visits)

Having more than 1 siblings and illiteracy increased risk of LRTI

3 Charland et al., (319) Canada Hospital based study 1996-2006, Bayesian ecological regression models,

Social deprivation reduced risk of infections and health seeking (probably due to less exposure opportunities), whereas material deprivation increased it

4 Cohen (63) Review Literature on perceived socio-economic status and unemployment

Lower perceived social status and unemployment were associated with greater susceptibility to infection

5 Colley et al., (304) UK Community based follow up of 13,687 children from birth (1946-1964)

Social class and air pollution did not affect respiratory virus illness, however smoking increased this risk

6 de Francisco et al., (305) Gambia Case-control study, 129 cases, 144 dead controls and 270 community based live controls

No associations between maternal education and literacy, socio-economic status and mortality from ALRI , however smoking increased this risk

7 Etiler et al., (65) Turkey Community based follow up of 204 infants (every 2 months)

No associations between household crowding, and parental smoking with ARI incidence, however cooking on wood and house ownership (socio- status) increased this risk

8 Forshey et al., (66) Peru population-based surveillance for ILI across 45 blocks in Iquitos City

Use of wood as a cooking fuel increased risk but household crowding did not

9 Launes et al., (314) Spain Case-control study (medical records review) 195 cases & 184 controls

Low education status was associated with higher risk of FluApdm09 infections

10 Gardner ((326) Review Articles on socio-economic status and risk of respiratory infection

Lower socio-economic status increased risk of infection

11 Gardner et al., (316) USA Birth cohort follow up, to 1 year of age

Having a school going sibling, being a girl and low socio-economic status increased risk of infections

12 Monto & Sullivan (204) USA Community based follow up of 1000 individuals (10% of population) for 11 years

Being female, and being a housewife increased risk i.e. adult females had more frequent illnesses than adult males; illnesses were less common in working women

13 Lenzi et al., (313) Brazil Disease notification forms of 4,740 laboratory confirmed influenza cases

level of schooling was associated with increased mortality from FluApdm09 whereas gender and race/ethnicity did not

14 Lowcock et al., (67) Canada Hospital based surveillance study

Material deprivation, low education increased risk of hospitalization with pandemic FluApdm09, whereas household crowding and social deprivation did not

73

Table 2.5: Socio-economic status and risk of respiratory virus infection-continued

Study name Country Study design Major finding

15

Maliszewski & Wei (306)

USA

Department of Public Health database, spatial regression analysis

Using public transport increased risk of hospitalization, whereas being poor or having low education did not

16 Mamelund (320) Norway Review of individual household data of 46,972 Spanish, 1918 influenza cases

Material deprivation (dilapidated house, poor sanitation & pollution), low social class, small house size and male gender increased risk of death from the 1918 Spanish influenza virus

17 Mayoral et al., (310) Spain Case-control study, 699 hospitalized FluApdm09 cases and 699 controls

Being overcrowded and low education increased risk of hospitalization with FluApdm09

18 Sims et al.,(309) UK Hospital based surveillance study recruiting 987 cases

Overcrowding, having more children and unemployment status increased risk

19 Badger et al., (210) USA Community based follow up of families (weekly)

Having a school attending sibling, household crowding and male gender increased risk of infection

20 Bansal et al., (318) USA Analysis of previous pandemic data from Vancouver city

School attendance age was associated with increased attack rates in all the past influenza pandemics

21 Rutter et al., (327) UK Use of NHS number database for all confirmed pandemic Flu Apdm09 deaths

High material deprivation was associated with increased mortality with FluApdm09

22 Townsend et al., (64) USA Family follow up study Being male and aggregation in school increased risk however household crowding did not

23 Sutmoller & Maia (307) Brazil Community based follow up (weekly visits) of 262 children

Crowding and maternal education increased risk ARI

24 Sydenstricker (328) USA Community based follow up of households

Low socio-economic status was associated with increased risk of incidence and mortality of RTI

25 Fox et al., (317) USA Community based flow up of 184 households 1975-79

Aggregation of children in schools increased virus infection rates

26 Thompson et al., (329) USA Hospital based survey of hospitalizations (logistic model)

Poverty (low income), male gender and indigenous ethnicity increased risk of hospitalization and death

27 Wenger et al., (315) USA Hospital based survey (248,889) of hospitalization of elderly (harmonic regression models)

Increased travel e.g. by aeroplanes increased transmission of respiratory viruses

28 Yousey-Hindes & Hadler (308) USA Birth cohort follow up study (17 years) geocoding

Poverty and household crowding increased risk of hospitalization (author attributes to low vaccine uptake or co-morbidities)

74

2.3. The nature of respiratory virus disease Part C: The incidence of acute respiratory

virus infections and influenza A viruses associated hospitalizations in North

West England, 2007 – 2012

Synopsis

This is the authors’ version of the paper submitted to Eurosurveillance for publication. This

paper presents the incidence of respiratory virus infections in order to elucidate the burden of

sickness due to these viruses by examining the association of infection with hospitalization and

mortality caused by influenza like illnesses (ILI), in NW England over the study period. An earlier

version of this paper was presented as a poster to the Spring Conference of the Society for General

Microbiology held in Manchester from 25 – 28 of March 2013, poster number MA16/18.

75

2.3.1. Abstract

The incidence of acute respiratory virus infections and influenza A viruses

associated hospitalizations in North West England, 2007 – 2012

Goka E.A1, Vallely P.J 1, Mutton K.J1,2, Klapper P.E,1,2

1: Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, University of Manchester,

2: Department of Clinical Virology, Central Manchester University Hospitals - NHS Foundation Trust

Correspondence author: E. A. Goka: Institute of Inflammation and Repair, Faculty of Medical and Human

Sciences, 1st Floor Stopford building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. Email:

[email protected]

Introduction: Incidence provides knowledge on the medical burden and aids public health

preparedness and interventions.

Methodology: We used ICD-10 J-codes, number of ILI hospitalized patients tested for

respiratory virus infection at the MMPL (January 2007-June 2012), and age-specific mid-year

population figures for NW England, to calculate ARI and influenza A associated

hospitalization using a Poisson regression.

Results: From week 13/2007-13/2012, 378,866 hospitalizations were associated with ARI and

24,607 patients hospitalized with ILI had a sample tested for a respiratory virus. ARIs

hospitalizations peaked in week 50/2008 (85.7/100,000) and week 51/2010/11 (83.1 per

100,000). Hospitalizations were higher in ≤5 children and the elderly >65 years old (annual

average 4,726.3 per 100,000 in ≤ 5 years old, 2,628.7/100,000 in 65-84 year olds,

6,167.4/100,000 in >85 years old compared to 189.51/100,000 in 25-65 year olds). Pandemic

influenza A(H1N1)pdm09 and the seasonal influenza A viruses associated hospitalizations

ranged from 192.8 to, 86.9 per 100,000 and 23.6 to 99.8 per 100,000 respectively. The role of

comorbidities, behavioral and socio-economic factors should be born in mind.

Conclusion: These results are in favour of influenza vaccination policy in children ≤5 years

and the elderly >65 years old. The results could also aid future public health preparedness.

Key words: incidence of hospitalization, acute respiratory virus infections, influenza A viruses.

76

2.3.2. Introduction

Population based studies have estimated the annual incidence of influenza like illnesses (ILIs) to lie

between 46.7 and 6,000 per 1,000 population. The Cleveland study (210), estimated an annual ILI

incidence of 6,200 per 1,000 population, whereas the Holland family study by Van Loghem (211)

put it at 4,280 per 1,000 population. In the Baltimore study, van Volkenburgh and Frost (208)

estimated that annually there are between 3,070 and 3,180 ILIs per 1,000 population.

Studies have indicated that 5-38% of all ILI develop into acute infections requiring hospitalization

or leading into death (8;234;236). In a hospital based study using the health management

organization database, Glezen et al., (216) indicated that the incidence of respiratory virus

associated acute respiratory disease lies between 120 and 290 per 1,000 population. According to

the 2011 review by Nair and others (228), globally, the annual incidence of influenza among

children ≤5 year old is 119 to 156 cases per 1,000 children (equivalent to 90 million cases). Around

20 million (13%) develop into acute lower respiratory tract infections (giving an influenza

associated ALRI incidence of 23 to 44 per 1,000 children) of which between 28,000 and 111,500

die. A review published in 2013, on global prevalence of pneumonia and its associated causes

[Walker et al., (4)], reported that in 2010-2011 the incidence of influenza was 982 (CI: 414–2699)

per 1,000 children ≤5 years old with 7.0% developing into severe disease, whereas the incidence of

influenza associated deaths was 137 (CI: 38–163) per 1,000 children.

In the UK, the European Paediatric Influenza Analysis project (EPIA) reported that between 2002

and 2008, the incidence of medically attended influenza like illnesses (ILI) in England was 581 and

409 per 100,000 for children aged ≤4 and 4-14 years respectively and influenza associated

incidence was 315 per 100,000 children (230). In 2006, Pitman et al., (7) estimated that influenza

causes between 19,000 to 50,000 hospitalizations and 18,000 – 24,800 deaths annually. Similar

estimates were reported by Fleming et al., (330) and Fleming et al., (331).

Knowledge of the incidence of diseases is important as it aids in; determination of disease burden,

planning of public health interventions (such as vaccination). It also informs on the cost-

effectiveness of disease management strategies, and on policy for public health preparedness

(8;332). However, the estimation of incidence of respiratory virus infections is difficult because the

frequency of respiratory virus infections varies considerably from year to year; in some years

epidemics occur, in others pandemics, and in yet others only minimal virus activity (224;333;333).

The variations can among other things be due to genetic changes in respiratory viruses (334),

77

changes in weather patterns, or the match between vaccine and circulating viruses or the yearly

differences in the number of people vaccinated (224;335;336). In 2009, the World Health

Organisation declared an influenza H1N1 pandemic which later spread to all parts of the world

(274). An understanding of the incidence of pandemic and the seasonal influenza A viruses

associated, and ARIs associated hospitalizations, in this period and its vicinity, could help generate

knowledge on their medical burden, and aid in future public health preparedness. However, the

incidence of ARI and influenza A viruses associated hospitalizations in the North West England,

between 2007 and 2012, is not known. This study used data obtained using ICD-10 J-codes

respiratory classifiers, on ARI associated hospitalizations, and laboratory confirmed cases of

influenza A viruses, to estimate the incidence of ARI and influenza associated hospitalization.

2.3.3. Methodology

2.3.3.1. Setting, data source and laboratory methods

The Manchester Microbiology Partnership Laboratory receives and tests respiratory virus samples

from hospitals, medical centres and surgeries located in North West England region, an area with a

population of about 7 million people (99). The data for samples tested at the MMPL contributes to;

the Health Protection Agency laboratory surveillance tool for influenza and other respiratory viruses

(DataMart), the National Health Service (NHS) National Lab Reporting Scheme (LabBase), the

Royal College of General Practitioners Weekly Returns Service (RCGP), and the HPA

complementary sentinel primary care scheme (337). We used this data to calculate the incidence of

influenza and acute respiratory virus infections (ARIs) associated hospitalizations in North West

England from January 2007 to June 2012. Details of the laboratory protocols used to test for the

various respiratory viruses have been published elsewhere (338).

2.3.3.2. Calculation of incidence and other statistical analyses

The number of ARI associated hospitalizations were calculated using the major complaint

[respiratory classifiers (J codes) of the International Conference for the Ninth Revision of the

International Classification of Diseases (ICD-10]. Incidence of ARI was calculated by dividing the

age-specific weekly number of ARIs hospitalizations by the age-specific mid-year population

figures for North West England for the years 2007 – 2011, and the 2011 census figures for

2011/2012 season, and rounded up for every 100,000 using a Poisson model. Cumulative annual

incidence rates were calculated by summing up the age-specific weekly incidence rates.

78

On the other hand, incidence of hospitalizations due to the pandemic influenza A(H1N1)pdm09

(FluApdm09) and seasonal influenza A viruses (SeasFlu A) were calculated by dividing the

expected number of hospitalizations for each respiratory virus by the age-specific population figures

and rounding up for every 100,000 population, in a Poisson regression model. To obtain the

expected number of hospitalizations, the age-specific observed number of hospitalizations caused

by each virus was divided by the total number of ILI samples, from hospitalized individuals, tested

for each virus at the MMPL and multiplied by the age-specific total number of ARIs events from

the ICD-10 J codes. Details of the ICD-10 codes and the scope they covered are provided in

Appendix I at the very end of this thesis.

The models goodness of fit, (how good the Poisson models fitted the observed frequencies of

respiratory virus infections) were assessed using the Pearson’s chi-square goodness of fit statistic

and the likelihood-ratio test and the Akaike information criterion. A difference in age distribution of

patients who visited GPs who had their samples tested for respiratory viruses, and those who were

hospitalized over the study period and the NW England general population was assessed using the

Chi-square test. Statistical differences in the incidence of ARI and influenza A viruses associated

hospitalizations by age group were evaluated by incidence rate ratios (IRR) and their 95%

confidence intervals at significance level of p = 0.05. All analyses were conducted using the

STATA software version 11 (STATACorp, College Station, TX, USA).

2.3.3.3. Ethical and research and development approval

Ethical approval for this study was granted by the Greater Manchester NHS Ethics Committee (Ref:

11/NW⁄0698) and the University of Manchester Research Ethics Office. Research and Development

approval was obtained from the Central Manchester Universities Hospitals NHS Foundation Trust

(Ref: R01835).

2.3.4. Results

2.3.4.1. Profile of patients with ARIs and the source population

Between January 2007 and June, 2012, a total of 30,975 samples were sent to the MMPL for testing

of one or more respiratory viruses of which 24,607 were from patients hospitalized with ILI. A

substantial proportion; 37.8% (11,715) were positive for one or more respiratory virus infection,

and 2,879 (24.6%) were pandemic influenza A(H1N1)pdm09, and 902 (7.7%) were seasonal

influenza A viruses.

79

Figures obtained using the ICD-10 J-codes indicated that, between week 13 of 2007 and week 13 of

2012, a total of 378,866 hospitalizations were associated with ARI. Whereas, 24,607 (79.4%) of the

30,975 patients who had a sample tested for respiratory viruses were admitted to a general ward

(GW) or the intensive care unit (ICU). The mid-year population figures for NW England indicated

that there were 6,863,700 in 2007, 6,874,100 in 2008, 6,897,900 in 2009, and 6,935,900 in 2010,

whereas the 2011 census indicated that there were 7,052,177 individuals. There was a statistically

significant difference, by age, between the people who had samples examined for respiratory virus

infections at MMPL and the general population; more children ≤5 years old (35.8%; 11,112/30,975)

had a sample taken for virus testing while only 6.1% (432,091/7,052,177) of the NW England

population were children ≤5 years, p <0.0001 (Table 2.6). Similarly there was a statistically

significant difference between the numbers of patients whose sample was tested for ILI, those that

were hospitalized, and patients hospitalized with ARI (as indicated by ICD-10 J-codes) and the

general population. For example 39.1% (9,628/24,607) ILI hospitalizations and 26.2%

(99,153/378,866) of patients hospitalized with ARI were aged ≤5 years old compared to 6.1%

(432,091/7,052,177) children ≤5 years in the NW England population, p <0.0001, and similar

differences were observed for the other age groups (Table 2.6).

Table 2.6: Demographics of the patients and that of NW England general population

Age

group

ILI

(%)

ILI_ Hosp

(%)

ARI_ Hosp

(%)

Popltn

(%)

pILI_Pop

ILIHos_Pop

pARI_Pop

<5

11,112

(35.8)

9,628

(39.1)

99,153

(26.2)

432,091

(6.1)

<0.0001

<0.0001

<0.0001

5-14 1,824 (5.9) 1,413 (5.7) 12,845 (3.4) 804,573 (11.4) <0.0001 <0.0001 <0.0001

15-24 4,129 (13.3) 2,912 (11.8) 6,498 (1.7) 946,477 (13.4) 0.64 0.02 <0.0001

25-39 5,065 (16.4) 3,484 (14.2) 12,413 (3.3) 1,346,316 (19.1) <0.0001 <0.0001 <0.0001

40-64 5,381

(17.4) 4,246 (17.3) 66,035 17.4) 2,351,565 (33.3) <0.0001 <0.0001 <0.0001

65-84 1,962 (6.3) 1,554 (6.3) 137,064 (36.2) 1,022,338 (14.5) <0.0001 <0.0001 <0.0001

>85 1,502 (4.9) 1,370 (5.6) 44,858 (11.8) 148,817 (2.1) <0.0001 <0.0001 <0.0001

Total 30,975 (100) 24,607 (100) 378,866 100 7,052,177 (100)

Notes: ILI = samples from patients with influenza like illness received at the MMPL (2007-2009) for testing of

respiratory virus infections. ILI_Hosp = Number of ILI these samples that were from hospitalized patients. ARI_hosp =

Number of patients categorized as being hospitalized of acute respiratory virus infection (2007-2012) as estimated using

the J6-ICD-10 codes from the Hospital Episodes (HES) data. Popltn is the age-specific NW England population

according to the 2011 population census figures. ILI_Pop is a the p value for the difference between observed number

of samples with ILI tested in each age group with the number of people in that age category in the population (in 2011

census). ILIHos_Pop is the same comparison but for ILI and again ARIHosp_Pop is the same comparing but for ARI

hospitalization

80

2.3.4.2. Incidence of hospitalizations associated with ARI

The number of ARI hospitalizations for each year by age, and the number of individuals in the

general population (according to mid-year population figures for the specific years), is given in

supplementary table 2.3S1. During the 2007/2008 influenza season there were 69,089

hospitalizations associated with ARI and the NW England age-specific population for this period

was 6,864,100. For the other periods, the number of ARI hospitalizations were 76,157 (2008/2009),

74,678 (2009/2010), 81,370 (2010/2011) and 77,563 for 2011/2012.

We used these ARI and population figures to calculate the incidence of age-specific ARI associated

hospitalization in NW England during this period and the results are presented in Table 2.7. The

weekly incidence ranged from 85.7 (week 50, 2008) as the highest to 11.3 as the lowest with and

average, minimum and maximum weekly rates of: 37.1 (21.1 – 64.4), 40.8 (21.8 – 85.7), 39.1 (15.4

– 64.9), 42.1 (11.3 – 83.2) and 41.2 (25.5 – 68.9) for the successive years. For each year, incidence

of ARI associated hospitalization peaked from week 39 to week 12 (Figure 2.9 A & B), and the

2008/2009 year had the highest peak followed by 2010/11, this was before the pandemic influenza

A(H1N1)pdm09 virus had emerged.

For age-specific rates, the incidence of ARI hospitalizations was higher among children ≤5 years

old and the elderly ≥65 years old compared to that for patients aged 4 to 64 years old (Table 2.7).

For example, between week 13 of 2007 and week 12 of 2008, there were 4,588.5 ARI associated

hospitalizations per 100,000 children ≤5 years old and 1,969.9 per 100,000 for patients 65-84 years

old and 5,472.8 per 100,000 among the elderly ≥85 years old. In a Poisson model, the risk of ARI

hospitalizations among children ≤5 years old, patients 65-84 years old and patients ≥85 years old

was significantly higher than that of patients 25-39 year old; For the 2007/2008 year: rate ratio -

RR: 39.0, 95% CI: 32.5 – 46.8, p <0.0001, RR: 16.7, 95% CI: 13.9 – 20.2, p = <0.0001 and RR:

46.5, 95% CI: 38.7 – 55.8, p = <0.0001 respectively. Similar results were obtained for the

2008/2009, 2009/2010, 2010/2011 and 2011/2012 influenza seasons.

For the 5 seasons, the average age-specific annual incidence of ARI associated hospitalizations was

4,726.3 per 100,000 in children ≤5 years old, 2,628.7 per 100,000 in patients ≥65 -84 years old and

6,167.4 per 100,000 individuals aged ≥85 years old compared to rates of 322.3 per 100,000 for

those aged 5-14 years old, 133.7/100,000 for 15-24 year olds, 189.5/100,000 among 25-39 year

olds, and 503.0 per 100,000 individuals aged 40-64 year old (and the rate ratios were also

statistically significant – data not shown).

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Table 2.7: Age-specific average annual incidence of ARI hospitalizations per 100,000

population in NW England 2007 - 2012

Age group

2007/08

2008/09

2009/10

2010-11

2011-12

Average annual

incidence

<5 4,588.47 4,798.26 4,816.33 4,940.71 4,487.71 4,726.30

5-14 284.94 317.99 359.78 346.74 301.90 322.27

15-24 117.71 136.16 143.23 156.62 114.64 133.67

25-39 182.44 189.23 185.44 221.41 169.05 189.51

40-64 414.42 571.44 560.66 635.89 332.59 503.00

65-84 1,969.84 2,850.08 2,658.40 2,848.54 2,816.49 2,628.67

>85 5,472.82 6,278.91 5,799.45 6,459.48 6,826.50 6,167.43

Average annual

incidence

1,861.52 2,163.15 2,074.76 2,229.91 2,149.84 2,095.84

Notes: Weekly age specific rates were calculated and average for all age groups in North West England from Week 13

in 2007 to week 13 of 2012. Annual rate is the cumulative weekly rates of hospitalizations associated with acute

respiratory virus infections deduced from ICD-10 J-codes. The age groups with the highest annual incidence rates are

bolded and highlighted in red. Goodness of fit test indicated that all the models fitted the data well (data not shown).

82

Figure 2.9: Weekly incidence of ARI hospitalizations in NW England 2007-2012

Notes: (A) weekly age specific incidence of ARIs were calculated for each age group using ICD-10 J-codes and

average for all age groups calculated. The annual rates were cumulative addition of all weekly rates. Highest weekly

rate 84.5/100,000, lowest weekly rate 11.31/100,000, average 40.83/100,000. The dip in week 0 (around December 30,

is due to holidays in the UK). (B) the same rate plotted linearly by year.

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2.3.4.3. Age-specific average annual incidence of hospitalization associated with the

pandemic and seasonal influenza A viruses

Sixteen thousand five hundred and seventy seven (16,577) of patients whose samples were tested

for the pandemic influenza A(H1N1)pdm09 virus were admitted to a GW or the ICU. A positive

diagnosis with FluApdm09 was made in 12.7% (2,106) of these. Conversely 21,129 patients who

were tested for seasonal influenza A virus were admitted to a GW or ICU and the virus was

identified in 3.7% (788) of them. Data from the ICD-10 J-codes showed that over the study period

there were 378,866 ARI associated hospitalizations.

In a Poisson regression model, for the 3 years the pandemic virus was in circulation, the average

annual incidence of Flu Apdm09 associated hospitalizations were 192.8 per 100,000 population in

the 2009/10 season, 86.9/100,000 in the 2010/11 and 89.2 per 100,000 in 2011/12 season. Similar to

ARI hospitalizations, the pandemic influenza A(H1N1)pdm09 virus associated hospitalizations

were higher among children ≤5 years old and the elderly ≥65 years old (incidence rate IR: 301.4 per

100,000 in children ≤5 years, and 108.1 and 300.6 per 100,000 population in 65-84 and >85 year

olds respectively – Table 2.8). The seasonal influenza A viruses caused substantial burden of

hospitalization, with annual rates ranging from 23.6 to 99.8 per 100,000 over the 5 seasons 2007 –

2012 – Table 2.8. Age-specific rates were also higher among children ≤5 years and the elderly ≥65

years old (105.7, 56.6 and 116.6 per 100,000 respectively).

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Table 2.8: Age-specific average annual incidence of hospitalization for pandemic and seasonal influenza A viruses

Pandemic influenza A(H1N1)pdm09 virus hospitalizations per 100,000 population

Seasonal influenza A viruses hospitalizations per 100,000 population

Age group

ARIs HES

ILI Hosp

Flu Apdm09

Hosp

2009/10 2010/11 2011/12 Average ILI Hosp

SeasFlu A Hosp

2007/08 2008/09 2009/10 2010/11 2011/12 Average

<5 99,153 6,137 390 289.75 249.93 364.57 301.42 8,778 126 81.21 150.38 22.92 73.5712 197.73 105.16

5-15 12,845 1,044 216 100.9 25.69 2.79 43.13 1,135 44 7.63 6.4 9.96 1.85 11.31 7.43

15-24 6,498 2,046 349 37.23 20.87 0.98 19.69 2,451 117 6.45 2.36 4.69 3.81 3.89 4.24

25-39 12,413 2,584 445 37.11 45.23 2.91 28.42 2,837 140 6.88 9.7 3.43 6.59 6.02 6.52

40-64 66,035 3,150 527 75.34 73.91 24.7 57.98 3,373 79 11.78 20.04 6.77 11.08 12.44 12.42

65-84 137,064 1,309 87 85 82.87 156.37 108.08 1,308 24 35.39 187.25 20.1 19.9 20.47 56.62

>85 44,858 307 92 724.38 105.46 71.9 300.58 1,247 258 118.74 322.4 97.46 44.43 0 116.61

Average 378,866 16,577 2,106 192.82 86.28 89.17 122.76 21,129 788 38.3 99.79 23.62 23.03 41.98 45.34

Notes: SeasFlu A - seasonal influenza A viruses, Flu A(H1N1)pdm09 - pandemic influenza A(H1N1)pdm09. The North West England mid-year population by age and

region figures for each year from 2006 - 2010, and for 2011/12 period, the 2011 Census population, were used to calculate the age-specific incidence rates. The population

datum was downloaded from the Office of the National Statistics UK http://www.statistics.gov.uk/hub/index.html. Flu A(H1N1)pdm09_Hosp - number of patients who

were positive for pandemic influenza A(H1N1)pdm09 virus who were hospitalized, SeasFlu A_Hosp – number of patients who were positive for seasonal influenza A

viruses who were hospitalized, ARI_Hosp – hospitalizations associated with acute respiratory virus infections as deduced from ICD-10 J-codes, ILI_Hosp – number of

patients whose samples were received at MMPL between 2007 and 2012, who were hospitalized. The age groups that had the highest risk of hospitalization are bolded and

highlighted in red. Goodness of fit test showed all the models fitted the data very well (data not shown).

85

2.3.5. Discussion and conclusion

During the study period, the highest weekly incidence of hospitalizations associated with ARIs

occurred in 2008/2009 and 2010/2011 seasons; 85.7 per 100,000 and 83.1 per 100,000

respectively. The 2008/2009 peak was in week 50 of 2008. The World Health organization

declared an influenza A(H1N1) pandemic in April, 2009 (274), therefore the pandemic virus had

not yet emerged by then. The peak could probably be due to seasonal influenza A viruses or other

respiratory viruses such as respiratory syncytial virus, human parainfluenza viruses etc.

In this study, for the entire study period, the average age-specific annual incidence of ARI

associated hospitalization ranged from 4,726.3 per 100,000 in children ≤5 years old, 322.3 per

100,000 for those aged 5-14 years old, 133.7/100,000 in 15-24 year olds, 189.5/100,000 in 25-39

year olds, 503.0 per 100,000 in 40-64 year old, 2,628.7 per 100,000 in patients 65 -84 years old

and 6,167.4 per 100,000 in the elderly >85 years old (with the 2008/09 and 2010/11 seasons

having the highest annual rates 2,163.2/100,000 and 2,229.9/100,000 respectively.

Hospitalizations were higher in ≤5 year old children and the elderly >65 year old. Our Results

agree with other studies that have also reported higher hospitalizations among these age groups.

The 2004 PRIDE study in Germany, by Forster et al., (260) reported an annual LRTI associated

hospitalization of between 2,843 and 3,042 per 100,000 population among children <3 years old.

In 2012, Stockman et al., (292) found LRTIs caused around 2,790 hospitalizations per 100,000

children <5 years in the USA. Two hospital based studies published in 2009 and 2012 from Kenya

reported rates of 1,912 to 5,528 per 100,000 population among children and 670/100,000 among

people of all ages (232;289). Two daces ago, Glezen et al., (216), using the hospital and health

care firm database in the USA, reported rates of 114 to 127 per 100,000 population.

We also investigated hospitalizations associated with the pandemic influenza A(H1N1)pdm09 and

the seasonal influenza A viruses. Flu Apdm09 associated hospitalizations ranged from 192.8 to,

86.9 per 100,000 population whereas the seasonal influenza viruses 23.6 to 99.8 per 100,000.

Again these findings are in agreement with previous studies; Nicholson et al., (251), Schanzer et

al., (249) and Schanzer et al., (250) in Canada. Specifically, Nicholson (251) reported that

influenza, RSV and metapneumovirus would cause around 517, 144 and 126 hospitalizations per

100,000 population respectively. Whereas Schanzer et al., (2006) and Schanzer et al., (2008)

indicated that between 1994 and 2000, influenza-associated hospitalizations ranged from 18-200

per 100,000 population. The largest burden was in infants 6 to 11 months of age (200 per

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100,000), 270–340 per 100,000 in adults 65 to 69 years old, compared to 65/100, 000 among

persons aged 20 years or older.

A weakness of this study is that we used the number of hospitalized patients who had their

samples sent to the laboratory, for identification of respiratory virus infections, to estimate the

expected number of hospitalizations with specific respiratory viruses. However, not all patients

who consult medical services with respiratory illness have samples taken for identification of

respiratory viruses. This might lead to a general underestimation of the incidence of influenza A

viruses associated hospitalizations during the study period. In addition, the age profiles of patients

included in this study significantly differed with that of the general population; 35.8%

(11,112/30,975) of samples received at the MMPL between 2007 and 2012 were from children ≤5

years old. Similarly 26.2% (99,153/378,866) of patients identified using the ICD-10 J-codes to

have been hospitalized with ARIs were children and majority of patient’s positive for any

respiratory virus (51.8%; 6,065/11,715) were ≤5 years old, yet according to the 2011 population

census figures, children ≤5 years old made up only 6.1% of the general population. This difference

might be because, due to concern for their children’s health, parents were more likely to take their

children’s to hospital, leading to more children being registered in the medical records. It could

also be due to selection or diagnostic bias (i.e. doctors were preferentially sending samples from <

5 year old children). However, as indicated above, our results compare well with previous studies,

which have also shown high attack and hospitalization rates of ILI in children. It is therefore very

unlikely that this result was due to selection or diagnosis bias.

Lastly, the role of socio-economic status in the incidence of ILI and hospitalizations has been

extensively covered in literature (10;15;23;62;63) although the impact of the same is not very

clear. While some researchers argue that the socially disadvantaged are more likely; to have more

respiratory virus infections (62;63), and more severe outcome (10;15), others found no such

evidence (64-67). Environmental, nutritional, and behavioral factors such as; overcrowded

household, poor nutrition, high prevalence of risky behaviours like smoking, ignorance about

vaccination and other types of medical care, prematurity and low birthweight have also been

identified as risk factors could affect the epidemiology of respiratory viruses (22;23;53). Also

chronic conditions such as chronic respiratory disease e.g. asthma, chronic heart disease, chronic

liver disease, chronic neurological disease, chronic renal disease influence severe disease (9-13)

However, we did not have this information and these factors were not controlled for in this study.

Bias due to the same should be noted when interpreting our results.

87

Despite any shortfall, this study has found that ARIs and influenza A viruses caused significant

burden of hospitalization in NW England between 2007 and 2012. Children ≤5 years and the

elderly >65 years old bore the highest brunt of this burden. This result supports the idea of

vaccinating children ≤5 years and the elderly >65 years old against influenza virus infections. Our

study spanned over a period where there was a pandemic of influenza A(H1N1)pdm09 virus.

Therefore our incidence estimates could in future be useful for the estimation of the expected

numbers of infections, hospitalizations, hence help with public health preparedness in form of

stock piling of vaccines, drugs, and in the organisation of hospital beds.

Funding: This work was supported by the University of Manchester.

Acknowledgements: The authors would like to acknowledge the University of Manchester, the

Manchester Academic Health Science Centre, the Central Manchester University Hospitals NHS

Foundation Trust and staff, and the Public Health England, for their support in this research, and

for the data respectively.

Conflict of interest: All authors, no conflict of interest.

88

2.3.6. Supplementary material

Table 2.3S1: Number of ILI hospitalizations and age-specific mid-year population figures for NW England 2007-2012

2007/08 2008/09 2009/10 2010/11 2011/12

Age group

HES_ARI Population HES_ARI Population HES_ARI Population HES_ARI Population HES_ARI Population Total (%)

<5 18,464 402,400

19,836 413,400 20,296 421,400 21,166 428,400 19,391 432,091 99,153 26.17

5-14 2,314 812,100 2,535 797,200 2,839 789,100 2,728 785,900 2,429 804,573 12,845 3.39

15-24 1,203 961,700 1,310 962,100 1,374 959,300 1,515 957,100 1,096 946,477 6,498 1.72

25-39 2,398 1,314,400 2,459 1,299,500 2,400 1,294,200 2,880 1,299,400 2,276 1,346,316 12,413 3.28

40-64 12,107 2,258,800

12,998 2,274,600 12,851 2,292,100 14,661 2,305,600 13,418 2,351,565 66,035 17.43

65-84 24,952 974,900

28,059 984,500 26,483 996,200 28,776 1,010,200 28,794 1,022,338 137,064 36.18

>85 7,651 139,800 8,960 142,700 8,444 145,600 9,644 149,300 10,159 148,817 44,858 11.84

Total 69,089 6,864,100

76,157 6,874,000 74,687 6,897,900 81,370 6,935,900 77,563 7,052,177 378,866 100.00

Notes: The number of patients hospitalised with ARI over the 5 influenza seasons obtained using ICD-10 J codes as in Appendix II, and UK age-

specific mid-year population (for 2007 to 2011) and the 2011 population census figures (for 2011/2012) obtained from the United Kingdom’s Office of

National Statistics. ARI – acute respiratory infections.

89

2.4. The nature of respiratory virus disease Part D: Virology of respiratory virus

infections

Synopsis

This subsection will presents the virology of all the respiratory viruses studied in this thesis i.e.

influenza A and B virus, respiratory syncytial virus (RSV), rhinovirus (RV), adenovirus (AdV),

human metapneumovirus (hMPV), human parainfluenza viruses types 1 to 4 (hPIV1-4), human

bocavirus (hBoV), and human coronaviruses (hCoV). The rationale for presenting these is to lay a

background to the research, to provide information that will help readers to understand the nature

of the respiratory viruses described in this thesis.

90

2.4.1. Virology of influenza viruses

Influenza viruses belong to the family Orthomyxoviridae, they are classified into three groups

i.e. influenza A, B and C based on the antigenic differences in their nucleoprotein (NP) gene and

matrix (M1) gene (130). Influenza A viruses are further subtyped based on the antigenic

properties of surface glycoproteins; the haemagglutinin (HA) gene and the neuraminidase (NA)

gene. Currently there are 18 HA (H1–H18) subtypes and 11 NA (N1–N11) subtypes

(130;339;340). Genetically, the difference between influenza A and B virus and influenza C is

that, the influenza C virus contains 7 RNA segments, not 8. This is because in influenza C, the

virus envelope glycoprotein haemagglutinin-esterase-fusion (HEF) functions both as

an HA and NA (341).

The influenza A virus genome is 14

kilobases long and consists of 8

single-stranded RNA segments of

negative polarity, In order of their

sizes: polymerase basic (PB) 2 and

1, polymerase gene (PA),

haemagglutinin (HA), nucleoprotein

(NP), neuraminidase (NA), matrix

gene (M) 1 and 2 and the non-

structural gene (NS) 1 and 2 –

Figure 2.10.

Figure 2.10: Diagram of an influenza A virus virion. Reprinted by permission from Macmillan

Publishers Ltd, Figure 1, Horimoto (342), Copyright Licence number 3234490934956.

The two smallest genes (NS and M) give rise to two mRNAs, one collinear and the other as a

consequence of a splicing event, therefore in total there are 10 gene products (341). The

polymerase complex PB1, PB2, and PA which assemble within the nucleocapsid have RNA

polymerase activity involved in transcription and replication of the viral RNA genome, the NP

enclose/binds the viral RNA (341;343;344). The M1 protein envelopes the nucleocapsid while

the M2 molecules assemble as tetramers that function as ion channels which are highly selective

for protons. The ion channels allow protons to enter virus particles during virion uncoating in

91

endosomes, and the M2 channel also causes the equilibration of pH between the acidic lumen of

the trans-Golgi network and the cytoplasm (345).

The virus also contains two non-structural proteins, the collinear NS1 and the spliced NS2

messenger RNA (mRNAs) of the NS gene. The NS1 and NS2 proteins have a regulatory

function including inhibition of cellular interferon response, inhibition of the export of poly-A

containing mRNA molecules from the nucleus and inhibition of splicing of pre-mRNA to

promote the synthesis of viral components in the infected cell (346). NS2 is a small molecule

with a molecular weight of 11,000 bound to M1 protein. Its function is believed to facilitate the

transport of newly synthesized ribonucleoproteins from the nucleus to the cytoplasm to

accelerate virus production (347).

The HA and NA genes are transmembrane glycoproteins, which form the surface of the virus

particles (348;349). The haemagglutinin is the principal antigen on the viral surface and is

responsible for viral binding to host receptors enabling entry into the host cell through

endocytosis and subsequent membrane fusion. Important residues on the HA gene include the

binding sites, glycosylation sites, major histocompatibility class I (MHC-I) epitope residues, and

antibody binding sites (348). The NA is an enzyme which cleaves α-ketosidic linkage between

the haemagglutinin and sialic acid residue on the surface of the infected cells. This prevents virus

aggregation, on the surface of infected cells, and releases the progeny virions from the surface of

infected cells enabling them to infect new cells hence aids in virus replication (350;351).

2.4.1.1. Host range of influenza viruses

Influenza B and C viruses are primarily human viruses and rarely infect animal host’s e.g. seals,

pigs, and dogs. On the other hand, influenza A viruses primary host are water fowls

(130;352;353). Figure 2.11 displays the host range for different HA and NA subtypes. Water

fowls, are infected with all the HA and NA subtypes; humans are infected by H1 to H3, H5, H7,

H9 and N1 and N2 subtypes; domesticated poultry can be infected by H4, 5, 7, 9, 10 and N1 – 9;

pigs may be infected with H1, 3 and N1 and 2 and are thought to be the mixing vessels, re-

assorting primarily human and avian viruses and enabling them to cross the species burrier

(130;352-354). Only a few HA and NA subtypes infect other hosts such as cattle, horses, seals,

and cats (Figure 2.11).

92

Figure 2.11: Ecology of influenza A viruses and interspecies transmission. Notes: H5N1

transmission from birds to humans was first noted in 1997 Hong Kong (355) and since then there have been

sporadic zoonotic outbreaks in Asia, the Middle East, Europe, and Africa (356). The H7N7 virus transmission from

chickens was first reported in 2003 in Holland (357) and since then there have also been sporadic zoonotic outbreaks

globally (358). In March, 2013, a novel influenza A (H7N9) virus was transmitted from chickens to humans in

China (359). A previously unidentified antigenic subtype of HA (H16) has been detected exclusively in shorebirds

(339), and a new subtype HA (H17) and H18N11 have been identified in bats (340). Reprint of Figure 1; Peiris et

al., (360), with permission from the American Society for Microbiology.

93

2.4.2. Virology of respiratory syncytial virus (RSV), human metapneumovirus (hMPV)

and human parainfluenza virus (hPIV)

RSV, hMPV and hPIV belong to the family Paramyxoviridae, a group of enveloped, single-strand,

non-segmented, negative-sense RNA viruses, 15 – 19 Kilobases long. In particular RSV and

hMPV belong to the subfamily Pneumovirinae, whereas hPIVs belong to the subfamily

Paramyxovirinae (361). Pneumoviruses differ from paramyxovirinae viruses in morphology of the

nucleocapsid (which is narrower in pneumoviruses), and in the number and types of encoded

genes (47;49;50;362;363).

The sizes of genomes of members of the paramyxoviruses range from 6 to 10 linked genes

existing as open reading frames, each end having noncoding regions, the leader on the 3' end and

trailer on the 5' end and the spaces between them having antigenic regions. RSV contains 10

genes, hMPV has 8 genes and the hPIV encodes 6 genes. Some of the proteins i.e. the

nucleocapsid (N), phosphoprotein (P), matrix (M), fusion (F), and the large polymerase (L) genes

are common to all the 3 viruses. In addition to these, RSV and hMPV also contain the

glycoprotein (G), M2 and small hydrophobic (SH) proteins, although the orientation of these

genes in the two viruses is different (49;50;362;363). The location of the SH and G proteins and F

and M2 proteins in the two viruses are interposed. In RSV, SH and G come first and are followed

by the F and M2, whereas in hMPV, F and M2 come first, followed by SH and G – Figure 2.12

(363). Further, RSV has the NS1 and NS2 proteins, whereas the hMPV and hPIV do not have

(361). On the other hand, in addition to the P, M, F, and L genes hPIV also encodes the

nucleoprotein (NP) and haemagglutinin neuraminidase (HN) genes – Figure 2.13. For this virus,

the number of proteins encoded in the P gene differs between parainfluenza viruses. While all

hPIVs encode the P protein, in some hPIV viruses the P gene also encodes other non-structural

proteins by 2 or 3 overlapping open reading frames in the P gene. Specifically, hPIV 1, 2 and 3

encodes the C protein, hPIV2 encodes the D protein and hPIV 3 and 4 encode the V protein by

editing the P mRNA through the addition of 2 G residues in the mRNA (47-50;362;363).

The P, L and N proteins form the viral nucleocapsid and are involved in the transcription and

replication of RNA. The P protein forms complex with the L protein; is highly phosphorylated;

and not highly conserved (361). The L protein contains all the enzymes needed for RNA synthesis

and has highly conserved regions (361;363). The P and L proteins are bound together by the N

94

gene. Just like in influenza viruses, the M protein of RSV, hMPV and hPIV form the inner layer of

the viral membrane and is also involved in ion channeling (361).

In RSV and hMPV, the G and F proteins form the viral coat whereas in hPIV, the coat is formed

by the HN and F proteins. These proteins are used for attachment to cell glycoproteins and fusion

of the viral membrane with the cellular membrane, respectively (363;364). The HN protein is

named HN because of its neuraminidase activities and is the major antigenic determinant of

paramyxoviruses. However, both RSV A and B viruses do not agglutinate red blood cells. The SH

protein is inserted in the membrane, however its function is not known. Accessory proteins C in

hPIV1 and 3, D in hPIV3 and V in hPIV2 and 4 play a similar role as influenza NS1 and NS2

proteins by being involved in activities that promote viral replication i.e. inhibition of interferon

production through degradation of signal transducers and activators of transcription factors 1 and

2 (STAT1 and/or STAT2), slowing the cell cycle, and are the determining feature of host range of

hPIVs (365;366).

Figure 2.12: Genomic maps of the Pneumovirinae. Notes: Genomic maps of the negative-sense, single-

stranded RNA genomes of hMPV and RSV are displayed in the 3' to-5' orientation. In hMPV, the F and M2 genes are

3' to the SH and G genes, whereas in RSV, the order of these genes is reversed. The RSV genome encodes two non-

structural proteins, NS-1 and NS-2 (shaded), that are not present in the hMPV genome. The M2 genes of both viruses

carry two ORFs (M2-1 and M2-2) (not shown). The M2 and L genes overlap in the RSV genome (triangle). The L

gene of each virus, encoding the viral RNA-dependent RNA polymerase, comprises two-thirds of the viral genome

and is shortened for figure clarity. The genomes are not drawn to scale. Reprint of Figure 2; Kahn (363), with

permission from the American Society of Microbiology.

95

Figure 2.13: Genomic structure of parainfluenza viruses. Reprint of Figure 1; Chambers and

Takimoto (364), with permission from John Wiley & Sons Ltd. Copyright Licence number 3234500648349.

Notes: In PIV 3 and 4, the P region also encodes the V protein.

2.4.3. Virology of coronaviruses (CoV)

Coronaviruses are enveloped, single stranded, positive sense RNA viruses. They are the largest

known RNA viruses with a genome size ranging from 27,317 nucleotides in hCoV-229E to 31,357

- 32,000 nucleotides in mouse hepatitis virus (MHV-A59) (367-369). The 5' contains the open

reading frame (ORF1a and 1b genes) with a methylated cap at the end. This gene occupies two-

thirds of the coronavirus, encodes the non-structural i.e. all the enzymes necessary for RNA

replication (mRNA synthesis and genome replication). The remainder of the virus genome, the 3'

end, encodes several structural proteins i.e. the spike (S), envelope (E), M and N, and in some

group 2 viruses and the Turkey coronavirus; the HE gene (367;369;370). The tip of the 3' end is a

polyadenylated (poly-A) tail and this enables coronaviruses RNA to function directly as mRNA, a

characteristic which is unlike other negative sense RNA viruses, therefore the virus is not

packaged with RNA dependent polymerase (371;372). Coronaviruses also encode a number of

accessory genes, they are interspersed between ORF1, S, E, M and N genes and are associated

with pathogenicity, however, their number and sequence vary significantly among different

coronavirus species (368) - Figure 2.14.

The ORF 1a and 1b encodes two replicase–transcriptases, polyprotein pp1a and pp1ab, which are

then automatically processed by viral proteases (chymotrypsin-like protease 3CLpro and a papain-

like cysteine protease PL1pro) into 16 putative enzymes, designated non-structural proteins 1 to 16

(nsp1 to nsp16) (367;369;373;374). The replicase polymerases are first synthesized after viral

infection, into negative strand RNA which serves as a temperate for the synthesis of a number of

subgenomic mRNAs (transcription) (367) including the RNA-dependent RNA polymerase (RdRp)

and other accessory proteins (104;373;375).

As for the structural proteins, the S protein forms the glycosylated spikes on the surface of the

virus, giving the characteristic name of coronaviruses as they are shaped like a crown and are

responsible for receptor binding and membrane fusion (367). In some group 2 viruses and the

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turkey coronavirus, the HE gene forms short spikes on the surface and performs a similar function

to influenza virus NA, specifically it cleaves 9-O-acetylated neuraminic acid glycans, hence

preventing virus aggregation on cell surfaces hence aiding virus replication (367). The function of

the N and M proteins of coronaviruses is similar to the functions of these genes in influenza, RSV,

hMPV and hPIV viruses; i.e. it forms the inner lining of the virus capsid. However in

coronaviruses, the viral envelope also contains the E protein which works in conjunction with the

M protein to facilitate virion formation and budding (376)

2.4.3.1. Host range of coronaviruses

Coronaviruses are classified into 4 Genera based on inter-genomic comparisons, with members of

the same group having similar non-structural proteins in the same genomic position (368;377).

Alphacoronaviruses and Betacoronaviruses infect mammals, Gammacoronaviruses infect birds

(367;368) - Table 2.9. Alphacoronaviruses includes 8 species; human coronavirus 229E (hCoV-

229E), human coronavirus NL63 (hCoV-NL63), Miniopterus bat coronavirus 1 (BatCoV-1),

Miniopterus bat coronavirus HKU8 (BatCoV- HKU8), Rhinolophus bat coronavirus HKU2

(BatCoV-HKU2), Scotophilus bat coronavirus 512 (BatCoV-512), and the porcine epidemic

diarrhoea virus (PEDV) (45;367;368;377;378). Betacoronaviruses include 7 species; Human

coronavirus HKU1 (hCoV-HKU1), Betacoronavirus 1, mouse hepatitis virus (MHV), Pipistrellus

bat coronavirus HKU5 (BatCoV-HKU5), Rousettus bat coronavirus HKU9 (BatCoV-HKU9),

Tylonycteris bat coronavirus HKU4 (BatCoV-HKU4) and Severe acute respiratory syndrome-

related coronavirus. Deltacoronaviruses include 3 species: Bulbul coronavirus HKU11 (BCoV-

HKU11), Munia coronavirus HKU13, and Thrush coronavirus HKU12, whereas

Gammacoronaviruses include 2 species; infectious bronchitis virus (IBV), and Beluga whale

coronavirus SW1 (378). The classification of the newly discovered novel Middle East respiratory

syndrome coronavirus (MERS) is still controversial (378).

97

Figure 2.14: Genomes of coronaviruses. Reprint of Figure 1, page 4 Brian and Baric (370), with

kind permission from Springer Science and Business Media, Copyright Licence number 3234501206733. Notes: The predicted protease cleavage sites for open reading frames (ORFs 1a and 1b) are indicated by

numbers and domains of known or predicted function are shaded (PL, papain-like protease; 3CL, poliovirus 3C-

like protease; TM, transmembrane domain; RdRp, RNA-dependent RNA polymerase; Z, zinc finger (metal-

binding) domain; Hel, helicase domain; C, conserved sequence domain). Genes 2–8 (or 9) are identified by their

transcript name (1a, 1b, etc.) or the abbreviated name of the protein product (S, spike; E, envelope; M,

membrane; N, nucleocapsid; HP, hydrophobic protein; HE, haemagglutinin-esterase; I, internal). The

nomenclature of the coronaviruses since changed by the International Committee on Taxonomy of Viruses -

ICTV (378). The Coronavirinae subfamily now has 4 Genera: Alphacoronaviruses (listed here as Group 1),

Betacoronavirus (listed here as Group 2), Deltacoronavirus, and Gammacoronaviruses (listed here as Group 3).

98

Table 2.9: The known coronavirus

Group Designation Species

Host

GegeBank Accession Number*

1 TGEV Transmissible gastroenteritis virus pig AJ271965 [g]

PRCoV Porcine respiratory coronavirus pig Z24675 [p]

FIPV Feline infectious peritonitis virus cat AY994055 [g]

FCoV Feline enteric coronavirus cat Y13921 [p]

CCoV Canine coronavirus dog D13096 [p]

hCoV-229E Human coronavirus 229E human AF304460 [g]

PEDV Porcine epidemic diarrhoea virus pig AF353511 [g]

hCoV-NL63 Human coronavirus NL63 human AY567487 [g]

BatCoV-61 Bat coronavirus 61 bat AY864196 [p]

BatCoV-HKU2 Bat coronavirus HKU2 bat AY594268 [p]

2 MHV Mouse hepatitis virus mouse AY700211 [g]

BCoV Bovine coronavirus cattle U00735 [g]

RCoV Rat coronavirus rat AF088984 [p]

SDAV Sialodacryoadenitis virus rat AF207551 [p]

hCoV-OC43 Human coronavirus OC43 human AY903460 [g]

HEV Haemagglutinating encephalomyelitis virus pig AF481863 [p]

PCoV Puffiness coronavirus puffin AJ544718 [p]

ECoV Equine coronavirus horse AY316300 [p]

CRCoV Canine respiratory coronavirus dog CQ772298 [p]

SARS-CoV SARS coronavirus Human* AY278741 [g]

HCoV-HKU1 Human coronavirus HKU1 human AY597011 [g]

BatSARS-CoV Bat SARS coronavirus bat DQ022305 [g]

3 IBV Infectious bronchitis virus chicken AJ311317 [g]

TCoV Turkey coronavirus turkey AY342357 [p]

PhCoV Pheasant coronavirus Pheasant AJ618988 [p]

GCoV Goose coronavirus goose AJ871017 [p]

PCoV Pigeon coronavirus pigeon AJ871022 [p]

DCoV Duck coronavirus mallard AJ871024 [p]

Notes: This table is a reprint of Table 1, page 197-98 of the review by Masters (368). Reprinted with

permission from Elsevier. Copyright Licence number 3234440722900. The nomenclature of the

coronaviruses since changed by the International Committee on Taxonomy of Viruses - ICTV (378). The

Coronavirinae subfamily now has 4 Genera: Alphacoronaviruses (comprising some of the species listed here in

Group 1), Betacoronavirus (some species listed here in Group 2), Deltacoronavirus, and Gammacoronaviruses

(containing some of the species listed here in Group 3). SARS coronavirus is classified as a Betacoronavirus,

after the final classification of this virus into Genera Betacoronavirus (379). There is a variation between authors

on the members of Gammacoronaviruses, Lai et al. (367) and Weiss et al., (45) included only IBV and TCoV.

The group bovine coronaviruses also includes other bovine like coronaviruses that infect ruminants e.g. the

samber deer coronavirus (SDCoV), White tailed deer coronavirus (WTDCoV), waterbuck coronavirus

(WBCoV) and giraffe coronavirus (GiCoV) (380). * While most coronaviruses infect only 1 or 2 animal species,

SARS coronavirus infects a variety of animals including humans, primates, Himalayan palm civets , dogs cats

and rodents (367). The classification of the MERS is still controversial (378).

99

2.4.4. Virology of the rhinoviruses

Rhinoviruses are icosahedron shaped, single stranded, positive sense RNA viruses, with a

genome 7 to 8.5 kilobases long (381). Their RNA genome has an untranslated 5' region of

approximately 650 base pairs linked with a VPg protein and a 6,500 base pair single ORF

which after translation is cleaved into 11 to 12 viral proteins, including the capsid protein and

other proteins (2A, 2B, 2C and 3A, 3B, 3C, 3D) required for viral replication (382;383) –

Figure 2.15. The virus capsid contains 4 proteins (1A, 1B, 1C, and 1D) and has star shaped

plateaus and deep depressions (canyons) on its surface which are used for receptor binding

(381). Rhinoviruses are divided into 4 groups, RV-A, B, C and D which together comprises

101 serotypes based on their phylogenetic similarities (381;382).

Figure 2.15: The genome map of human rhinoviruses. Notes: The 5' untranslated region (5' UTR)

is linked to VPg(3B), and encodes several important RNA structures which function during RNA synthesis and

genome translation. The single open-reading frame (ORF) encodes a polyprotein, which is cleaved in a series of

co-translational and post-translational reactions to provide all mature viral proteins required to establish and

perpetuate an infection. The capsid proteins (red) and proteins involved in replication functions (blue) are common

to all HRV, and all sequences share analogous cleavage sites delineating these locations in the polyprotein. The

illustrated genome is hrv-35; the base numbering system in this panel is for that sequence. B: The HRV sequences

in the RNA genome alignment (Table S2) were queried pairwise at each position in the alignment. All sequences

were given equivalent weight. The arithmetic average of the scores (0 or 1 for each pair) was reported for each

position, re-averaged over a sliding window of 30 adjacent residues (+/- 15), then plotted relative to the alignment

as a whole. The strongest sequence conservation is in the 5' IRES region. The lowest conservation is in the 5'

spacer region, upstream of the IRES, and also in the capsid coding regions, where the 1B, 1C and 1D troughs

correspond to the respective, mapped immunogenic surface loops. The averaged identity across all HRVs,

considering all alignment positions, is indicated with the blue, horizontal line. Reprint of Figure SA2;

Palmenberg et al., (382), with permission from The American Association for the Advancement of Science

(AAAS). Copyright Licence number 3234461243814.

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2.4.5. Virology of adenovirus and human bocavirus (AdV and hBoV)

Both adenovirus and human bocavirus are DNA non-enveloped icosahedral viruses,

adenoviruses have a double stranded DNA (dsDNA), the hBoV have a single stranded DNA

(ssDNA) (43;384;385).

The DNA of adenoviruses is 34-36 kilobases long with inverted terminal repeat sequences

ranging from 36 to 200 base pairs. The virion consists of a protein capsid enclosing the DNA

(43;384). The DNA has approximately 47 genes (open reading frames ORFs) i.e. early (E1A,

E1B, E2, 3, and 4), intermediate (IX and IVa2), late (L1, 2, 3, 4, 5) genes, and the p23 protein –

Figure 2.16 (43;384). Out of the 47 proteins, 33 (including the early and late genes) are

conserved among all adenoviruses (386;387), and five proteins i.e. the hexon fibre, panton

base, and the E3 transcription genes are hypervariable (388).

The hexon (gene II), panton (gene III), and polypeptide II, III and IV (Fibre protein) form the

virus capsid, and together, these proteins determine the virus antigenicity (43). The penton

consists of a base and a fibre, the fibre protein binds to cell receptors, the VIII and IX proteins

stabilize interactions between hexons, VI protein disrupts host cell membranes during infection,

whereas the IIIa protein is involved in virus assembly. The nucleoprotein is composed of five

genes; i.e. the V, VII, µ, terminal protein, and the p23 genes. The VII and µ proteins bind the

genome together. Polypeptide V (core protein) links the virus core to the capsid, whereas the

p23 protein is used for protein synthesis (43). The E3 proteins facilitate immune evasion by the

virus (389;390).

The human adenoviruses are classified into 56 serotypes and 7 species (A, B, C, D, E, F and

G), with subspecies B being further divided into B1 and B2. The classification is based on the

viruses haemagglutination characteristics (384;391;392) and degree of base sequence

homology as shown by similarity of restriction fragments cut by restriction enzymes; BamHI,

BclI, Bgll, BglII, BstEII, EcoRI, HindIII HpaI, Sall, SmaI, XbaI, and XhoII among different

species. The classification puts a within subgroup homology of >50%, and between subgroup

homology of <25% (393-395). The patterns obtained after restriction digestion are named using

the name of the gene in which they occur e.g. A, B, C - G combined with Arabic letters (e.g.,

Ad3p, Ad3p1, H8a, H8p, H8k) - (396;397).

Bocaviruses are simple and small viruses with negative or positive strand ssDNA 4 to 6

kilobases long with inverted terminal repeats, which are able to infect only dividing epithelial

or lymphoid cells (385). The genome of human bocavirus consists of three open reading frames

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(ORFs) encoding the NS1, the capsid proteins (VP1 and VP2) and the nucleoprotein (NP1) –

Figure 2.17 (398). Just like in adenoviruses, the capsid of bocavirus is made of proteins,

responsible for receptor binding. The capsid contains a phospholipase (PL2) which participates

in viral entry into host cells, the protein also determines host specificity (385). The virus is

classified into 4 species (hBoV1 to 4) based on phylogenetic grouping (399).

Figure 2.16: Genome organization of hAdV-C2, hAdV-F40, and hAdV-D53. Notes: Protein

encoding regions are shown as boxes. Boxes above the black line represent open reading frames (ORFs) that are

encoded on the forward (or upper) strand. Boxes underneath the black line represent ORFs that are encoded on the

reverse (or lower) strand. (a) HAdV-C2. (b) HAdV-F40. (c) HAdV-D53. Reprint of Figure 2; Torres et al.,

(387), with permission from Multidisciplinary Digital Publishing Institute (MDPI).

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Figure 2.17: Map of the hBoV genome. Notes: (A) Schematic map of isolate ST1 of hBoV showing the

three ORFs as arrows: NS1, 1,920 nt (nucleotides 183-2102), 639 aa; NP-1, 660 nt (nucleotides 2340–2999), 219

aa; and VP1_VP2, 2,016 nt (nucleotides 2986–5001), 671 aa. (B) A map showing the location of the 26 nucleotide

differences that were detected between two isolates of hBoV. The horizontal line represents the sequence of ST1,

and each vertical line represents a nucleotide difference to ST2. In two cases where several differences were

located close together, a longer vertical line representing four differences was used. The asterisks mark the three

differences that resulted in a predicted amino acid change. Reprint of Figure 2; Allander et al., (398), with

permission from National Academy of Sciences, U.S.A.

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2.5. The nature of respiratory virus infections Part E: Genetic mutations associated

with pathogenicity of pandemic influenza A(H1N1)pdm09 virus: A review

Synopsis

This is the authors’ version of the paper submitted to Archives of Virology. This paper

discusses the association between mutations in the pandemic influenza A with severe or fatal

disease. Animal models have indicated that certain virus genetic mutations affect severity of

influenza disease. This study aimed at summarizing the available evidence on the role of

mutations on disease outcome in humans.

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2.5.1. Abstract

Mutations associated with severity of the pandemic influenza

A(H1N1)pdm09 in humans: A systematic review and

meta-analysis of epidemiological evidence

Goka E.A1, Vallely P.J 1, Mutton K.J 1,2, Klapper P.E 1,2


2: Department of Clinical Virology, Central Manchester Universities NHS Trust

Corresponding author: Edward Goka, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, 1st

Floor Stopford building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

E-mail: [email protected]

Objective: Mutations on the haemagglutinin (HA), non-structural protein 1 (NS1) and polymerase

basic protein 2 (PB2) of influenza viruses have been associated with virulence. This study investigated

the association between mutations on these genes, in influenza A(H1N1)pdm09 and risk of severe or

fatal disease.

Methodology: The review is conducted using studies identified from the MEDLINE, EMBASE and

WEB of Science electronic databases, and reference lists of published studies. The PRIMA and

STROBE guidelines were followed in assessing the quality of studies and conducting the write-up.

Results: Eighteen (18) studies, from all continents, were included in the systematic review (recruiting

patients 0 – 77 years old). During the two years the pandemic influenza A(H1N1)pdm09 virus

circulated, mutation D222G was associated with a significant increase of severe disease (pooled RD:

11%, 95% CI: 3.0% - 18.0%, p = 0.004); and risk of fatality (RD: 23%, 95% CI: 14.0% – 31.0%, p =

<0.0001): No association was observed between mutations HA-D222N, D222E, PB2-E627K and NS1-

T123V and severe/fatal disease. The results suggest that no virus quasi-species bearing virulence

conferring mutations in the HA, PB2 and NS1 predominating in humans. This result reaffirms previous

reports suggesting the importance of PB2, NS1, and HA genes working together to cause serious

disease. However issues of sampling bias, and bias due to uncontrolled confounders such as;

comorbidities, and viral and bacterial co-infection, should be born in mind.

Conclusion: Influenza A viruses should continue to be monitored for the occurrence of virulence

conferring mutations on HA, PB2 and NS1. There are suggestions that respiratory virus co-infections

also affect virus virulence. Studies investigating the role of genetic mutations on disease outcome

should make efforts to also investigate the role of co-infections.

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2.5.2. Introduction

2.5.2.1. The virulence genes of influenza A viruses

The history of influenza viruses has shown that the viruses that caused pandemics had changes in

the surface and internal proteins (400). The 1957 pandemic H2N2 influenza virus had

haemagglutinin (HA), neuraminidase (NA) and polymerase basic protein 1 (PB1) genes from

avian virus and the remainder of the genes derived from a previously circulating human virus.

Similarly, the 1968 pandemic H3N2 virus had avian HA and PB1 segments in a background of

human viral genes (266;271;342;400-403). It has been suggested that changes in the HA, PB and

NS1 genes affect virus severity.

Changes in the antigenic sites, or in the vicinity of the antigenic sites of the HA gene result in

antigenic drift and compromises vaccine and immune effectiveness, with some novel strains

causing pandemics with high virulence (348;404-408). Also changes in the HA receptor binding

sites or in molecules in their vicinity affect viral virulence (409). Similarly glycosylation in the

vicinity of the cleavage site promotes folding of the HA and can lead to masking or unmasking

of proteolytic cleavage or antigenic sites and affect viral pathogenicity (348). The polymerase

basic protein 2 (PB2) gene has been shown to affect host specificity in influenza H5N1, H7N7

and in the 1918 H1N1 virus (20;34;37). The NS1 protein was implicated in the pathogenicity of

the highly pathogenic H5N1 avian influenza virus and the 1918 H1N1 virus (19;36). Mutations

in the PB2, HA, and NS1 affect viral virulence by: determining host specificity, enhancing the

ability of hybrid viruses to efficiently replicate in humans - enhancing efficiency of viral

attachment, entry and release, facilitating antagonism of the interferon pathways and evasion of

the immune response (18-20;34-37).

2.5.2.2. The HA genes’ virulence related functional sites

HA is an envelope glycoprotein of 550 amino acids (348); HA1-329 residues, and HA2-221

residues (348). The HA gene contains three major functional sites; the receptor binding sites

(RBS), the antigenic sites and the cleavage site.

HA1 187 and 222, determine the receptor-binding specificity of the HA: D187/D222 for α(2,6)

receptors in humans, D187/G222 for α(2,6) and α(2,3) receptors in swine, and E187/G222 for

α(2,3) receptors in avian (18;407;410-416). In the 1918 virus, a single mutation (D222G)

reduced the binding affinity for α (2,6) receptors (414;415) and the infectivity of the virus (18),

while a double variant D187E/D222G rendered the HA non-binding to α(2,6) receptors and the

virus non-infectious (414;415;417).

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Out of the 329 HA1 residues, 131 are antigenic sites, 181 are non-antigenic sites and the status of

the others is unknown. The antigenic sites are further classified into 5 antigenic epitopes

(Supplementary table S1), designated Sa, Sb, Ca1, Ca2 and Cb in influenza H1N1 viruses and A,

B, C, D and E in influenza H3N2 viruses (348;418-421). In general, most of the antigenic

changes in influenza A(H1N1) and A(H3N2) have been proceeded by at least 3 substitutions in

positively selected sites (406). However a deletion of K at position 130 (K134 in H3 numbering)

was responsible for the antigenic difference between the A/Bayern/7/95 and A/Beijing/262/95

influenza A(H1N1) subtypes (404). The positively selected antigenic sites on H1N1-HA1 have

previously been summarized by Cox et al., (422), Brownlee and Fodor (421), Igarashi et al.,

(408), Liao et al., (406), Huang et al, (423), Stray and Pitman (424), and Shen et al., (407). We

have summarized the findings from these studies in supplementary Table 2.5S1. Further,

supplementary Table 2.5S2 details the amino acid mutations in these HA antigenic sites that

have occurred in influenza A(H1N1) main strains that have circulated since 1918.

Cleavage of the HA gene is important for viral infectivity as it is a prerequisite for the fusion of

the viral and cell membranes (425). N-glycosylation is achieved by addition of glycans to

asparagine (N) residues (426). Influenza A (H1N1) viruses possess 5 glycosylation sites N71,

N104, N142 (variant - 144), N177 (variant – 172 and 179), and N286 (H1- numbering)

(427;428). The A/SC/1918 and CA/07/2009 had only one glycosylation site N104 (408;427),

however after 1918, H1N1 viruses gained the N286 and N142 and N177 before 1957, the other

site N71 appeared in 1987 (427). The seasonal influenza A(H1N1) viruses that circulated prior to

the 2009 pandemic virus had 4 glycosylation sites (dropped the 286) - (427).

2.5.2.3. The PB2 genes’ virulence related functional sites

The PB1, PB2, and PA form the polymerase responsible for replication and transcription of the

viral RNA in the nucleus of infected cells using a cap-snatching mechanism (429;430). The

location of the cap binding site on PB2 is controversial, Honda et al., (431), Palese and Shaw

(432), and Li et al., (430) suggested N-site residues 242–252 and C-site residues 533–577,

Fechter et al., (433) a more central site involving residues F363 and F404 and Poole et al.,

(432;434) the C-terminal residues 1–269 and 580–683 respectively. One of the most commonly

identified virulence markers is mutation (PB2-E627K); the glutamic acid (E) is generally found

in avian influenza viruses, while human viruses have a lysine (K), therefore this mutation

determines host specificity (435). Other important PB2 mutations include D701N, S714R,

S678N and L13P in (436-440), and in the pandemic influenza A(H1N1)pdm09 virus PB2-

T588I, A271T, K340N and D567G (417;441-443). The pandemic influenza A(H1N1)pdm09

(Flu Apdm09) virus has avian PB2 gene (266), hence need for the 627K mutation for it to

107

efficiently replicate in humans. However, some researchers (436;440;444-446) indicated that

E627K has little effect on transmissibility of Flu Apdm09, yet others found it is (447) yet others

suggested that this might be compensated by Q591R (440;448).

2.5.2.4. The NS1 genes’ virulence related functional sites

Depending on the virus strain NS1 consists of 124–237 amino acids and is expressed exclusively

in infected cells and contains the N-terminal RNA-binding domain (residues 1–73) and the C-

terminal effector domain (residues 73–237) (449). The effector domain containing several other

domains, including cleavage and CPSF-binding domain (residues 175-210), poly(A)-binding

protein (PABP) domain (residues 218-225), nuclear localization signal 1 (residues 34-38),

nuclear localization signal 2 (residues 211-216), nuclear export signal (residues 132-141) and

e1F4G1 domain (residues 81-113) (450). Residue 186 and the 103 and 106 prevent transport of

cellular mRNA to the cytoplasm by interaction with poly (A) – binding protein II (PABII) (451-

453), whereas amino acids 215 to 237 have been identified as the binding site for PABII (451).

Wang et al., (454), Cheng et al., (455) and Yin et al., (456) indicated that the dsRNA-binding

residues in the N-terminal include; T5, P31, D34, R35, R38, K41, G45, R46 and T49. Further, a

five-residue short peptide sequence (123-IMDKN-127) counteracts the PKR-mediated antiviral

response (457;458). The effector domain residues; T89/M93, P164/P167 and L141/E142 bind to

p85b and induce the phosphatidylinositol 3-kinase (PI3K)/Akt signalling pathway (346;459),

whereas R38A/K41A and E96A/E97A are responsible for TRIM25 binding (460-463). Studies

by Donelan et al., (464) and Talon et al., (465) indicated that the NS1 R38A/K41A mutations

abolished binding to dsRNA-binding and IFN antagonism. While Gack et al., (460), Mibayashi

et al., (461); Bornholdt and Prasad (462) and Chien et al., (463) indicated that mutations

R38A/K41A and E96A/E97A NS1, in contrast to NS1 wild type, led to abolishment of TRIM25

binding and dysfunctional RIG-I. The mutations implicated in the pandemic influenza

A(H1N1)pdm include the NS1 T123V (466;467)

2.5.2.5. Rationale for conducting the review

An understanding of the positively selected important mutations on influenza A virus HA, PB2

and NS1 genes, associated with severe outcome, could help future research for vaccine and drug

development. It could also help with patient management, and public health surveillance and

preparedness. Due to the ever changing nature (antigenic drift and shift) of influenza viruses, the

World Health Organisation (WHO) Global Influenza Surveillance and Response System

(GISRS) coordinates the global risk assessment of influenza viruses yearly. One hundred and

twenty two (122) National Influenza Centres, designated by national authorities in 92 countries

108

sends around 150,000 to 200,000 respiratory specimens to the 5 global collaborating centres (the

United Kingdom and Australia National Institute for Medical Research, US Centres for Disease

Control and Prevention, Chinese Centres for Disease Control and Prevention, and National

Institute of Infectious Diseases in Japan) for antigenic characterization to identify novel variants

twice a year (468;469). An additional approach is the filed vaccine trials, which are undertaken

by multiple sites in North America, Europe and Australia. The case-control and cohort studies

identify and characterise influenza viruses, document observed mutations and observed

responsiveness of the circulating viruses to that seasons vaccine (470;471). Lastly, independent

studies are conducted by hospitals, university institutions, government ministry of health and

other bodies of which some are published and others are not. Some of the virus sequences are

deposited on GenBank, the Influenza Research Database, and Research Collaboration for

Structural Bioinformatics Protein Data Bank. A major problem with the above methods is most

of the studies, while using laboratory confirmed influenza viruses, do not include virulence (i.e.

severe disease or death), as an endpoint, or give a clear link between specific mutations and

disease outcome (472;473). Therefore most of the evidence on the role of mutations on severity

are based on animal and cell line experiments (439;441;447;474-481) and evidence from human

infections is scanty. The December 2009 WHO report (482) and the November 2010 European

Centres for Disease Control and Prevention report (483) summarized on mutations in the

pandemic virus and disease severity but both dwelled only on the HA D222G mutation and no

study has systematically reviewed the evidence on the association between mutations in the HA,

PB2 and NS1 genes in the pandemic Flu A(H1N1)pdm09 and patient outcome, hence the

importance of this review.

2.5.2.6. Aims and objectives of the review

This study aims to review available epidemiological evidence on mutations on PB2, NS1 and

HA gene of influenza A (H1N1)pdm09 and their association with disease outcome. Also, it has

been suggested, by some studies, that respiratory virus co-infections affect disease severity

(76;77;484-487). Currently most of the studies on genetic mutations and virus virulence do not

investigate the role of co-infections in those circumstances. Our other study [Goka et al., 2013

(338)] investigated the association between co-infection among influenza A viruses and other

respiratory viruses (that have circulated between 2007 and 2012, including the pandemic

influenza A virus), and disease outcome. It is therefore imperative to investigate what other

factors, apart from co-infections, could contribute to severe disease. This review will help

answer, indirectly, the research question: “Did mutations previously describes to increase

109

severity of influenza A viruses occur in pandemic influenza A(H1N1)pdm09 virus that circulated

between 2009 and 2012?”

The objectives of the review therefore were:

1. To investigate the occurrence of HA mutations D222E, D222G and D222N, PB2

mutation E627K or D701N and NS1 mutations T123V, R38A, K41A, E96A or E97A in

mild, severe and fatal cases of influenza A(H1N1)pdm09.

2. To determine which mutation should be regarded as one that posed a major public health

concern.

3. To determine whether any other unknown new mutations on the HA, PB2 and NS1 gene

were associated with severe disease outcome.

2.5.3. Methodology

2.5.3.1. Protocol for the review

The authors of this manuscript developed the protocol of this review. The protocol was not

published or deposited to any online server.

2.5.3.2. Search strategy

The MEDLINE, EMBASE and WEB of Science databases were searched for primary

epidemiological studies on the role of the PB2, NS1 and HA genes in driving influenza virus A

severity. Specifically the search aimed at identifying literature on PB2 gene and its role in

promoting virus replication under high temperature, NS1 and inhibition of interferon production

and the role of HA mutations on glycosylation site, antigenic sites and receptor binding sites or

its vicinity, and observed outcomes in patients (mild, severe or fatal). Websites of health

organisations e.g. the WHO, UK Health Protection Agency, Centre for Disease control -USA,

World Influenza Network Centre, were visited to check Influenza bibliography references listed

therein or any published reports on influenza. Also reference lists of good quality studies,

identified through the electronic sources, were examined to look for studies addressing the

question under review.

For the electronic databases, the search technique involved combining a number of subject

headings, keywords, and scoping for text words including: Orthomyxovirus, influenza virus,

influenza A virus type H1N1, 2009 pandemic influenza virus, influenza A(H1N1)pdm09,

influenza virus haemagglutinin, HA gene, PB2 gene, non-structural protein 1, NS1, evolution,

molecular evolution, mutation, genetic mutation, virus mutation, genetic evolution, antigenicity,

110

prognosis, virulence, virulence, virus virulence, severity, severe disease, mild disease,

pathogenicity, hospitalization, admission, ICU, death, fatality, and mortality. A copy of the

electronic search conducted for this review on EMBASE is provided in supplementary Table S4,

at the last pages of this thesis as an example.

2.5.3.3. Study assessment tool and study selection criteria

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline

for reporting systematic reviews and meta-analysis (109) and the Strengthening the Reporting of

Observational Studies in Epidemiology (STROBE) (108) tools were used as checklist for

writing this review and for critical appraisal of identified epidemiological studies. The principal

investigator of this review carried out the online and manual searches. Titles and abstracts were

first reviewed for relevance and those that were clearly not relevant (e.g. mutations on HA, PB2

and NS1 genes of pandemic Flu A(H1N1)pdm09 induces virulence in ferrets, mouse adapted

influenza A(H1N1) PB2, E627K mutation of H9N2 avian influenza virulence), were removed.

Papers that met minimum inclusion criteria were downloaded and read in full. A range of

epidemiological studies i.e. cross-sectional, case-control, or cohort studies, with endpoints of

laboratory confirmed influenza virus infection and prognosis of the patients i.e. mild, severe or

fatal [according to November 2009 WHO criteria for clinical classification of the disease due to

the pandemic virus (488)] were included. Due to inadequacy of genetic studies of large sample

sizes, studies with a sample size of ≥5 were included. Studies that did not investigate the role of

either PB2, NS1 and HA genes in driving influenza virus A viruses virulence, or did not report

the outcome which was being considered by this study, or were not written in English language,

were poorly designed and conducted e.g. the analysis did not give exact mutations that were

identified, or did not link observed mutations to prognosis and disease outcome, were excluded.

2.5.3.4. Assessment of bias in the studies

Since the included studies are not randomized control trials, they would be prone to bias due to

uncontrolled confounding factors such as: the study population and period of virus identification

[the representativeness of the population to the general population of viruses which were

circulating arising mainly due to differential surveillance (systematic differences in the way the

virus was identified from various study groups i.e. random sampling of severe or mild cases), or

what laboratory protocols used (PCR or not), and the possibility of contamination of samples];

data sources and measurement of outcomes (mutations and clinical disease outcomes) and the

type of outcome measure adopted e.g. classifying patients as mild, or severe (clinicians will use

different guidelines, what was classified as severe by one clinician might be classified as mild by

another. Bias due to co-infection with other respiratory viruses and bacteria, some of the patients

111

may have been infected with more than one respiratory virus and this, might have also

influenced the outcome. Bias by indication, such as comorbidities, immune status. Bias due to

other confounders such as, age of patients, vaccination status, immune status, and the kind of

clinical treatment they were given. All the studies included in this review were assessed using

the PRIMA guideline and a summary of the scores for the 18 included studies is provided in

supplementary Table 2.5S3.

2.5.3.5. Data extraction and statistical analysis

A data extraction form was designed by the principal investigator of this study and confirmed by

the co-authors. Data extracted from the studies included: the study setting i.e. author and year of

publication, where and when it was conducted, study design i.e. the viruses and genes that were

analysed, type of diagnostic method(s) used, observed mutations, prognosis in patients (mild,

moderate, severe, disease or death(s) associated with specific mutations). Association between

mutation type and disease outcome was assessed using risk differences. The differences between

these statistics was assessed using difference of two proportions (at significance level of α =

0.05), differences and results summarized using forest plots.

2.5.4. Results

2.5.4.1. Characteristics of the included studies

Two thousand Three hundred and ninety three (2,393) were obtained from the searches

conducted on MEDLINE, EMBASE and WEB of Science of which 307 were from MEDLINE,

703 from EMBASE and 1,383 from WEB of Science. Manual search of other published papers

yielded 25 papers. After removing the duplicates and irrelevant papers, 98 papers were reviewed.

Eighty (80) more papers were found ineligible to be included and were excluded, and 18 papers

were included in the systematic review (Figure 2.18). The included studies were conducted in

2009 and 2010 and published between 2010 and 2013.

Details of the studies included in this review are summarized in Table 2.10. Identified studies

were from all over the world; One from North America, 3 from South America, 6 from Europe, 1

from the Middle East, 5 from Asia, and 2 from Africa. Out of the 18 studies, 4 [Potdar et al.,

(489), Tse et al., (490), Baldanti et al., (491), and the Promed Email (492)] were conducted

during the first wave (April – September 2009), the majority, 9 [Puzelli et al., (493), Kilander et

al., (494), Mak et al., (495), Miller et al., (496), Graham et al., (497), Farooqui et al., (498), Chen

et al., (499), Venter et al., (500), and Akcay Ciblak et al., (501)] covered the 1st and second

wave, 1 [Vazquez-Perez et al., (502)] covered only the 2nd wave, 2 [Wedde et al., (503) and

Moussi et al., (504)] covered the 1st and 3rd wave, and 2 [Ferriera et al., (505) and Barrero et al.,

112

(506)] covered all the 3 waves of the 2009 influenza A(H1N1) pandemic – Table 2.10. The

number of viruses sequenced ranged from 13 to 357; 11 studies only sequenced the HA gene, 3

sequenced the HA, PB2 and NS1 genes, 2 sequenced the HA and PB2/NS1 and 1 sequenced

only the PB2 – Table 2.5S3.

2.5.4.2. Possible sources of bias in the included studies

The PRIMA guideline was used to assess bias in the included studies and the results are

presented in supplementary Table 2.5S3. The first issue concerns possibility of selection bias.

None of the included studies sequenced all the Flu Apdm09 viruses identified during the study

periods. It is understandable, because such an exercise would be very expensive. In that case,

samples were selected from the pool of the Flu Apdm09 viruses, with each study adopting its

own criteria to achieve representativeness. The age of the severe and fatal cases was mainly

people aged >5 -30 years and very few children ≤5 years old. This is not surprising as the

epidemiological studies have documented that the pandemic virus peaked in the 5 – 19 year olds

(213). Some studies did not control for comorbidities and other factors known to affect diseases

severity. Only one study each investigated bacterial, and other respiratory virus co-infections

respectively. Therefore these shortfalls should be born in mind when interpreting the results of

this review.

113

Figure 2.18: Number of studies identified, excluded and included in the review on

genetic mutations in pandemic influenza A(H1N1)pdm09 and severity

Records identified through

database searching

(n = 2,393)

Additional records identified

through other sources

(n = 25)

Records after duplicates removed

(n = 2,145)

Records screened

(n = 270)

Records excluded (n = 172)

• Were animal models or

cell line experiments

• Were poorly designed

• Discussed genetic

evolution of the genes

only, with no link to

outcome

Full-text articles assessed

for eligibility

(n = 98)

Full-text articles excluded (n = 80)

• Incomplete outcome data

e.g. the exact mutations,

how many patients had it

• Did not meet minimum

inclusion criteria

Studies included in

quantitative synthesis

(meta-analysis)

(n = 18)

114

Table 2.10: Characteristics of the studies included in the analysis of the effect of HA, NS1 and

PB2 mutations on virulence of pandemic influenza A(H1N1)pdm09 viruses

Author & study design

Period

Sample size

Patients

Age (yrs)

Mutations

Ref

1. Puzelli et al., (2010),

cross-sectional survey -

Italy

May/09 - Feb/10

169

1 - 77

HA- D222G & D222E.

(493)

2. Kilander et al., (2010),

cross-sectional study -

Norway

May/09 – Jan/10 266 All ages HA- D222G, D222E,

& D222N

(494)

3. Mak et al., (2010),

cross-sectional survey –

Hong Kong

May/09 –Jan/10 458 All ages HA-D222G, D222E & D222N (495)

4. Miller et al (2010), cross-

sectional survey -

Scotland, UK.

?/2009 - ?/10

58 All ages HA-D222G, D222E

and D222N

(496)

5. Graham et al., (2011),


Canada

Apr/09 - Dec/09

235 1 - 77 HA-N87K, P137S, N228D, S74N, R205K, N56D,

Y230H, A203T, Q293H, N473R, A186T, D222E,

D222G, E374K, NS1-E55G, PB2-D567G, K480R,

K526R

(497)

6. Potdar et al., (2010),


India.

May/09– Sep/09 13 3 - 42 HA-D222G, Q223R, T197A, Q293H(Q310H), NS1-

G28S, I123V, PB2- E627, D701 and A271

(489)

7. Farooqui et al., (2011),

cross-sectional study of

patients presenting to

hospital with ILI -

China

Dec/09 – Mar/10 172 7.2 – 34.8 HA- E374K, P83S, I191L, T197A, S203T, I321V,

S128P, A392V, D127E, K54N, S323F and H399P

(498)

8. Tse et al., (2011),

cohort study of patients

admitted with influenza –

China

Apr – May/09 20 18 - 85 HA-D222G/N (490)

9. Chen et al., ( 2010),

cohort study of patients

with influenza virus

admitted to hospital –

Hong Kong

May – Dec/09 117 18 - 59 HA-D222G (499)

10. Wedde et al.,(2013),

cross-sectional survey of

fatal, hospitalized and

community pdm09

positive cases –

Germany

Oct-Apr/09/10

Oct-Apr/10/11

357 0 - 73 HA-D222G/E/N (503)

11. Baldanti et al., (2010),


hospitalized and

outpatients with ILI –

Pavia & Milan, Italy

Apr-Nov/09 131 All ages HA-D222G/E/N (491)

115

Table 2.10: Characteristics of the studies included in the analysis on the effect of HA, NS1 and

PB2 mutations on virulence of pandemic influenza A(H1N1)pdm09 viruses – continued

Author

Period

Sample size

Patients

Age

Mutations

Ref

12. Moussi et al., (2013),

outpatient and

hospitalized individuals

with pdm09 –

Tunisia

09-10 and 10-11

specific months

not given

50

15 - 47

HA-D222G/E, K374E

(504)

13. Ferreira et al., (2011),


fatal and nonfatal

patients with ILI –

Brazil

Apr/09 – Jun/10 90 All ages HA-D222G/N/S, S203T, Y230H, E235K,

M257V, Q293H, & E391K

(505)

14. Vazquez-Perez et al.,

(2013) –

Mexico

Sep/09 – Apr/10 77 26 – 50.5 HA-D222G/E/N, N27D, N48D, V169I, G172N,

W194R, A203T, I338V, Y528C, PB2 – E191G,

Q194H, NS1 – V65M, L95P, I123V, I128T,

V180I, L181F & P215L

(502)

15. Venter et al., (2012)

Cross-sectional study of

outpatients and

hospitalized individuals -

South-Africa

Feb/09 – Dec/10 30 All ages HA-D222G, S185T, Q293H, PB2-E627K, (500)

16. Promed Email (2009)

Outbreak investigation &

hospital based

surveillance -

Netherlands

Jul-Aug/09 33 All ages PB2-E627K (492)

17. Akcay Ciblak et al.,

(2013)cross-sectional

study of severe mild

and fatal cases –

Turkey

May/09 – Jan/10 13 11days –

59 yrs

HA-D222G, S203T, E374K, PB2-E627K,

V655I

(501)

18. Barrero et al., (2011)

Cross-sectional study

of fatal severe and

mild cases –

Argentina

May/09 – Jan/10 21 1 - 54 HA-S203T, P83S, T197A, I321V, PB2-S199A,

NS1-I123V

(506)

Notes: All ages – studies sequences part of the samples from the state public health surveillance of the pandemic.

Although the papers indicated here as all ages didn’t report the specific age ranges for the included samples, the

manuscripts do describe or attempt to describe that all efforts were made to take a sample of the viruses which

would be representative of all the strains and patient groups, therefore all age groups were represented. Sample size

represents number of viruses that were sequenced for HA, PB2 or NS1 or all of these, it does not represent the

number of respiratory samples that were tested for Flu Apdm09 or number of Flu Apdm09 viruses identified. In

most of the studies, the number of viruses sequenced was a small fraction of all the Flu Apdm09 viruses positive in

patient’s samples. In most cases, some criteria for selecting a representative sample of all positives was used.

116

2.5.4.3. Association between mutations in influenza A(H1N1)pdm09 and severe disease

2.5.4.3.1. HA-D222G mutation and severity

Overall, the evidence suggests that the mutation HA-D222G was associated with severe disease

and mortality. Most of the risk differences on severity and death, and the pooled risk differences

(RD) were statistically significant (pooled RD: 11%, 95% CI: 3.0% - 18.0%, p = 0.004 for

severity and RD: 23%, 95% CI: 14.0% – 31.0%, p = <0.0001 for fatality) – Figure 2.19. It

should however be noted that the I2 statistic for severity indicates that the heterogeneity was

statistically significant (I2: 86.5, p = <0.0001), whereas that for mortality was not. Observational

studies are subject to bias due to uncontrolled confounders. Of the 8 studies included in the

severity analysis, 3; Chen et al., (499), Wedde et al., (503), and Vazquez-Perez et al., (502)

controlled for comorbidities such as pregnancy, hypertension, congenital heart disease,

myocarditis, asthma, diabetes and obesity, whereas the other 5 studies; Puzelli et al., (493),

Kilander et al., (494), Mak et al., (495), miller et al., (496), and Moussi et al., (504) did not

(supplementary Table 2.5S3). Further only Vazquez-Peres investigated and reported bacterial co-

infection (Table 2.5S3), and together these factors might have caused the slight differences.

However, despite this, we think that the evidence supports the hypothesis that D222G increased

risk of severity and mortality.

2.5.4.3.2. Mutation HA-D222E and D222N and disease outcome

Figures 2.20 and 2.23 presents the results of the analysis on association between D222E and

D222N and severity. For the former, there was no evidence for or against the role of this

mutations and disease severity (pooled RD: -2.0%, 95% CI: -5.0% - 1.0%, p = 0.2, and for RD:

1.0%, 95% CI: -3.0% - 6%, p =0.54). However none of the associations was statistically

significant. More research could, in due course, shed more light on the role of D222E.

As for the mutation D222N, all the risk differences (for individual studies and the pulled RDs)

suggested a positive association between this mutation and more severe disease and death

(pooled RD: 2.0%, 95% CI: 1.0% - 5.0%, p = 0.24 for severity analysis and RD: 5.0%, 95% CI:

1.0% - 9.0%, p = 0.09 for the fatality analysis). However, apart from Tse et al., (490), none of

the RDs for the individual studies were statistically significant (Figure 2.21). The problem here

could also be due to bias by uncontrolled confounders, as the I2 statistics in the mortality was

statistically significant (I2: 73.5, p = 0.001).

117

Study name RD 95% CI p value

Ref

HA-D222G & Severe disease

Puzelli et al., (2010) 0.04 (-0.03 - 0.10) 0.253 (493)

Kilander et al., (2010) 0.09 (-0.02 - 0.19) 0.083

(494)

Mak et al., (2010) 0.04 (-0.01 - 0.07) 0.003 (495)

Miller et al., (2010) 0.00 (-0.14 - 0.14) 1.000 (496)

Chen et al., (2010) 0.12 (0.03 - 0.21) 0.007

(499)

Wedde et al., (2013) 0.42 (-0.28 - 0.56) <0.0001 (503)

Moussi et al., (2013) -0.05 (-0.30 - 0.19) 0.662 (504)

Vazquez-Perez et al., (2013) 0.21 (0.04 - 0.38) 0.016

(502)

Overall 0.11 (0.03 - 0.18) 0.004

I2 = 86.5, p = <0.0001

HA-D222G & Mortality

Puzelli et al., (2010) 0.10 (-0.10 - 0.31) 0.329

(493)

Kilander et al., (2010) 0.30 (0.13 - 0.47) 0.001

(494)

Miller et al., (2010) 0.09 (-0.05 - 0.22) 0.198

(496)

Potdar et al., (2010) 0.25 (-0.12 - 0.62) 0.182

(489)

Tse et al., (2011) 0.20 (-0.22 - 0.62) 0.351

(490)

Chen et al., (2010) 0.75 (0.35 - 1.15) <0.0001

(499)

Wedde et al., (2013) 0.33 (0.18 - 0.48) 0.019

(503)

Ferreira et al., (2011) 0.25 (0.09 - 0.41) 0.002

(505)

Moussi et al., (2013) 0.13 (-0.36 - 0.61) 0.611

(504)

Vazquez-Perez et al., (2013) 0.15 (0.005 - 0.30) 0.043

(502)

Overall 0.23 (0.14 - 0.31) <0.0001

I2 = 43.2 p = 0.07

Figure 2.19: Mutation HA-D222G and risk of severe disease and mortality

Notes: RD – risk difference = risk in severe or fatal cases minus risk in mild cases, calculated using the random

model. The squares represent the estimated risk difference, the diamond represent their summary, the horizontal

lines give their 95% confidence intervals and the size of the squares represent the weight of the study. HA –

haemagglutinin gene of influenza A(H1N1)pdm09 virus. All studies recruited patients of all age groups presenting

with ILI at hospital, as outpatients or hospitalized, or had died.

Risk difference and 95% CI

Mild Severe

1 / 117 2 / 43

0 / 205 3 / 34

0 / 239 9 / 219

0 / 26 0 / 9

0 / 60 7 / 57

0 / 258 21 / 50

1 / 8 3 / 42

0 / 27 5 / 24

-0.25 -0.13 0.00 0.13 0.25


Dead Mild

1 / 9 1 / 117

8 / 27 0 / 205

2 / 23 0 / 26

2 / 8 0 / 5

5 / 10 3 / 10

3 / 4 0 / 60

13 / 39 0 / 258

7 / 28 0 / 62

1 / 4 1 / 8

4 / 26 0 / 27

-0.25 -0.13 0.00 0.13 0.25

118


Ref

HA-D222E & Severe disease

Puzelli et al., (2010) 0.10 (-0.07 - 0.27) 0.235 (493)

Kilander et al., (2010) -0.04 (-0.11 - 0.23) 0.196 (494)

Mak et al., (2010) -0.02 (-0.04 - 0.001) 0.070 (495)

Miller et al., (2010) 0.22 (-0.05 - 0.49) 0.113

(496)

Chen et al., (2010) -0.05 (-0.11 - 0.01) 0.120

(499)

Wedde et al., (2013) -0.04 (-0.09 - 0.01) 0.122

(503)

Moussi et al., (2013) 0.02 (-0.14 - 0.18) 0.805

(504)

Vazquez-Perez et al., (2013) 0.08 (-0.04 - 0.21) 0.201

(502)

Overall -0.02 (-0.05 - 0.01) 0.199

I2 = 28.2, p = <0.20

HA-D222E & Mortality

Puzelli et al., (2010) 0.09 (-0.38 - 0.19) 0.517 (493)

Kilander et al., (2010) 0.001 (-0.10 - 0.11) 0.985 (494)

Miller et al., (2010) 0.00 (-0.08 - 0.08) 1.000 (496)

Wedde et al., (2013) 0.02 (-0.07 - 0.11) 0.674 (503)

Vazquez-Perez et al., (2013) 0.08 (-0.04 - 0.20) 0.208 (502)

Overall 0.01 (-0.03 - 0.06) 0.544

I2 = 0 p = 0.77

Figure 2.20: Mutation HA-D222E and risk of severe disease and mortality







Severe Mild

18 / 43 37 / 117

1 / 34 15 / 205

0 / 219 4 / 239

2 / 9 0 / 26

0 / 57 3 / 60

1 / 50 15 / 258

1 / 42 0 / 8

2 / 24 0 / 27

-0.25 -0.13 0.00 0.13 0.25


Dead Mild

2 / 9 37 / 117

2 / 27 15 / 205

0 / 23 0 / 26

3 / 39 15 / 258

2 / 26 0 / 27

-0.25 -0.13 0.00 0.13 0.25

119


Ref

D222N & Severe disease

Kilander et al., (2010) 0.05 (-0.03 - 0.13) 0.185 (494)

Mak et al., (2010) 0.01 (-0.01 - 0.03) 0.273 (495)

Miller et al., (2010) 0.00 (-0.24 - 0.24) 1.000 (496)

Chen et al., (2010) 0.05 (-0.11 - 0.01) 0.120 (499)

Baldanti et al., (2010) 0.07 (-0.03 - 0.18) 0.175 (491)

Wedde et al., (2013) 0.03 (-0.02 - 0.09) 0.254 (503)


Overall 0.02 (-0.01 - 0.05) 0.236

I2 = 33.4, p = 0.17

D222N & Mortality

Kilander et al., (2010) 0.03 (-0.02 - 0.14) 0.104 (494)

Miller et al., (2010) 0.07 (-0.21 - 0.08) 0.079 (495)

Tse et al., (2011) 0.30 (-0.60 - 0.002) 0.002 (490)

Balanti et al., (2010) 0.44 (0.24 - 0.63) 0.634 (491)

Wedde et al., (2013) 0.04 (-0.03 - 0.11) 0.113 (503)

Ferreira et al., (2011) 0.08 (-0.05 - 0.20) 0.198 (505)


Overall 0.05 (0.01 - 0.09) 0.089

I2 = 73.5 p = 0.001

Figure 2.21: Mutation HA-D222N and risk of severe disease and mortality







Severe Mild

2 / 34 1 / 205

3 / 219 1 / 239

1 / 9 3 / 26

0 / 57 3 / 60

2 / 27 0 / 81

2 / 50 2 / 258

3 / 24 0 / 27

-0.25 -0.13 0.00 0.13 0.25


Dead Mild

1 / 27 1 / 205

1 / 23 3 / 26

0 / 10 3 / 10

10 / 23 0 / 81

2 / 39 2 / 258

3 / 28 2 / 62

2 / 26 0 / 27

-0.25 -0.13 0.00 0.13 0.25

120

2.5.4.4. Association between PB2-E627K mutation and severe disease

Five (5) of the included studies reported on the impact of E627K mutation on severe or fatal

disease (Figure 2.22). Four of the 5 studies did not identify E627K either in the mild or severe

cases. Similarly no association was noted in a suspected E627K Flu Apdm09 outbreak in the

Netherlands as reported in a 2009 Promed-Email. An investigation of suspected E627K Flu

Apdm09 virus was conducted among individuals who had camped at the West Frisian Islands in

the Netherlands, following isolation of this virus in a diabetic index case. The mutation was later

also identified in 2 of the 10 contact patients from across the country who had also camped at the

same site and time with the index case, and they had all mild disease. The email further reports

failure to identify the mutation in a further 22 samples of patients hospitalized with pandemic

influenza A(H1N1)pdm09 between July and August, 2009.


Ref

PB2-E627K & Severe/Fatal disease

Akcay Ciblak et al., (2013) 0.00 (-0.3 - 0.30) 1.000 (501)

Venter et al., (2012) 0.00 (-0.14 - 1.14) 1.000 (500)

Potdar et al., (2010) 0.00 (-0.27 - 0.27) 1.000

(489)

Graham et al., (2011) 0.00 (-0.15 - 0.15) 1.000

(497)

Promed-Email -0.27 (-0.54 - 0.11) 0.043 (492)

Overall -0.03 (-0.10 - 0.45) 0.510

I2 = 88.0, p <0.0001

Figure 2.22: Mutation PB2-E627K and risk of severe disease and mortality



lines give their 95% confidence intervals and the size of the squares represent the weight of the study. PB2 –

polymerase basic protein 2 of influenza A(H1N1)pdm09 virus. All studies recruited patients of all age groups

presenting with ILI at hospital, as outpatients or hospitalized, or had died.

The absence of PB2-E627K mutation in patients agrees with the proposition that the pandemic

Flu Apdm09 virus did not require E627K mutation to cause serious disease (436;440;444-446).

However, the fact that they had severe disease suggest that other factors might have caused the

serious illness. In two of the included studies HA-D222G was identified in 2/8 (25%) and in 2/6

(33.3%) of patients who had died [in Potdar et al., (489) and in Akcay Ciblack et al., (501)

respectively]. However in the former, the PB2 genes in the 2 dead patients were not sequenced.

Either the D222G or other factors could explain the outcomes. All the studies included in this

analysis did not control for comorbidities and bacterial and viral co-infections and this should be

born in mind when interpreting our analysis.


Severe/Fatal Mild

0 / 9 0 / 4

0 / 18 0 / 10

0 / 8 0 / 5

0 / 8 0 / 105

0 / 22 3 / 11

-0.25 -0.13 0.00 0.13 0.25

121

2.5.4.5. NS1-T123V and other mutations and disease severity

Mutation NS1-T123V was not associated with increased risk of severe or fatal disease; both of

the studies included in this analysis did not identify this mutation in either mild or severe/fatal

cases respectively (Figure 2.23).

A number of other mutations in the pandemic influenza A(H1N1)pdm09 have been reported and

some have been indicated to be associated with disease outcome (507;508). We identified three

studies that investigated the association between mutations HA-Q293H (Q310H) and disease

outcome. The evidence was inconclusive; two of the 3 studies reporting on Q293H found it

increased severity, whereas one found it did not, although only one of the three statistics was

significant (Figure 2.23).

Similarly, 1 of the 3 studies included in the S203T analysis found the mutation significantly

reduced risk (RD: -0.23, 95% CI: -0.7% - - 33.0%, p = <0.0001), while the other 2, one Found

no difference and the other found a positive association (Figure 2.23). The analysis on additional

mutations on HA therefore had significantly heterogeneous outcomes (I2 = 84.1, p = 0.002 and I2

= 79.8, p = 0.01), this could again be due to bias caused by differences in patients underlying

conditions, vaccination status, age and other factors. Suffice to add that none of the studies

included in these two analyses investigated bacterial and viral co-infections.

The NS1 mutations T123V and E55S lie in the region responsible for induction of the PKR

signalling pathway and the RNA binding domain respectively (346), whereas the HA- 293

(Q310) and 203 (S220) are antigenic sites (408;423). In general, our findings on the role of these

mutations should be taken cautiously as we did not find many studies that clearly reported the

number of patients with these mutations who had mild or severe disease respectively. The

importance of these mutations is yet to be elucidated.

122


Ref

NS1-T123V & Severe/Fatal disease

Potdar et al., (2010) 0.00 (-0.27 - 0.27) 1.00

(489)

Barrero et al., (2011) 0.00 (-0.17 - 0.17) 1.00 (506)

Overall 0.00 (-0.14 - 0.14) 1.00

I2 = 0, p = 1

HA-Q293H (Q310H) Severe/Fatal disease

Graham et al., (2011) 0.13 (-0.07 - 0.33) 0.22 (497)

Ferreira et al., (2011) -0.08 (-0.27 - 0.11) 0.430

(505)

Venter et al., (2012) 0.50 (0.24 - 0.76) <0.0001 (500)

Overall 0.17 (-0.14 - 0.48) 0.270

I2 = 84.1, p = 0.002

HA-S203T (S220T) Severe/Fatal disease

Graham et al., (2011) -0.23 (-0.34 - 0.12) <0.0001 (497)

Farooqui et al., (2010) 0.00 (-0.32 - 0.32) 1.00

(498)

Ferreira et al., (2011) 0.11 (-0.08 - 0.30) 0.26 (505)

Overall -0.06 (-0.30 - 0.19) 0.66

I2 = 79.8 p = 0.01

Figure 2.23: Mutation NS1-T123V and others and severe disease and mortality



lines give their 95% confidence intervals and the size of the squares represent the weight of the study. NS1 – non-

structural protein. HA – haemagglutinin gene of influenza A(H1N1)pdm09 virus. All studies recruited patients of all

age groups presenting with ILI at hospital, as outpatients or hospitalized, or had died.


Severe Mild

0 / 8 0 / 5

0 / 9 0 / 12

-0.25 -0.13 0.00 0.13 0.25


Severe Mild

11 / 28 28 / 105

6 / 28 18 / 62

9 / 18 0 / 10

-0.25 -0.13 0.00 0.13 0.25


Severe Mild

1 / 28 28 / 105

6 / 6 4 / 4

22 / 28 42 / 62

-0.25 -0.13 0.00 0.13 0.25

123


This review found that mutation HA-D222G significantly increased risk of severe disease and

fatality. Mutation HA-D222N was also found to be positively associated but this was not

statistically significant. However, there was no evidence linking mutation HA-D222E with

severe disease. The D222G and D222N single and mixed variants have been found in pandemic

viruses from approximately 20 countries, including Norway, Mexico, Ukraine and the USA

(482;507;508). Influenza viruses constantly change their genetic material - antigenic drift

(348;404;407), historical understanding of positively selected sites may help understand the

relevance of observed mutations. Position 222 resides in the receptor binding site of the HA

protein and may possibly influence binding specificity. The HA from the 1918 H1N1 pandemic

switched from avian to human receptor specificity through mutation at two positions G187D and

D222G (415); the A/New York/1/18 strain of the 1918 pandemic possessed a glycine (G) at

position 222 and this markedly affected receptor binding, reducing α2-6 preference and

increasing α2-3; the A/Memphis/42/1983, had an asparagine (N); whereas the 2009 pandemic

influenza virus A/C/04/2009 had an aspartic acid (D) on this position (Table 2.5S2). The results

of our study could therefore be useful in public health applications. Other HA mutations reported

by studies summarized in this review include; D293G (Q310H), S203T (S220T), E374K,

N156D, and N370H in the UK substitution (12). The importance of these mutations is yet to be

elucidated.

No association was observed between mutations PB2-E627K and NS1-T123V and severe and

fatal disease. Mutation PB2-E627K has previously been describe in animal models to be

associated with severe disease, the glutamic acid (E) is generally found in avian influenza

viruses, while lysine (K) is found in human viruses; i.e. this mutation determines host specificity

(435). In experiments with single gene reassortants of influenza viruses, it was shown that

changes in the NP, basic polymerase-2 (PB2), and M1 proteins were involved in host restriction

in monkeys (509), while attenuation of human viruses was achieved in human volunteers by

changes in the NP, non-structural- 1 (NS1), and PB1 and PB2 proteins (510). The virulence of

the pandemic virus has been reported to be affected by E627K by some studies is (447), but said

to not be necessary by others (436;440;444-446). The finding in this review supports the latter.

The NS1 mutations T123V and E55S lie in the region responsible for induction of the PKR

signalling pathway and the RNA binding domain respectively (346) and therefore aids in virus

replication. In this review, viruses that circulated had the mild PB2 and NS1 phenotypes

(489;497;506;508;511;512). Other mutations on the NS1 and PB2 genes reported by studies

included in this review include; PB2-K340N and NS1-G28S, E55G, G154R and T215P

124

(482;508). It should be noted that our results are prone to sampling, diagnostic/information bias.

None of the included studies sequenced all the viruses they identified. Each study used their own

sampling method to achieve representativeness of the strains circulating during the study period.

There is a possibility that the samples included were not representative enough of the virulence

mutations in circulation and that could affect the outcome in this study. Also not all included

studies sequences the PB2 and NS1 genes, most of the studies sequenced only the HA gene.

Perhaps there could be a different outcome if all the 18 included studies had sequenced the PB2

and NS1 genes in addition to the HA gene.

The limitations of this study include possible reporting, publication or selection bias. A number

of factors e.g. underlying chronic conditions, age, vaccination and patient’s immune status, and

co-infections between influenza and other respiratory viruses and with bacteria may affect the

severity of influenza virus infections. However, some of the studies reviewed here did not

control for these factors (Table 2.5S3) and this might bias the reported outcome. Regarding

selection or reporting bias, we might not have been able to identify and include other studies. In

addition, our inclusion of only studies that were written in the English language, might have

further confounded this problem. However, three electronic data bases were used (MEDLINE,

EMBASE and WEB of Science). In addition, official websites of different organisations were

visited to identify recommended references. It is believed that such an extensive effort

significantly reduced, if not eliminated, reporting, selection, and publication bias. Suffice to add

that a systematic error may have occurred in the designing and execution of studies included in

this review e.g. preferential admission and sample collection of only severe cases, laboratory

contamination or poor laboratory techniques would cause this study to inherit selection bias or

information bias. However, a standardised method for scrutinising the quality of studies included

in this study (PRISMA and STROBE) was adopted. Where studies had shortfalls, these shortfalls

have been mentioned as part of the reporting process (Table 2.5S3).

Studies have well documented the role of bacterial co-infection in causing ARIs and pneumonia.

A 2012 review by Punpanich and Chotpitayasunondh (15) reported that 43% of the pandemic

influenza A(H1N1)pdm09 virus associated paediatric deaths had bacterial co-infection. Similar

reports have been made by Ruuskanen et al., (59). On the other hand, respiratory virus co-

infections have been associated with severe disease outcome (76;513-517). However, in this

review, only Vazquez-Perez (502) and Barrero et al., (506) reported on patterns of bacterial co-

infections and respiratory virus co-infections respectively. Respiratory virus infection might lead

to destruction of epithelial cells, giving a leeway to bacterial infection [Peltola and McCullers

(60) and Bakaletz et al., (61)], or it could be the other way round. Sequential studies

125

investigating initial virus vs. bacterial infection and the epidemiology of subsequent co-

infections and disease outcome, might help explain the causal relationships.

It would have been good if the role of mutations in seasonal influenza A (H1N1), influenza A

(H3N2), influenza B virus (Flu B), and other respiratory viruses including; respiratory syncytial

virus (RSV), rhinovirus (RV), adenovirus (AdV), human metapneumovirus (hMPV), and human

parainflueza virus types 1 to 4 (PIV1-4), on disease outcome were also explored. However,

mutations in the other respiratory viruses are not routinely investigated and reported. Li et al.,

(518) compared the positively selected sites of pandemic influenza, vs. seasonal human, avian,

and swine influenza viruses in 2009 and 2010. They identified a number of sites on the HA gene

that underwent differential selection (HA-86, 94, 153, 160, 202, 234, 250, 303, 374, 399, 473,

and 573) (Supplementary Figure 2.5S1) yet none of these have been observed by the studies

included in our review. Mutations in the antigenic sites of seasonal influenza A(H1N1) virus

were well summarized by Shih et al., (519). In addition, the positively selected sites for influenza

A(H3N2)-HA have been described by Wiley et al., (520), Bush et al., (521), Shih et al., (519),

Suzuki and Gojobori (522), Liao et al., (406) (Figure S2) whereas the 9 glycosylation sites [N63,

N81, N122, N126, N133, N144, N165, N246 and N276] in influenza H3N2 viruses, gained since

their first appearance in 1968, have been documented by Abe et al., (523), Blackburne et al.,

(524), and Seidel et al., (525). As influenza viruses also occur as co-infections, the impact of

mutations in other respiratory viruses on disease severity should be born in mind when

interpreting our results.

In conclusion, this review has found an association between the mutation HA-D222G and severe

and fatal disease. It has also established that during the two years the pandemic influenza

A(H1N1)pdm09 virus circulated, no virus quasispecies bearing virulence conferring mutations

on all the major virulence conferring genes (HA, PB2 and NS1) predominated in humans. This

result reaffirms previous reports suggesting the importance of PB2, NS1 and HA mutations

working together to cause serious disease. The circulating influenza A viruses bearing a glycine

on position 222, the receptor binding site of influenza A viruses, should continue to be monitored

for the occurrence of other virulence conferring mutations on HA, PB2 and NS1. Respiratory

virus co-infection has been reported to increase disease severity (75), future studies on role of

genetic mutation on severity of influenza virus should make efforts to control for other

respiratory virus co-infections.

126

Funding: This work received no funding from any funding organization.


Manchester Academic Health Science Centre.


127


Table 2.5S1: Summary map of influenza A(H1N1)-HA gene antigenic sites by study

Region

Region Position as reported by different authors

Caton

Brownlee

Stray

Huang

Liao

All_PR34

Caton

Brownlee

Stray

Huang

Liao

All_PR34

Sa Ca1 121 121 35 35 124 124 36 36 125 125 125 43 43 43 127 127 127 169 169 169

128 128 128 128 128 170 170 170 129 129 129 129 171 171

155 155 172 172 172 156 156 156 156 173 173 173 173 157 157 157 157 179 179

158 158 158 204 204 159 159 159 205 205 205

160 160 160 160 206 206 162 162 162 162 207 207 207 207 163 163 163 163 163 208 208 208

164 164 164 209 209 165 165 165 165 212 212 166 166 166 166 166 216 216 216 167 167 167 238

247 247 239 239 248 248 240 240 240 240

241 241 242 242

Sb 244 244 153 153 153 245 245 183 183 183 271 271 186 186 186

187

187

Ca2

188

188

130 130 130

189 189 189 189 189

132

132

190 190 190 190 190 133 133 133 133 191 191 191 191 137 137

192 192 192 192 139 139 193 193 193 193 193 193 140 140 140 140

194 194 194 194 141 141 141 141 195 195 195 142 142 142 142 142

196 196 196 143 143 143 197 197 197 144 144 144 144

198 198 198 145 145 145 146 146

Cb

149

149

47

47

221

221

51

51

222

222

54 54 54

224 224

224

224

57 57

225 225 225

225

69 69 69

237

237

70

70

71 71 71 H1c 73 73 86 86 74 74 74 94 94 75 75 271 271

76 272 272 77 77 273 273 273

78 78 78 274 274 274 79 79 79 275 275

80

80 80 80

276

276

81 81

81

277 277 277 277

82 82

82

82

279

279

83 83

83

280

280

85 85

281

281

115

115

282

282

149

149

286

286

252 252

295

295

253 253

310 310 310

255 255 256 256 259 259 260 260 261 261 262 262 263 263 264 264 265 265 266 266

267 267

128

Notes: Antigenic sites as reported by Caton et al., (418), Brownlee et al., (421), Huang et al., (423), Liao et al., (406).

The other authors numbering was based on influenza A/PR/8/1934 virus. A number of methods are used to identify

positively selected sites on the HA gene including: a serological method, the haemagglutination inhibition assay (HI);

genetic methods such as: pairwise analysis of the genome and determination of positively selected codons by

comparing differences between non-synonymous (non-silent) vs. synonymous (silent) codon substitutions (ω=dN/dS),

mapping amino acid changes using maximum parsimony tree, Shannon entropy (amino acid variation) and likelihood

ratio (for a given antigenic site - LR), or use of the hamming distance to determine the percentage difference between

vaccine strains (Pepitope), and determination of amino or 3D crystal structure of HA constructed using homology

modelling (405;406;423;424;519;521;526-529). The HI and 3D genetic comparison the 3D being the most commonly

used. The studies here used one or more of these methods to determine that these sites are antigenic sites.

129

Table 2.5S2: Mutations on the H1-HA antigenic sites in representative strains 1918-2009

Site

Position

No site mutated

Site

Position

No site mutated

Sa

Ca1 Q121 T35

S124 V36 V125 V43 S127 T169V 1

F128

K170E/K/G/K 4

E129

K171

Y155H

G172E/D/N 3

A156E/K/N/A 4

S173G/N 2

G157

S179K/N/S 3

A158K/R/E/K/E/A 6

D204E/V/N/A 4

S159C/S/K 3

Q205

S160

Q206K/R/Q 3

Y162

S207N/T/A/S 4

R162K/R/K 3 L208I/L 2 N164 Y209 L165 A212E/A 2 L166 V216 W167 R238 Y247 D239G/N/G/D 4 W248 Q240

A241E 1

G242 Sb

M244I/M 2

C153 N245 V183A/I 2 A271

K186

G187E/G 2

Ca2

K188

K130R 1

E189

E132

V190

I133

L191

T137E/T 2

V192

S139

L193

W140 W194 P141

G195 N142K/N 2 V196I/V/I 3 H143

H197 E144N/T/D 3 H198 T145V/S 2

T146N 1 Cb

V149

V47 S221 E51 K222N/H/N/R 4 H54 N224S 1 K57 R225K 1 L69 V237

G70

K71N/K 2

H1c

N73S/N 2

L86P/S/L/S 4

I74V/I 2

S94

A75

A271

G76

F272

W77

A273

L78I 1

L274M 1

L79 N275S/N/S/E 4 G80 R276 N81 G277N 1 P82 G279

E83K/E 2 S280 D85E 1 G281 Y115 I282 V149 D286N/D 2

E252K/E 2 K295T 1 P253 Q310 D255

T256K A259 E260 A261

T262N/T 2 G263 N264 L265 I266V 1 A267V 1

Notes: Letter before the number indicate the amino acid at that position in 1918 and letters after the number indicate

substitutions observed in either of the following reference strains: A/Puerto Rico/8/1934, A/Bellamy/1942,

A/USSR/90/1977, A/Memphis/42/1983, A/Singapore/6/1986, A/Taiwan/1/1986, A/Texas/36/1991, A/Bayern/7/1995,

A/Beijing/262/1995, A/New Caledonia/20/1999, A/Brisbane/59/2007, and A/CA/7/2009. Although some studies

summarized antigenic changes in influenza A(H1N1), no prior study summarized all the antigenic sites. In order to

130

understand the changes in the antigenic sites of influenza A (H1N1), sequences of representative strains from 1918 to

2009 were downloaded from GenBank, aligned and analysed. A total of eleven HA sequences for representative

strains of influenza A(H1N1): A/Puerto Rico/8/1934 (accession number AF117241.1), A/Bellamy/1942

(CY009276.1), A/USSR/90/1977 (DQ508897.1), A/Memphis/42/1983 (CY019061.1), A/Singapore/6/1986

(CY020477.1), A/Taiwan/1/1986 (DQ508873.1), A/Texas/36/1991 (DQ508889.1), A/Bayern/7/1995 (EF566037.1),

A/Beijing/262/1995 (EF541421.1), A/New Caledonia/20/1999 (EF566076.1), A/Brisbane/59/2007 (CY163616.1)

A/CA/07/2009 (KC781785.1) were downloaded from GenBank and A/CA/04/2009 (3LZG:E) from the RCSB Protein

DataBank. For translation of the nucleotide sequences to protein sequences, the Fasta formats of were uploaded onto

the European Molecular Biology Laboratory (EMBL-EBI) protein conversion tool – Transq available at

(http://www.ebi.ac.uk/Tools/st/emboss_transeq/). Multiple alignment was conducted using ClustalW multiple

alignment in Bioedit Sequence alignment Editor version 7.1.3.0 (103). Changes in the antigenic sites were inspected

visually using conservation plot view and details of the changes at specific sites are summarized.

131

Supplementary Table 2.5S3: Assessment of bias in studies included on the review:

Mutations in FluApdm09 and severity

No

Study name

Ref

Sample size

Study design & sampling procedure

Selection bias

Genes of interest

sequenced

Demographic

characteristics

Comorbidities

Co-infection,

viral or bacterial

Overall Assessment

1 Puzelli et al., (2010) (493) 169 convenient sample of patients diagnosed with pdm09

possible HA Given Not given

Not investiga

ted

Ok

2 Kilander et al., (2010) (494) 266 samples from severe and mild cases selected through frequency matching

No HA Not given

Not given

Not investiga

ted

Good

3 Mak et al., (2010) (495) 458 serial mild and severe cases tested at the laboratory

Possible HA Not given

Not given

Not investiga

ted

Ok

4 Miller et al., (2010) (496) 58 hospitalized and community cases

No HA Not given

Not given

Not investiga

ted

Ok

5 Graham et al., (2011) (497) 235 sample of mild and severe cases from 11 of the 12 provinces

No HA, NS1 & PB2

Given Not given

Not investiga

ted

Good

6 Potdar et al., (2010) (489) 13 representative viruses from major cities

Possible HA, NS1 & PB2

Given Not given

Not investiga

ted

Ok

7 Farooqui et al., (2011) (498) 10 patients presenting to hospital with ILI

Possible HA Given Given Not investiga

ted

Excellent

8 Tse et al., (2011) (490) 20 patients admitted to hospital who were positive for pdm09

Possible HA Given Given Not investiga

ted

Excellent

9 Chen et al., (2010) (499) 117 patients admitted to hospital who were positive for pdm09

No HA Yes Given Not investiga

ted

Excellent

10 Baldanti et al., (2010) (491) 131 hospitalized and community cases

No HA Yes Not given

Not investiga

ted

Good

11 Wedde et al., (2013) (503) 357 fatal, hospitalized and community cases

HA Yes Given bacterial co-

infection investigated but

not reported

Excellent

12 Ferreira et al., (2011) (505) 90 fatal and nonfatal cases with ILI

No HA Not given

Not given

Not investiga

ted

Ok

132

Supplementary Table 2.5S3: Assessment of bias in studies included on the review:

Mutations in FluApdm09 and severity – continued

No

Study name

Ref

Sample size

Study design & sampling procedure

Selection bias

Genes of interest

sequenced

Demographic

characteristics

Comorbidities

Co-infection,

viral or bacterial

Overall Assessment

13 Moussi et al., (2013) (504) 50 a sample of outpatient, hospitalized and fata pdm09 patients

Possible HA Yes Not given

Not investiga

ted

Good

14 Vazquez-Perez et al., (2013)

(502) 77 hospitalized and ambulatory patients with pdm09

No HA, NS1 & PB2

Yes Given bacterial co-

infection given

Excellent

15

Venter et al., (2012)

(500)

HA-28 PB2-30

in and outpatients with Flu Apdm09 infections

No

HA, PB2

Not

given

Not

given

Not

investigated

Good

16 Promed Email (2009) (492) 33 cohort of people who had camped with the index case and a selection of patients hospitalized with Flu Apdm09 July-Aug 2009

No PB2 Given Given for

index case, others

not given

Not investiga

ted

OK

17 Akcay Ciblak (2013) (501) 13 samples from severe, mild and fatal cases

No HA, PB2 Given Not given

Not investiga

ted

Good

18 Barrero et al., (2011) (506) 21 fatal, severe and mild cases

No HA, NS1 Given Given Flu B, RSV,

hPIV3, AdV

Excellent

133

Figure 2.5S1: Sites under differential selection between isolates from seasonal human and the

pandemic 2009 clusters. Reprint of Table 3 from Li et al., (2011) (518) with permission from BioMed Central.

134

Figure 2.5S2: Updated evolutionary dynamics of positively selected sites on the HA1 domain of

human influenza A/H3N2. Reprint of Supplementary Figure S3 (A) in Liao et al., (2010) (406) with permission

from Elsevier Copyright Licence No: 3367641189939.

135

Table 2.5S4: Search history on EMBASE for review number 1: Mutations associated with

severity of pandemic influenza A(H1N1)pdm09 viruses: A systematic review and meta-

analysis

# ▲ Searches Results Search Type

1 Orthomyxovirus/ 6257 Advanced

2 Influenza virus A/ 16372 Advanced

3 Influenza virus A H1N1/ or influenza A (H1N1)pdm09.mp 9206 Advanced

4 hemagglutinin/HA.pm 8000 Advanced

5 nonstructural protein 1.mp/ or NS1.mp 1321 Advanced

6 Polymerase basic protein 2.mp/ or pb2.mp. 4285 Advanced

7 1 or 2 or 3 or 4 or 5 or 6 42454 Advanced

8 MUTATION/ 160398 Advanced

9 virus mutation/ 10120 Advanced

10 antigenic changes.mp. 463 Advanced

11 evolution/ 68043 Advanced

12 molecular evolution/ 40605 Advanced

13 molecular epidemiology/ 4539 Advanced

14 genetics/ 532926 Advanced

15 8 or 9 or 10 or 11 or 12 or 13 or 14 740760 Advanced

16 virulence/ 18997 Advanced

17 Virus virulence/ 9965 Advanced

18 pathogenicity/ 56117 Advanced

19 fatality/ 76264 Advanced

20 mortality/ 502041 Advanced

21 prognosis/ 417500 Advanced


23 7 and 15 and 22 703 Advanced

136

2.5.6.1. The cause of influenza pandemics

Influenza pandemics have been documented since the 16th century at intervals varying from 11 to 42

years. In the 20th century, there has been no recognisable pattern with pandemic in 1918/19,

1957/58, 1968/69 and 1977-78 (261), and in the 21st century 2009/2012 (274). Antigenic shift, i.e. a

notable genetic change in the circulating strain of influenza virus to a novel virus, is responsible for

pandemics because the virus can easily spread because a large portion of the human population is

susceptible to infection with the new virus (530). Evidence from studies summarized in Table

2.5S5 and Figure 2.5S3 indicate that in the last decades influenza events have been marked by

antigenic shifts and antigenic drifts and the antigenic shifts have been due to the introduction of

interspecies genomes e.g. avian or swine into the human viruses. For example Morens et al., (266)

and Zimmer and Bulke (403) have indicated that the 1918-19 pandemic was due to an introduction

of a novel swine adapted avian virus, which circulated in swine shortly before the start of the

pandemic (Figure 2.5S3). Interspecies gene exchanges was also responsible for the appearance of

the pandemic influenza A(H2N2) virus in 1957, a reassortment of the human 1918 H1N1 virus with

the avian virus and then the pandemic influenza A(H3N2) virus in 1968 whose PB1 and HA genes

were of avian origin and the rest were human (266;403).

Since then, there appeared to be a break and then in 1998, a new triple-reassortant influenza

A(H3N2) virus derived from North American avian, classical swine A(H1N1) and human influenza

A(H3N2) viruses, caused outbreaks in North American swine (531;532). Mixing of the triple-

reassortant influenza A(H3N2) with established swine lineages gave rise to the influenza A(H1N1)

and influenza A(H1N2) reassortant swine viruses (533;534). The first case of human disease from

the 1998 swine influenza triple-reassortant influenza A (H1N2) virus was a 17-year-old who had

been exposed to pigs at a slaughterhouse in Wisconsin (533). Investigators later reported 11 known

human cases of infection with the triple-reassortant viruses between 2005 and 2009; most of these

patients had been exposed to swine (534). The pandemic influenza A(H1N1)pdm09 was a

reassortment of 2 viruses i.e. the 1998 triple-reassortant influenza A(H1N2) North American swine

virus, and the Eurasian H1N1 swine. In particular, 6 out of the 8 genes; the PB2 and PA, PB1, HA,

NP and NS were from the reassortant influenza A(H1N2) - introduced into swine populations

around 1998; the HA, NP, and NS genes descended directly from the classic swine influenza A

virus of North American lineage, which can be traced back to the 1918 virus; and NA and M were

from the Eurasian swine virus, introduced from birds around 1979 (266;535) - Figure 2.5S3.

137

Figure 2.5S3: Genetic Relationships among Human and Relevant Swine Influenza Viruses,

1918–2009.Yellow arrows reflect exportation of one or more genes from the avian influenza A virus gene pool. The

dashed red arrow indicates a period without circulation. Solid red arrows indicate the evolutionary paths of human

influenza virus lineages; solid blue arrows, of swine influenza virus lineages; and the blue-to-red arrow, of a swine-

origin human influenza virus. All influenza A viruses contain eight genes that encode the following proteins (shown

from top to bottom within each virus): polymerase PB2, polymerase PB1, polymerase PA, haemagglutinin (HA),

nuclear protein (NP), neuraminidase (NA), matrix proteins (M), and non-structural proteins (NS). The genes of the 1918

human and swine H1N1 and the 1979 H1N1 influenza A viruses were all recently descended from avian influenza A

genes, and some have been “donated” to the pandemic human H1N1 strain. Reproduced with permission from

Morens et al., (266), Copyright Massachusetts Medical Society

138

Table 2.5S5: Evidence on the origin of the pandemic influenza A(H1N1)pdm09 virus

genomes, 1918-2009

Author

Study design

Sample size

Study focus

Smith et al., (535)

Cross-sectional

survey

3,042 complete genome

sets i.e. 1,759 human, 166

swine and 1,117 avian

Historical perspective of mixing of avian, swine

and human influenza viruses and origins of

2009 H1N1pdm

Miotto et al., (438) Cross-sectional

survey

92,343 public database

records

Mapping of human influenza adaptive

mutations and adaptive signatures

Zimmer et al., (403) Systematic review Not specified Historical perspectives of mixing of influenza

virus genomes and origin of 2009 H1N1

Morens et al., (266) Systematic review Not specified Historical perspectives of mixing of influenza

virus genomes and origin of 2009 H1N1

Rambaut et al., (400) Cross-sectional

survey

1,302 A/H1N1 and A/H3N2 Reassortment

Bragstad et al., (536) Cross-sectional

study

234 A/H1N1, A/H3N2 and

A/H2N2

Reassortment and evolution of human

influenza viruses

Nelson et al., (402) Cross-sectional

study

71 H1N1 Reassortment events and genetic evolution

Nelson et al., (537) Cross-sectional

survey

284 viruses Reassortment events and genetic evolution

Garten et al., (538) Cross-sectional

survey

56 viruses Phylogenetic analysis

139

Part III: Review of available literature on patterns of co-infections and

association between co-infections and disease severity

3.1. Co-infections; patterns and severity Part A: The patterns of co-infection between

influenza A and other respiratory viruses and its effect on viral load and

interferon production: A systematic review and meta-analysis

Synopsis

This is the authors’ version of the paper submitted to Lancet Infectious Diseases for publication.

Since recent literature indicates that a good proportion of respiratory viruses’ infections occur as

co-infections, there is need to understand which pairs are most likely to occur as this could be used

to calculate population attributable fractions associated with the said pairings. This paper reviews

the patterns of co-infections between influenza A and other respiratory viruses.

140

3.1.1. Abstract

The patterns of co-infection between influenza A and other respiratory

viruses and its effect on viral load and interferon production:

A systematic review and meta-analysis

Edward A. Goka 1, Pamela J. Vallely 1, Kenneth J. Mutton 1,2, Paul E. Klapper 1,2



Corresponding author: Edward Goka, Institute of Inflammation and Repair, Faculty of Medical and Human

Sciences, 1st Floor Stopford building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.


Running head: Co-infection patterns, influenza & respiratory viruses

141

Summary

Objectives: Respiratory virus infections have mainly been regarded as single infections,

however research suggest a substantial number occur as co-infections. Knowledge on the burden

of co-infection in influenza would guide, clinical practice, vaccine and drug research. This

review summarizes the pattern of co-infections between influenza A viruses and other

respiratory viruses and its impact of viral load and interferon production.

Methods: A meta-analysis and a review was conducted using studies obtained from MEDLINE,

EMBASE and WEB of Science, websites of health organisations and reference lists of published

studies. Thirty (30) studies that recruited in and outpatients or only hospitalized children or

adults and children were included. The quality of studies was assessed using the PRISMA and

STROBE Guidelines.

Results: Co-infections with influenza A ranged from 1% to 26%. Respiratory syncytial virus

(RSV), human bocavirus (hBOV), rhinovirus (RV) and adenovirus (AdV) were the most

common viruses to co-infect with influenza A virus especially among children: overall co-

infection; RSV (26%) among children ≤5 years old hospitalized with influenza A illness; hBoV

(21%) among hospitalized and outpatient children ≤18 years old; RV (12%) among hospitalized

children ≤5 years old; and AdV 4.0% - 11.3% among hospitalized and outpatient children ≤5

years old. This was followed by human coronavirus (hCoV), human metapneumovirus (hMPV)

and least common being human parainfluenza virus types 1 to 4 (hPIV1-4). Co-infection patterns

varied with age, study design, season, year and region the study was conducted. There is no

enough evidence for or against the association between co-infection and viraemia or cytokine

release. The possibility of bias due to lack of information on patient’s influenza vaccination or

other predisposing factors should be born in mind when interpreting our results.

Conclusion: Routine testing of these viruses among patients with influenza infection is

recommended. Research aimed at elucidating the mechanisms of virulence or avirulence in these

co-infections is worth pursuing.

Key words: influenza A virus, co-infections, mixed infections, viral load, interferon release

142

3.1.2. Introduction

A substantial number of studies have reported viruses occuring as as dual or multiple infections

and that there is a relationship between the presence or absence of certain viruses (539-543).

For example, Greer et al., (541) reported that the presence of rhinoviruses (RV) led to a

reduced probability of co-infection of adenoviruses (AdV), human coronavirus (hCoV), human

bocavirus (hBoV), human metapneumovirus (hMPV), respiratory syncytial virus (RSV),

influenza A virus (Flu A), and parainfluenza viruses 1-4 (hPIV1-4). Whereas Linde et al., (544)

and Casalegno et al., (545) have suggested that rhinovirus interfered with the survival of the

pandemic influenza A(H1N1)pdm09 virus (Flu Apdm09).

Influenza viruses, and members of the paramoyxoviridae; [RSV, hMPV and hPIV1-4], are

enveloped single stranded negative sense RNA viruses. They all have surface attachment

proteins, penetrate cells through membrane fusion and replicate in the nuclease for influenza

viruses (432) and the cytoplasm for paramyxoviruses (361;546). On the other hand coronavirus

and rhinovirus are respectively enveloped and non-enveloped single stranded positive sense

RNA viruses. They also attach to cell membranes and replicate in the cytoplasm (367;381),

whereas bocavirus and adenovirus are respectively single strand and double stranded, circular

and linear DNA non-enveloped viruses (43;385). Both bocavirus and adenovirus enter the cell

through endocytosis and replicate in the nucleus (43;385). Respiratory viruses dsRNA and

ssRNA are recognized by toll like receptors: TL2, TLR4 and TLR6 on the external surface of

the cell; TLR3 TLR7, TLR8 and TLR9 in the endosome; and by the protein kinase RNA -

activated (PKR), the melanoma differentiation associated gene 5 (MAD-5), the retinoic

inducible gene I (RIG-I) and the 2',5'-Oligoadenylate synthetase (2',5'-OAS1&2) in respiratory

epithelial cells and dendritic cells (17;547-550), which then trigger production of cytokines

such as tumour necrosis factor (TNF) and type 1 proinflammatory cytokines; interleukin-6 (IL-

6), interleukin-18 (IL-18), interferon-alpha (IFN-α), and interferon-beta (IFN-β) (361;551;552).

Biomedical studies have indicated that the severity of influenza A virus infection is mediated

by the amount of cytokines produced following virus infection (553-555). The cytokines causes

damage by causing; loss of cell adhesion, cell membrane hyperpermeability, and release of

mitochondria reactive species, which leads to damage of blood vessels, cell death, and organ

failure (553).

143

3.1.2.1. Rationale for conducting this review

Co-infection among respiratory virus infections has been associated with increased risk of

hospitalization to a general ward (GW), admission to the intensive care unit (ICU) or death (75-

77;86;91;94;513;514). Among other factors, the ability of respiratory viruses to cause a severe

infection depends on their ability to antagonize the interferon pathways (19;34;37-44;46-

50;363;385;556). In influenza, RSV and hBoV, the NS1 protein is used to block the interferon

production (38-42;556). The E protein in AdV, and hCoV spike protein has also been reported

to perform a similar function (43-46;385), and the P, C, D and V proteins in hPIV1 and 3,

hPIV3, and hPIV2 and 4 respectively (47-50;363). The impact of the interaction of these

molecules during co-infection and how it affects length of viral shedding, viral load, interferon

production, and disease severity is not well understood. An understanding of the patterns of co-

infections with influenza A viruses would aid in determining which viruses would result in

severe disease (viral load and interferon production); calculation of population attributable risk

of hospitalization, admission to ICU or mortality associated with co-infections; and in

modelling the economic benefit of public health control measures.

Some studies have reviewed the magnitude of co-detection (84;540;557). Sly et al.,(557)

reported that co-detection may range from 4% to 44%, and almost a similar range of co-

infection patterns (14.3% - 47.0%) was reported by Stefanska et al., (84) review. A number of

studies found RSV and RV as the most frequently identified pairing (76;80;302;514;558), yet

others found RSV and human hBoV (559); RSV and AdV pairing (79;82;560); and some have

even suggested that coronavirus and influenza virus pairing is low (75;82). However, no

attempt has been made to summarize information on co-infections between influenza A viruses

and other respiratory viruses; which respiratory virus infections are most commonly involved in

such co-infections and how that influences virus replication and interferon production.

3.1.2.2. Aims and objectives of the systematic review and meta-analysis

This study aimed at summarizing the evidence on the patterns of co-infections between

influenza A virus and other respiratory virus infections and its impact on interferon production

and viral load with the following objectives:

1. To investigate the burden of specific types of co-infections between influenza A and

other respiratory virus infections.

2. To review evidence documenting; successful respiratory virus pairings that lead to

higher viral loads, and those that trigger more interferons when they co-infect.

144

The review was conducted to answer one of our research questions: “Which respiratory viruses

most commonly co-infect with influenza A viruses?” and therefore aimed to:

a) Generate knowledge on the role of co-infection in acute respiratory tract infection

(ARI).

b) Summarize evidence needed for prioritizing and making policy on the importance of

multiple testing of respiratory virus infections in patients presenting with influenza

virus infections, prioritizing research and development on combined respiratory viruses

vaccines and drugs, and calculation of population attributable fraction for co-infections.

3.1.3. Methodology

3.1.3.1. Review protocol

The supervisors of this PhD, who are coauthors of this manuscript, guided the principal

investigator in developing a review protocol. We did not deposit the protocol on any online

server.

3.1.3.2. Search strategy

A search for primary studies on the epidemiology of co-infections between influenza A viruses

and other respiratory virus infections was performed on: MEDLINE, EMBASE and WEB of

Science databases; websites of the World Health Organisation (WHO), UK Health Protection

Agency (HPA), United States Centres for Disease Control, World Influenza Network Centre,

UK’s Medical Research Council; and reference lists of well conducted studies. In the electronic

search, specific key words or text words and subject heading were used and combined

including: Respiratory tract Infections, respiratory virus infections. respirovirus infections,

Orthomyxovirus, Orthomyxoviridae infections, influenza virus, influenza human, influenza A

virus H1N1 subtype, influenza A virus H3N2 subtype, influenza A virus H2N2 subtype, avian

influenza, influenza in birds, influenza A virus H5N1 subtype, influenza A H7N9 subtype,

influenza A H7N7 subtype, 2009 pandemic influenza virus, pandemic influenza, 2009 H1N1

influenza, influenza A(H1N1)pdm09, Flu A, Picornaviridae infections, rhinovirus, human

rhinovirus, rhinovirus infection, RV, Adenoviridae, Adenoviridae infections, adenovirus,

adenovirus infection(s), human adenovirus, human adenovirus infection, AdV,

Paramyxoviridae infections, respiratory syncytial virus(es), respiratory syncytial virus

infection(s), RSV, metapneumovirus, human metapneumovirus, metapneumovirus human,

hMPV, parainfluenza virus 1 human, parainfluenza virus 2 human, parainfluenza virus 3

human, parainfluenza virus 4 human, hPIV1, hPIV2, hPIV3, hPIV4, Parvoviridae Infections,

145

bocavirus, bocavirus human, human bocavirus, bocavirus infection, coronavirus, coronavirus

infection(s), human coronavirus NL63, human coronavirus 229E, human coronavirus OC43,

human coronavirus HKU1, coinfection, co-infection(s), mixed infection, dual infection(s),

multiple infection(s), immune response, innate immune response , cytokine storm, cytokine,

cytokine release, interferon production, tumour necrosis factor alpha, gamma interferon,

interleukin 6, interleukin 12, interleukin 1, severe influenza, vascular hyperpermeability,

pathogenesis, viral load, virus titres. A result of the search on MEDLINE is provided in

supplementary Table 3.1S2 as an example.

3.1.3.3. Assessment of study quality and selection process

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)

guideline (109) was followed to write the reviews. In addition, The “Strengthening the

Reporting of Observational Studies in Epidemiology (STROBE)” tool for critical appraisal of

epidemiological studies (108), was used to assess the studies identified in the search. The

principal investigator of the study conducted the online and manual search of eligible studies.

Titles and abstracts of the studies identified online and through the other described means were

quickly scoped and scanned for relevance. Studies that were clearly not relevant (e.g. with the

title; respiratory viruses in bats or pigs, correlation between bocavirus infection and viral loads,

epidemiology of parainfluenza viruses), were removed. Papers that looked relevant were

downloaded and read in full.

3.1.3.4. Exclusion and inclusion criteria

Relevance of a study to be included depended on: whether it reported the patterns of co-

infections between influenza A viruses and other respiratory virus infections; and on the other

part of the study, description of viral loads and interferon release. Other factors considered

included: whether or not the following variables were reported; the source of samples i.e.

hospital or community based; the sample size; type of samples used to identify viruses e.g.

throat swabs or nasopharyngeal aspirates; method used to identify viruses [real time

polymerase chain reaction (TR-PCR), ordinary PCR, immunofluorescence tests or other test

types]. Because it is now generally acknowledged that use of diagnostic methods other than

RT-PCR or PCR would result in low virus yields (47), only studies that used RT-PCR or PCR

for virus identification were included. For part A, studies that did not report co-infection with

influenza A viruses were excluded. Some of the studies were left out because there were not

enough studies of the same age and study design in other pairs of respiratory virus co-

infections. For part B, studies that did not report viral load or interferon levels were excluded.

146

For both arms, studies that were not written in the English language, or were poorly designed

were excluded.

3.1.3.5. Assessment of bias

Influenza vaccine effectiveness studies suggest that vaccination increases the risk of non-

influenza respiratory virus infection (561-563). In addition, other factors including: chronic

conditions such as asthma, heart disease, COPD; obesity, socio-economic status, pregnancy,

prematurity and low birthweight, nutrition status and breastfeeding in infants, active and

passive smoking, immune status affect risk of infection with respiratory viruses

(9;10;13;21;23;53;564) also affect susceptibility to infection. Therefore studies were eligible

for inclusion if they recruited healthy adults or children presenting to hospitals, clinics,

emergency departments, or case-control studies. The studies must have documented at least two

of the above mentioned factors in their study design, so as to eliminate confounding by

indication and by those factors. Other issues considered included selection bias (how

participants were recruited, study population and demographic characteristics of the groups). A

summary of the inclusion criteria used and the rationale behind it is provided in Table 3.1. The

identified observational studies were appraised for presence of these factors at selection and

analysis stages of the review. Studies were scored based on this criteria using the method

described in the PRISMA and STROBE guidelines and a summary of the scores for all the 30

studies included in this review is provided in Supplementary table 3.1S1.

3.1.3.6. Data extraction and statistical analysis

An abstraction form was designed capturing the details of the study i.e. author, year and place

conducted, sample size, total number of respiratory viruses found, number of single and mixed

influenza A virus infections. A meta-analysis summarizing the observed number of co-

infections between influenza A virus and other respiratory viruses was conducted using the

random model. All analyses were conducted using the Comprehensive Meta-Analysis software

– version 2 (BIOSTAT, Englewood, NJ 07631 USA) and results summarized using forest plots

and tables. Also summed up in tables were results on association between viral load and

interferon levels and co-infection.

147

Table 3.1: Inclusion criteria for studies in this systematic review and meta-analysis of

patterns of co-infections between influenza and other respiratory viruses

Item

Description

Rationale

Study design

Descriptive prospective or retrospective studies, or case-control studies that investigated co-infections among patients seen as outpatients at a hospital, clinic, medical centre, admitted to a general ward, the ICU, or case control studies on the same.

These would give a pictures on the epidemiology of co-infections among patients. It would have been ideal to recruits community based studies as these would give the true incidence of respiratory virus co-infections but such studies are rare, also the thesis leans towards severity, therefore epidemiology of co-infections among those with serious disease is a more suitable statistic fitting the objectives and aims of this thesis.

Sample size Studies that recruited 30 or more participants

It is generally accepted in epidemiology and statistics that a sample size of 30 is a baseline for investigating epidemiology of disease.

Diagnostic test PCR It is now regarded the standard test, to eliminate detection bias only studies that used PCR were included.

Age Studies that recruited either only young children, or a mixture of these, or only adults

We planned to conduct stratified analysis in the systematic review e.g. Flu A+RSV co-infection among hospitalized children ≤5 years old; Flu A+RV A co-infection among in and outpatients of all age groups. Therefore selection of studies of such designs would be appropriate.

Vaccination status, comorbidities and other confounders

Studies that reported at least 2 of the factors known to affect susceptibility to infections with respiratory viruses

Epidemiological evidence indicate that influenza vaccination protects against Flu but probably increases risk of infection with other respiratory viruses. Similarly it is argued that among other factors, socio-economic status, ones gender, predispose individuals to infection with respiratory viruses. Since this paper was trying to estimate the epidemiology of co-infections, having these reported in the inclusion criteria would help bring the estimates closer to the true value.

3.1.4. Results

3.1.4.1. Characteristics of the included studies

The search on the MEDLINE, EMBASE and WEB of Science in total identified 11,794 papers

with the majority (76.05%; 8,969) identified from the Web of Science, and the others from

EMBASE and MEDLINE – Figure 3.1. Of these 9,271 were not downloaded after scrutinising

the title or abstract and finding the study irrelevant. A further 2,493 of the 2,523 studies that

were downloaded and reviewed were excluded either because; they did not report patterns of

co-infections between influenza A and other respiratory viruses; or were duplicates of other

included studies; or were poorly designed. Seven papers were selected from references of other

well conducted primary studies. In the end, 30 studies were included (Figure 3.1).

Studies included in the meta-analysis originated from all parts of the world (12 from Europe, 8

from North and South America, 4 from the Middle East, 4 from Australasia and 2 from Africa).

The included studies can be divided into 4 categories; those that recruited hospitalized children

≤5 years old (8), hospitalized children < 18 years old (5), in and outpatient under-five children

148

(6), and in and outpatients of all age groups (11). The sample sizes of the 29 studies varied

considerably, with 75 as the smallest and 8,173 as the largest sample size and together this

comprised of 36,185 individuals from which a total of 4,382 influenza viruses were identified,

representing an influenza A prevalence of 12.1% (range 2.1% - 55.8%) - Table 3.2. Of the

4,382 influenza A infections, 13.1% (717) occurred as mixed infections. The rates of co-

infection between influenza A and other respiratory viruses varied from 1.5% to 68.8%.

Figure 3.1: Number of studies identified, excluded and included in the review on patterns

of co-infection between influenza and other respiratory viruses

Excluded because they were

• Irrelevant

• Duplicates

• Poor study design

• Did not use PCR for diagnosis

• Had incomplete outcome data

2,493

Total number of

studies identified

11,794

Included in the

systematic review

& meta-analysis

30

Excluded after scanning title or

abstract and found irrelevant

9,271

Full text downloaded or

copied and scoped through

2,523

149

Table 3.2: Characteristics of the studies included in the meta-analysis of patterns of co-

infections with influenza A viruses

Study design

Study name Country Diagnostic method

Sample size

No of viruses

identified

Flu A Viruses

Prevalence

No of co-infection

Period study conducted

Hospitalized children < 5yrs

1 Aberle et al., (80) Australia PCR 772 596 (77.2) 26 (3.4) 10 (38.5) Oct 00 – Jul 04 2 Calvo et al., (85) Spain RT-PCR 749 520 (69..5) 16 (2.1) 8 (50.0) Sep 00 – Jun 03 3 Jennings et al., (565) New Zealand PCR 75 65 (87.0) 10 (13.3) 1 (10.0) Jul 01 – Nov 01

4 Boivin et al., (566) Canada RT-PCR 259 164 (63.3) 49 (19.0) 19 (38.8) Dec 01 - Apr 02

5 O'Callaghani-Gordo et al., (567) Mozambique PCR 333 185 (55.6) 41 (12.3) 17 (41.5) Feb 99 – May 00

6 Khamisa et al., (568) Oman RT-PCR 259 130 (50.0) 11 (4.3) 5 (45.5) Dec 07 – Dec 08

7 Wolf et al., (569) Israel RT-PCR 516 293 (57.0) 77 (15.0) 53 (68.8) Nov 01 - Oct 02

8 Foulongne et al., (570) France RT-PCR 589 275 (46.7) 18 (3.1) 1 (5.6) Nov 03 - Oct 04

In & out patients children < 5yrs

9 Kristoffersen et al., (571) Norweigh RT-PCR 452 130 (28.8) 33 (7.3) 1 (3.0) Nov 06 – Jun 07 10 Martin et al., (572) USA RT-PCR 893 566 (63.3) 127 (14.2) 25 (20.0) Sep 03 – Sep 04 11 Cilla et al., (513) Spain PCR 315 217 (68.9) 25 (8.0) 7 (28.0) Nov 04 – Oct 06 12 Malekshahi et al., (573) Iran RT-PCR 202 92 (45.5) 10 (5.0) 1 (10.0) Mar 08 – May 09 13 Naghipour et al., (574) Iran RT-PCR 261 108 (41.4) 11 (4.2) 1 (9.1) Nov 03 – Mar 04

14 de Vos et al., (575) Belgium RT-PCR 404 272 (67.3) 37 (9.2) 9 (24.3) Nov 04 – May 05

Hospitalized children < 18yrs

15 Libster et al., (93) Argentina RT-PCR 251 251 (100) 251 (100) 47 (18.7) May 09 – Jul 09 16 Do et al., (517) Vietnam RT-PCR 309 222 (72.0) 51 (16.5) 21 (41.2) Nov 04 – Jan 08 17 Rhedin et al., (91) Sweden RT-PCR 502 309 (61.6) 83 (16.5) 12 (14.6) Jul 09 – Dec 09 18 Weissbrich et al., (559) Germany PCR 786 357 (45.4) 98 (12.5) 9 (9.2) Jan 02 – Sep 05

19 Franz et al., (516) Germany RT-PCR 404 315 (78.0) 9 (2.2) 1 (11.1) Nov 06 – Oct 08

In & out patients all ages

20 Esper et al., (81) USA RT-PCR 496 306 (61.7) 229 (46.2) 30 (13.1) Sep 09 – Nov 09 21 Languna-Torres et al., (576) C. America RT-PCR 1756 434 (24.7) 130 (7.4) 6 (4.6) Aug 06 – Apr 09 22 Nisii et al., (577) Italy RT-PCR 544 203 (37.3) 108 (19.9) 19 (17.6) May 09 – Dec 09

23 Renois et al., (578) France RT-PCR 95 65 (68.4) 30 (31.6) 5 (16.7) Oct 09

24 Tanner et al., (579) UK RT-PCR 4821 2447 (50.8) 469 (9.7) 52 (11.1) Sep 09 – Apr 10

25 Wallace et al., (580) UK PCR 240 146 (60.8) 134 (55.8) 2 (1.5) Oct 99 – Mar 00

26 Peci et al., (90) Canada PCR 1018 668 (65.6) 452 (44.4) 149 (33.0) Apr 09 – Feb 10

27 Drews at al., (75) USA PCR 4336 1341 (30.9) 304 (7.0) 28 (9.2) Jan 91 - Dec 95

28 Druce et al., (582) Australia PCR 4,254 1583 (37.0) 314 (7.4) 18 (5.7) Jan 02 - Nov 03

Hospitalized all

age groups

29 Echenique et al., (94) USA PCR 1,192 666 (55.9) 314 (47.1) 24 (7.6) Oct-Dec 09

30 Pretorious et al., (581) South Africa PCR 8173 4666(57.1) 704 (8.6) 145 (20.6) Feb 09 – Dec 09

Notes: Numbers in parentheses represent percentages; prevalence of influenza A is percentage of sample which had a

positive diagnosis with influenza virus, co-infection is percentage of all influenza A viruses identified, predominant co-

infecting virus is a percentage total number of co-infections with influenza A virus identified. RT-PCT – real time

polymerase chain reaction, PCR – polymerase chain reaction. RSV - respiratory syncytial virus, RV - rhinovirus, AdV -

adenovirus, hMPV - human metapneumovirus, hCoV human coronavirus, hBoV - human bocavirus hPIV3 -

parainfluenza virus types to 4.

150

3.1.4.2. Co-infection patterns between influenza A and other respiratory viruses

Overall 16.0% to 26.0% of children 0 to 18 years old hospitalized with influenza A virus had

RSV co-infection compared to RSV co-infection proportion of 4.2% - 7.0% among outpatient

children and adults (Figure 3.2). In this analysis, only studies that recruited in and out young

and adult patients were heterogeneous (I2 = 97.1, p = <0.0001). The heterogeneity was caused

by 4 of the 7 studies; Esper et al., (81) Druce et al.,(582) Peci et al., (90) and Drews et al., (75),

probably because Esper’s study covered only the winter season, whereas Peci’s study covered

both the winter and summer seasons and RSV is known to have strong seasonal trends,

circulating mainly during winter (300), or because of the differences in the region the studies

were conducted; Druce et al., (582) in Australia, Peci et al., (90) in Canada. Therefore the

pooled estimate might have been biased towards the null due to the heterogeneity, and our

result may represent an underestimation of the true co-infection rate in this age group.

Although the burden of bocavirus and coronavirus were similar in studies that recruited

hospitalized <18 year old children (7.5% and 7.1%), bocaviruses were detected in relatively

higher proportions by studies that recruited both hospitalized and outpatients (summary

proportion 21.0%) compared to a co-infection proportion of 1.8% for coronaviruses (Figure 3.3

and 3.5). Apart from reporting low detection of coronaviruses, studies included in the

coronavirus analysis were heterogeneous (I2 = 68.8, p = 0.04 and I2 = 80.7, p = 0.006). We

could not identify a specific reason to explain the heterogeneity, it could probably be due to

seasonal trends, differences in the places the studies were conducted or because of the

differences in the sensitivity of the PCR assays used for the identification of coronaviruses.

As for the other viruses, the proportion of hMPV was 4.5% and AdV, 3.7% for studies that

recruited hospitalized under-five year old children. However, the proportion of RV co-infection

was 9.0% among hospitalized patients of all age groups and AdV, 11.3% among studies that

recruited both hospitalized and outpatient under-five year old children (Figure 3.4, 3.6 and 3.7),

whereas, the proportions of co-infection for RV and hMPV were much lower (range 0.2% to

3.9%), among studies that recruited both in and outpatients. Apart from analyses in RV, there

was no heterogeneity in the studies included in the other analyses (Figure 3.4, 3.6 and 3.7).

Lastly, parainfluenza viruses were the most unlikely viruses to co-infect with influenza A

viruses; only 1.2% in hospitalized under-five children and 2.0% in studies that recruited both

hospitalized and outpatients (Figure 3.8).

3.1.4.3. Association between co-infection with viral load and interferon production

Four of the included studies reported the relationship between co-infection patterns and

quantity of virus shed or innate immune responses (Table 3.3). Apart from Martin et al., (82)

151

who reported reduced viral loads associated with RV co-infection, the others did not find any

association. Also, in all the 4 studies identified and included, RV was the major co-infecting

virus, perhaps this explains why no association was reported. However, due to a limited

number of studies identified on this, we are unable to understand the true impact of co-

infections on viral load and immune response, more studies are needed.

Study name Co-infection 95% CI Statistics for each study Reference

(%)

Event rate & 95% CI

No of events

Hospitalized Children

≤5 years old

Boivin et al., (2003) 0.37 (0.18 - 0.60) (566)

Aberle et al., (2005) 0.23 (0.11 - 0.43) (80)

Calvo et al., (2008) 0.50 (0.27 – 0.73) (85)

Jennings et al., (2004) 0.10 (0.01 – 0.47) (565)

Foulongne et al., (2006) 0.56 (0.01 – 0.31) (570)

Overall 0.26 (0.13 – 0.45)


≤18 years old

Do et al., (2011) 0.12 (0.05 – 0.24) (517)

Libster et al., (2010) 0.17 (0.13 – 0.22) (93)

Overall 0.16 (0.12 – 0.21)

In and out patients

≤ 5 years old

Cilla et al., (2008) 0.14 (0.04 – 0.43) (513)

Kristoffersen et al.,(2011) 0.03 (0.00 – 0.19) (571)

Malekshahi et al., (2010) 0.10 (0.01 – 0.47) (573)

De Vos et al., (2009) 0.03 (0.00 – 0.17) (575)

Overall 0.07 (0.03 – 0.16)

In and out patients

all age groups

Nisii et al., (2010) 0.03 (0.01 – 0.08) (577)

Esper et al., (2011) 0.004 (0.00 – 0.03) (81)

Laguna-Torres et al., (2010) 0.03 (0.01 – 0.08) (576)

Druce et al., (2004) 0.01 (0.00 – 0.02) (582)

Tanner et al., (2012) 0.04 (0.03 – 0.07) (579)

Peci et al., (2013) 0.24 (0.20 – 0.28) (90)

Drews et al., (1997) 0.23 (0.20 – 0.28) (75)

Overall 0.04 (0.02 – 0.11)

Figure 3.2: Influenza A/RSV co-infection patterns by patient group and age. Notes:

Hospitalized ≤5 years old children I2 = 53.5, p = 0.06, hospitalized <18 years old children I2 = 0, p = 0.40, in

and outpatients ≤5 years old children I2 = 0, p = 0.40, in and outpatients all age groups I2 = 97.1, p = <0.0001.

6 / 51

42 / 251

-0.25 -0.13 0.00 0.13 0.25

7 / 18

6 / 26

8 / 16

1 / 10

1 / 18

-0.25 -0.13 0.00 0.13 0.25

2 / 14

1 / 33

1 / 10

1 / 37

-0.25 -0.13 0.00 0.13 0.25

3 / 108

1 / 229

4 / 130

11 / 1326

20 / 469

106 / 452

70 / 304

-0.25 -0.13 0.00 0.13 0.25

152

Study name Co-infection 95% CI Statistics for each study

Reference

(%)

Event rate & 95% CI

No of events


≤18 years old

Rhedin et al., (2012) 0.05 (0.02 – 0.12) (91)

Weissbrich et al., (2006) 0.10 (0.05 – 0.18) (559)

Overall 0.08 (0.04 – 0.13)

In and out patients

≤ 5 years old

Cilla et al., (2008) 0.36 (0.16 – 0.62) (513)

De Vos et al., (2009) 0.17 (0.08 – 0.32) (575)

Naghipour et al., (2007) 0.09 (0.01 – 0.44) (574)

Overall 0.21 (0.10 - 0.38)

Figure 3.3: Influenza A/hBoV co-infection patterns by patient group and age. Notes:

Hospitalized <18 years old children I2 = 20.8, p = 0.26, in and out patients ≤5 years old children I2 = 33.8, p =

0.22

Study name Co-infection 95% CI Statistic for each study

Reference

(%)

Event rate & 95% CI

No of events

Hospitalized

all age groups

Echineque et al., (2013) 0.05 (0.03 - 0.09) (94)

Pretorious et al., (2009) 0.13 (0.11 - 0.16) (581)

overall 0.09 (0.04 - 0.20)

In and out patients

all age groups

Nisii et al., (2010) 0.04 (0.01 – 0.09) (577)

Esper et al., (2011) 0.08 (0.05 – 0.01)

(81)

Druce et al., (2004) 0.01 (0.01 – 0.02) (582)

Tanner et al., (2012) 0.04 (0.02 – 0.06) (579)

Wallace et al., (2004) 0.02 (0.00 – 0.06)

(580)

Peci et al., (2013) 0.09 (0.07 – 0.12) (90)

Drews et al., (1997) 0.03 (0.02 – 0.06)

(75)

Overall 0.04 (0.02 – 0.07)

Figure 3.4: Influenza A/RV co-infection patterns by patient group and age. Notes:

Hospitalized all age groups I2 = 92.1, p = <0.0001, in and out patients all age groups I2 = 89.0, p = <0.0001

4 / 83

9 / 98

-0.25 -0.13 0.00 0.13 0.25

5 / 14

6 / 37

1 / 11

-0.25 -0.13 0.00 0.13 0.25

17 / 314

92 / 704

-0.25 -0.13 0.00 0.13 0.25

4 / 108

19 / 229

12 / 1326

17 / 469

2 / 132

40 / 452

10 / 304

-0.25 -0.13 0.00 0.13 0.25

153


Reference

(%)

Event rate & 95% CI No of events

Hospitalized Children ≤18 years old

Do et al., (535) 0.157 (0.08 – 0.284)

(517)

Rhedin et al., (87) 0.012 (0.00 – 0.012) (91)

Franz et al., (536) 0.111 (0.02 – 0.500)

(516)

Overall 0.071 (0.06 – 0.203)

In and out patients all age groups

Esper et al., (77) 0.022 (0.01 – 0.052) (81)

Druce et al., (543) 0.003 (0.00 – 0.008)

(582)

Renois et al., (539) 0.033 (0.01 – 0.202) (578)

Overall 0.012 (0.00 – 0.054)

Figure 3.5: Influenza A/hCoV co-infection patterns by patient group and age. Notes: Hospitalized ≤5 years old children I2 = 68.8, p = 0.04, in and out patients all age groups I2 = 80.7,

p = 0.006.


Reference

(%)

Event rate & 95% CI

No of events


≤5 years old

Boivin et al., (2003) 0.02 (0.003 – 0.13) (566)

Wolf et al., (2006) 0.05 (0.02 – 0.13) (569)

Foulongne et al., (2006) 0.06 (0.01 – 0.31) (570)

0.05 (0.02 - 0.10)

In and out patients

all age groups

Druce et al., (2004) 0.001 (0.0001 – 0.01) (582)

Tanner et al., (2012) 0.004 (0.001 – 0.02) (579)

0.002 (0.001 – 0.01)

Figure 3.6: Influenza A/hMPV co-infection patterns by patient group and age.

Notes: Hospitalized ≤5 years old children I2 = 0, p = 0.67, in and out patients all age groups I2 = 49.3, p

= 0.16.

1 / 49

4 / 77

1 / 18

-0.25 -0.13 0.00 0.13 0.25

1 / 1326

2 / 469

-0.25 -0.13 0.00 0.13 0.25

5 / 229

4 / 1326

1 / 30

-0.25 -0.13 0.00 0.13 0.25

5 / 229

4 / 1326

1 / 30

-0.25 -0.13 0.00 0.13 0.25

154


Reference

(%)

Event rate & 95% CI

No of events


Boivin et al., (2003) 0.02

(0.003 – 0.13)

(566)

Khamis et al., (2012) 0.07 (0.009 – 0.35) (568)

Overall 0.04 (0.009 – 0.14)


≤18 years old

Libster et al., (2010) 0.01 (0.002 – 0.03) (93)

Rhedin et al., (2012) 0.04 (0.012 – 0.11) (91)

Overall 0.02 (0.004 – 0.07)

In and out patients

≤5 years old

Martin et al., (2012) 0.11 (0.07 – 0.18) (82)

O’Callaghan-Gordo et al., (2011) 0.06 (0.02 – 0.17) (583)

de Vos et al., (2009) 0.17 (0.08 – 0.32) (575)

Overall 0.11 (0.07 – 0.18)

In and out patients

all age groups

Esper et al., (2011) 0.01 (0.009 – 0.04)

(81)

Laguna Torres et al., (2010) 0.02 (0.004 - 0.06) (576)

Tanner et al., (2012) 0.02 (0.007 – 0.03) (579)

Peci et al., (2013) 0.00 (0.001 – 0.03) (90)

Drews et al., (1997) 0.01 (0.003 – 0.03) (75)

Overall 0.01 (0.007 – 0.02)

Figure 3.7: Influenza A/AdV co-infection patterns by patient group and age. Notes:

Hospitalized ≤5 years old children I2 = 0, p = 0.39, Hospitalized <18 years old children I2 = 63.85, p =

0.10, in and out patient ≤5 years old children I2 = 19.38, p = 0.29, in and out patients all age groups I2 =

0, p = 0.45.

1 / 49

1 / 15

-0.25 -0.13 0.00 0.13 0.25

2 / 251

3 / 83

-0.25 -0.13 0.00 0.13 0.25

14 / 127

3 / 51

6 / 37

-0.25 -0.13 0.00 0.13 0.25

2 / 229

2 / 130

7 / 469

1 / 452

3 / 304

-0.25 -0.13 0.00 0.13 0.25

155


Reference

(%)

Event rate & 95% CI

No of events


Libster et al., (2010) 0.012 0.004 – 0.04

(93)

Rhedin et al., (2012) 0.012 0.002 – 0.08

(91)

Overall 0.012 0.005 – 0.03

In and out patients

all age groups

Nisii et al., (2010) 0.111 0.064 – 0.19

(577)

Esper et al., (2011) 0.013 0.004 – 0.04 (81) Druce et al., (2004) 0.002 0.001 – 0.01 (582)

Tanner et al., (2013) 0.013 0.006 – 0.03 (579) Peci et al., (8) 0.02 0.010 – 0.04

(90)

overall 0.016 0.005 – 0.06

Figure 3.8: Influenza A/hPIV1-4 co-infection patterns by patient group and age. Notes:

Hospitalized <18 years old children I2 = 0, p = 1.0, in and out patients all age groups I2 = 91.8, p = <0.0001.

Table 3.3: Influenza A virus co-infection and viral load/interferon production

Study name

Viruses covered

Main finding

Esper et al., (81)

Single Flu A (30), co-infections: RV (19),

hCoV (5), hPIV (3), AdV (2), RSV (1).

No difference in viral load were observed

between single and mixed infections.

Martin et al., (82)

Single Flu A (102), co-infections: AdV (14),

others = 11, specific pairing not given.

Co-infection did not affect viral load.

Aberle et al., (80)

Single Flu A (26), co-infections: RV (9)

RV co-infection did not increase type 1

interferon (INF) production, other

respiratory viruses co-infection did.

Marcos et al., (584)

Single Flu A (160),co-infections: RV (14),

hBoV (3), hCoV (3), hPIV (3), EV (5)

No difference in viral load in single and

mixed infections; (5.08±1.4 vs. 5.6±1.4, p =

50.108).

Notes: RSV - respiratory syncytial virus, RV - rhinovirus, AdV - adenovirus, hMPV - human metapneumovirus,

hCoV human coronavirus, hBoV - human bocavirus, hPIV1-3 - human parainfluenza virus types 1 to 3. Study by

Marcos et al., (584) was not included in the meta-analysis because the patterns of co-infections were not given.

3 / 251

1 / 83

-0.25 -0.13 0.00 0.13 0.25

12 / 108

3 / 229

3 / 1326

6 / 469

9 / 452

-0.25 -0.13 0.00 0.13 0.25

156


Pooled proportions from the meta-analysis indicate that co-infections with influenza A range

from 1% to 26.0% (Table 3.4). The evidence further shows that RSV, RV, hBoV and AdV are

the most common viruses to co-infect with influenza A viruses, followed by hCoV (1.2% and

7.1%), hMPV (0.2% and 4.5%), the least common being hPIV1-4 (1.2% and 1.6%). However

these rates differ with age, study design, season, year and region the study was conducted in;

young age and being hospitalized being associated with higher co-infection rates. Estimation of

the population attributable risk (PAR) due to a certain exposure requires knowledge of the

prevalence of the same in the general population. Our results could be useful for the same.

Influenza A virus, RSV, AdV and hBoV produce dsRNA and dsDNA in their replication cycle

(361;385;432), their co-infection is likely to be associated with heightened cytokine release and

severe disease. One study included in this review (81) found an association between rhinovirus

co-infection and reduced severity of influenza disease. However, we did not find enough studies

that investigated the patterns of interferon release and viral load associated with co-infections

with the other respiratory viruses, therefore more studies are needed to explore this hypothesis.

Table 3.4: Summary of pooled proportions of co-infections

Co-infecting virus

Overall % co-infection

among hospitalized children

0 – 18 years old

Overall % co-infection among in

and outpatients of all age groups

Flu A/RSV

16.0 – 26.0

4.2 – 6.8

Flu A/RV 3.9 – 9.0

Flu A/AdV 1.8 – 11.3 1.1 – 11.3

Flu A/hMPV 4.5 0.2

Flu A/hPIV1-4 1.2 1.6

Flu A/hBoV 7.5 - 20.9 N/A

Flu A/hCoV 7.1 1.2

The influenza attack rate, calculated from a pooled sample size of all the studies included in the

meta-analysis, is around 12.1%. This finding agrees with reports by the World Health

Organization and other investigators that suggest that, at any particular time, the respiratory

viruses’ attack rates lie between 10% and 20% (1;204). Overall the proportion of co-infections

with influenza virus ranged from 1.5% - 68.8% (Table 3.1) whereas the pooled proportions

ranged from 1% to 26.0%. The high prevalence of co-infection with human bocavirus reported

by studies that recruited both in and outpatient is in agreement with reviews conducted by

Schildgen et al., (87) and Zhenqiang et al., (88) who reported that over 40 studies conducted

globally identified hBoV occurring mainly together with other viruses, especially among

children with acute respiratory virus infections (ARIs).

157

Circulation of some respiratory viruses vary considerably with season and year (224;300). In

discussing their findings, Druce et al.,(582) stated that over the period they conducted their

study, there was lower than expected coronavirus activity, a factor that might have accounted for

the low proportions of coronavirus co-infections reported in their study. In this study, there were

low proportions of co-infection with coronavirus and in studies that recruited both hospitalized

and outpatients. It could be that the indeed coronaviruses rarely co-infect with influenza A

viruses or that the observed rates are biased by seasonal variations. Suffice to state that unlike

influenza, RSV, RV, AdV, hMPV and hPIV1-4, most studies do not test for hCoV, which could

be the reason for observed low proportions of hCoV co-infection. Esper et al., (81) called for the

use of more advanced methods for detection of co-infections such as deep sequencing, we add

that there should also be efforts to develop more sensitive RT-PCR assay(s).

A number of other factors affect the epidemiology of respiratory virus disease including: obesity,

pregnancy, prematurity and low birthweight, nutrition status and breastfeeding in infants, active

and passive smoking, immune status and vaccination (9;10;13;21;23;53;564). We made

deliberate effort when selecting studies to ensure that those included had controlled for at least

two of the known risk factors to virus infection. However, in those studies where only age and

sex were controlled for, e.g. O’Callaghan-Gordo et al., (583), Cilla et al., (513), Malekshahi et

al., (573), De Vos et al., (575) and a few others there is a possibility that their reports might be

biased and this should be born in mind when interpreting our results. Influenza vaccine

effectiveness studies suggest that vaccination increases the risk of non-influenza respiratory virus

infection (561-563). In this review, most of the studies did not control for patients vaccination

status, with only Renois et al., (578) mentioning the same. The observed epidemiology of co-

infections may have also been affected by patients influenza vaccination status and this should

also be born in mind.

There is a possibility that some studies that were published in languages other than English

might have found different results than those summarized here. Our choosing of only studies that

were published in English might possibly have introduced reporting or publication or selection

bias. The type of diagnostic method employed for identification of respiratory viruses greatly

affects the observed virus yield with RT-PCR generally being regarded as better than cell culture

due to the fact that some of the respiratory viruses cannot be easily grown in laboratory

environments (585). In order to eliminate measurement bias, only studies that used conventional

PCR or RT-PCR were included.

In conclusion, this meta-analysis has found RSV, hBoV, RV and AdV as the most common

viruses to co-infect with influenza A viruses mainly in children ≤5 years old. We did not find

158

enough studies that investigated association between co-infection and viraemia or cytokines over

production. Routine testing of these and the other respiratory virus infections among patients

with influenza A virus infection is recommended so as to help in patient management. Future

research should be directed at understanding the biomedical processes associated with different

respiratory virus co-infections with influenza A viruses so as to elucidate mechanisms of

virulence or avirulence.

Funding: This work had no funding.


Manchester Academic Health Science Centre.


159


Table 3.1S1: Assessment of bias in included studies – patterns of co-infections review

No

Study name

Ref

Study design

Selection

bias

Predisposing factors reported

Overall assessment

1

Aberle et al., (2005)

(80) infants<1 yr old hospitalized with ALRI, 4 yrs prospective

No

prematurity, age and sex

Ok

2

Calvo et al., (2008)

(85) children <2 years old hospitalized with ALRI, 3 yrs prospective

No

age, sex, prematurity, underlying disease(myocardiopathy and interauricular communication), COPD, sickle cell anaemia, antibiotic treatment

Good

3

Jennings et al., (2004)

(565) infants <1 year old, 4 months prospective

No

age, sex, antibiotic treatment

OK

4

Boivin et al., (2003)

(566) children <3 years old admitted with ARI, 6 months prospective

No

age sex, underlying conditions (not specified)

Good

5

O'Callaghani-Gordo et al., (2011)

(567) outpatient infants <1 year old with ARI, 1 year prospective

No

age, sex

OK

6

Khamis et al., (2012)

(568) children <1 year old, admitted with RTI

No

age, gender, birthweight, breast feeding, asthma, congenital heart disease, secondary smoke exposure

Good

7

Wolf et al., (2006)

(569) children <5 years old admitted with LRTI, 1 year prospective

No

age, sex, breastfeeding, prematurity, asthma, antibiotic treatment

Excellent

8

Foulongne et al., (2006)

(570) children <5 years hospitalized with ARI, 1 year prospective

No

age, sex, prematurity, congenital heart disease, gastroesophageal reflux disease, primary immunodeficiency diseases, mitochondrial cytopathy

Good

9

Kristoffersen et al., (2011)

(571) children <5 years old admitted with LRTI, 1 year prospective

No

age, sex, prematurity, asthma, breastfeeding, congenital heart disease, antibiotic treatment, steroid treatment

Excellent

10

Martin et al., (2012)

(572) outpatient and hospitalized children <4 year old, 1 year retrospective

No

age, sex, asthma and other chronic respiratory, neurologic disease, cardiac disease, renal disease, gastroenteric diseases, malignancies, metabolic diseases, haematological diseases, metabolic diseases

Excellent

11

Cilla et al., (2008)

(513) children <3 years old attended at emergency department

No

age, sex, asthma

Excellent

12

Malekshahi et al., (2010)

(573) in and outpatient children <6 years old, retrospective

No

age and sex

OK

13

Naghipour et al., (2007)

(574) in and outpatients children <5 years old, 3 months prospective

No

age sex, socio-demographic, clinical, therapeutic

Ok

14

de Vos et al.,(2009)

(575) in and outpatients children <5 years old with ARI, 5 months prospective

No

age and sex

OK

160

Table 3.1S1: Assessment of bias in included studies – patterns of co-infections review

No

Study name

Ref

Study design

Selection bias

Predisposing factors reported

Overall assessment

15

Do et al., (2011)

(517) children <13 years old hospitalized with ARI

No

age, sex, birthweight, number of family members, exposure to smoke, breastfeeding

Excellent

16

Rhedin et al., (2012)

(91) children <17 years old admitted with ARI

No

age, sex, prematurity, underlying disease, COPD, immunosuppression, bacterial infection

Excellent

17

Weissbrich et al., (2006)

(559) children < 18 years old admitted with ARI, 4 years prospective

No

age and sex

OK

18

Franz et al., (2010)

(516) children <16 years old admitted with LRTI, 2 years prospective

No

age, sex, corticosteroids and antibiotic treatment

Good

19

Esper et al., (2011)

(81) in and outpatients of all age groups, 3 months prospective

No

age and sex

OK

20

Languna-Torres et al., (2010)

(576) in and outpatients all age groups, 3 years prospective

No

age, sex, antibiotic treatment

Good

21

Nisii et al., (2010)

(577) in and out patients, all age groups, 6 months prospective

No

age and sex

OK

22

Renois et al., (2010)

(578) in and out patients, all age groups, 1 month perspective

No

age, sex, vaccination, antibiotic treatment, antiviral treatment

Good

23

Tanner et al., (2012)

(579) in and outpatients all age groups, 6 months retrospective

No

age and sex

OK

24

Wallace et al., (2004)

, (580) in and outpatients, all age groups

No

age and sex

OK

25

Peci et al., (2013)

(90) in and outpatients 11 months prospective

No

age, sex, chronic diseases (not specified)

Excellent

26

Drews at al., (1997)

(75) in and outpatients, all age groups, 4 years retrospective

No

age, sex, race

Good

27

Druce et al., (2004)

(582) in and outpatients, all age groups, 1 year retrospective

No

age and sex

OK

28

Libster et al., (2010)

(93) hospitalized infants <1 year old 3 months prospective

No

age, sex, asthma, chronic lung disease, metabolic disorder, renal disorder, cancer, immunosuppression, neurologic disorder, malnutrition, exposure to secondary smoking

Excellent

29

Pretorious et al., (2012)

(581) patients of all age groups hospitalized with ALRI, 1 year prospective

No age and sex

OK

30 Echenique et al., (2012) (94) patients of all age groups hospitalized with ALRI, 3 months retrospective

No Age, sex, asthma, hepatic disease, renal disease, cardiac disease, neurologic disease, immunocompromised, HIV+ve

Excellent

161

Table 3.1S2: Search history on MEDLINE for review number 2: Co-infection patterns

between influenza and other respiratory viruses and its effect on viral load and interferon

production: A systematic review and meta-analysis

#

▲

Searches Results Search

Type

1

Virus Diseases/ or Respiratory Tract Infections/ or Respiratory Syncytial Viruses/ or

Respirovirus Infections/ or Respiratory Syncytial Virus Infections/ or Viruses/ or

Influenza, Human/

112137 Advanced

2 Orthomyxoviridae Infections/ or Influenza A Virus, H1N1 Subtype/ 16518 Advanced

3 Rhinovirus/or coronavirus/ or coronaviridae/ or human coronavirus 2863 Advanced

4 Adenoviridae/or bocavirus/or human bocavirus/ or bocavirus infection 24161 Advanced

5 Respirovirus Infections/ or Paramyxoviridae Infections/ or Parainfluenza Virus 1,

Human/ or Parainfluenza Virus 3, Human/ or Parainfluenza Virus 2, Human/ 7315 Advanced

6 1 or 2 or 3 or 4 or 5 148070 Advanced

7 co-infections.mp. 1178 Advanced

8 multiple infections.mp. 1144 Advanced

9 dual infections.mp. 396 Advanced

10 7 or 8 or 9 2666 Advanced

11 6 and 10 264 Advanced

12 Virulence/ 37326 Advanced

13 "Severity of Illness Index"/ 163726 Advanced

14 Infant/ or "Length of Stay"/ or Aged/ or Hospitalization/ or Adult/ or Adolescent/ or

Middle Aged/ 6295599 Advanced

15

Respiratory Distress Syndrome, Adult/ or Middle Aged/ or Influenza, Human/ or

Intensive Care/ or Aged/ or Hospital Mortality/ or Patient Admission/ or Intensive

Care Units/ or icu admission.mp.

3857750 Advanced

16 Interferon-alpha/ or Interferon Type I/ or interferon production.mp. or

Immunomodulation/ 36968 Advanced

17 Viral Load/ 21901 Advanced


19 11 and 18 197 Advanced

162

3.2. Co-infections patterns and severity Part B: Single and multiple respiratory virus

infections and severity of respiratory disease: A systematic review and meta-

analysis

Synopsis

This is the authors’ version of this paper, published in the journal of Paediatrics Respiratory

Reviews, on 1st November, 2013. dio: 10.1016/j.prrv.2013.11.001. Reprinted from Goka et al.,

(586) with permission from Elsevier, Copyright Licence number 3286790553919. A poster of an

earlier version of this paper was presented at the XV International Symposium on Respiratory Viral

Infections. 14 – 17 March 2013, Rotterdam, the Netherlands. Poster number P7. This review is to

summarize the available evidence on impact of co-infections on severity of respiratory virus

infections.

163

3.2.1. Abstract

Single and multiple respiratory virus infections and severity of

respiratory disease: A systematic review and meta-analysis

Goka E.A1, Vallely P.J 1, Mutton K.J 1,2, Klapper P.E 1,2



Corresponding author: Edward Goka, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences,

1st Floor Stopford building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.


Objective: There are suggestions that virus co-infections may influence the clinical outcome of

respiratory virus illness. We performed a systematic review and meta-analysis to summarize the

evidence on association between co-infection and disease severity.

Methods: MEDLINE, EMBASE, WEB of Science, major websites and reference lists of published

studies were used. The review followed the PRISMA and STROBE guidelines. Studies that recruited

hospitalized individuals, in and out patients or case-control studies of children ≤18 years old, or both

adults and children were included. Datum was analysed using odds ratios and results summarized using

forest plots and tables.

Results: Eighteen (18) studies from all over the world (published 2003-2013) were included in the

review. Most of the studies 55.6% (10/18) recruited children ≤6 years old. Evidence on the role of co-

infection in increasing disease severity was inconclusive. In 3 out of 6 studies, co-infection significantly

increased risk of admission to general ward (OR: 2.40, 95% CI: 1.29 - 4.44, p = 0.005; OR: 2.84, 95%

CI: 1.05 – 7.66, P = 0.04 and OR: 2.33, 95% CI: 1.06 – 5.10, p = 0.04), one found it did not (OR: 0.59,

95% CI: 0.40 - 0.93, p = 0.02) and the other 2 had insignificant results. Similarly on risk of admission to

ICU, some studies found that co-infection significantly increased risk of admission to ICU (OR: 2.89,

95% CI: 1.41 – 5.94, p = 0.004 and OR: 2.90, 95% CI: 1.57 – 5.3, p = <0.0001), whereas others did not

(OR: 0.18, 95% CI: 0.05 – 0.75, p = 0.02 and OR: 0.34, 95% CI: 0.20 – 0.57, p = <0.0001). There was a

significant positive association between respiratory virus co-infections and risk of pneumonia. However,

the associations between respiratory virus co-infections and risk of bronchiolitis, and Flu A virus co-

infections and risk of admission to a GW or ICU were not statistically significant.

Conclusion: The influence of co-infections on severe viral respiratory disease is still unclear. The

observed conflict in outcomes could be because they were conducted in different seasons and covered

different years and periods. It could also be due to bias towards the null, especially in studies where only

crude analysis was conducted. Future studies should employ stratified analysis.

164

3.2.2. Introduction

Respiratory viruses including; influenza virus types A and B (Flu A/B), respiratory syncytial virus

(RSV), rhinovirus (RV), adenovirus (AdV), human metapneumovirus (hMPV), human

coronavirus (hCoV), human bocavirus (hBoV) and human parainfluenza viruses type 1, 2 and 3

(hPIV1-3), have been singly or jointly detected from patients suffering from respiratory diseases

(203;205;206;587). Incidence studies have indicated that 5-38% of respiratory infections develop

into acute respiratory tract infections (ARIs) with severe signs and symptoms including wheezing,

bronchiolitis, croup, high fever and pneumonia with subsequent increases in hospitalization to a

general ward (GW), admission to intensive care unit (ICU), or mortality

(17;204;216;217;330;588). A number of factors have been attributed to the severity of respiratory

viral disease including; underlying chronic diseases such as chronic respiratory diseases, diabetes,

chronic liver disease, chronic heart disease, chronic renal disease; and other factors such as

immunodeficiency, old age, young age, pregnancy, viral genome mutations (1;9;17;589). There

are suggestions that the presence of more than one type of virus in the respiratory specimen may

also affect the clinical presentation of respiratory tract infection (77;80;81;85). However, the

relationship between co-infection and severity of illness remains unclear.

3.2.2.1. Rationale for conducting the review

Knowledge on the impact of respiratory virus co-infections on disease outcome is needed to direct

policy on multiple respiratory virus testing, and prioritizing of research on integrated vaccine,

drug, and multi-targeted diagnostic tests. Although some researchers have reviewed the

association between respiratory virus infections and disease outcome, most of the reviews have

concentrated on outcomes in single infections. For example a meta-analysis published in 2013 by

Luksic et al., (590) looked at the association between severe LRTI and infection with specific

respiratory virus infections. Similarly a 2010 and 2011 meta-analyses by Nair et al., (227;228)

analysed the burden of ALRI due to RSV and influenza viruses respectively. Although a few

reviews have covered the association between respiratory virus co-infections and disease outcome;

a 2011 review by Sly et al., (83), a 2013 review by Stefansca et al., (84), the 2013 reviews by Dat

Tran (591) and Alvarez et al., (13), however, all of them have been narrative reviews. In addition,

some of the published reviews on the subject e.g. one by Ochoa Sangrador et al., (592), are in

addition to being narrative, also not in English Language. To our knowledge no prior published

study has synthesized the available evidence on co-infections in a systematic review and meta-

analysis, hence the importance of this paper.

165

3.2.2.2. Objectives and aims of the review and meta-analysis

This study investigated the relation between co-infection among respiratory viruses and co-

infection between influenza and other respiratory viruses and clinical outcome with the following

objectives:

a) To investigate the disease outcome (admission to a general ward, the intensive care unit,

bronchiolitis and pneumonia) in single vs. multiple respiratory virus infections as reported

by studies recruiting hospitalized individuals, outpatients, or community based controls of

all age groups globally.

b) To determine whether infection with influenza A viruses alone has a different disease

outcome, i.e. risk of hospitalization, admission to a general ward, ICU and death,

compared to co-infections with respiratory viruses.

c) To explore the bias and confounding factors associated with observed measures of

outcome in observational studies on respiratory virus co-infections.

The review was conducted to answer our research question: “What is the disease outcome in

single respiratory virus infections and in co-infections in general?” and therefore aimed to:

c) Generate knowledge on the role of co-infection in acute respiratory tract infection (ARI)

leading to hospitalization to a general ward or the ICU, bronchiolitis or pneumonia.

d) Highlight the confounding and bias when crude analysis is applied and the importance or

need of conducting stratified analysis in research on respiratory virus co-infections.

e) Summarize evidence that would be needed in prioritizing and making policy on the

importance of multiple testing of respiratory virus infections in patients presenting with

influenza like illness.

3.2.3. Methodology

3.2.3.1. Review protocol

A protocol for this review was developed together with the coauthors of this study as supervisors

of the principal investigator. The protocol was however not published or deposited to any online

server.

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3.2.3.2. Literature search

We searched the electronic databases; MEDLINE, EMBASE and WEB of Science for primary

epidemiological studies on the role of co-infections in causing severe clinical disease; i.e. risk of

hospitalization to the GW, admission to ICU or death, and risk of developing bronchiolitis and

pneumonia. We also searched websites of health organisations e.g. the World Health Organisation

(WHO), United Kingdom’s Health Protection Agency (HPA), United States of America’s Centre

for Disease Control (CDC), World Influenza Network Centre, for bibliography or any published

reports on respiratory viruses’ co-infections and patient outcome. The MEDLINE and EMBASE

system have studies published from May, 1946 to date, whereas the Web of Science has studies

published from 1945 to date. The search was refined to include studies published in the English

language related to medicine in general or to specific branches i.e. infectious diseases, virology,

internal or respiratory system, pathology and critical care. Reference lists of good quality studies,

were also manually searched to identify studies addressing the question under review.

For the electronic databases, the search technique involved combining a number of subject

headings and keywords and the scoping of text words; words used included: Viruses, virus

diseases, virus infection, respiratory tract infections, respirovirus, respirovirus infections, lower

respiratory tract infection(s), upper respiratory tract infection(s), orthomyxoviridae,

orthomyxoviridae infections, orthomyxovirus, influenza human, influenza A virus, influenza A

virus H1N1 subtype, 2009 H1N1 influenza, influenza A(H1N1)pdm09, influenza A virus H3N2

subtype, rhinovirus, human rhinovirus, rhinovirus infection, adenovirus, adenovirus infection(s),

respiratory syncytial virus, respiratory syncytial virus infection(s), metapneumovirus,

metapneumovirus human, parainfluenza virus 1 human, parainfluenza virus 2 human,

parainfluenza virus 3 human, bocavirus, bocavirus infection, coronavirus, coronavirus infection,

co-infection(s), mixed infection, dual infection(s), multiple infection(s), virulence, virus virulence,

prognosis, pathogenicity, virus pneumonia, bronchiolitis, viral bronchiolitis, hospital,

hospitalization, hospital care, hospital admission, patient admission, length of stay, intensive care,

critical care, intensive care unit, ICU admission, fatality, mortality, death. Results of the electronic

search conducted for this review on Web of Science are provided in supplementary Table 3.2S3 as

an example.

167

3.2.3.3. Study quality assessment and selection criteria

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline

(109) was followed to write the reviews. In addition, The “Strengthening the Reporting of

Observational Studies in Epidemiology (STROBE)” tool for critical appraisal of epidemiological

studies (108), was used to assess the studies identified in the search. The principal investigator of

the study conducted the online and manual search of eligible studies. First, only the title and

abstract of the studies were reviewed and those that were clearly not relevant (e.g. paper looked at

epidemiology of single respiratory virus infections, discussed bacterial vs respiratory virus co-

infections, reported circulation of virus strains), were removed. Those papers that seemed relevant

were downloaded and read in full. Descriptive prospective or retrospective cross-sectional, cohort

or case-control studies that investigated co-infection as a risk factor for disease outcome and

included the outcome measures; hospitalization to a general ward, admission to ICU, bronchiolitis

or pneumonia were included. Studies that investigated exposures other than those investigated in

this review i.e. did not include influenza and ≥4 of the other respiratory viruses considered as

exposures of interest in this study, did not give risk outcome in co-infections vs. single infections,

did not report risk of hospitalization to ICU, or general ward, bronchiolitis and pneumonia, did not

use PCR or RT-PCR as a diagnostic method, were conducted among patients with underlying

chronic diseases or impaired immunostatus, were duplicates of other included studies or had data

incompatible with odds ratios calculation, (i.e. with some cells having a zero) were excluded. In

some instances, where a paper seemed relevant but tables giving details of the outcome by virus

types was missing, the corresponding authors were contacted to provide these details if available.

3.2.3.4. Assessment of bias

Since this review investigated association between co-infection and disease severity, consideration

was made on the factors that might contribute to wrong evidence from the observational studies.

Issues considered included: declaration of conflict of interest, selection bias (how participants

were recruited, study population and demographic characteristics of the groups and if some

patients were excluded whether there was a statistical difference between included and excluded

patients), detection bias (sample size and laboratory methods used for virus identification),

reporting bias (preferences in what to report, outcome indicator used, validity of the calculations

and statistical methods used, the control/baseline population used for calculating the odds ratios),

confounding by indication or contraindication (whether a table on patients comorbidities and other

baseline characteristics was provided and whether these factors were controlled for in odds ratio

168

calculation), and publication bias. A summary of the inclusion criteria used and the rationale

behind it is provided in Table 3.5.

The whore body of the identified observational studies was appraised for presence of these factors

at selection and analysis stages of the review. Studies were scored based on this criteria using the

method described in the PRISMA and STOBE guidelines and a summary of the scores for all the

18 studies included in this review is provided in supplementary Table 3.2S2. To investigate

presence of publication bias, a funnel plot of the observed odds ratios from the included studies

and study sample sizes was fitted, and correlation between these variables measured using the

Spearman’s rank correlation test. All statistical analysis for bias were conducted using STATA

software version 11.0 (STATACorp, College Station, USA) and the Microsoft Excel processor.

Table 3.5: Inclusion criteria for studies in this systematic review and meta-analysis on

respiratory viruses single and multiple infection and severity

Item

Description

Rationale

Study design

Descriptive prospective or retrospective cross-sectional, cohort or case-control studies that investigated the impact of co-infections on admission to a general ward, the ICU, bronchiolitis and pneumonia

Since respiratory virus infections are diseases of short duration, one of the suitable study designs to explore associations is through observational studies. We anticipated that most of the published studies would have adopted this approach. The indicators for outcome were chosen because literature suggests that these are the best proxies for measuring outcome in infectious diseases.

Sample size Studies that recruited 42 or more participants

Our sample size calculations to detect the required power for studies with binary outcomes indicated that any sample size of ≥42 would have sufficient power to measure association with β - false negatives - type II error threshold of 80%. Details of the sample size calculation are provided in Appendix IX.

Diagnostic test PCR It is now regarded the standard test, to eliminate detection bias only studies that used PCR were included.

Age Studies that recruited either young children only or a mixture of these or only adults

We planned to conduct stratified analysis in the systematic review e.g. admission of children ≤5 years old to the ICU. Therefore selection of studies of such designs would be appropriate. It was also anticipated that most published studies on the subject under review would have been conducted in young children. However the review aimed at exploring the impact of co-infections across all age groups, hence the criteria.

169

3.2.3.5. Data extraction from the studies

A data extraction form was designed by the principal investigator based on directions from the

supervisors and advisor on what variables should be included in this study. Data extracted from

the studies included: Place it was conducted, year it was conducted (specific dates in month and

year of start and end of study), author and year of publication, participants age range, diagnostic

method(s) used for virus identification, number of participants (sample size), sources of

participants e.g. hospitalized individuals or patients attended at the emergency department,

community based controls (in case-control studies), clinic or medical centre. Results counts:

number of single or multiple virus infections observed, number of virus single and multiple for

specific outcomes e.g. hospitalization to a general ward, odds ratios or risk ratios and 95%

confidence interval. Confounding factors controlled for e.g. comorbidities – asthma, diabetes, age.


The exposure of interest was co-infection among eleven respiratory viruses i.e. Flu A/B, RSV,

RV, AdV, hMPV, hCoV, hBoV and PIV1-4. Association between co-infection and severe disease

(admission to general ward or ICU, bronchiolitis or pneumonia) was assessed using odds ratios

and 95% confidence intervals calculated using single infection(s) as the baseline, or single

influenza A infection as the baseline, in the analysis of influenza co-infections and severity of

respiratory disease. Results from individual studies were summarized using forest plots and tables

and all analyses were done using the Comprehensive Meta-Analysis software – version 2

(BIOSTAT, Englewood, NJ 07631 USA).

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3.2.4. Results

3.2.4.1. Characteristics of the studies included in this review

A summary of the number of studies that were retrieved from each database and the studies that

were selected and included in this systematic review is provided in Figure 3.14. Out of the 3,423

papers identified through electronic and manual search, ninety two (92) papers were reviewed of

which 18 were included. All the studies that were eligible for inclusion were published between

2003 and 2013, owing to the use of PCR diagnosis as one of the inclusion criteria.

Studies included in this review were from all over the world, i.e. 5 of the included studies were

from Europe, 4 from North America, 3 from South America, 4 from Asia and 2 from Africa. The

details of the included studies are provided in Table 3.5. A large number of the studies, 13/18

(72.2%), involved patients hospitalized to a general ward or the intensive care unit with acute

respiratory infections, some (3/18; 16.7%) recruited in and outpatients, and 2/18 (11.1%) were

case-control studies recruiting hospitalized patients and healthy controls. The highest proportion

of studies 55.6% (10/18) recruited children <6 years old, 6 (33.3%) studies included children aged

≤18 years old, 3 (16.7%) included both adults and children. Most of the studies 14/18 (77.8%)

applied a prospective design covering periods ranging from 3 months to 4 years, and 4/18 (22.2%)

analysed patients data retrospectively. Together all the studies recruited 8,372 people with 48 as

the smallest sample size and 1,192 as the largest sample size, the majority recruiting between 200

and 900 patients.

3.2.4.2. Factors associated with positivity and co-infection rates

Positivity rates ranged from 54.8% to 96.1% (mean 69.7%) whereas co-infection ranged from

5.7% to 62.0% (mean 22.1%). Respiratory syncytial virus was the most predominant co-infecting

virus with most of the studies reporting RSV being the most common among all the viruses

involved in the co-infections (Supplementary Table 3.2S1). RSV was reported as the most

frequent co-infecting with adenovirus by Huguenin et al., (593) and Martin et al., (82) co-infecting

with bocavirus by Cilla et al., (513) and Franz et al., (516) and co-infecting with influenza A virus

by Boivin et al., (566). There was a weak negative correlation between age and high positivity/co-

infection rate, such that studies that recruited young children were more likely to report high rates

of infection and co-infection (correlation coefficients - 0.26 and - 0.35 for infection and co-

infection respectively). In studies that recruited both adults and children, the proportion of co-

infections were 7.4% and 14.4% of all the identified viruses, compared to co-infection rates of

5.7% to 62.0% in studies that recruited children < 6 years old (Table 3.6).

171

Figure 3.14: Number of studies that were identified, included and excluded in the review on

respiratory virus co-infections and disease severity. Notes: ICU – intensive care unit, GW - general

ward.

Records identified through

database searching

(n = 3,366)

Additional records identified

through other sources

(n = 57)

Records after duplicates removed

(n = 3,404)

Records screened

(n = 194)

Records excluded (n = 102)

• Did not compare disease

outcome in single vs.

multiple infections

• Were on epidemiology of

respiratory viruses as

single infections

• Were animal models

• Sample size was too small

i.e. <42

Full-text articles assessed

for eligibility

(n = 92) Full-text articles excluded (n = 74)

• Outcome measure

irrelevant e.g. interferons,

fever, severity scale

• Were conducted in

patients with known

chronic condition e.g.

organ transplant patients

• Did not use PCR as a

diagnostic tool

Studies included in

quantitative synthesis

(meta-analysis)

(n = 18)

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Table 3.6: Characteristics of studies included in the review on association between single and

multiple infections and severity of respiratory disease

No

Study name (Ref No.) Country

Study design

Age group

Sample size & +ve rate

No & co-inf. rate

Protocol used

Outcome measure of interest

1

Richard et al., (76)

France

hospitalized GW or ICU with severe bronchiolitis, 2 yrs. prospective

< 1 yr.

180 (96.1)

44 (25.4)

RT-PCR, PCR & tissue culture. All RVIs except hBoV

admission to ICU

2 Cilla et al., (513) Spain

attended at paediatric emergency dept, 2 yrs. prospective

<3 yrs. 315 (66.9) 61 (27.0) PCR & Direct IF, tissue culture. All RVIs

admission to ICU admission to GW

3 Huguenin et al (593) France

hospitalized to GW or ICU with acute bronchiolitis, 1 yr. prospective

< 1 yr. 138 (91.0) 85 (62.0) RT-PCR & direct IF assay. All RVIs

admission to ICU,

4 Franz et al., (516)

Germany

admitted with LRTI, 2 yrs. prospective

<16 yrs. 404 (78.0) 127 (34.0) RT-PCR, All RVI’s

pneumonia

5 Singleton et al., (92) Alaska USA

hospitalized & community controls, 2 years prospective

<3 yrs. 865 (71.2) 35 (5.7) RT-PCR, All RVIs except hBoV

admission to GW bronchiolitis, pneumonia

6 Martin et al., (82)

USA outpatients and hospitalized 1 yr. retrospective

<4 yrs. 893 (63.0) 103 (18.0) RT-PCR All RVIs except hBoV & RV

admission to ICU, admission to GW

7 Boivin et al., (566) Canada

admitted to paediatrics dept. with ARIs 6 months prospective

<3 yrs. 259 (61.9) 23 (14.0) RT-PCR, All RVIs except hBoV & hCoV

bronchiolitis pneumonia

8 Camargo et al., (594) Brazil

hospitalized to GW or ICU, 3 months prospective

Children & adults

159 (65.4) 15 (14.4) RT-PCR, All RVIs

admission to ICU

9 Do et al., (517) Vietnam

hospitalized to GW & ICU with ARI, 3 yr. prospective

<13 yrs. 309 (72.0) 62 (20.0) RT-PCR, All RVIs

Admission to ICU bronchiolitis pneumonia

10 Venter et al., (595) South Africa

outpatients, hospitalized patients and healthy controls,2 years retrospective

< 5 yrs. 610 (83.6) 279(54.7) RT-PCR and IFA assays All RVI’s

admission to ICU, admission to GW, pneumonia

11 Sung et al., (596) Taiwan

admitted with LRTI, 8 months prospective

<3 yrs. 48 (70.83) 8 (23.5) RT-PCR & direct IF, All RVI’s

pneumonia

12 O’Çallaghani-Gordo et al., (583) Mozambique

outpatients, 1 year prospective

<1 yr. 333 (55.6) 38 (20.5) PCR. All RVIs except hBoV & hCoV

admission to GW

13 Rhedin et al., (91)

Sweden

admitted to paediatric ward 6 months prospective

< 17 yrs. 502 (61.6) 45 (14.6) RT-PCR All RVIs admission to ICU

14 Marcone et al., (514)

Argentina in and outpatients 2 years prospective

<6 yrs. 620 (76.8) 61 (12.8) RT-PCR & IF All RVI’s except hBoV & hCoV

admission to a GW

15 Guerrier et al., (515) Cambodia

hospitalized with ALRI 3 year prospective

<5 yrs. 1,006 (54.8) 60 (10.9) RT-PCR All RVIs

bronchiolitis & pneumonia

16 Echenique et al., (94) USA

hospitalized to a GW or ICU 2 months retrospective

Children & adults

1,192 (55.2) 49 (7.4) RT-PCR All RVIs

admission to ICU

17 Libster et al., (93) Argentina

hospitalized to a GW or ICU. 3 months prospective

<18 yrs. 391 (64.2) 47 (18.7) RT-PCR Flu A, RSV, AdV & PIV1-3

admission to ICU

18 Bicer et al., (89) Turkey

Hospitalized to GC or ICU, 1 year retrospective

<9 yrs. 155 (66.5) 21 (13.5) RT-PCR All RVIs

pneumonia

Notes : RT-PCR – real time polymerase chain reaction, IF – immunofluorescence assay, ICU intensive care unit, GW

– general ward, RVIs - respiratory virus infections, hBoV –human bocavirus, hCoV – human coronavirus, RSV

respiratory syncytial virus, AdV – adenovirus, PIV1-3 – human parainfluenza virus types 1 to 3.

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3.2.4.3. Assessment of bias in the studies

3.2.4.3.1. Study design, year of study and viral genetics

Some of the bias which still exists in this systematic review and meta-analysis include: differing

types of viruses that circulated in the different years and seasons the studies were conducted, the

adoption of crude analysis i.e. comparison of disease outcome in single vs. multiple respiratory

virus infections, and possible role of viruses’ genetic mutations on disease outcome.

It is known that circulation of respiratory viruses differ yearly and between winters and summers

(9;204;300). For example, the influenza strains that have been in circulation globally have been

changing (402). Particularly the influenza A(H1N1) viruses that have circulated over the last decade

which include: A/New Caledonia/20/1999, the A/Solomon Islands/3/2006, the A/Brisbane/59/2007

and the pandemic influenza virus A/California/04/2009. Under the study hypothesis, it was

impossible to stratify studies based on year and season they were conducted and studies from

different seasons were included. Also, the crude analysis approach to investigation of the

association between co-infections and disease outcome, employed by different authors, was a bias

which remained. Some authors did not declare whether a conflict of interest exists, as the format of

the journals they published with does not include such a statement and this should be born in mind.

Lastly, genetic mutations are known to influence disease outcome in respiratory viruses such as

influenza virus. The role of genetic mutation on disease outcome was beyond the scope of this

review and an attempt to review the same has been made elsewhere (by systematic review 1 in this

thesis).

3.2.4.3.2. Publication bias

A total of 25 odds ratios were calculated from the 18 studies as described in Figures 3.16 to 3.20.

The sample sizes ranged from 48 to 1,192 (Table 3.6). The highest odds ratio was 3.12 as reported

by Guerrier et al., (515) on co-infection and risk of pneumonia, and the lowest odds ratio was 0.18

reported by Martin et al., (82) in co-infection and risk of admission to a general ward. The funnel

plot (Figure 3.10) indicates that there was no publication bias as the ORs were symmetrically

distributed around the combined OR (with some ORs falling on the lower left corner of the funnel

as well), and the p-value of the Spearman’s rank correlation test was not significant (p = 0.58).

174

Figure 3.10: Funnel plot of observed odds ratios to check publication bias

3.2.4.4. Co-infection and risk of hospitalization to a general ward

Evidence from the review of the role of co-infections on risk of admission to a general ward is

inconclusive as 3 of the 6 included studies [Cilla et al., (513), O’Callaghani-Gordo et al., (583), and

Marcone et al., (514) found a significant positive association (OR: 2.40, 95% CI: 1.29 – 4.44, p =

0.005, OR: 2.84, 95% CI: 1.06 – 7.66, p = 0.04 and OR: 2.33, 95% CI: 1.10 – 5.10, p = 0.04)],

whereas one study, Singleton et al., (92), found insignificant positive association – Figure 3.11.

Further, two studies [Martin et al., (82) and Venter et al., (595)] found a significant and an

insignificant negative association in risk of hospitalization to a general ward (OR: 0.59, 95% CI:

0.38 – 0.93, p = 0.02 and OR: 0.97, 95% CI: 0.67 – 1.4, p = 0.86 respectively).

The I2 statistic indicates that the studies were significantly heterogeneous (I2 = 76.8, P = <0.0001).

The heterogeneity means that the individual and the pooled odds ratios are biased towards the null,

this is probably due to the difference in the number and types of viruses identified by the studies;

some viral co-infections of low severity influence the estimates of co-infection patterns, which

results in bias of severe illness towards the null, when crude analysis is applied. Our result therefore

might be an underestimation of the true impact of co-infections on risk of admission to a GW. The

above complexity emphasizes the importance of identifying individual viral agents in influencing

the outcome of disease, with and without co-infection. We did not find enough studies in which

researchers have compared the relationship between specific respiratory virus co-infections e.g.

FluA+RSV or RV+AdV and disease severity because all the included studies conducted crude

analysis. Therefore, this question needs research investigation where stratified analysis is applied.

175

Study name

Age (yrs)

Co-infecting viruses

Reference

Children <6 years

Martin et al., (2012) ≤1 Flu A, RSV, AdV, hMPV, hPIV1-4, hCoV

(82)

Cilla et al., (2008) ≤3 Flu A/B/C, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, hBoV

(513)

O’Çallaghan-Gordo et al., (2011) ≤1 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4

(583)

Venter et al., (2011) ≤5 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, hBoV

(595)

Singleton et al., (2010) ≤3 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV

(92)

Macorne et al., (2013) ≤6 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4

(514)

Overall

I statistic 76.8, p = 0.001

Figure 3.11: Respiratory virus co-infections and risk of admission to a general ward. Notes: Odds ratios are for occurrence of event (hospitalization

to a general ward) in multiple infections (group A) vs. single infections (group B) as the baseline. The squares represent the estimated odds ratios, the diamond

represent their summary, the horizontal lines give their 95% confidence intervals and the size of the squares represent the weight of the study. Study by Cilla et al., (513)

was included because emergency department is an outpatient service, whereas study by Ventre et al., (184) was included because controls are unhospitalized individuals,

hence in some way similar to outpatients.

Events / Total Odds ratio and 95% CI

Odds Lower Upper Group-A Group-B ratio limit limit p-Value

66 / 103 347 / 463 0.59 0.38 0.93 0.024

41 / 61 76 / 165 2.40 1.29 4.43 0.005

7 / 28 19 / 185 2.84 1.05 7.66 0.039

177 / 279 148 / 231 0.97 0.67 1.39 0.859

31 / 299 4 / 67 1.82 0.62 5.35 0.275

52 / 60 308 / 415 2.33 1.06 5.10 0.035

1.47 0.86 2.51 0.159

0.1 0.2 0.5 1 2 5 10

Favours A Favours B

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3.2.4.5. Co-infection and risk of admission to intensive care unit (ICU)

All the studies included in this analysis recruited children <6 years old. We conducted separate

analysis of studies that recruited hospitalized children <1 year old and those that included both in

and outpatient children <6 years old. In the latter, co-infection was associated with a significant

reduction in risk of admission to the ICU (pooled OR: 0.32, 0.19 – 0.52, p = <0.0001, and

individual studies: OR: 0.34 – 95% CI: 0.20 – 0.57, p = <0.0001 and OR: 0.18, 95% CI: 0.04 –

0.75, p = 0.02). However, among studies that recruited hospitalized children <1 year old, there

was conflicting evidence with one study Richard et al., (76) reporting co-infection increase risk

(OR: 2.89 95% CI: 1.41 – 5.93, p = 0.004), and the other Huguenin et al., (593) finding an

insignificant negative association (OR: 0.45, 95% CI: 0.12 – 1.65, p = 0.82) – Figure 3.12. This is

probably because Huguenin et al., (593) identified very few viruses in their study (5 co-infections

out of 85 and 5 single infections out of 41).

The differences in the findings between studies that recruited hospitalized individuals and those

that recruited both in and outpatients underscores the importance of study design in influencing

results. Hospitalized individuals may represent a cohort of more severe patients, and this might

probably have skewed the outcomes in hospitalized individuals towards a more severe outcome

[e.g. in Richard et al., (76)]. Conversely, the outpatient’s cohort, in studies that recruit both in and

outpatients, may have milder or less severe disease, that’s pulling the odds ratios towards the null.

However our interpretation of this result is hampered by the small numbers of studies included in

each analysis. Literature has shown that among other factors patients underlying chronic

conditions such as asthma, may influence severe disease outcome among patients with respiratory

virus single and multiple infections (513;597). Children are known to suffer asthma, leukaemia,

hay fever and other chronic conditions, therefore this factor should be taken into consideration

when interpreting this result.

177

Study name

Age (yrs)


Reference

Hospitalized Infants

Richard et al., (2008) ≤1 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (76)

Huguenin et al., (2012) ≤1 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, hBoV (517)

Overall

I2 statistic 83.4, p = 0.014

In and outpatient children

Venter et al., (2011) ≤5 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, hBoV (595)

Martin et al .,(2012) ≤4 Flu A, RSV, AdV, hMPV, hPIV1-4, hCoV (82)

Overall

I2 statistic 88.94, p = <0.0001

Figure 3.12: Respiratory virus co-infections and risk of admission to an intensive care unit. Notes: Odds ratios are for occurrence of event

(hospitalization to a general ward) in multiple infections (group A) vs. single infections (group B) as the baseline. The squares represent the estimated odds ratios, the

diamond represent their summary, the horizontal lines give their 95% confidence intervals and the size of the squares represent the weight of the study.



30 / 44 58 / 136 2.89 1.41 5.94 0.004

5 / 85 5 / 41 0.45 0.12 1.65 0.228

1.24 0.20 7.61 0.818

0.1 0.2 0.5 1 2 5 10

24 / 279 50 / 231 0.34 0.20 0.57 0.000

2 / 103 46 / 463 0.18 0.04 0.75 0.019

0.32 0.19 0.52 0.000

0.1 0.2 0.5 1 2 5 10

Favours A Favours B

178

3.2.4.6. Co-infection and risk of bronchiolitis

This analysis found an insignificant positive association between co-infection and risk of

developing bronchiolitis (pooled OR: 1.29, 95% CI: 0.79 – 2.11, p = 0.31), and for specific studies

(OR: 2.09, 95% CI: 0.67 – 6.58 and OR: 1.15, 95% CI: 0.67 – 2.0, p = 0.61). The two studies

included in this analysis recruited hospitalized children ≤5 years old and used RT-PCR for

identification of viruses; Boivin et al., (566) identified Flu A/B, RSV A/B, AdV, hMPV and PIV1-4

co-infections to which Guerrier et al., (515) added hBoV and hCoV. There was no heterogeneity in

their findings (I2 = 0, p = 0.36) (Figure 3.13). It would have been good if we were able to compare

this result with studies recruiting both hospitalized and community based controls or outpatients.

More studies are needed to further explore this possible positive association.

3.2.4.7. Co-infection and risk of pneumonia

Respiratory viruses have previously been identified as significant causes of community acquired

viral pneumonia (14), however the role of co-infection among respiratory viral infections has not

been previously explored. In this review all the studies had recruited hospitalized children. We

grouped the studies according to the age of the participants (children ≤5 years old and children ≤16

years old). Among children ≤5 years old, the pooled odds ratio indicate that co-infection

significantly increased the risk of pneumonia (summary OR: 2.17, 95% CI: 1.10 – 4.37, p = 0.03 -

Figure 3.14), but the study by Guerrier et al., (515) found a significant outcome (OR: 3.12, 95% CI:

1.75 – 5.54, p = <0.0001).

In studies that recruited children ≤16 years old, the pooled odds ratio suggested a positive

association between co-infection and risk of pneumonia (OR: 1.29, 95% CI: 0.79 – 2.11), but this

was not statistically significant (p = 0.30). As for the individual studies, only Franz et al., (516)

reported a statistically significant positive association (OR: 1.68, 95% CI: 1.03 – 2.75, p = 0.04).

The lack of statistical significance in this analysis is probably because the studies did not identify a

high number of co-infections; only 7 co-infections developed pneumonia in Boivin et al., (566)

study, 3 in Sung et al., (596), 9 in Bicer et al., (89) and 11 in Do et al., (517). Literature has shown

that bacterial co-infection aggravates disease severity (such as pneumonia complication) in patients

infected with respiratory viruses (94;277;597;598). A 2013 review by McCullers et al., (599) and a

2012 review by Viasus et al., (600) suggest that different influenza virus strains, will have different

ability to prime the host for secondary bacterial infection, their genetic fitness playing a role in the

same. This insight might offer some explanation to the mechanism behind the observed severity in

some respiratory virus co-infections.

179

Study name

Age (yrs)


Reference

Boivin et al., (2003) ≤3 Flu A/B, RSV, AdV, hMPV, hPIV1-4 (566)

Guerrier et al., (2013) ≤5 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hBoV, hCoV (515)

Overall

I2 statistic 0, p = 0.36

Figure 3.13: Respiratory virus co-infections and risk of bronchiolitis. Notes: Odds ratios are for occurrence of event (bronchiolitis) in multiple infections

(group A) vs. single infections (group B) as the baseline. The squares represent the estimated odds ratios, the diamond represent their summary, the horizontal lines give

their 95% confidence intervals and the size of the squares represent the weight of the study. For this category, we identified only studies that recruited hospitalized children

<5 years old.



19 / 23 99 / 141 2.09 0.67 6.58 0.207

25 / 59 191 / 491 1.15 0.67 2.00 0.606

1.29 0.79 2.11 0.313

0.1 0.2 0.5 1 2 5 10

Favours A Favours B

180

Study name

Age (yrs)


Reference

Children ≤ 5 years old

Boivin et al., (2003) ≤3 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (566)

Sung et al., (2011) ≤3 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (596)

Guerrier et al., (2013) ≤5 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (515)

Overall


Children ≤ 16 years old

Bicer et al., (2013) ≤9 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (89)

Do et al., (2011) ≤13 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (517)

Franz et al., (2010) ≤16 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (516)

Overall

I2 statistic 31.6, p = 0.23

Figure 3.14: Respiratory virus co-infections and risk of pneumonia. Notes: Odds ratios are for occurrence of event (pneumonia) in multiple infections

(group A) vs. single infections (group B) as the baseline. The squares represent the estimated odds ratios, the diamond represent their summary, the horizontal lines give

their 95% confidence intervals and the size of the squares represent the weight of the study. All studies included in this analysis had recruited hospitalized children. When

a combined analysis is conducted co-infection is associated with a significantly increases the risk of pneumonia by 60% OR: 1.60, 95% CI: 1.02 – 2.50, p = 0.03, I2 = 47.

6, p = 0.09.



7 / 23 39 / 141 1.12 0.43 2.94 0.813

3 / 8 5 / 26 2.53 0.45 14.28 0.295

40 / 59 198 / 491 3.12 1.75 5.54 0.000

2.17 1.08 4.37 0.029

0.1 0.2 0.5 1 2 5 10

9 / 21 28 / 82 1.46 0.55 3.88 0.449

11 / 62 35 / 160 0.77 0.36 1.63 0.497

45 / 97 70 / 206 1.68 1.03 2.75 0.039

1.29 0.79 2.11 0.303

0.1 0.2 0.5 1 2 5 10

Favours A Favours B

181

3.2.4.8. Influenza A virus single and mixed infections and disease severity

The preceding sections looked at disease outcome in single vs. multiple infections involving all

respiratory viruses in general, with a condition that influenza was among the single or mixed

infections. That was done to explore whether or not disease in single respiratory virus infections is

more or less severe than mixed infections. As it was anticipated that crude analysis may obscure

the true effect of specific virus pairings, however, we did not find enough studies to conduct crude

analysis as two-by-two virus co-infection level to include in a meta-analysis. In trying to start this

exploration, a decision was made to investigate disease outcome in single vs. mixed influenza A

virus infections.

Seven studies were included in this analysis of which two investigated risk of admission to a

general ward in ≤5 year old children, and 5 reported admission to the ICU in children ≤18 years

old respectively (Figure 3.15). Regarding risk of admission to a general ward, both the pooled

odds ratio and that of the individual studies showed a positive association (pooled OR: 1.24, 95%

CI: 0.33 – 3.84, p = 0.85 and odds ratios of 1.01 and 1.31 for individual studies), but none of these

was statistically significant (Figure 3.20). Similarly, on admission to the ICU, both the individual

and pooled odds ratios showed positive association. For example among children ≤18 years old,

the pooled estimate was: OR: 1.46, 95% CI: 0.75 – 2.83, but again this was not statistically

significant (p = 0.26). Suffice to add that among studies that recruited both young children and

adults [Camargo et al., (594) and Singleton et al., (92)], the estimates were in conflict. Camargo et

al., (594) found that co-infection was actually protective against admission to ICU, however this

was also not statistically significant. There is therefore a need for more well designed studies

investigating the impact of co-infection on the outcome of influenza virus infection.

182

Study Name Age (yrs) co-infecting viruses

References

Flu viruses co-infection & admission to GW

Venter et al., (2011) ≤5 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, hBoV (595)

Singleton et al., (2010) ≤3 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (92)

Overall (91)

I2 statistic 0 p = 0.87

Flu viruses co-infection & admission to ICU

Do et al., (2011) ≤13 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV (517)

Libster et al., (2010) ≤18 Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, hBoV (93)

Rhedin et al., (2012) ≤17 Flu A, RSV, AdV, hPIV1-3 (91)


Camargo et al., (2012) young & old Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, (594)

Echenique et al., (2013) young & old Flu A/B, RSV, RV, AdV, hMPV, hPIV1-4, hCoV, (94)

Overall


Figure 3.15: Influenza A viruses single and multiple infections and disease severity

Notes: GW – general ward, ICU – intensive care unit. Odds ratios are for occurrence of event (admission to GW or ICU) in influenza A virus co-infections (group A) vs.

single influenza A virus infections (group B) as the baseline. The squares represent the estimated odds ratios, the diamond represent their summary, the horizontal lines

give their 95% confidence intervals and the size of the squares represent the weight of the study. Studies included in the analaysis of risk of admission to a GW included

those that recruited both hospitalised and outpatients whereas studies on risk on admission to the ICU recruited patients admitted to a general ward and ICU.

2 / 10 2 / 17 1.87 0.22 15.85 0.567

11 / 47 36 / 204 1.43 0.67 3.07 0.360

2 / 12 9 / 71 1.38 0.26 7.32 0.707

1.46 0.75 2.83 0.263

0.1 0.2 0.5 1 2 5 10

1 / 6 6 / 27 0.70 0.07 7.22 0.766

6 / 24 47 / 290 1.72 0.65 4.57 0.274

1.51 0.61 3.71 0.371

0.1 0.2 0.5 1 2 5 10

Favours A Favours B



7 / 11 4 / 7 1.31 0.19 9.10 0.783

31 / 54 4 / 7 1.01 0.21 4.97 0.988

1.12 0.33 3.84 0.852

0.1 0.2 0.5 1 2 5 10

183


In conclusion, this review found inconclusive results on the role of co-infections among

respiratory viruses on risk of admission to a general ward or the ICU; some studies found co-

infection increased the risk yet others did not. Similarly, some studies found that co-infection

significantly increased risk of pneumonia whereas others did not. Both of the studies that reported

on bronchiolitis found insignificant positive associations. Seven of the 18 included studies

reported disease outcome in single vs. multiple influenza A virus infection. Five out of the seven

found a positive association between co-infection and risk of admission to a GW or ICU, but none

was statistically significant.

Some of the included studies conducted both crude and multivariate analysis; controlling for co-

morbidities, patients age, gender, immune status and other covariates, whereas others only carried

out crude analysis (because they didn’t find statistically significant results). Since we did not find

enough studies that had carried out multivariate analysis, and because among the different studies

that carried out multivariate analysis the types of covariates controlled for were different, only

crude figures were used to conduct this meta-analysis. The estimated odds ratios might be biased

or confounded and could have been different if controlled for confounding factors and this should

be born in mind when interpreting the results of this review. Furthermore one of the main

objectives of this review was to investigate whether co-infection could increase disease severity

across the age spectrum or it would only be a burden in children ≤5 years or the elderly >65 years

old (holding other factors constant). However we did not find enough studies that recruited both

adults and children, only 2 out of the 18 included studies met this criteria. The results here means

this review was unable to answer this question, therefore more studies recruiting both adults and

children need to be carried out in order to answer it.

The studies included in the analysis on co-infection and risk of admission to the ICU were

heterogeneous (I2 = 83.4, p = 0.014 and 88.9, p = <0.001 – Figure 3.12). The differences in the

findings between these underscores the importance of study design in influencing results.

Evidence from other studies indicate that the rate of co-infection is higher when studies recruit

hospitalized patients and is lower when they recruit both hospitalized and outpatients or when only

outpatients are recruited (80;81;85;93;576;577). In this review, the studies that recruited

hospitalized patients; Libster et al., (93), Richard et al., (76) and Do et al., (517) reported higher

co-infection rates (19%, 25% and 20% respectively), and were more likely to find a positive

184

association between co-infection and severe outcome. Conversely, studies that recruited both

hospitalized and outpatients Martin et al., (82) and Venter et al., (595) were more likely to find no

association between co-infection and severe disease. A cohort of more severe patients among

hospitalized individuals [Richard et al., (76) and Do et al., (517)], might probably have skewed the

outcomes in these studies towards a more severe disease.

An additional bias in the studies included in this review may have been introduced by the crude

analysis adopted by many authors, as it may have introduced bias towards the null (odds ratios

being close to 1). For example, in a study where RSV/hBoV co-infections were predominant

[Cilla et al., (513) Supplementary Table 3.2S1, Figure 3.16], a significantly increased risk of

admission to a general ward was reported - and where RSV/AdV and RSV/RV co-infections were

predominant, a reduced risk of admission to a general ward was reported [Martin et al., (82), and

Venter et al., (595) – (Supplementary Table 3.2S1, Figure 3.11)]. This could be because some

viral co-infections of low severity influenced the estimates of co-infection patterns towards the

null when crude analysis was applied. Future studies should employ stratified analysis on the

effect of co-infections on disease outcome where effects of specific pairs of viruses e.g. Flu

A/RSV, RSV/hMPV or RSV/AdV are investigated so as to elucidate the type of virus pairs which

increase or decrease disease severity. There is overwhelming evidence of bacterial co-infection

among patients with ARIs and pneumonia. For example, in their 2012 review, Punpanich and

Chotpitayasunondh (15) reported that 43% of the pandemic influenza A(H1N1)pdm09 virus

associated paediatric deaths had bacterial co-infection. Similar reports have been made by

Ruuskanen et al., (59). In explaining the causal pathway to this, Peltola and McCullers (60) and

Bakaletz et al., (61) indicated that the destruction of respiratory epithelium by viruses may

increase bacterial adhesion leading to more pneumonia, bronchiolitis and other severe outcomes.

This suggests a possible mechanism behind the observed severity in patients with respiratory virus

co-infections. However, such causal hypothesis can only be best explored by sequential studies

where it is investigated which among the two pathogens, bacteria or viruses, infect patients first.

We therefore call for research investigating virus/bacteria colonization in order to shed more light

on this important subject.

Influenza A viruses, RSV, hMPV and AdV follow seasonal patterns, with higher virus activity in

winters and minimal activity in summers. RV circulate all year round whereas hPIV1-4 are

predominantly in summer (9;204;300). As the studies included here spanned over different time

185

periods, this might also have introduced some bias in the observed patterns of co-infections

reported by different studies, hence the outcomes, and this should be born in mind when

interpreting our results. It is possible that some of the co-infections indicated by different studies

were nosocomial infections, however, in all the included studies, ascertainment of disease status

was performed during the time of hospitalization or during the first consultation, ruling out the

possibility of nosocomial infections. Polymerase chain reaction (PCR) give better sensitivity and

specificity than other diagnostic methods as previously discussed by Henrickson (585) and Lee et

al., (238). In this review, only studies that used RT-PCR, PCR were included. If there is any yet

unknown systematic error due to application of RT-PCR or PCR, then the effect would be carried

over into the results of our study. However, at the present time, PCR remains the gold standard for

diagnosis, as some of the respiratory viruses cannot be cultured in laboratories; hence we believe

that the results summarized here closely resemble the epidemiological situation.

Literature has suggested that virus-virus interactions may influence host immune response in

driving other respiratory viruses’ virulence or a virulence. Respiratory virus proteins are detected

by host cell tall like receptors; TL2, TLR4 and TLR6 on the surface of the cell; TLR3 TLR7,

TLR8 and TLR9 in the endosome, and by the protein kinase RNA - activated (PKR), the

melanoma differentiation associated gene 5 (MAD-5), the retinoic inducible gene I (RIG-I) and

the 2',5'-Oligoadenylate synthetase (2',5'-OAS1&2) in respiratory epithelial cells and dendritic

cells, which in turn triggers host production of cytokines including; tumour necrosis factor (TNF),

type 1 proinflammatory cytokines; interferon-alpha (IFN-α), and interferon-beta (IFN-β),

interleukin-6 (IL-6), interleukin-18 (IL-18) (17;552), which counteract virus infection. Depending

on the type of virus, infection may lead to cytokine storm resulting into severe disease

characterised by organ failure. Casalegno et al., (545) and other researcher (541;544) suggested

that rhinoviruses interfered with circulation of other viruses, and some studies (80;81) indicated

that co-infections with rhinoviruses resulted in low risk. However the precise mechanisms in co-

infections that may affect virulence are not well understood and more research is needed to

understand the biomedical processes in respiratory virus co-infections and the co-infection

patterns that may increase or decrease virulence.

Studies have indicated that a number of other factors affect severity of respiratory virus disease

including: obesity, socio-economic status, pregnancy, prematurity and low birthweight, nutrition

status and breastfeeding in infants, active and passive smoking, immune status and vaccination

186

(9;10;13;21;23;53;564). It is observed here that most descriptive studies on epidemiology of

respiratory virus single and multiple infections do not collect information on patients

characteristics with regard to these factors. Similarly, most of the included studies did not.

Specifically, information on pregnancy was reported in only 2 studies Camargo et al., (594) and

Echenique et al., (94), nutrition status by Ventre et al., (184) and Libster et al., (93), and passive

smoking by Echenique et al., (94) and Libster et al., (93). It should therefore be born in mind that

the observed outcomes might have been affected by one or more of these factors. It is

recommended that researchers should endeavour to collect and control factors.

Also, studies included in this review were only those written in the English language. This might

have introduced selection bias into our review and this should be born in mind. Since reviews

mainly rely on published data, there is a danger of publication bias, where published studies

represent only those studies that had found a positive and significant association between exposure

and outcome. However we constructed a funnel plot of odds ratios reported by included studies

against their sample sizes and the results indicated that there was no publication bias (Spearman’s

rank correlation test was not significant, p = 0.58). In addition, we employed a standard search

strategy, making sure that we are able to capture all the possible studies covering the subject under

study. The search was performed on MEDLINE, EMBASE and WEB of Science, databases which

summarize publication in a wide variety of medical journals. We also manually searched studies

of good quality, for primary studies to include in the review and in this way hope to have

eliminated any study selection or publication bias.

In conclusion, this review found inconclusive results on the role of co-infections on severity of

respiratory disease. Many of the problems in interpretation of the evidence were because the

authors adopted crude analysis. Future studies should employ stratified analysis on the effect of

co-infections on disease outcome where the effects of specific pairs of viruses e.g. Flu A/RSV,

RSV/hMPV or RSV/AdV are studied so as to elucidate the type of virus pairs which increase or

decrease disease severity.

Funding: This work had no funding.


Manchester Academic Health Science Centre


187


Supplementary Table 3.2S1: Number of co-infections for different respiratory viruses in included studies

Study name Sample

size

No. RVIs

found

No Co-infects

Flu A

RSV

RV

AdV

hMPV

hBoV

hCoV

PIV

Highest

Co-infection

Severity

1

Richard et al., (76) 180 173 44 5 36 22 0 8 0 5 0 14 ↑

2 Cilla et al., (513) 315 226 61 7 34 9 1 8 37 8 16 14 ↑

3 Huguenin et al., (593) 152 138 85 1 54 10 30 15 28 1 17 26 ↓

4 Franz et al., (516) 404 315 127 1 29 30 19 2 19 6 5 14 ↑

6 Martin et al., (82) 893 563 103 25 55 0 59 18 0 30 25 24 ↓

7 Boivin et al., (566) 265 164 23 21 22 0 3 12 0 0 0 18 ↑

8 Camargo et al., (594) 159 104 9 9 2 2 4 2 0 0 0 4 ↓

9 Venter et al., (595) 610 510 279 11 70 95 20 20 26 22 15 ? ↓

10 O’Çallaghani-Gordo et al., (583) 333 185 30 11 0 21 17 6 0 0 3 9 ↑

11 Rhedin et al., (91) 502 309 45 12 1 30 11 1 18 11 9 9 ↓

12 Marcone et al., (514) 620 476 61 3 31 48 0 12 0 0 5 30 ↑

14 Echneque et al., (94) 1,192 666 49 24 13 28 13 1 0 1 8 17 ↑

15 Libster et al., (93) 251 251 47 47 10 10 7 6 0 1 8 10 ↑

Notes: RVIs - respiratory virus infections, RSV - respiratory syncytial virus, RV - rhino virus, hMPV – human metapnuemovirus, hCoV - human coronavirus, Flu A/B - influenza A or B,

AdV - adenovirus, PIV1-4 - parainfluenza virus types 1 to 4. ↑ Study reported increased risk associated with co-infections, ↓ study found co-infections reduced severity? – number not

given. In bold is the co-infection pair that was found to be the most predominant by the study and the actual number of such co-infection is provided in column “Highest Co-infection”.

The number of studies in this table is less than the number of studies included in the review because some of the included studies did not give details of the number of specific viruses

involved in co-infections.

188

Table 3.2S2: Study quality assessment criteria – bias and other study characteristics

Study Ref

No conflict of interest

Study design Selection bias

Demographic

characteristics given

Detection bias

Validity of calculations

Controlled for confounders

Selective outcome reporting

Poor or inadequate reporting

Overall assessment

Richard et al., (2008)

(76)

Not given

hospitalized with LRTI 2 years prospective

No

Yes

No

Yes

Yes

No

No

Excelle

nt

Cilla et al., (2008) (513) Not given paediatric emergency dept, 2 years prospective

No Yes No Yes No No No Excellent

Huguenin et al (2012) (593) Yes hospitalized with LRTI, 1 year prospective

No Yes No Yes Not shown

Yes No Good

Franz et al., (2010) (516) Yes admitted with LRTI, 2 years prospective

No Yes No Yes No Yes No Good

Singleton et al., (2010) (92) Not given hospitalized & community controls, 2 years prospective


Martin et al., (2011) (82) declared outpatients and hospitalized patients, 4 years retrospective

No Yes No Yes Yes No No Excellent

Boivin et al., (2003) (566) Yes outpatients and hospitalized, 1 year retrospective

No Yes No Yes No No No Excellent

Camargo et al., (2012) (594) Yes admitted to paediatrics dept. with ARIs, 6 months retrospective

No Yes No Yes No No No OK

Do et al., (2011) (517) Yes hospitalized with LRTI, 3 months retrospective


Venter et al., (2011) (595) Not given hospitalized with LRTI , 3 years prospective


Sung et al., (2011) (596) Not given admitted with LRTI, 8 months retrospective

No Yes No Yes Yes Yes No Excellent

O’Çallaghani-Gordo et al., (2011)

(583) Not given outpatients, 1 year prospective


Rhedin et al., (2012) (91) Yes admitted to paediatric ward, 6 months prospective


Marcone et al., (2013) (514) Yes in and outpatients, 2 years prospective


Echenique et al., (2013) (94) declared hospitalized to a GW or ICU, 2 months retrospective


Libster et al., (2010) (93) Yes hospitalized to a GW or ICU, 3 months prospective


Bicer et al., (2013) (89) Yes Hospitalized to GC or ICU, 1 year prospective


Guerrier et al., (2013) (515) Not given Hospitalized to GC or ICU, 3 years prospective

No Yes No Yes No No No Good

189

Table 3.2S3: Search history on Web of Science for review number 3: Single and multiple

respiratory virus infections and severity of respiratory disease: A systematic review and

meta-analysis

Web of Science® now with books

Search History

Set

Results

Combine

Sets

AND

OR

Delete Sets

# 9 2,515 #6 AND #5 AND #4

Refined by: Web of Science Categories=( INFECTIOUS DISEASES

OR VIROLOGY OR MEDICINE GENERAL INTERNAL OR

RESPIRATORY SYSTEM OR PATHOLOGY OR CRITICAL CARE

MEDICINE ) AND Document Type=( ARTICLE )

Databases=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH,

BKCI-S, BKCI-SSH Timespan=All Years

Lemmatization=On

Select to

combine

sets.

Select to

delete this

set.

# 8 2,839 #6 AND #5 AND #4

Refined by: Web of Science Categories=( INFECTIOUS DISEASES

OR VIROLOGY OR MEDICINE GENERAL INTERNAL OR

RESPIRATORY SYSTEM OR PATHOLOGY OR CRITICAL CARE

MEDICINE )



Lemmatization=On

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combine

sets.

Select to

delete this

set.

# 7 6,022 #6 AND #5 AND #4


Select to

combine

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190


Lemmatization=On

sets. set.

# 6 633,281 #3 OR #2 OR #1



Lemmatization=On

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combine

sets.

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delete this

set.

# 5 1,573,622 Topic=(virulence or virus virulence or prognosis or pathogenicity

or virus pneumonia or bronchiolitis or viral bronchiolitis or

hospital or hospitalisation or hospitalization or hospital care or

hospital admission or patient admission or length of stay or

intensive care or critical care or intensive care unit or icu

admission or fatality or mortality or death)



Lemmatization=On

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sets.

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set.

# 4 71,699 Topic=(coinfection or coinfections or mixed infection or dual

infection or dual infections or multiple infections)



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sets.

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set.

# 3 21,058 Topic=(respiratory syncytial viruses or respiratory syncytial virus

infections or metapneumovirus or metapneumovirus human or

parainfluenza Virus 1 or human parainfluenza virus 2 or human

parainfluenza virus 3 or bucavirus or human bocavirus or

bocavirus infection or coronavirus or human coronavirus or

coronavirus infection)



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sets.

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set.

# 2 35,454 Topic=(orthomyxoviridae or orthomyxovirus infection or

orthomyxovirus or influenza A virus or influenza human or

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191

influenza A virus or H1N1 subtype or 2009 H1N1 influenza or

influenza A(H1N1)pdm09 or influenza A virus H3N2 subtype)



Lemmatization=On

sets. set.

# 1 627,947 Topic=(viruses or virus diseases or virus infection or respiratory

tract Infections or respirovirus infections or lower respiratory tract

infections or upper respiratory tract infections)



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set.

AND

OR

192

Part IV: Co-infections and risk of hospitalization and mortality in NW

England 2007 – 2012

4.1. Co-infections and risk of hospitalization and mortality Part A: Single, dual and

multiple respiratory virus infections and risk of hospitalization and

mortality

Synopsis

This is the authors’ version of the paper published in Epidemiology and Infection. On 28

January, 2014. Reproduced from Goka et al., with permission from Cambridge University

Press, Copyright Licence No: 3373750155325. This paper discusses the association between

mixed infections and severe disease outcome. The dataset was the electronic records of all

samples, from the North West region of England, received and tested for respiratory virus

infections between January 2007 and June, 2012. Some of the datum included in this study was

also presented as a poster at the poster day of the faculty of Medical and Health Sciences of the

University of Manchester held on 26th of January 2012.

193

Single, dual and multiple respiratory virus infections

and risk of hospitalization and mortality

E. A. Goka 1, P. J. Vallely 1, K. J. Mutton 1,2, P. E. Klapper 1,2


2: Department of Clinical Virology, Central Manchester University Hospitals - NHS Foundation Trust

Correspondence author: E. A. Goka: Institute of Inflammation and Repair, Faculty of Medical and Human

Sciences, 1st Floor Stopford building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. Email:

[email protected]

Running head: Single/mixed respiratory viruses & severity

194

4.1.1. Abstract

Respiratory virus infections cause a significant number of hospitalization and deaths globally.

This study investigated the association between single and multiple respiratory virus infections

and risk of admission to a general ward, ICU or death among patients aged 0 – 105 years old

[mean = 24.4 (SD±24.1) years], from North West England, that were tested for respiratory

virus infections between January 2007 and June 2012. The majority of infections were in

children ≤5 years old. Dual or multiple infections occurred in 10.4% (1,214/11,715) of patients,

whereas single infection occurred in 89% (10,501/11,715). Rhinovirus was the most common

co-infecting virus (occurring in 69.5%; 844/1,214 of the co-infections). In a multivariate

logistic regression model, multiple infections were associated with an increased risk of

admission to a general ward (OR: 1.43, 95% CI: 1.2 – 1.7, p = <0.0001). On the other hand,

patients with RSV and hPIV1-3, as a single infection, had a higher risk of being admitted to a

general ward (OR: 1.49, 95% CI: 1.28 – 1.73, p = <0.0001 and OR: 1.34, 95% CI: 1.003 – 1.8,

p = 0.05); admitted to the ICU or dying (OR: 1.5, 95% CI: 1.20 – 2.0, p = 0.001 and OR: 1.60,

95% CI: 1.02 – 2.40, p = 0.04). This result emphasises the importance of RSV, hPIV and mixed

infections and calls for research on vaccines, drugs and diagnostic tests targeting these

respiratory viruses.

Key words: respiratory virus infections, single and multiple infections, hospitalization,

parainfluenza virus types 1 to 3, influenza A(H1N1)pdm09 virus.

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4.1.2. Introduction

Around 5.0% - 38.0% of respiratory virus infections develop into acute respiratory tract

infection (ARIs) leading to hospitalization and mortality (204;216;233;234;237;251).

Thompson et al., (253) estimated deaths associated with influenza A/B (Flu A/B) and

respiratory syncytial virus (RSV) between 1976 and 1999 in the USA. They reported that

between 1990 and 1999 there were an average of 8,097 (SD: 3084) influenza A/B/pneumonia

associated deaths and 2,707 (SD: 196) RSV/pneumonia associated deaths annually, with 90.0%

of influenza and 78.0% of RSV-associated deaths occurring in the elderly ≥65 years old. In the

United Kingdom a study by Nicholson (601) estimated that between 1975 and 1990 there were

between 6,244 (SD: 1,416) and 29,646 (SD: 6,723) influenza-related deaths annually. Two

recent meta-analyses on influenza and respiratory syncytial virus (RSV) in under-five year old

children (227;228) estimated that globally, 13.0% of all children with influenza infection

developed acute lower respiratory tract infections (ALRI) - equivalent to 20 million (95% CI:

13–32 million) ALRI cases per year, and that in 2008, influenza-associated ALRI caused

between 28,000 and 111, 500 deaths in children younger than 5 years. Nair et al., (227)

reported that 22.0% of ALRIs were associated with RSV (incidence 59.1 per 1,000 children in

developing countries and 24.0 per 1,000 children in developed countries respectively) -

equivalent to a global health burden of 33.8 million new cases each year; of which 3.4 million

cases (9.0%) required hospitalization, and 66,000 to 199,000 died. An earlier review by

Williams et al., (3) suggested that globally, ARIs were responsible for 1.9 million (95% CI 1.6–

2.2 million) deaths among under 5 children.

Some studies have reported an association between co-infection and severe disease outcome

(75-77;91;94;514), however, a number of other studies have reported no significant differences

in disease outcome between single or multiple virus infections (89;602;603). Our other study

(338) investigated the association between co-infection between pandemic influenza

A(H1N1)pdm09 (Flu Apdm09), seasonal Flu A (Seas Flu A) and other respiratory viruses and

risk of hospitalization and mortality. This study compared the disease outcome in single RSV,

rhinovirus (RV), adenovirus (AdV), human metapneumovirus (hMPV), human parainfluenza

viruses types 1 to 3 (hPIV1-3), influenza B, pandemic influenza A(H1N1)pdm09, and seasonal

influenza A viruses. It also investigated the association between single and multiple infections,

in general, and disease outcome. Knowledge on single infections will help determine the

relative important of these respiratory viruses in causing acute respiratory infections requiring

hospitalization or resulting in deaths, whereas an understanding of disease outcome in co-

infections may help generate information needed for patient management, direct research

196

efforts towards development of multi-targeted diagnostic tests, integrated vaccine, and

combined treatment for all respiratory viruses.

4.1.3. Methodology

4.1.3.1. Study design and setting

This descriptive cross-sectional study retrospectively analysed the results of respiratory virus

samples of patients 0 - 105 years old that were received and tested for ten respiratory virus

infections; Flu Apdm09, SeasFlu A (e.g. H3N2), Flu B, RSV, RV, AdV, hMPV, and hPIV1-3,

at Manchester Microbiology Partnership Laboratory (MMPL) between January 2007 and June

2012. The samples were from outpatients, hospitalized patients, or patients who had died, and

they originated from medical centres, surgeries and hospitals located in the North West region

of England, an area serving a population of over 7 million people (99). In total 30,975 samples

were received at the MMPL between January 2007 and June 2012, of which 35.9% (11,112)

were from children ≤5 years and 64.1% (19,863) were from patients >5 years old [mean 24.4

(SD±24.1) years, median 20.0 (25 – 75 percentiles 0.8 - 41.0 years)], and these proportions

were statistically significant (p = <0.0001).

4.1.3.2. Virus identification, inclusion and exclusion criteria

The types of samples that were tested for respiratory virus infection included: nose and throat

swabs (VNT), nasopharyngeal aspirates (VNPA), and swabs from other parts of the body

(VSW). Details of the laboratory protocols used to test for the viruses were published

elsewhere Goka et al., (338). Briefly well validated in-house polymerase chain reaction (RT-

PCR) assays were used for the identification of influenza and other respiratory virus infections.

A second RT-PCR assay by Ellis et al., (102) was used to further type the pandemic influenza

A(H1N1)pdm09 virus.

The majority, but not all samples were tested for all respiratory viruses as tests were conducted

according to the physicians’ request. All samples that were received at the MMPL and tested

for respiratory virus infections during the study period were eligible for inclusion. Samples that

did not have test results because of failure of PCR or insufficient sample, or had no outcome

[i.e. hospitalization to the general ward (GW) or intensive care unit (ICU) or died], were

excluded.


Descriptive statistics were calculated to explore the distribution of respiratory virus single, dual

and multiple infections by age and patients gender. Pearson’s chi-square test was used on this

197

categorical data at significance level of p = 0.05. Multiple logistic models, controlling for age,

season, gender and an interaction factor between age and co-infection, were used to measure

the association between single, dual, and multiple respiratory virus infections and risk of

admission to a general ward, admission to the ICU or death. First a univariate logistic model

was calculated and then a second covariate was added to the model up to the last and the best

model chosen. The significance of each covariate was assessed using the likelihood-ratio test.

Association was measured using odds ratios (OR) and 95% confidence intervals (CI) at

significance of p = 0.05. All analyses were conducted using STATA software version 11

(STATACorp, College Station, TX, USA).

4.1.3.4. Ethical and research and development approval

Ethical approval for this study was granted by the Greater Manchester NHS Ethics Committee

(Ref: 11/NW⁄0698) and the University of Manchester Research Ethics Office. Research and

Development approval was obtained from the Central Manchester Universities Hospitals NHS

Foundation Trust (Ref: R01835).

4.1.4. Results

4.1.4.1. Respiratory virus infections

During the 6 years of the study, 30,975 samples were tested and 11,715 (37.8%) were positive

for one or more respiratory virus of which, dual or multiple infections occurred in 10.4%

(1,214/11,715), and single infections occurred in 89.6% (10,501/11,715). Among dual

infections, the most common combination was RV/RSV; 30.2% (367) of all co-infections,

followed by RV/AdV 13.9% (169), RSV/AdV 7.2% (88), RV/hPIV3 5.2% (63), RV/hMPV

5.0% (61), AdV/hMPV 2.1% (25), RSV/hMPV 1.5% (18), AdV/hPIV3 1.4% (17) and

RV/hPIV1 1.3% (16) – (Table 4.1). Among triple and multiple infections, rhinoviruses and

respiratory syncytial viruses predominated with the RV/RSV/AdV triple infection comprising

32 (2.7%) of all co-infections, followed by a triple infection involving the three human

parainfluenza viruses hPIV1/hPIV2/hPIV3 16 (1.3%) and multiple infections of

RV/hPIV1/hPIV2/hPIV3 and RSV/hPIV1/hPIV2/hPIV3 - 13 (1.1%). In total rhinovirus was

involved in 844 of the 1,214 co-infections (69.5%) making them the most common co-infecting

virus. The pattern of co-infections between the pandemic influenza A(H1N1)pdm09 and

seasonal influenza A viruses and other respiratory viruses was presented elsewhere, Goka et al.,

(338).

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4.1.4.2. Demographic and other factors associated with single or multiple infections

Age and Sex: Of the 11,715 patients that were positive for one or more virus, the majority

51.2% (6,065) were children ≤5 years old (mean age for all positive cases = 16.5 (SD: ±21.4)

years, range 0 – 99; median 3.0 (25 – 75 percentiles 0.42 - 28.0 years), and ≤5 year old children

had also the highest positivity rate (54.6%) – supplementary Table S1. Comparatively, the

proportion of ≤ 5 years olds among PCR negative and excluded cases were 26.6%

(4,579/17,248) and 23.3% (468/2,012) respectively, and these differences were statistically

significant (�2 = 1900, p = <0.0001 for positive and negative and �2 = 559.6, p = <0.0001 for

positive and excluded cases). The distribution of single and multiple infections by age and

gender is given in Table 4.2 (and Table 4.1S1). Overall children had a higher risk of having a

mixed infection, with 83.5% (1,014/1,214) of mixed infections occurring in under-five year old

children. Similar proportions were observed for specific respiratory virus infections. However,

apart from RV, Flu B and hMPV, the attack rates for the proportion of single and multiple

respiratory viruses did not significantly differ by sex.

Seasonality: There was a four-fold increase in the number of samples received at the MMPL

during the first and third waves of the 2009 influenza pandemic compared to the other periods

of the study (Supplementary Table 4.1S2A&B). Specifically 25.9% (8,032/30,975) of the

samples were tested between April – October 2009, and 25.3% (7,849/30,975) between

November 2010 – March 2011 compared to range of 474 and 2,196 in pre, inter and post-

pandemic periods. Correspondingly, the majority of the respiratory viruses 20.3%

(2,831/11,715) and 32.1% (3,756/11,715) were identified during these periods (Supplementary

Table 4.1S2A&B). The type of virus that predominated each period also differed; influenza

viruses were predominant during the 1st and 3rd wave (44.7% and 36.0% of all viruses

identified in the 2 periods respectively), whereas RV and RSV predominated in the pre, inter

and post-pandemic periods (RV mainly in summer and RSV mainly in winter) – Table

4.1S2A&B. However overall RV was the most predominant virus comprising 31.9%

(3,740/11,715) of all viruses identified between January 2007 and June 2012 followed by the

pandemic influenza A(H1N1) 24.6% (2,789/11,715) and RSV 24.2% (2,832/11,715).

4.1.4.3. Single and multiple infections and risk of hospitalization to a general ward, or

admission to an intensive care unit (ICU) and death

Of the 10,501 patients with single respiratory virus infections, 1,738 (16.6%) were seen as

outpatients, 8,009 (76.3%) were admitted to a general ward, 530 (5.1%) were admitted to the

ICU, and 224 (2.1%) died. Out of the 1,214 patients who had co-infections, 147 (12.2%) were

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seen as outpatients, 992 (81.7%) were admitted to a general ward, 57 (4.7%) were admitted to

the ICU, and 18 (1.5%) died. In stratified analysis multiple infection in children ≤5 years old

was associated with an increased risk of admission to a general ward (OR: 1.32, 95% CI: 1.10 –

1.63, p = 0.01). In a multiple logistic model controlling for age and season, the risk was slightly

higher (OR: 1.43, 95% CI: 1.20 – 1.71, p = <0.0001) – Table 4.3.

We wanted to know whether the disease outcome in specific single respiratory virus infections

was less, or more severe, compared to other single respiratory virus infections. In this analysis,

stratified analysis showed that seasonal influenza A virus, and influenza B virus increased risk

of admission to a general ward among young children and adults >5 years old (OR: 2.37, 95%

CI: 1.85 – 3.04, p = <0.0001 for seasonal Flu A and OR: 1.31, 95% CI: 1.01 – 1.70, p = 0.04),

whereas RSV increased risk of admission to a general ward in children ≤5 years old (OR: 1.18,

95% CI: 1.03 – 1.40, p = 0.05). In a multiple logistic model, controlling for age and season,

RSV and hPIV1-3 single infections were associated with significant increase in risk of

admission to a general ward compared to other single respiratory virus infections (OR: 1.49,

95% CI: 1.28 – 1.73, p = <0.0001 and OR: 1.34, 95% CI: 1.003 – 1.80, p = 0.05).

Regarding risk of admission to the ICU/death, in general, multiple infection was associated

with an increase in the risk of admission to the ICU (OR: 1.15, 95% CI: 0.86 – 1.55), compared

to single infection but this was not statistically significant (p = 0.34). For specific viruses, RSV

and hPIV3 single infections were associated with an increased risk of admission to an

ICU/dying than single other respiratory virus infections (OR: 1.51, 95% CI: 1.20 – 2.0, p =

0.001 and OR: 1.60, 95% CI: 1.02 – 2.40, p = 0.04) for RSV and hPIV1-3 respectively – Table

4.4. Stratified analysis indicated that the risk of admission to the ICU associated with RSV and

hPIV were only significant among patients aged >5 years old (OR: 2.42, 95% CI: 1.36 – 4.33, p

= 0.003 for RSV and OR: 1.92, 95% CI: 1.10 – 3.74, p = 0.05 for PIV1-3).

In the multivariate logistic regression, separate models controlling for age group, season, sex,

and an interaction factor of age and co-infections were employed. After assessment using the

likelihood-ratio test, it was observed that models that included both age and season were the

best, probably because the distribution of single and multiple virus infections differed

statistically by age and season. For example, the result of likelihood test for a logistic model

measuring association between single rhinovirus infection and risk of admission to a general

ward; one without covariates, one with age group, and another with age group and season

included were: �2 = 3.74, P = 0.05 and �2 = 7.7, p = 0.02, indicating that both age and season

were significant variables.

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4.1.5. Discussion and Conclusion

In general, patients with dual or mixed respiratory virus infections had an increased risk of

being admitted to a general ward compared to those with single infections. Patients with RSV

and hPIV1-3 single infections had a higher risk of being admitted to a general ward or to the

ICU or dying. The finding that co-infections increase disease severity is in agreement with

several other studies (75-77) that have reported an association between co-infections and

disease severity. Drews et al., (75) showed that co-infection increased risk of admission to a

general ward 3-fold, similarly Richard et al., (76) and Do et al., (517) also found that co-

infection increased risk of admission to ICU 3-fold (OR: 2.7, 95% CI: 1.2 -6.2 and OR: 3.0,

95% CI: 1.6 – 5.6). Whereas the latter finding is in agreement with other studies

(3;89;223;227;228;602-605) that observed severe disease in single respiratory virus infections.

The majority of single and multiple respiratory virus infections were in children < 5 years old

(Table 4.2). Also, in general, multiple respiratory virus infections caused more severe disease

in children ≤5 years old whereas for single virus infections: RSV accounted for severe disease

in both ≤5 years children (GW) and patients >5 years old (ICU/death); seasonal influenza A

virus (GW), influenza B (GW), and hPIV1-3 (GW) had higher impact on patients aged >5 year

old.

In this study, the pandemic influenza A(H1N1) was the predominant virus during the 1st and

3rd waves of the 2009 influenza pandemic, whereas RV and RSV were the main viruses in

summer and winter periods of the pre-pandemic and post-pandemic periods (Supplementary

Table S2). However, considering the entire study period RV was the most common virus

(31.9%; 3,740/11715), followed by the pandemic influenza A(H1N1)pdm09 virus and RSV. A

study by the European Paediatric Influenza Analysis (EPIA) project (230) on respiratory virus

activity between 2002 and 2008 in England, reported influenza A and RSV as the predominant

viruses. Elsewhere, epidemiological studies from across the globe (222;223;227;228;241;606)

have identified RSV as the most common virus among patients visiting outpatient clinics with

influenza like illnesses or hospitalized with acute respiratory tract infection or from the

community, followed by rhinovirus, and influenza A viruses.

RV, RSV and AdV caused the highest co-infection burden [occurring in 69.5% (844/1,214),

52.2% (634/1,214) and 32.5% (394/1,214) of all co-infections respectively]. Among others, RV

was detected in 367 co-infections with RSV and 169 with AdV (Table 4.1A). Our findings

agree with (89;94;602) but differ with (87;514). The high RV and RSV co-infections could

because they circulated all year round (RV mainly in summer, but a good number were also

identified during winter and vice versa for RSV - Table 4.1B). Further, hPIV1-3 were the

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fourth most important co-infecting virus, involved in 17.6% (213/1,214) of co-infections and

most of the mixed infections. This cannot be explained by seasonal variations as hPIV1-3

circulated predominantly in spring and summer. Monto and Sullivan (204) and Macorne et al.,

(514) also found hPIV1-3 predominating in spring and summer whereas Bicer at al., (89)

reported that hPIVs circulate all year round. More research is needed to understand the

biomedical processes that favour these co-infections.

A shortfall of this study is that we did not control for other covariates known to affect disease

outcome; including patient’s immune status and co-morbidities, and genetic mutations

occurring in influenza A viruses and this should be born in mind when interpreting our results.

This study included both children and adults, however children ≤5 years old differ considerably

with adults in their exposure to infections, immunology and anatomy. The odds ratios among

different age strata were different amongst themselves and also differed with the crude odds

ratios which suggests effect modification by age, however we did not have serological data to

ascertain the patient’s immune status. In addition, the adjusted odds ratios differed with crude

odds ratios by more than 10% suggesting age was also a confounder. We therefore reported

both stratum specific and adjusted odds ratios. Further, samples were not tested for human

bocavirus and coronaviruses which are known to co-infect with other respiratory viruses

(87;88).

It is well understood that genetic mutations may affect the severity of respiratory viruses. For

example the severity of influenza viruses may be influenced by genetic mutations in the

haemagglutinin (HA), neuraminidase (NA), non-structural protein 1 (NS1), and polymerase

basic 1 gene (PB1) (18;342;401;415;436;438;461;464). Nevertheless, a UK study that

investigated the association between mutations in the pandemic influenza A(H1N1)pdm09 and

disease outcome (607) observed that similar mutations (39/40) occurred in fatal and none-fatal

cases suggesting that no significant virulence related mutations occurred in influenza

A(H1N1)pdm09 during the study period. It is thus unlikely that the severity observed in this

study was due to virus mutations in the influenza A viruses. However, as mutations on other

respiratory viruses; RSV, RV, AdV, hMPV, and hPIV1-3 are largely not studied, the

importance of these on virulence should be born in mind when interpreting the results of this

study.

In summary, in this study, co-infection was associated with an increased risk of admission to a

general ward, whereas single RSV or hPIV1-3 infections were associated with an increased risk

of admission to a general ward, the ICU or death. Evidence on the severity of disease by

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laboratory confirmed respiratory virus infections is often lacking, the findings of this study

offer a better understanding of the importance of respiratory viruses and help define a place for

vaccine and drug development especially vaccines and drugs which may target several of these

viruses. It also elucidates the importance of research on development of multi-target diagnostic

tests.



Manchester Academic Health Science Centre, and the Central Manchester University Hospitals

NHS Foundation Trust and staff for their support in this research.


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Table 4.1A: Patterns of single and multiple infections between respiratory viruses

Virus

n %

Single infections Flu Apdm09 2,742 23.4 SeasFlu A 843 7.2 Flu B 602 5.1 RV 2,896 24.7 RSV 2,198 18.8 AdV 366 3.1 hMPV 432 3.7 hPIV1 97 0.8 hPIV2 44 0.4 hPIV3 281 2.4 Dual infections Flu B+RV 8 0.1 Flu B+RSV 15 0.1 Flu B+AdV 7 0.1 Flu B+hMPV 5 0.1 Flu B+hPIV3 1 0.01 RV+RSV 367 3.1 RV+AdV 169 1.4 RV+hMPV 61 0.5 RV+hPIV1 16 0.1 RV+hPIV2 13 0.1 RV+hPIV3 63 0.5 RSV+AdV 88 0.8 RSV+hMPV 18 0.2 RSV+hPIV1 4 0.03 RSV+hPIV2 7 0.1 RSV+hPIV3 4 0.03 AdV+MPV 25 0.2 AdV+hPIV1 3 0.03 AdV+hPIV3 17 0.2 hPMV+hPIV3 6 0.05 hPIV1+hPIV2 1 0.01 hPVI1+hPIV3 1 0.01 hPIV2+hPIV3 1 0.01 Triple infections Flu B+RV+AdV 3 0.03 RV+RSV+AdV 32 0.3 RV+RSV+hMPV 6 0.05 RV+RSV+hPIV1 1 0.01 RV+RSV+hPIV2 1 0.01 RV+RSV+hPIV3 3 0.03 RV+AdV+hMPV 6 0.05 RV+AdV+hPIV1 1 0.01 RV+AdV+hPIV2 1 0.01 RV+AdV+hPIV3 9 0.1 RV+hMPV+hPIV3 1 0.01 RV+hPIV1+hPIV3 1 0.01 RSV+hMPV+hPIV3 1 0.01 AdV+hMPV+hPIV2 1 0.01 AdV+PIV1+hPIV2 1 0.01 hPIV1+PIV2+hPIV3 16 0.1 Multiple infections RSV+hPIV1+hPIV2+hPIV3 13 0.1 RV+hPIV1+hPIV2+hPIV3 13 0.1 AdV+hPIV1+hPIV2+hPIV3 4 0.03 hMPV+hPIV1+hPIV2+hPIV3 4 0.03

11,715

100.00

Notes: Flu Apdm09 - Pandemic influenza A(H1N1)pdm09, SeasFlu A - seasonal influenza A virus, Flu B -

influenza B virus, RSV - respiratory syncytial virus, RV - rhinovirus, AdV - adenovirus, hMPV - human

metapneumovirus, hPIV 1-3 - parainfluenza virus types 1 to 3. Patterns of co-infections between pandemic

influenza A(H1N1)pdm09 and seasonal influenza A viruses with other respiratory viruses have been

presented elsewhere (338).

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Table 4.1B: The likelihood and burden of co-infection for specific viruses

Virus type

Number of infections

Single

infections Co-infections

likelihood Burden of

co-infection

n (%) n (%) n (%) n (%)

Flu Apdm09 2,879 (24.6) 2,742 (94.7) 137 (4.9) 137 (11.3)

SeasFlu A 902 (7.7) 843 (93.5) 59 (6.5) 59 (4.9)

Flu B 679 (5.8) 602 (89.1) 74 (11.0) 74 (6.1)

RSV 2,832 (24.2) 2,198 (77.6) 634 (22.4) 634 (52.2)

RV 3,740 (31.9) 2,896 (77.4) 844 (22.6) 844 (69.5)

AdV 760 (6.5) 366 (48.2) 394 (51.8) 394 (32.5)

hMPV 576 (4.9) 432 (75.0) 144 (25.0) 144 (11.9)

hPIV1-3 635 (5.4) 422 (66.5) 213 (33.5) 213 (17.6)

Total 11,715 (100) 10,501 (89.6) 1,214 (10.4) 1,214 (100)

Notes: Flu A pdm09 – pandemic influenza A(H1N1)pdm09 virus, SeasFlu A – seasonal influenza A viruses,

RSV - respiratory syncytial virus, RV - rhinovirus, hMPV – human metapnuemovirus, hCoV - human

coronavirus, Flu A/B - influenza A or B, AdV - adenovirus, hPIV1-3 parainfluenza virus types 1 to 3. %

number of infections is percentage of total number of infections (11,715), % single infections is percentage of

total number of specific infection e.g. for pandemic influenza A(H1N1)pdm09 - 97.4% = 2,742/2,895), %

likelihood of co-infections is percentage of total number of specific infection e.g. for pandemic influenza

A(H1N1)pdm09 – 4.7% = (137/2,895) and % burden of co-infections is percentage of total number of co-

infections (1.214). Considering likelihood of co-infections, AdV were more likely to occur as co-infection with

other respiratory viruses with 51.8% of all AdV occurring as co-infections. However, regarding the virus

which caused the highest burden of co-infections, then RV leads with 69.5% of all co-infections being caused

by RV.

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Table 4.2: Demographic and other characteristics of single and multiple respiratory

virus infections in NW England, 2007 – 2012

Single

Infections n (%)

Multiple Infections

n (%)

p

Age ≤5 years

Flu Apdm09 409 (14.9) 75 (54.7) <0.0001*

Seas Flu A 119 (14.1) 35 (59.3) <0.0001*

Flu B 133 (22.1) 40 (54.1) <0.0001*

RSV 2,013 (91.3) 580 (92.5) 0.34

RV 1,591 (54.9) 721 (85.4) <0.0001*

AdV 235 (64.8) 349 (88.6) <0.0001*

hMPV 291 (67.4) 119 (82.3) <0.0001*

hPIV1-3 265 (62.8) 174 (81.7) <0.0001*

Total 5,051 (48.1) 1,014 (83.5) <0.0001* Male Sex

Flu Apdm09 1,218 (46.4) 70 (51.1) 0.29

Seas Flu A 307 (52.4) 32 (56.1) 0.59

Flu B 262 (44.7) 43 (58.9) 0.02*

RSV 1,163 (53.7) 354 (57.2) 0.13

RV 1,494 (52.0) 501 (60.1) <0.0001*

AdV 193 (54.1) 235 (60.6) 0.07

hMPV 236 (54.4) 94 (65.3) 0.03*

hPIV1-3 241 (57.2) 132 (62.3) 0.23

Total 5,110 (50.9) 712 (59.3) <0.0001*

Notes: Distribution of single infections compared to multiple infections by age and sex. For each virus, the

total number of single or multiple infections were evaluated, i.e. how many single infections occurred in

children ≤5years and how many multiple infections occurred in this age group. For example, out of the 3740

RV infections, 2,896 were single infections and 844 were mixed infections. Out of the 2,896 single RV

infections, 1,591 (54.9%) were aged ≤5 years, and out of the 844 mixed infections and 721 (85.4%) were aged

≤5 years. The Pearson’s chi-square test (�2) was used to measure the differences in frequency of single and

multiple infections, for example in RV, the Chi square statistic is a comparison between the percentages of

children ≤ 5 years with single infections (54.9%) and with mixed infections (85.4%).* - the p value was

statistically significant at α = <0.05.

206

Table 4.3: Risk of hospitalization to a general ward in single and multiple respiratory virus infections

Virus/Co-infection

Hospitalized

Risk estimates

Total ≤5 yrs >5 yrs

≤5 yrs

>5 yrs

Adjusted

n (%) n (%) n (%)

OR (95% CI) p OR (95% CI) p OR (95% CI) p

Multiple Infections 992(87.1)* 844 (88.3)* 148 (80.9) 1.32 (1.10 - 1.63) 0.01* 1.09 (0.75 - 1.59) 0.64 1.43 (1.20 – 1.71) <0.0001*

Single infections 8,009 (82.2) 3,979 (85.1) 4,030 (79.4)

Single Flu Apdm09 1,910 (76.2)* 315 (82.6) 1,595 (75.1)* 0.79 (0.60 - 1.60) 0.10 0.64 (0.56 - 0.73) <0.0001* 0.62 (0.55 – 0.70) <0.0001*

Single other viruses 6,099 (84.2) 3664 (85.4) 2,435 (82.5)

Single SeasFlu A 726 (88.1) 93 (80.9) 633 (89.3)* 0.73 (0.46 - 1.17) 0.20 2.37 (1.85 - 3.04) <0.0001* 1.17 (0.93 – 1.47) 0.17

Single other viruses 7,283 (81.6) 3,886 (85.2) 3,397 (77.8)

Single Flu B 482 (83.4) 106 (84.1) 376 (83.2)* 0.92 (0.57 - 1.50) 0.75 1.31 (1.01 - 1.70) 0.04* 1.23 (0.97 – 1.56) 0.08


Single RSV 1,757 (86.2)* 1,613 (86.4)* 144 (84.7) 1.18 (1.0 - 1.40) 0.05* 1.45 (0.95 - 2.21) 0.09 1.49 (1.28 – 1.73) <0.0001*


Single RV 2,182 (81.6) 1,218 (84.2) 964 (78.6) 0.90 (0.76 - 1.07) 0.25 0.93 (0.80 - 1.10) 0.38 0.98 (0.87 – 1.10) 0.77

Single other viruses 5,827(82.4) 2,761 (85.5) 3,066 (79.7)

Single AdV 281 (82.7) 190 (86.8) 91 (75.2 ) 1.15 (0.77 - 1.72) 0.49 0.78 (0.51 - 1.18) 0.25 1 .03 (0.77 – 1.38) 0.83


Single hMPV 341 (84.4) 234 (85.1) 107 (83.0) 1.10 (0.71 - 1.40) 0.99 1.27 (0.80 - 2.80 0.32 1.20 (0.91 – 1.58) 0.2


Single hPIV1-3 330 (85.7)* 210 (86.4) 120. (84.5) 1.12 (0.77 - 1.63) 0.56 1.42 (0.90 - 2.26) 0.13 1.34 (1.003 – 1.80) 0.05*


Notes: In comparisons among single infections, the risk of admission to a general ward in the primary virus (e.g. RSV single infections) was compared with the risk in all other

respiratory virus single infections as a baseline. On the other hand, in crude analysis of risk in single vs. multiple infections, the risk in single infections was used as the baseline. *

- the p value was statistically significant at α = <0.05, Flu Apdm09 - Pandemic influenza A(H1N1)pdm09, SeasFlu A - seasonal influenza A viruses, Flu B - influenza B virus,

RSV - respiratory syncytial virus, RV - rhinovirus, AdV - adenovirus, hMPV - human metapneumovirus, hPIV1-3 - human parainfluenza virus types 1 to 3. Adjustment was for

age group and season.

207

Table 4.4: Risk of admission to ICU/Death in single and multiple respiratory virus infections

Virus/Co-infection

ICU/Dead

Risk estimates

Total ≤5 yrs >5 yrs

≤5 yrs

>5 yrs

Adjusted

n (%) n (%) n (%)

OR (95% CI) p OR (95% CI) p OR (95% CI) p

Multiple Infections 75 (33.8) 39 (43.3) 56 (26.9) 1.19 (0.77 -1.86) 0.43 0.94 (0.68 - 1.30) 0.71 1.15 (0.86 – 1.55) 0.34

Single infections 754 (30.3) 278 (39.0) 544 (28.2)

Single Flu Apdm09 236 (28.4) 26 (27.7) 210 (28.5) 0.68 (0.43 -1.10) 0.11 1.23 (1.0 - 1.55) 0.09 0.89 (0.72 – 1.10) 0.21

Single other viruses 518 (31.2) 351 (35.9) 167 (24.5)

Single SeasFlu A 19 (16.2)* 4 (15.4) 15 (16.5) 0.33 (0.11 - 0.96) 0.04* 0.53 (0.30 - 0.91) 0.03* 0.42 (0.26 – 0.72) 0.001*


Single Flu B 24 (20.0)* 7 (25.9) 17 (18.3) 0.64 (0.27 - 1.52) 0.31 0.60 (0.35 - 1.03) 0.06 0.59 (0.37 - 0.93) 0.02*


Single RSV 161 (36.5)* 139 (35.4) 22 (45.8)* 1.01 (0.78 - 1.32) 1.0 2.42 (1.36 - 4.33) 0.003 1.51 (1.20 – 2.0) 0.001*


Single RV 223 (31.2) 145 (38.9) 78 (22.9) 1.28 (0.99 - 1.66) 0.06 0.77 (0.58 -1.03) 0.08 1.04 (0.86 – 1.26) 0.68


Single AdV 26 (30.6) 18 (38.0) 8 (21.1) 1.15 (0.63 - 2.10) 0.65 0.73 (0.33 - 1.61) 0.44 1.02 (0.62 – 1.60) 1.0


Single hMPV 28 (30.0) 16 (28.1) 12 (35.3) 0.71 (0.39 - 1.29) 0.25 1.53 (0.75 - 3.11) 0.25 1.03 (0.64 – 1.60) 1.0


Single hPIV1-3 37 (40.2)* 22 (40.0) 15 (40.5)*

1.24 (0.71 - 2.16) 0.44 1.92 (1.10 - 3.74) 0.05* 1.60 (1.02 – 2.40) 0.04*


Notes: In comparisons among single infections, the risk of admission to ICU or death in the primary virus (e.g. RSV single infections) was compared with the

risk in all other respiratory virus single infections as a baseline. On the other hand, in crude analysis of risk in single vs. multiple infections, the risk in single

infections was used as the baseline. * - the p value was statistically significant at α = <0.05, Flu Apdm09 - Pandemic influenza A(H1N1)pdm09, SeasFlu A -

seasonal influenza A virus, Flu B - influenza B virus, RSV - respiratory syncytial virus, RV - rhinovirus, AdV - adenovirus, hMPV - human

metapneumovirus, hPIV1-3 - human parainfluenza virus types 1 to 3. Adjustment was for age group and season.

208


Table 4.1S1A: Respiratory viruses’ positivity rates by age group NW England, Jan 2007 – Jun 2012

Age

group

ILI

RVI

Flu A

pdm09

Seas Flu A

Flu B

RSV

RV

AdV

hMPV

hPIV1-3

n

(%)

n

(%)

n

(%)

n

(%)

n

(%)

n

(%)

n

(%)

n

(%)

n

(%)

≤5 11,112 6,065 (54.6) 484 (6.8) 154 (1.5) 173 (1.6) 2,593 (26.8) 2,312 (26.9) 586 (6.1) 410 (5.1) 439 (5.1)

5-14 1,824

753 (41.3) 334 (24.8) 62 (4.4) 90 (5.2) 42 (3.5) 174 (17.1) 49 (4.1) 13 (1.4) 34 (3.3)

15-24 4,129 1,349 (32.7) 519 (16.7) 138 (4.1) 121 (3.2) 29 (1.0) 499 (18.6) 25 (0.8) 28 (1.1) 25 (0.9)

25-39 5,065 1,322 (26.1) 617 (15.8) 165 (4.0) 130 (2.8) 34 (1.0) 294 (9.9) 47 (1.3) 44 (1.4) 28 (0.9)

40-64 5,381 1,390 (25.8) 696 (17.3) 92 (2.2) 89 (1.9) 63 (1.8) 336 (12.5) 36 (1.0) 55 (1.9) 77 (2.9)

65-84 1,962

353 (18.0) 121 (7.3) 28 (1.8) 50 (3.0) 37 (3.0) 84 (10.2) 9 (0.7) 19 (1.9) 22 (2.7)

≥85 1,502

483 (32.2) 108 (25.9) 263 (19.6) 26 (1.7) 34 (11.4) 41 (18.1) 8 (2.7) 7 (3.0) 10 (4.4)

30,975 11,715 (38.8) 2,879 13.3 902 (3.5) 679 (2.4) 2,832 (12.6) 3,740 (19.7) 760 (3.4) 576 (3.1) 635 (3.3)

Notes: Number and percentage of samples that were positive for a respiratory virus out of all samples tested for that particular virus. RVI – positive for any respiratory

virus infection, ILI – number of all patients that had samples tested for influenza like illness in that particular age group, Flu Apdm09 – pandemic influenza

A(H1N1)pdm09, Flu A seasonal influenza A viruses, RSV – respiratory syncytial virus, RV – rhinovirus, AdV – adenovirus, hMPV – human metapneumovirus,

hPIV1-3 – human parainfluenza virus types 1-3. Data similar to the one in this table was the one used to dray Figure 4.2. This table is given here for comparison

purposes as the data in the preceding paper were for 5 out of the 6 years (June 2007 – June 2011), whereas the data herein has one additional year (June 2011- June

2012) added to the same.

209

Table 4.1S1B: Age distribution of patients that were positive for any respiratory virus infection NW England Jan 2007 – Jun 2012

Age group

ALL_ILI (%) ILI (%) Flu Apdm09 Seas Flu A Flu B

RSV

RV

AdV

hMPV

hPIV1-3

≤5 11,112 (35.9)

6,065 (51.8) 484 (16.8) 154 (17.1) 173 (25.5) 2,593 (91.6) 2,312 (61.8) 586 (77.1) 410 (71.2) 439

(69.1)

5-14 1,824 (5.9) 753 (6.4) 334 (11.6) 62 (6.9) 90 (13.3) 42 (1.5) 174 (4.7) 49 (6.4) 13 (2.3) 34 (5.4)

15-24 4,129 (13.3)

1,349 (11.5) 519 (18.0) 138 (15.3) 121 (17.8) 29 (1.0) 499 (13.3) 25 (3.3) 28 (4.9) 25 (3.9)

25-39 5,065 (16.4)

1,322 (11.3) 617 (21.4) 165 (18.3) 130 (19.1) 34 (1.2) 294 (7.9) 47 (6.2) 44 (7.6) 28 (4.4)

40-64 5,381 (17.4)

1,390 (11.9) 696 (24.2) 92 (10.2) 89 (13.1) 63 (2.2) 336 (9.0) 36 (4.7) 55 (9.5) 77 (12.1)

65-84 1,962 (6.3) 353 (3.0) 121 (4.2) 28 (3.1) 50 (7.4) 37 (1.3) 84 (2.2) 9 (1.2) 19 (3.3) 22 (3.5)

>85 1,502 (4.8) 483 (4.1) 108 (3.8) 263 (29.2) 26 (3.8) 34 (1.2) 41 (1.1) 8 (1.1) 7 (1.2) 10 (1.6)

Total 30,975 (100)

11,715 (100) 2,879 (100) 902 (100) 679 (100) 2,832 (100) 3,740 (100) 760 (100) 576 (100) 635 (100)

Notes: Flu Apdm09 - Pandemic influenza A(H1N1)pdm09, SeasFlu A - seasonal influenza A virus, Flu B - influenza B virus, RSV - respiratory syncytial virus, RV -

rhinovirus, AdV - adenovirus, hMPV - human metapneumovirus, hPIV1-3 - human parainfluenza virus types 1 to 3

210

Table 4.1S2A: Number of respiratory virus infections identified in each season NW England 2007 - 2012

Jan-Oct Nov-Mar Apr-Oct Nov-Mar Apr-Oct Nov-Mar Apr-Oct Nov-Mar Apr-Oct Nov-Jun

2007

2007-2008

2008

2008-2009

2009

2009-10

2010

2010-2011

2011

2011-12

Total

Flu Apdm09

-

-

-

-

1,063

448

14

1,351

3

-

2,879

SeasFlu A

8

35

15

118

394

25

1

178

1

132

902

Flu B

3

34

22

20

10

9

11

569

1 -

679

RSV

72

481

120

468

86

748

52

791

11

3

2,832

RV

147

288

292

259

559

652

500

464

363

216

3,740

AdV

26

46

41

63

119

128

82

202

33

20

760

hMPV

16

70

25

64

21

118

26

158

32

46

576

hPIV1-3

31

77

60

63

129

49

115

43

58

10

635

RVIs

295

1,031

575

1,055

2,381

2,177

801

3,756

502

427

11,715

ILI

474

1,337

1,071

1,675

8,032

4,624

1,849

7,849

2,196

1,868

30,975

Notes: Some samples had more than one virus identified from them. Flu A pdm09 – pandemic influenza A(H1N1)pdm09 virus, SeasFlu A – seasonal influenza A

viruses, RSV - respiratory syncytial virus, RV - rhinovirus, hMPV – human metapnuemovirus, hCoV - human coronavirus, Flu A/B - influenza A or B, AdV -

adenovirus, hPIV1-3 parainfluenza virus types 1 to 3. The number of influenza like illnesses (ILI) was high in summer of 2009 (7,672) compared 5,819 in winter of

2010/2011, however more laboratory confirmed cases of respiratory virus infections (RVIs) were identified during the 2010/2011 winter season.

211

Table 4.1S2B: Proportion of each respiratory virus identified in each season NW England 2007 - 2012

Months & year Virus type

Jan-Oct

Oct-Nov

Mar-Oct

Nov-Mar

Apr-Oct

Nov-Mar

Apr-Oct

Nov-Mar

Apr-Oct

Nov-Jun

Total

No of viruses

2007

2007-2008

2008

2008-2009

2009

2009-2010

2010

2010-2011

2011

2011-2012

n

%

Flu Apdm09 0.00 0.00 0.00 0.00 44.65 20.58 1.75 35.97 0.60 0.00 2,879 24.58

Seas Flu A 2.71 3.39 2.61 11.18 16.55 1.15 0.12 4.74 0.20 30.91 902 7.70

Flu B 1.02 3.30 3.83 1.90 0.42 0.41 1.37 15.15 0.20 0.00 679 5.77

RSV 24.41 46.65 20.87 44.36 3.61 34.36 6.49 21.06 2.19 0.70 2,832 24.17

RV 49.83 27.93 50.78 24.55 23.48 29.95 62.42 12.35 72.31 50.59 3,740 31.92

AdV 8.81 4.46 7.13 5.97 5.00 5.88 10.24 5.38 6.57 4.68 760 6.49

hMPV 5.42 6.79 4.35 6.07 0.88 5.42 3.25 4.21 6.37 10.77 576 4.92

hPIV1-3 10.51 7.47 10.43 5.97 5.42 2.25 14.36 1.14 11.55 2.34 635 5.42

No. Positive 295 1031 575 1055 2381 2177 801 3756 502 427 11,715 100.00

No. of Samples 474 1,337 1,071 1,675 8,032 4,624 1,849 7,849 2,196 1,868 30,975

Positivity (%) 62.24 77.11 53.69 62.99 29.64 47.08 43.32 47.85 22.86 22.86 37.82

Notes: Some of the samples had more than one virus identified from them. Not all samples were tested for all viruses, positivity is overall positive rate. Flu

Apdm09 - Pandemic influenza A(H1N1)pdm09, SeasFlu A - seasonal influenza A virus, Flu B - influenza B virus, RSV - respiratory syncytial virus, RV -

rhinovirus, AdV - adenovirus, hMPV - human metapneumovirus, hPIV1-3 - human parainfluenza virus types 1 to 3

212

Table 4.1S3: Number of hospitalisations and deaths by virus type

Flu Apdm09 SeasFlu A FluB RSV RV AdV hMPV hPIV Single Multiple RVI Total

Age group Hosp died Hosp died Hosp died Hosp died Hosp died Hosp died Hosp died Hosp died Hosp died Hosp died Hosp died Hosp died

≤5 390 17 126 2 139 4 2102 21 1816 9 478 6 329 2 363 3 3979 41 844 11

5206

52 8883 124

5-14 216 5 44 0 63 2 30 0 109 3 33 0 7 0 21 0 449 8 30 1

535 9 1263 28

15-24 349 25 117 2 96 2 22 0 393 2 18 0 22 0 21 0 982 27 25 2

1022 29 2838 98

25-39 445 37 140 2 103 5 22 1 213 4 35 1 37 0 20 1 946 47 25 2

1003 49 3362 171

40-64 527 48 79 2 76 4 48 6 244 10 29 2 44 1 67 2 978 73 43 1

1098 74 3954 254

>65 179 19 282 1 59 1 59 3 99 3 13 0 20 0 20 3 675 28 25 1

724 23 2766 180

Total 2106 151* 788 9 536 18 2283 31* 2874 31 606 9 459 3 512 9* 8009 224 992 18*

9588 242* 23066 855

Risk of Hospitalization 0.732 0.87 0.79 0.81 0.77 0.80 0.80 0.81 0.76 0.82 0.82 0.74

Risk of death

0.05

0.01

0.03

0.01

0.01

0.01

0.01

0.01

0.02

0.01

0.02

0.03

Total # infections 2879

902

679

2832

3740

760

576

635 10501

1214

11715

30975

Notes: Flu A pdm09 – pandemic influenza A(H1N1)pdm09 virus, SeasFlu A – seasonal influenza A viruses, RSV - respiratory syncytial virus, RV - rhinovirus, hMPV – human

metapnuemovirus, hCoV - human coronavirus, Flu A/B - influenza A or B, AdV - adenovirus, hPIV1-3 parainfluenza virus types 1 to 3. * Means the event was statistically

significant

213

4.2. Co-infections and risk of hospitalization and mortality Part B: Influenza A

viruses dual and multiple infections with other respiratory viruses and risk

of hospitalization and mortality

Synopsis

This is the authors’ version of the paper published in Influenza and other Respiratory

viruses. Reprint of Goka et al., (338). doi: 10.1111/irv.12020. Epub 19 October 2012.

Reproduced with permission from John Wiley & Sons Ltd, Copyright Licence number

3273250460066. This paper discusses the co-infection pattern between influenza A viruses and

other respiratory viruses and how that affected disease outcome. Similar to the preceding paper,

the data here were for samples, tested at the Manchester Microbiology Partnership Laboratory

(MMPL) between January 2007 and June 2012.

214

4.2.1. Abstract

Influenza A viruses dual and multiple infections with other respiratory

viruses and risk of hospitalization and mortality

Goka E.A1, Vallely P.J 1, Mutton K.J 1, 2, Klapper P.E 1, 2

1: Institute of Inflammation and Repair, University of Manchester,

2: Clinical Virology, Central Manchester University Hospitals - NHS Foundation Trust

Correspondence author: E. A. Goka: Institute of Inflammation and Repair, Faculty of Medical and Human Sciences,

1st Floor Stopford building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. Email:

[email protected]

Introduction: Recent literature suggests that dual or multiple virus infections may affect disease

severity. However, few studies have investigated the effect of co-infection in influenza A

viruses.

Objectives: To identify the association between influenza A viruses and respiratory viruses co-

infections with disease outcome.

Methodology: Electronic data of results of samples from North West England tested between

January 2007 and June 2011 was analysed for patterns of co-infection between influenza A

viruses and 8 respiratory viruses. Risk of hospitalization to ICU or GW in single vs. co-

infections was assessed using logistic regression.

Results: Of the 25,596 samples analysed for respiratory viruses 40.7% (10,501) were positive

for any virus. Co-infections were detected in 4.7% (137/2,879) of all patients with influenza

A(H1N1)pdm09, and 7.3% (57/779) of those with other influenza A viruses infections. Co-

infection between seasonal influenza viruses and influenza B virus was associated with a

significant increase in the risk of admission to ICU/death (OR: 22.0, 95% CI: 2.21 – 219.8, p =

0.008). RSV/Flu A co-infection also increased this risk but was not statistically significant. For

influenza A(H1N1)pdm09, RSV and AdV co-infection increased risk of hospitalization to

general ward whereas Flu B increased risk of admission to ICU, but all of these were not

statistically significant.

Conclusion: Co-infection is a significant predictor of disease outcome; integration of respiratory

viruses’ co-infections into public health reports could also contribute to the building up of

evidence. Research on combined treatment, introduction of an integrated vaccine for all

respiratory viruses and development of multi-target rapid diagnostic tests is recommended.

Key words: Co-infection, dual or multiple infections, influenza A viruses, respiratory virus

infections, respiratory syncytial virus, rhinovirus.

215

4.2.2. Introduction

Influenza virus and other acute respiratory tract infections (ARIs) cause considerable mortality

and morbidity worldwide with each winter season having between 10% and 20% of populations

suffering from influenza (1) and at least 2.2 million deaths occurring from ARI throughout the

world (3). In the United States of America (USA), influenza is responsible for 20-40 million

outpatient visits, 330,000 hospitalizations and 30,000 deaths annually (8). Whereas a UK study

study estimated that influenza causes between 779,000 and 1,164,000 general practice

consultations, 19,000 and 31,200 hospital admissions and 18,500 and 24,800 deaths per annum

(7).

Although respiratory viral infections have traditionally been thought to be caused by single

viruses, an increasing number of reports have reported respiratory viruses occurring as dual or

multiple virus infections (75-77;79-82;86;513;541). There are suggestions that respiratory viral

co-infections affect disease severity with some studies suggesting that dual and multiple

infections increase severity of respiratory disease (75-77;86;513), while others have found either

no association (560;578;593;608;609), or that dual or multiple infection may actually be

protective (82). Influenza A viruses cause varying disease severity in different persons. For

example, the pandemic influenza A(H1N1)pdm09 virus caused widely differing outcomes

ranging from mild respiratory illness to severe disease or death in some cases (278;610). Thus

whilst there is currently some conflicting data on the subject, most studies have been in infants

less than 1 year old where the infection pattern is different from adults (due to pre-existing

passive immunity in adults) and very few studies have investigated the effect of co-infection in

influenza A viruses on disease outcome. This study aimed to identify the association between the

pandemic influenza A(H1N1)pdm09 and seasonal influenza A viruses, dual or multiple

infections with other respiratory viruses and severity of influenza disease.

4.2.3. Methodology

4.2.3.1. Study Design and setting

Electronic data on samples that were sent to the Manchester Microbiology Partnership

Laboratory (MMPL) in the North West of England, for viral detection, between 1st January,

2007 and 23rd June, 2011, was interrogated to determine the association between a positive

diagnosis of influenza A(H1N1)pdm09 or other influenza A viruses, and influenza B virus (Flu

B) or other respiratory viruses including respiratory syncytial virus (RSV), rhinoviruses (RV),

adenoviruses (AdV) and parainfluenza viruses 1-3 (hPIV1-3), and severity of influenza disease.

The MMPL is a reference virology laboratory for the North West region of England receiving

216

respiratory viral samples from all hospitals, medical centres and surgeries located in the region

and caters for a population of 6.9 million people (101).

The database records only results for samples from patients whose reason for admission or

medical consultation was due to respiratory virus infection. Apart from indicating types of all

tests that were requested and results of testing, the database also contains information on

patients’ age (in years), date sample was collected and date received, medical facility which

submitted the sample or the secondary locations and the type of sample that was submitted. A

one step reverse transcriptase real-time polymerase chain reaction (RT-PCR) protocol was used

in the identification of respiratory viruses. Nucleic acid was extracted from samples using the

Qiagen total nucleic acid extraction kit run on the Qiagen Biorobot MDX (Qiagen, Crawley,

United Kingdom). Reverse transcription was accomplished using the Invitrogen Superscipt III

platinum one step RT-PCR Kit (Invitrogen, Paisley, United Kingdom). Influenza A and B

viruses were tested using a duplex assay and additionally for influenza A (H1N1) using the

Health Protection Agency (HPA (H1)v) assay (102). Duplex assays of well characterised ‘in-

house’ RT-PCR assays (unpublished) were used for the identification of respiratory syncytial

virus, metapneumoviruses, adenoviruses, rhinoviruses and triplex assays for parainfluenza

viruses 1-3. All the PCRs were run on the ABI7500 real-time PCR instrument (Applied

Biosystems, Warrington, United Kingdom). Positive amplification was determined using

amplicon specific probes labelled with MGB or TAMRA (Applied Biosystems). Samples that

were submitted by attending physicians for respiratory virus identification included: nose and

throat swabs (VNT), throat swabs (VTS), nasopharyngeal aspirates (VNPA), and other types of

swabs taken from the mouth and nose area (VSW).

All patients with a positive polymerase chain reaction (PCR) test for influenza A(H1N1)pdm09

virus or seasonal influenza A viruses who were hospitalized, seen as outpatients, or who died

within the study period were eligible for inclusion. All entries where outcome datum was

missing i.e. either because PCR test was not performed, or where there was insufficient sample,

were excluded from the analysis. In addition, for the calculation of measures of association,

entries that did not have information as to whether the patient was seen as an outpatient,

admitted to general ward, or the ICU were omitted.

217


Descriptive statistics were computed to describe co-infection patterns among different age and

sex and differences calculated using Pearson’s Chi square (�2) statistic. The measure of

outcome was the risk of hospitalization to a general ward or the ICU or death and the exposure

of interest was whether or not the patient had dual or multiple respiratory viral infections.

Association between influenza A viruses and respiratory viruses co-infection and risk of

hospitalization and admission to ICU or death were assessed using simple logistic regression

models. Further, multivariate logistic regression models, including age and interaction term as

covariates, were employed and the significance of the covariate assessed using the likelihood

ratio test. For each of the co-infections with respiratory viruses, separate multivariate logistic

models, controlling for age and an interaction term between age and co-infections, were applied

to assess the risk of hospitalization using other respiratory virus single infections as the baseline.

Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs) with significance

level of p = 0.05. All analyses were done using the STATA software, version 11.0.

(STATACorp, Texas 77845, USA).

The study received ethical approval (National Health Service - Research Ethics Committee

(NHS-REC), reference number 11/NW/0698) and the University of Manchester Research Ethics

Office. In addition, the Central Manchester Universities Hospitals NHS Foundation Trust also

granted Research and Development approval for this project (reference number R01835).

4.2.4. Results

4.2.4.1. Identification of influenza and respiratory viral infections Jan 2007 – June 2011

Of the 27,575 samples that were received at the MMPL between the 10th of January 2007 and

23rd of June 2011, a large proportion 25,596 (92.8%) had complete information on the RT-PCR

results on respiratory viral infections and were included in the analysis, 1,979 (7.2%) had this

information missing and were excluded. Of the 25,596 samples included in this study, 40.7%

(10,501) were positive for any of the respiratory virus of which 14.4% (3,658/25,489) were

positive in a generic influenza A PCR with 2,879 typed as influenza A(H1N1)pdm09 virus and

779 as seasonal influenza A viruses. The positivity rates for the other respiratory infections

were: influenza B 2.7% (679/25,043), RV 20.9% (3,262/15,635), RSV 14.8% (2,825/19,067),

AdV 3.9% (738/18,890), hMPV 3.4% (530/15,586), hPIV1 - 1.1% (175/15,662), hPIV2 – V

0.8% (119/15,660) and hPIV3 - 2.8% (436/15,660) – Figure 4.1.

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4.2.4.2. Characteristics of included and excluded patients

There was a significant difference in age between included and excluded patients such that

young patients were more likely to be included than adults. For example, 35.0% (8,959/25,595)

of included patients were infants < 1 year old vs. a proportion of 19.7% (389/1,979) among

excluded patients. The age differences for included and excluded patients for the other age

groups were 6.5% vs. 4.0 in 1-5 year olds, 8.3% vs. 5.2% in 5-17 year olds, 7.8% vs. 8.9% in 17-

25 year olds, 12.6% vs. 15.7% in 25-40 year olds, 17.6% vs. 29.1% in 40-65 year olds, 6.5% vs.

15.0% in 65 – 85 year olds and 5.6% vs. 2.5% among those >85 years old and these differences

were statistically significant (p <0.0001). However there were no significant gender differences

between the 2 groups (% female included: excluded =48.7% vs. 50.0%, p = 0.285). The

difference in number of samples that were tested for different viruses’ e.g. 25,489 for all

influenza A viruses and 15,660 for hPIV 2 and 3 were because, as a clinical procedure and in

compliance with ethical issues, only tests requested by the physicians who submitted the samples

were conducted.

4.2.4.3. Respiratory viruses’ positivity rates and subjects demographics

The positivity of influenza A and B virus infection was significantly lower in infants <1 year of

age but peaked in 5 – 17 year olds and in people aged 17 to 65 years: (positivity proportions

4.7% vs. 17.4% – 23.7% for influenza A virus infection and 1.0% vs. 4.7% - 5.6% for influenza

B virus infection respectively). It was lower among those aged 65 to 85 years, presumably

because of immunity derived from previous infections with strains antigenically related to the

influenza strains that circulated between 2009 and 2011, but increased in those older than 85

years. On the other hand, other respiratory viruses, they were predominantly more in children <

5 years old – Figure 4.2. Consequently, the majority of study participants who were positive for

RV, RSV, AdV, hMPV, hPIV 1-3 were aged ≤5 years whereas participants who were positive

for influenza A, and B, were mainly adults, and these differences were statistically significant p

= < 0.0001 (Table 4.5).

219

Figure 4.1: Schematic diagram of tests conducted, results, included and excluded

patients. Reprint of Figure 1. Notes: All Flu A = All influenza A viruses i.e. both pandemic influenza

A(H1N1)pdm09 and seasonal influenza A viruses, RV = rhinovirus, RSV = respiratory syncytial virus, AdV =

adenovirus, MPV = human metapneumovirus, hPIV1 – 3 = human parainfluenza virus types 1 to 3.

27, 575 Samples collected

from patients throughout NW

Positive

• 3,658 All Flu A

• 679 Flu B

• 3,262 RV

• 2,825 RSV

• 738 AdV

• 530 hMPV

• 175 PIV1

• 119 PIV2

• 436 PIV3

1,979 Samples excluded

• Insufficient

sample

• Not tested

Samples tested

• 25,489 All Flu A

• 25,043 Flu B

• 15,635 RV

• 19,067 RSV

• 18,890 AdV

• 15,586 hMPV

• 15,662 PIV1

• 15,660 PIV2

• 15,660 PIV3

Negative

• 21,831 All Flu A

• 24,364 Flu B

• 12,373 RV

• 16,242 RSV

• 18,152 AdV

• 15,056 hMPV

• 15,487 PIV1

• 15,541 PIV2

• 15,224 PIV3

2,879 Flu A(H1N1)pdm09

• 2,742 Single infections

• 137 Co-infections

779 Seasonal influenza viruses

• 722 Single infections

• 57 Co-infections

25,596 Samples

included

220

Figure 4.2: Respiratory viruses positivity rates by age group. Reprint of Figure 2. Notes: Age

groups were demarcated in such a way as to make the best depiction of the distribution of respiratory viruses

taking into account previously published literature indicating that identification of respiratory viruses is higher

among children < 1 year old and among school going children. People aged 25-40 were grouped together as they

are generally considered fit, those aged 40-65 starting to age, 65-85 aged and > 85 very old and very susceptible

to respiratory infections.

Regarding, positivity rates by gender, males were more likely to have a positive diagnosis of

other respiratory viruses than females, and apart from hPIV1 and hPIV3, these differences

were statistically significant (Flu A p= <0.0001, Flu B p = 0.007, RV p = 0.001, RSV p = 0.02

and AdV p = 0.003 and hMPV p = 0.004, PIV2 p = 0.04), however, such differences were not

observed in influenza A and B virus infections - (data not shown). The gender related

differences in positive rates for other respiratory viruses may relate to more men than women

consulting medical services, for example, 8,286 male samples were submitted for

identification of rhinovirus virus compared to 7,153 female samples, and the male/female

ratios for the other samples were: influenza B 12,145/11492, RSV 9,899/8,909, AdV

9,805/8,827, hMPV 8191/7,176, hPIV1 8,301/7167, hPIV2 8,299/ 7167 and hPIV3

8,299/7167, or that gender is a risk factor for respiratory virus infection. Because positivity

was biased towards males, it was not surprising that the majority of study subjects, positive

for any of the other respiratory viruses, were males – Table 4.5, and the gender differences

were also statistically significant.

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Table 4.5: Demographic characteristics of patients positive for any respiratory virus

Characteristics (n = 10, 501)

n % p

Age ≤5

All Flu A 639 17.5 <0.0001

Flu B 173 25.5 <0.0001

RV 2,307 70.7 <0.0001

RSV 2,593 91.8 <0.0001

AdV 586 79.4 <0.0001

hMPV 410 77.4 <0.0001

PIV1 120 68.6 <0.0001

PIV2 89 74.8 <0.0001

PIV3 312 71.6 <0.0001

Male Sex

All Flu A 1,561 47.6 <0.0001

Flu B 306 46.2 0.007

RV 1,819 56.3 0.001

RSV 1,517 54.7 0.021

AdV 409 56.8 0.03

hMPV 315 59.4 0.004

PIV1 104 59.8 0.104

PIV2 75 63 0.04

PIV3 251 57.7 0.086

Notes: �² = Pearson’s Chi-square test, significance was at α = 0.05, All flu A = influenza A(H1N1)pdm09 and

seasonal influenza A viruses, RV = rhinovirus, RSV = respiratory syncytial virus, AdV = adenovirus, hMPV =

human metapneumovirus, PIV1-3 = human parainfluenza types 1 to 3. Reprint of Table 1.

222

4.2.4.4. Respiratory viral infections yield by type of sample

Influenza viruses were mainly identified from nose and throat swabs (VNT), with half (50.3%)

of the Flu A viruses and 44.3% of Flu B viruses identified from VNT. The majority of

respiratory viruses, i.e. rhinoviruses (59.3%), RSV (74.9%), AdV (53.9%), hMPV (66.0%) and

hPIV 1-3 (47.4% - 72.3%), were identified from nasopharyngeal aspirates (VNPA). Swabs

where the site of swabbing was not given (VSW) and throat swabs (VTS) yielded almost equal

proportions of influenza and respiratory viruses. This trend is probably because more VNTs

were obtained from adults. The physiological changes of the nasopharynx (narrowing of the

orifice) and other morphological changes beginning at six (6) months and continuing through

adolescence make it painful to draw adequate nasopharyngeal aspirates in adults (611).

4.2.4.5. Seasonal distribution of respiratory viral infections

Influenza A viruses, rhinoviruses and adenoviruses showed no distinct seasonal patterns.

Specifically, there was little influenza A activity during the 2007/2008 summer season and

2008/2009 winter seasons. However, from April, 2009, a rise in the number of influenza A

viruses was noted coinciding with the WHO declaration of the influenza A(H1N1)pdm09

pandemic. There was little influenza activity between April and October 2010 but the virus

returned in the 2010/2011 winter season. Influenza B virus activity, however, was low during

the study period although the virus co-circulated with the influenza A(H1N1)pdm09 virus in

winter 2010/2011. Conversely, RSV and hMPV had a clear seasonal pattern, predominating

during winter and subsiding during summer; and this trend was the same throughout the study

period, whereas Hpiv1-3 were predominatly in spring and summer (Figure 4.3).

4.2.4.6. Flu A(H1N1)pdm09 and seasonal Flu A, pattern of co-infections and patients

demographics

Co-infection was identified in 4.7% (137/2,879) of patients who had influenza A(H1N1)pdm09

with rhinovirus as the most common, accounting for 39.4% (54/137) of all co-infections,

followed by respiratory syncytial virus (27.7%; 38/137), influenza B virus 11.7% (16/137),

adenovirus (10.2%; 14/137), human metapneumovirus (2.9%; 4/137), parainfluenza type 1

viruses (2.2%; 3/137) and parainfluenza type 3 viruses (0.7%; 1/137). Among patients who

were positive for seasonal influenza A viruses, co-infections occurred in 7.3% (57/779). In this

group, RSV was the most predominant virus, occurring in 36.8% (21/779) of all co-infections,

followed by influenza B viruses (28.1%; 16/779), rhinoviruses (12.3%; 7/779), adenoviruses

(8.8%; 5/779) and human metapneumovirus (7.0%; 4/779). Tables 4.6 and 4.7. Thus RSV co-

infection was the highest among seasonal influenza A viruses, whereas rhinovirus was more

frequent in influenza A(H1N1)pdm09 co-infections. The difference is likely because RSV

223

circulates only during winter seasons while rhinoviruses circulate throughout the year. Few

patients were infected by more than three respiratory viruses (Figure 4.4).

Figure 4.3: Seasonal distribution of respiratory viral infections Jan 2007 – Jun 2011. Reprint of Figure 3.

224

Figure 4.4: Co-infections patterns, influenza A viruses vs. other respiratory viruses

225

Co-infections were predominantly identified in children ≤5 years old. Thus 83.3% (45/54) of

patients with Flu A(H1N1)pdm09/RV co-infection 92.1% (35/38) Flu A(H1N1)pdm09/RSV,

100% (14/14), Flu A(H1N1)pdm09/AdV, 75%; (3/4) Flu A(H1N1)pdm09/hMPV and 68%

(11/16) Flu A(H1N1)pdm09/Flu B were under five years of age, and these differences were

statistically significant (p = 0.002 to p = <0.0001) – Table 4.6. A similar age distribution was

observed among patients who were diagnosed with seasonal influenza A virus co-infections -

Table 4.7. As for gender, apart from hMPV co-infection, there were no significant differences

in the proportion of males and females diagnosed with single versus dual or multiple

respiratory viral infections. The male to female ratios among different types of co-infections

were 57.4%/43.6% for Flu A(H1N1)pdm09/RV, 50%/50% for Flu A(H1N1)pdm09/RSV –

Table 4.6. Similar proportions were observed among seasonal influenza A viruses co-infections

- Table 4.7.

4.2.4.7. Risk of hospitalization, admission to ICU and death associated with influenza

A(H1N1)pdm09 co-infections

Seventy six point two percent (76.2%) of patients who had single influenza A(H1N1)pdm09

were hospitalized in a general ward. Comparatively, a higher proportion of patients with RSV

and AdV co-infection were hospitalized in a general ward (86.1%; 31/36 and 92.9%; 13/14

respectively), but the general ward hospitalization among patient with RV and influenza B co-

infection was lower than that in single influenza A(H1N1)pdm09 (75.0%; 9/12 and 73.5%;

36/49 respectively) – Table 4.6. The majority of co-infections were in children < 5 years old, so

when patients were stratified by age group, rates of admission to a general ward were still high

among patients co-infected with either Flu B, RV, RSV or AdV than those who had single Flu

A(H1N1)pdm09 infection (Supplimentary Table 4.2S1). On the other hand, only RSV and AdV

co-infection had higher hospitalization among patients aged five years of age or older – Table

4.6. However in a simple and multiple logistic regression, controlling for age and age*co-

infection interaction factor, these increases in risk were not statistically significant.

Regarding risk of admission to the ICU or death, 28.4% of patients with single influenza

A(H1N1)pdm09 infection were admitted to the ICU or died compared to rates of 57.1% among

those with influenza B co-infection and 27.8% and 28.6% among those with RV and RSV co-

infection respectively. Young age was again also associated with higher proportions of

hospitalization to ICU/death due to co-infection but again, in simple and multivariate logistic

regression models this increase in risk was not statistically significant.

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Table 4.6: Influenza A(H1N1)pdm09 co-infections, demographics and

risk of hospitalization, admission to ICU and death

Demographic & clinical characteristics

Flu A(H1N1) only

(n = 2,742)

A/H1N1 + Flu B

(n = 16)

A/H1N1 + RV

(n = 54)

A/H1N1 + RSV

(n = 38)

A/H1N1 + AdV

(n = 14)

Age ≤ 5 years - n (%) 383 (15.2) 11 (68.8) 45 (83.3)* 35 (92.1)* 14 (100)*

Sex Male - n (%) 1,406 (53.6) 5 (31.1) 31 (57.4) 19 (50.0) 8 (57.1)

Hospitalized

All - n (%) 1,910/2,506 (76.2) 9/12 (75.0) 36/49 (73.5) 31/36 (86.1) 13/14 (92.9)

≤5 years - n (%) 315 (82.3) 4 (100) 17 (94.4) 24 (85.7) 10 (90.9)

> 5 years - n (%) 1,595 (75.1) 5 (62.5) 19 (61.3) 7 (87.7) 3 (100)

ICU/Dead

All - n (%) 236/832 (28.4) 4/7 (57.1) 5/18 (27.8) 2/7 (28.6)

≤5 years - n (%) 26 (27.6) 2(100) 1 (50.0) 2 (33.3)

> 5 years - n (%) 210 (28.4) 2 (40.0) 4 (25.0) 0 (0)

Notes: Difference in number of single respiratory virus and co-infections by age and sex was assessed using

the Chi-square test, * means that the statistic was significant at α = 0.05, single influenza A virus infection

was used as a baseline for calculation of odds ratios, multivariate model adjusted for age and co-

infection*age mixed variable. Flu A(H1N1) = influenza A(H1N1)pdm09 virus, Flu B = influenza B virus,

RV = rhinovirus, RSV = respiratory syncytial virus, AdV = adenovirus.

4.2.4.8. Risk of hospitalization, admission to ICU and death associated with seasonal

influenza A virus co-infection

Seasonal influenza A viruses were in circulation throughout the study period, they also co-

circulated with the pandemic influenza A(H1N1)pdm09 virus between April, 2009 and June,

2011. Among patients who had single seasonal influenza virus infection, 86.1% were

admitted to the general ward compared to 91.7% of those with influenza B co-infection and

80% and 75% of those who had RSV, AdV and hMPV co-infection respectively – Table 4.7,

supplementary Table 4.2S2. The percentage of co-infected patients who were hospitalized

differed with age with younger patients having higher proportions than those aged 5 years or

older. In simple and multiple logistic regression models, the increased risk of hospitalization

due to co-infection between seasonal influenza A viruses and Flu B, RSV or AdV was not


227

Regarding co-infection risk of admission to the ICU among patients infected with seasonal

influenza A viruses, only the influenza B co-infection with seasonal influenza A viruses was

associated with a significant increase in the risk of hospitalization to the ICU, in simple and

multivariate logistic models. Specifically, out of 5 patients who had Flu B/Flu A co-infection,

80% (4/5) were admitted to ICU or died, compared to rate of 14.8% (17/115) among those

who had single other influenza A viruses infection (OR: 23.10, 95% CI: 2.4 – 219.0, p =

0.006). Age adjustment slightly altered this estimate but a positive association was still

maintained (OR: 22.0, 95% CI: 2.21 – 219.8, p = 0.008). RSV co-infection also increased risk

of admission to ICU but this increase was not statistically significant.

Table 4.7: Other influenza A viruses co-infections, demographics

and risk of hospitalization, admission to ICU or death

Demographic & Clinical features

NDFlu A only

(n = 772)

NDFlu A + Flu B

(n = 16)

NDFlu A + RSV

(n = 21)

NDFlu A + AdV (n = 5)

NDFlu A + hMPV (n = 4)

Age ≤ 5 years, n (%) 120 (16.6) 14 (87.5)* 19 (90.5)* 5 (100) 2 (50.0)

Sex = Male, n (%) 221 (47.8) 8 (50.0) 6 (28.6) 1 (25.0) 3 (75.0)*

Hospitalized

All - n (%) 607/705 (86.1) 11/12 (91.7) 16/20 (80.0) 4/5 (80.0) 3/4 (75.0)

≤5 years - n (%) 94 (81.0) 4 (100) 14 (82.4) 3 (100) 1 (50.0)

>5 years - n (%) 513 (87.1) 7 (87.5) 2 (66.7) 1 (50.0) 2 (100)

ICU/Dead

All - n (%) 17/115 (14.8) 4/5 (80.0)* 1/5 (20.0)

≤5 years - n (%) 4 (15.4) 1 (100) 0 (0)

>5 years - n (%) 13 (14.6) 3 (75.0)* 1 (50.0)

Notes: * = the statistic was significant at α = 0.05, single seasonal influenza A virus infection was used as a

baseline for calculation of all odds ratios, multivariate model adjusted for age and co-infection*age mixed

variable. NDFlu A = seasonal influenza A viruses, Flu B = influenza B virus, RSV = respiratory syncytial

virus, AdV = adenovirus, hMPV = human metapneumovirus.

228


The overall positive rate for any respiratory virus of 40.7% (10,501/25,596) in this study is

generally lower than positive rates of between 60% -96% identified by most studies which

recruited hospitalized patients (76;86;517;560;593) but is generally higher than rates reported

by studies which recruited patients from the emergency department or recruited patients

presenting with influenza like illnesses to the general practitioners (GPs) (range 15% -31.0%)

(75;513;578). Co-infections occurred in 4.7% (137/2,879) of patients who had a positive

diagnosis of influenza A(H1N1)pdm09 and 7.3% (57/779) of samples with seasonal influenza

A virus infections. The rates of co-infections observed in this study are similar to those

observed in studies which recruited similar study populations; however, they are generally

lower than those which recruited infants and children. For example among studies of both

adults and children (75;81;302) in USA, France, and Madagascar, reported co-infection rates

of 5.0% (66/1,347), 13.1% (30/226) and 29.4% (53/389) respectively. In contrast in studies

conducted in infants and children (76-78;608;612) rates of 16.8% - 36.1% have been

reported.

Co-infection between seasonal influenza A viruses and influenza B virus was associated with

a significant increase in risk of admission to ICU or death whereas co-infection between

influenza A(H1N1)pdm09 and AdV and RSV were associated with insignificant increases of

risk of admission to a general ward. The findings of this study are in agreement with previous

studies which implicated AdV, RSV and flu B virus co-infection in aggravating respiratory

disease outcome (75-77;517;612;613). Further, in this study, RV co-infection was associated

with an insignificant increase in risk of hospitalization to a general ward, ICU or death among

children < 5 years old but no such increases among adults. Reports from other studies on role

of RV on disease outcome have been contradictory with some studies indicating that they

decreased severity (80;81) and others indicating they increase the risk (614;615). More

recently, studies from Australia, Sweden and USA have suggested that rhinoviruses reduce or

interfere with circulation of influenza viruses (82;541;544;545). The mechanisms driving

virus virulence in co-infections are not clearly understood. However, some authors have

suggested that it could either be due to virus-virus interactions in the form of protein-protein

or protein-RNA interactions such as the direct or indirect heterologous transactivation (616)

that aid in their co-existence and replication.

In this study, co-infections were predominantly among children ≤5 years old which could

introduce confounding into the calculated measures of risk. Therefore in the logistic

229

regression models, age was adjusted for so as to eliminate any change of such confounding.

Also, in this study, the positivity of most of the respiratory viruses was higher in males than in

females and, apart from hPIV1 and hPIV3, these differences were statistically significant.

This could be either because of differences in the health seeking behaviours of men and

women or because gender is a risk factor for respiratory virus infection. Some previous

studies have also reported higher positivity rates in males (578;617;618) while others have

reported higher rates in females (75;302;582;619;620).

The weaknesses of this study are that it was retrospective and because the database included

limited information on disease outcome, therefore we could not measure the impact of other

covariates like disease comorbidities. Most of the co-infections (range: 68.8% - 100%)

occurred in children ≤5 years old Tables 4.6 and 4.7. Children could suffer from chronic

conditions like leukaemia, cystic fibrosis asthma and immune suppressing infections like

HIV, therefore these factors should be born in mind when interpreting our results. Also, as

genetic mutations i.e. antigenic drift and reassortment can play a major role in the virulence of

influenza A viruses, it would have been useful to sequence the identified influenza A viruses.

However as no major mutation to influence disease virulence was observed during the period

of study it is unlikely that major change occurred as exemplified by, similar mutations (39/40)

occurring in almost all fatal and non-fatal influenza A(H1N1)pdm09 infections in 2010/11

winter season in the UK (607).

Further, excluded patients significantly differed by age from included patients such that

infants and children were more likely to be included than adults (e.g. inclusion: exclusion

rates 35.0%/19.7% vs. 29.1%/17.62% among those aged 40-65 years) and these differences

were statistically significant p = <0.0001. If there was a systematic error leading to young

children’s samples not being tested than older patients, it might have introduced diagnostic

bias leading to an exaggeration of the observed number of co-infections among young

children. However, this study investigated co-infections among patients primarily infected

with influenza virus infection. As influenza is an acute respiratory infection; the nature of the

sickness would drastically reduce the possibility of patients not seeking medical treatment or

physicians not requesting viral testing. We therefore believe that the information contained in

the database closely estimates the epidemiology of influenza A and respiratory viral infections

in North West England. However, the possibility of diagnostic bias should be born in mind

when interpreting our results.

230

In conclusion, despite some shortcomings, this study found that influenza B co-infection,

among patients infected with seasonal influenza A virus, significantly increased the risk of

admission to ICU or death. It also observed positive associations between influenza A

(H1N1)pdm09 and RSV, AdV and risk of hospitalization to the general ward, although these

associations were not statistically significant. We recommend comprehensive testing and a

careful integration of the patterns of co-infections among respiratory viruses into public health

reports so as to help accumulate evidence on the role of co-infections on patient outcome in

respiratory infections. Such information would be vital in shaping policy on an integrated

investigation and treatment of respiratory viral infections, introduction of an integrated

vaccine for all respiratory viruses and development of multi-target rapid diagnostic tests.


Acknowledgements: The authors would like to acknowledge the University of Manchester,

the Manchester Academic Health Science Centre, and the Central Manchester University

Hospitals NHS Foundation Trust and staff for their support in this research.


231

4.2.6. Supplementary tables

The supplementary data shows the odds ratios for all the co-infections

Table 4.2S1: Odds ratios for influenza A(H1N1)pdm09 co-infections and risk of hospitalization and ICU/death

Flu A(H1N1) Flu A(H1N1) + Flu B Flu A(H1N1) + RV Flu A(H1N1) + RSV A/H1N1 + AdV

n % n % n % n % n %

Hospitalization

Crude 1,910/2,506 (76.2) 9/12 (75.0) 36/49 (73.5) 31/36 (86.1) 13/14 (92.9)

≤ 5 years 315/383 (82.3) 4/4 (100.0) 17/18 (94.4) 24/28 (85.7) 10/11 (90.9)

> 5 years 1,595/2,123 (75.1) 5/8 (62.5) 19/31 (61.3) 7/8 (87.5) 3/3 (100.0)

Odds Ratios* OR (95% CI) p OR (95% CI) p OR (95% CI) p OR (95% CI) p

Crude baseline 0.94 (0.25 - 3.47) 0.92 0.86 (0.46 - 1.64) 0.66 2.0 (0.75 - 5.0) 0.17 4.10 (0.53 - 31.0) 0.18

≤ 5 years baseline (-) (-) 3.67 (0.48 - 28.05) 0.21 1.30 (0.44 - 3.85) 0.64 2.16 (0.27 – 17.15) 0.50

> 5 years baseline 0.55 (0.13 - 2.32) 0.42 0.52 (0.25 - 1.10) 0.08 2.32 (0.28 – 18.90) 0.43 (-) (-)

Age-adjusted baseline 0.98 (0.26 - 3.60) 0.96 0.92 (0.48 - 1.75) 0.79 2.20 (0.85 - 5.74) 0.12 4.64 (0.60 - 35.73) 0.14

n % n % n % n %

ICU/Dead

Crude 236/832 (28.4) 4/7 (57.1) 5/18 (27.8) 2/7 (28.6)

≤ 5 years 26/94 (27.6) 2/2 (100.0) 1/2 (50.0) 2/6 (33.3)

> 5 years 210/738 (28.5) 2/5 (40.0) 4/16 (25.0) 0 (0.0)

Odds Ratios OR (95% CI) p OR (95% CI) p OR (95% CI) p

Crude baseline 3.40 (0.75 - 15.20) 0.11 0.97 (0.34 - 2.75) 0.96 1.01 (0.19 - 5.24) 0.99

≤ 5 years baseline (-) (-) 2.62 (0.16 - 43.37) 0.50 1.31 (0.23 - 7.57) 0.77

> 5 years baseline 1.67 (0.28 - 10.10) 0.57 0.83 (0.27 - 2.63) 0.76 (-) (-)

Age-adjusted baseline 3.40 (0.75 - 15.16) 0.11 0.97 (0.34 - 2.75) 0.96 1.01 (0.19 - 5.24) 0.99

232

Table 4.2S2: Odds ratios for other Influenza A viruses co-infections and risk of hospitalization, admission to ICU or death

NDFlu A only ND Flu A + Flu B NDFlu A + RSV NDFlu A + AdV NDFlu A + hMPV

n % n % n % n % n %

Hospitalization

Crude 607/705 (86.1) 11/12 (91.7) 16/20 (80.0) 4/5 (80.0) 3/4 (75.0)

≤ 5 years 94/116 (81.0) 4/4 (100.0) 14/17 (82.4) 3/3 (100.0) 1/2 (50.0)

> 5 years 513/589 (87.1) 7/8 (87.5) 2/3 (66.7) 1/2 (50.0) 2/2 (100.0)

Odds Ratios OR (95% CI) p OR (95% CI) p OR (95% CI) p OR (95% CI) p

Age-adjusted 2.0 (0.24 – 15.00) 0.54 0.83 (0.26 – 2.68) 0.76 0.76 (0.08 – 7.00) 0.81 0.55 (0.06 – 5.41) 0.61

ICU/Dead

Crude 17/115 (14.8) 4/5 (80.0) 1/5) (20.0)

≤ 5 years 4/26 (15.4) 1/1 (100.0) 0/0 (0.0)

> 5 years 13/89 (14.6) 3/4 (75.0) 1/2 (50.0)

Odds Ratios OR (95% CI) p OR (95% CI) p

Age-adjusted 22.0 (2.21 – 219.84) 0.008* 1.48 (0.14 – 14.86) 0.74

Notes: * odds ratio was statistically significant

233

4.3. Co-infections and risk of hospitalization and mortality Part C: Influenza A

viruses co-infection with human coronavirus and bocavirus and risk of

hospitalization: Use of SYBR Green and TaqMan RT-PCR assays for virus

identification

Synopsis

This is the authors’ version of the paper submitted to Diagnostics Microbiology and

Infectious Disease Journal. During the study period, coronaviruses and bocavirus were not

being tested at the MMPL. This paper presents pan-coronavirus primers designed and used for

identification of hCoV in samples that were positive for influenza A virus at MMPL from June

2011 to June 2012. The samples were also tested for hBoV using previously published primers.

234

Influenza A viruses co-infection with human coronavirus and bocavirus

and risk of hospitalization: Use of SYBR Green and

TaqMan RT-PCR assays for virus identification

Edward A. Goka a, Pamela J. Vallely a, Kenneth J. Mutton a,b, Paul E. Klapper a,b

a Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, University of Manchester,

Oxford Road, Manchester, M13 9PL, United Kingdom. b Department of Clinical Virology, Central Manchester Universities NHS Trust, Oxford Road, Manchester, M13

9WL, United Kingdom.

Corresponding author: Edward Goka, Institute of Inflammation and Repair, Faculty of Medical and Human

Sciences, 1st Floor Stopford building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.


235

4.3.1. Abstract

Human coronaviruses (hCoV) and bocavirus (hBoV) have been associated with lower

respiratory tract infection (LRTI); for coronaviruses, the Middle East respiratory syndrome

(MERS) in 2012/13 and severe acute respiratory syndrome (SARS) in 2003. This study

investigated the association between their co-infection with influenza A viruses and severity. We

tested 217 samples from adults and children [mean age 37.7 (SD±30.4)] with seasonal influenza

A viruses (SeasFlu A) identified between 06/2011 - 06/2012 in NW England for hCoV and

hBoV using real-time RT-PCR. A total of 12 hCoV and 17 hBoV were identified in the 217

influenza A positive samples. The risks of admission to a general ward were: thirty nine point

four percent (39.4%; 54/137) in single SeasFlu A virus infection compared to 58.3% (7/12) in

SeasFluA/hBoV and 22.2% (2/9) in SeasFluA/hCoV co-infections respectively (OR: 2.19 95%

CI: 0.65 – 7.33, p = 0.21 and OR: 0.93, 95% CI: 0.42 – 2.06, p = 0.86). Whereas the risk of

admission to the intensive care unit were: 12.6% (12/95) in single SeasFlu A virus vs. 22.2%

(2/9) in SeasFluA/hCoV and 16.7% (1/6) in SeasFluA/hBoV (OR: 2.0, 95% CI: 0.33 – 10.70, p

= 0.48 and OR: 1.40, 95% CI: 0.14 – 13.98, p = 0.77), but none of these associations was

statistically significant in the logistic regression model controlling for age and season. More and

larger studies are needed to confirm the role of hCoV, hBoV co-infection in increasing influenza

A severity. Our hCoV RT-PCR protocol appeared to be of adequate analytical sensitivity for

diagnosis.

Key words: influenza A virus, coronavirus, bocavirus, co-infection, severity, hospitalization

236

4.3.2. Introduction

Influenza A viruses are a significant cause of acute lower respiratory tract infections (ALRI),

with 13.0% of all children with influenza infection developing ALRI - equivalent to 20 million

(95% CI: 13–32 million) ALRI cases per year (228); and according to these authors, in 2008,

between 28,000 and 111,500 children younger than 5 years died of influenza-associated ALRI.

On the other hand, the human coronavirus (hCoV) and human bocavirus (hBoV) are emerging

important respiratory viruses (87;368). Some recent studies have indicated that respiratory virus

co-infections increases disease severity (75;76;513;517), however the pattern of co-infection

between influenza A viruses and hCoV and hBoV and their association with severe disease has

not been clearly elucidated.

Coronaviruses infect a wide variety of hosts (animals and humans); Alphacoronaviruses and

Betacoronaviruses infect mammals, whereas Gammacoronaviruses infect birds (367). Most of

the human coronaviruses (Alphacoronaviruses 229E and NL63 and Betacoronaviruses OC43,

HKU1) cause mild influenza like illnesses, however in 2003, the severe acute respiratory

syndrome (SARS) coronavirus caused a very severe form of respiratory disease in humans

globally (45;621;622), resulting in over 900 deaths (623). On 21 September 2012, a novel

coronavirus was identified in lower respiratory tract of an adult male Qatari national in London

(624). Three months earlier (13 June 2012), Dutch researchers identified a similar virus in a

Saudi Arabian national presenting with pneumonia (625-627). As of 20th September 2013, the

novel coronavirus, now known as the Middle East respiratory syndrome coronavirus (MERS-

CoV) had caused a total of 130 laboratory confirmed and 17 probable cases globally. Of the 130

confirmed cases, 58 (45.0%) died (623).

Human bocavirus, was first discovered in respiratory specimens of patients with lower

respiratory disease in 2005 by Allander et al., (398) using a random polymerase chain reaction

(PCR)-cloning-sequencing protocol. Reviews by Schildgen et al., (87) and Zhenqiang et al., (88)

showed that, since 2005, over 40 studies conducted globally identified human bocavirus in

children with acute respiratory virus infections; and that most of these prevalence studies found

hBoV occurring mainly together with other viruses.

The emergence of the human coronaviruses and bocavirus as significant pathogens in LRTI,

created a need for the characterization of the epidemiology of these viruses. However these

viruses are not at present routinely tested in clinical settings. We designed primers and optimized

a real-time polymerase chain reaction (RT-PCR) assay for identification of 15 coronaviruses and

used the RT-PCR targeting the NS1 region of the hBoV described by Qu et al., (106) to

237

investigate the patterns of co-infections between influenza A virus with hCoV and hBoV and

their association with severe clinical disease.

4.3.3. Methodology

4.3.3.1. Clinical samples and setting

From 1st May to 30th August 2013, we tested for hCoV and hBoV in residual nucleic acids

extracts from 217 samples randomly sampled from a set of samples received at the Manchester

Micribiology Partnership Laboratory (MMPL) between 24th June 2011 and 30th June, 2012 and

were positive for influenza A viruses in a well characterised in-house duplex real-time polymeras

chain reaction assay. Nucleic acids were extracted from combined nose and throat samples in

virus transport medium using Qiagen Biorobot MDX and the QIAamp Virus Biorobot Mdx kit.

For influenza A viruses, reverse transcription was accomplished using the Invitrogen Superscipt

III platinum one step RT-PCR Kit (Invitrogen, Paisley, United Kingdom). Positive amplification

for influenza A viruses was determined using amplicon specific probes quenched with

tetramethylrhodamine (TAMRA) and dihydrocyclopyrroloindole tripeptide minor groove binder

(MGB) (Applied Biosystems). Influenza A(H1N1) was subtyped using the Health Protection

Agency (HPA (H1)v) assay (102). The same samples were also previously tested for other

respiratory virus infections: influenza B virus (Flu B), respiratory syncytial virus (RSV),

rhinoviruses (RV), adenovirus (AdV), human metapneumovirus (hMPV), and human

parainfluenza virus types 1 to 3 (hPIV1-3), using well characterised and validated in-house real-

time PCR assays. The PCRs were run on the ABI7500 real-time PCR instrument (Applied

Biosystems, Warrington, United Kingdom). The MMPL is a reference laboratory for the North

West England which has a population of around 7 million people (99). Samples were for patients

age between 1 and 98 years old (mean age 37.7, SD±30.4), seen as outpatients (at medical

centres, clinics or hospitals), or admitted to a hospital in North West England.

4.3.3.2. Primers, templates and probes for hCoV, hBoV

Primers for hCoV were designed using the BLAST program [GenBank, Bethesda MD, 20894

USA]. Complete genomic sequences for 15 coronavirus subtypes (151 in total); the 4 human

coronaviruses and 11 representatives of animal coronaviruses (Table 4.8), were downloaded

from the GenBank (and details of their GenBank accession numbers available on request). The

retrieved sequences were aligned with BioEdit Sequence alignment Editor version 7.1.3.0 (103)

and, where more than one sequence for each subtype was available, a consensus sequence for

each subtype generated. All the consensus sequences were then aligned to generate one

consensus for all consensuses (Figure 4.5) which was then blasted on Primer Blast on the

238

GenBank website, limiting the search to nucleotide collection (nt) database and organism

Coronaviridae. One pair of primers targeting the region of the replicase open reading frame 1b

(ORF1b) gene responsible for the transcription of the non-structural protein 15 (nsp15;

XendoU/NendoU) uridylate specific endonuclease, which is highly conserved among all

coronaviruses (104;105) was selected.

Table 4.8: Sequences of coronaviruses used in the design of pan-coronavirus primers

(downloaded from GenBank on 18th December, 2011)

Coronavirus

Number identified and downloaded

1

Human coronavirus HKU1

20

2

Human coronavirus NL63

2

3 Human coronavirus OC43

4

4 Human coronavirus 229E

1

5 Bovine coronavirus

12

6 Porcine haemagglutinating virus

2

7 Murine hepatitis virus

21

8 Equine coronavirus

3

9 Rat coronavirus

1

10 Bat coronavirus

31

11 Avian infectious bronchitis virus

41

12 Canine coronavirus

1

13 Sambar deer coronavirus

1

14 Bat SARS coronavirus

10

15 Duck coronavirus

1

Total

151

The positions of the original primer were shifted so that the primers can amplify a fragment or

template of 95 base pairs; forward primer: 5'-TGGGGAGTAATGAACCCGGTA-3' and reverse

primer: 5'-ACATGTAAAAGAGCTAATAACAC-3' and template

TGGGGAGTAATGAACCCGGTAATGTCGGTGGTAATGATGCTCTGGCAACCTCCACTA

TCTTTACACAAAGCCGTGTTATTAGCTCTTTTACATGT (corresponding to position nt

20062–20134 on the genome of Bovine coronavirus, accession number DQ811784.2 on

GenBank website). The primers were synthesized by Eurofins MWG Operon, Ebersberg

Germany. We also designed a probe 5' -FAM-TTGTGTAAAGATAGTGGAGGTT-MGB - 3',

239

which was synthesized by Applied Biosystems, Warrington, UK. To ensure that the region

amplified by the primers (the template) is specific to human coronaviruses (and would not

amplify sequences from other organisms) we blasted the template and the forward and reverse

primer sequences on GenBank, and assessed the ‘‘specificity’’ by scores on the query coverage,

maximum identity, and E value.

....|....| ....|....| ....|....| ....|....| ....|....| ....|....|

21845 21855 21865 21875 21885 21895

Consensus WYHYVHRGWH BWHSWNNBKK HRVYWKHBKN VKNYNHYRNH MBAGATTGGT CTCGGGYGTT

---------- ---------- ---------- ---------- ---------- ----------

....|....| ....|....| ....|....| ....|....| ....|....| ....|....|

21905 21915 21925 21935 21945 21955

Consensus CCATTAGACC CCTCATTACT TGGGCCATTA CAGCCACCAT TACTACGAGA CCGTTGGAKK

F Primer -------ACC CCTCATTACT TGGGCCATTA CA-------- ---------- ----------

....|....| ....|....| ....|....| ....|....| ....|....| ....|....|

21965 21975 21985 21995 22005 22015

Consensus BSDDAGWDRT SYDWHNYBDH RNDDHWWNHD WRNRRAWBYD SANVNDBNYD WWMNNWBHYD

R Primer ---------- ---------- ---------- ---------- ---------- ----------

....|....| ....|....| ....|....| ....|....| ....|....| ....|....|

22025 22035 22045 22055 22065 22075

Consensus NYRDMMDWHN NVDWYNHVDB DHKKNRVRWD DVVBNNTBNV TRBYRNVVNT YYBDNYNHSV

---------- ---------- ---------- ---------- ---------- ----------

Figure 4.5: A section of the consensus sequence (showing the region identical in all

coronaviruses, derived from consensus sequences for 15 coronaviruses) that was used to

design pan-coronavirus primers Notes: F Primer – Forward primer, R Primer – Reverse primer. The sequence of region conserved in all

coronaviruses is underlined, the positions here are in positive sense orientation, because of variations in the sizes of

coronavirus genomes, in bovine coronavirus, the amplicon region lies between nucleotide 20062 and 20156.

For human bocavirus, the primer and template described by Qu et al., (106) GenBank accession

no. NC_007455, were used; forward primer 5'-TAATGACTGCAGACAACGCCTAG-3',

reverse primer 5'-TGTCCCGCCCAAGATACACT-3' (Eurofins MWG Operon, Ebersberg

Germany) template sequence

(TAATGACTGCAGACAACGCCTAGTTGTTTGGTGGGAGGAGTGCTTAATGCACC

AGGATTGGGTGGAACCTGCAAAGTGTATCTTGGGCGGGACAG); and probe 5' FAM -

TTCCACCCAATCCTGGT - MGB - 3' (Applied Biosystems, Warrington, UK).

4.3.3.3. Determination of analytical sensitivity and reproducibility of RT-PCR protocols

A series of experiments were conducted to determine the analytical sensitivity and

reproducibility of the PCRs, and to optimize the primer and probe concentrations. In determining

240

the analytical sensitivity of hCoV, both a live coronavirus 229E (Public Health England Culte

Collection, Salisbury UK) and RNA transcribed from the templates (or amplicon sequences)

inserted into a bacterial plasmid pEX-A vector (Eurofins MWG Operon, Ebersberg Germany)

were used. Both the coronavirus and bocavirus amplicon sequences were inserted into a pEX-A

plasmid vector (Eurofins MWG Operon, Ebersberg Germany) and utilized as positive control

material and quantification standard for the assays. For use in the coronavirus RT-PCR, RNA

was synthesized from the coronavirus pEX-A insert (the map of the pEX-A vector and sequence

of the insert is provided in supplementary Figure 4.3S7). To transcribe the RNA, the pEX-A

plasmid was first linearized using Bacillus amyloliquefaciens (BamHI) restriction enzyme (New

England BioLabs, Ipswich USA) and ribonucleic acid (RNA) transcribed using the T7

polymerase enzyme using the T7 High Yield RNA synthesis Kit (New England BioLabs,

Ipswich USA).

To determine the analytical sensitivity, seven 1:4 serial dilutions (in duplicate) of the RNA

transcribed from the pEX-A plasmid vector (Eurofins MWG Operon, Ebersberg Germany) and

RNA extracted from the live human coronavirus (Public Health England Culture Collection,

Salisbury UK) were used in standard curve experiments. Details of the standard curve

experiments calculations done are provided in supplementary text provided in pages following

this paper]. To optimize the primer concentration, duplicate preparations of coronavirus 229E

and pEX-A RNA template, 20µM, 10µM, 7.50µM, and 5µM serial dilutions of forward and

reverse primer and 10µM, 6.60µM, 3.3µM, 0.37µM probe concentrations were set up in

MicroAmp Fast Reaction PCR tubes and mounted on a 48 well plate (details of the optimisation

experiments are provided in supplementary text ahead). Similarly, for analytical sensitivity of the

hBoV PCR, 1:4 serial dilutions (in duplicates) of the pEX-A plasmid (Eurofins MWG Operon,

Ebersberg Germany) were used in standard curve experiments (details of the hBoV standard

curve experiments and the calculation of number of plasmid copies in serial dilutions are

provided in supplementary text).

4.3.3.4. PCR for identification of coronaviruses and bocavirus in samples

The Power SYBR Green RNA-to-CT 1-Step Kit (Applied Biosystems) was used in a one-step

RT-PCR for detection of coronavirus, the reaction mixture comprised: 10µl of Power SYBR

Green RT-PCR Master Mix (2X), 2.5µl forward and reverse primer (20µM), 1µl of Arrayscript

RT Enzyme mix (125X), 2µl of RNAse free water and 3µl of RNA template in positive control

well or sample in test wells. For identification of bocavirus the Power SYBR Green PCR Master

Mix (Applied Biosystems) was used: reaction mixture 10µl of Power SYBR Green PCR Master

241

Mix (2X), 2.5µl forward and reverse primer (20µM), 2µl RNAse free water and 3µl of template

in positive control well or sample in test wells. RT-PCR experiments were conducted on the

StepOne and StepOne Plus Real Time PCR machine (Applied Biosystems) with cycling

parameters for coronavirus of: 48oC for 30 minutes, 95oC for 10 minutes; followed by 40 cycles

of denaturing at 95oC for 15 seconds and 57oC for 1 minute; and a melting curve of 95oC for 15

seconds, 57oC for 15 seconds and 95oC for 15 seconds. The bocavirus PCR used 95oC for 10

minutes, 40 cycles of 95oC for 15 seconds and 60oC for 1 minute and a melting curve of 95oC for

15 seconds, 60oC for 15 seconds and 95oC for 15 seconds. The number of viral copies was

calculated using the absolute quantification method published by Brankatschk et al., (107).

4.3.3.5. Confirmation of positive samples using MGB probes

Samples that were positive for coronavirus or bocavirus were confirmed with a TaqMan based

RT-PCR. The probe concentrations were first optimized using duplicate preparations of 10µM,

6.6µM, 5µM, 3.3µM, 0.3µM and 0.12 probe concentrations. Each well contained 10µl of

TaqMan Fast Universal PCR Master Mix (2X) (Applied Biosystems), 2.5µl of probe, 2.5µl

forward and reverse primer, 2µl of RNAse free water and 3ul of RNA template. The coronavirus

PCR also contained the 1µl Multiscribe Reverse Transcriptase (Applied Biosystems) and 1µl of

RNase inhibitor. The cycling conditions were; for coronavirus experiments: 50oC for 2 minutes,

95oC for 20 seconds, followed by 40 cycles of 95oC for 1 second and 57oC for 20 seconds;

whereas for bocavirus: 95oC for 20 seconds, followed by 40 cycles of 95oC for 1 second and

60oC for 20 seconds. An amplification plot from one of the probe confirmation experiments is

provided in supplementary Figure 4.3S6 as an example.

4.3.3.6. Polyacrylamide gel (PAGE) electrophoresis

Amplicons for coronavirus and bocavirus PCR were visualized using precast 8% TBE

polyacrylamide gel 1mm 15 well (Applied Biosystems) run on an XCell SureLock™ Mini-Cell

Electrophoresis chamber (Invitrogen Novex Mini cell), filled with 200 ml and 600ml 1X BTE

Running Buffer in upper and lower chambers respectively. Ten (10µl) of PCR product was

mixed with 9µl of Deionised water and 1µl 5X DNA Loading Buffer (Bioline, London) and

loaded into the wells and the positive control well loaded with HyperLadder V (Bioline). The

loaded XCell SureLock™ Mini-Cell chamber was run at 200V, 18mA for 35 minutes, stained

with GelRed Nucleic Acid Stain (Biotium, Hayward, UK) for 30 minutes, and visualised under

UV light on an Alphaimager 2200 machine (Alpha Innotech, San Leandro, California).

242


Two by two tables were drawn to describe the patterns of co-infection between influenza A

virus, human coronavirus and human bocavirus and differences in distribution of single and dual

infections by age, sex, and season. Differences were assessed using the Pearson’s chi-square

(�2) statistic, and for small samples, the Fisher’s exact test. Association between single and

mixed virus infection and risk of admission to a general ward (GW) or intensive care unit (ICU)

were assessed using simple logistic regression models. We then employed multivariate logistic

regression models, including covariates; age, season, sex and an interaction term between age

and co-infections, and the significance of the covariate was assessed using the likelihood ratio

test. Association between co-infection and risk of hospitalization to GW or ICU was measured

using odds ratios (ORs) and 95% confidence intervals (CIs) and significance determined at p =

0.05. All statistical analyses were performed with STATA software, version 11.0. (STATACorp,

Texas 77845, USA).

4.3.3.8. Ethics

Ethical approval for the study was obtained from the National Health Service - Research Ethics

Committee (NHS-REC), reference number 11/NW/0698), the University of Manchester

Research Ethics Office. Research and Development R&D approval was obtained from the

Central Manchester Universities Hospitals NHS Foundation Trust (reference number R01835).

4.3.4. Results

4.3.4.1. Efficiency, analytical sensitivity and reproducibility of the hCoV and hBoV PCRs

The optimum concentration for primers was 2.5µl (20µM) whereas the optimum concentration

of the probe was 2.5µl (6.6µM). The optimum annealing temperature for the primers was 57oC.

At this temperature, the wells that contained coronavirus 229E and RNA template; gave the

lowest threshold cycle (Ct value), (range 19 - 38), highest fluorescence signal (range 0.6 - 19.2),

and the PCR product melted at 76.2 oC, which is within the Tm for coronaviruses PCR products

(75.0 oC and 80.0oC) ((Supplementary Figures 4.3S1-3). Analytical sensitivity of the PCR assays

was tested by seven 1:4 serial dilutions (104-100) for the hCoV and eleven serial dilution (106-

100) for the hBoV. The assay hCoV assay was able to detect down to 2.6 copies/µl, whereas the

hBoV was able to detect down to 4.9 copies/µl. Under optimum conditions, the duplicate

samples gave reproducible results and the experiments were repeated 3 times giving a total

number of 21 and 33 replicates. Supplementary Figures 4.3S4 and 4.3S5 show the results (Ct

values and corresponding number of copies) obtained from the serial dilutions of the hCoV RNA

(transcribed from the pEX-A vector), as an example. Our results compare well with previously

243

published protocols of hCoV and hBoV. For example a SYBR Green RT-PCR assay for

identification of hCoV, published in 2007, Escutenaair et al., (628) reported that their assay was

able to detect down to 10 RNA copies/µl, whereas Vijgen et al., (629) pancoronavirus assay

(published in 2008) was far less sensitive with a lower detection limit of 5.0 × 103 RNAcopies

per microliter sample, Adach et al., (630) (1 and 10 genome copies). Further, hBoV assay by

Neske et al., (631) was able to detect up to 3.8 × 108 to 3.8 × 103 copies/µl and Lu et al., (632)

(10 copies). Our assays were therefore of good analytical sensitivity and could be used in

diagnosis of hCoV. However further experiments using statistically significant number of patient

samples, including those negative for influenza viruses to assess specificity, before use in clinical

settings is recommended. Use of Qu et al., (106) hBoV NS1 primers for diagnosis is also

recommended. Polyacrylamide gel results indicated that the size of the PCR products obtained

from the coronavirus 229E and bocavirus was similar to the coronavirus and bocavirus inserts

into the pEX-A plasmid vector (a picture of the PAGE result for coronavirus is provided in

Figure 4.6).

Figure 4.6: Polyacrylamide gel electrophoresis of hCoV PCR products. Notes: PCR products

from two experiments, one where each well contained 3µ l of hCoV229E RNA (Exp1) and another 5µ l, to increase

yield (Exp2) and products from RNA produced from the pEX-A vector. HyperLadder V marks are from bottom,

25bp, 50bp, 75bp, 100bp up to 500. The marks at 50bp are due to primer dimers which are known to form in SYBR

Green PCRs.

244

4.3.4.2. Respiratory virus infections in samples that were positive for influenza A viruses

Of the 217 influenza A virus samples included in this study, the majority 95.4% (207) were

seasonal influenza A viruses and only 10 (4.6%) were pandemic influenza A(H1N1)pdm09

infections. This was because the pandemic virus did not circulate in high numbers during the

2011/2012 season. The World Health Organisation (WHO) had declared the end of the pandemic

on 10th August 2010 (633). Human coronaviruses were found in twelve of the 217 samples

whereas human bocavirus in 17 of the 217 samples. The Ct values for hCoV positive samples

ranged from 21.95 to 35.0 (mean 31.3) corresponding to 122 and 75 copies/µl respectively,

whereas for hBoV it ranged from 26.95 to 36.0 with (mean of 32.75) corresponding to 123 and

92 copies/µl, indicating low viral loads. Previous studies have also reported finding hBoV low

and not high viral loads Zhenqiang et al., (88).

When results of respiratory virus tests conducted in the hospital were considered, 149 (72.0%) of

the 207 seasonal influenza A viruses were single infections, and more than one virus was

detected in 58 (28.0%) cases. Only 1 of the 10 patients with pandemic influenza A(H1N1)pdm09

virus infection was also infected with human coronavirus and rhinovirus. Of the 34 co-infections

between seasonal influenza A and other respiratory viruses; 19 cases were infected with hMPV,

4 with RV, Flu B (2), RSV (2) and 1h PIV3 virus (Table 4.9).

Table 4.9: Number of co-infection between influenza A viruses and human coronavirus,

human bocavirus and other respiratory viruses in NW England between June 2011 and

June 2012

Virus

Total No of Infections

Seasonal Flu A Co-infections

FluApdm09

Co-infections

n (%) n (%) n (%)

hCoV 12/217 (5.5) 11/207 (5.3) 1/10 (10.0)

hBoV 17/217 (7.8) 17/207 (8.2)

FluB 2/217 (0.9) 2/207 (1.0)

RSV 2/217 (0.9) 2/207 (1.0)

RV 14/217 (6.5) 13/207 (6.3) 1/10 (10.0)

AdV 4/217 (1.8) 4/207 (1.9)

hMPV 19/217 (8.8) 19/207 (9.2)

hPVI3 1/217 (0.5) 1/207 (0.5)

Total 59/217 (29.0) 58/207 (28.0) 1/10 (10.0)

Notes: Multiple infections not shown, 4 of the 17 hBoV were involved in a triple infection 2 involving

seasonal influenza A virus and adenovirus and 2 of influenza A and rhinovirus.

245

4.3.4.3. Demographic characteristics, seasonality of infections and disease outcome

The differences in patients distribution by age, sex, and season are given in (Table 4.10). The

majority of both single and dual virus infections occurred during the winter season, with summer

season having low virus activity, whereas the majority of hCoV and other respiratory virus co-

infections were in ≤5 year children (41.2% in SeasFluA/Other RVIs in children ≤5 year vs.

22.8% single influenza A virus infections, p = 0.03, and 36.4%) SeasFluA/hCoV co-infections in

adults 18-65 years old, vs. 14.1% - 25.3% among those with single influenza A; not statistically

significant). A higher proportion 58.3% (7/12) of those co-infected with human bocavirus were

hospitalized to a general ward compared to (39.4% (54/137) among those with single seasonal

influenza A virus infections (OR: 2.19, 95% CI: 0.65 – 7.33, p = 0.21). On the other hand, a

higher proportion of patients infected with hBoV 16.7% (1/6) or hCoV 22.2% (2/9) were

admitted to the ICU compared to 12.6% (12/95) among those with single seasonal influenza A

viruses (OR: 1.40, 95% CI: 0.14 – 13.98, p = 0.77 and OR: 2.0, 95% CI: 0.33 – 10.70, p = 0.48),

however none of these associations was statistically significant (Table 4.11).

Table 4.10: Demographic and other characteristics of patients that were positive for

influenza A virus infection in NW England between June 2011 and June 2012

Characteristic

Single Flu A

n = 149

Flu A + hCoV

n = 11 P

Flu A + hBoV

n = 13 P

Flu A +

Other RVIs

n = 34

P

Age - n (%)

≤5 yrs 34 (22.8) 2 (18.2) 0.53 3 (23.1) 0.61 14 (41.2) 0.03

5-18 yrs 11 (7.4) 0 (0.0) 0.44 1 (7.7) 0.65 2 (5.9) 0.55

18-45 yrs 38 (25.3) 4 (36.4) 0.34 3 (23.1) 0.57 9 (26.5) 0.92

45-65 yrs 21 (14.1) 4 (36.4) 0.07 2 (15.4) 0.58 4 (11.8) 0.49

> 65 yrs 41 (30.2) 1 (9.1) 0.16 4 (30.8) 0.51 5 (14.7) 0.12

Sex - n (%)

Female 68 (49.3) 7 (63.6) 0.27 6 (46.2) 0.82 17 (50.0) 0.92

Season - n (%)

Jun - Oct 2011

8 (5.4)

0 (0.0)

0.56

0 (0.0)

0.50

3 (8.8)

0.33

Nov 11 -Mar 2012 125 (83.9) 10 (90.9) 0.46 13 (100) 0.11 27 (79.4) 0.53

Apr - Jun 2012 16 (10.7) 1 (9.1) 0.67 0 (0.0) 0.24 4 (11.8) 0.53

Notes: Flu A = seasonal influenza A viruses e.g. H3N2, hCoV = human coronavirus, hBoV = human bocavirus,

other RVIs = other respiratory viruses (rhinovirus = 14, influenza B = 2, respiratory syncytial virus = 2, adenovirus

= 4, human metapneumovirus = 19 and parainfluenza virus type 3 = 1).

246

Table 4.11: Disease severity in single influenza A virus infections vs. in co-infection with

hCoV, hBoV and in multiple infection with other respiratory viruses

Notes: SeasFlu A = Seasonal influenza A virus, hCoV = human coronavirus, hBoV = human bocavirus, Other

RVIs = other respiratory viruses (rhinovirus = 14, influenza B = 2, respiratory syncytial virus = 2, adenovirus = 4,

human metapneumovirus = 19 and parainfluenza virus type 3 = 1). NA = odds ratio was not calculated, logistic

model controlled for age and season. For calculation of odds ratios for admission to a general ward or intensive

care unit, the outpatient group was used as the baseline.


This study did not find a significant association between hCoV and hBoV co-infection with

seasonal influenza A viruses and risk of admission to GW or ICU. This could mainly be due to

the small number of samples that were tested for the presence of hCoV and hBoV. Some studies

(90;94;302;564;602) have reported that co-infections are predominantly in younger and older

patients, in this study, a higher proportion of other respiratory virus infections were in children

≤5 years old and a higher percentage of coronaviruses co-infections (36.4%) were in adults 18 to

65 years old, although this was not statistically significant, but suggests high prevalence of

hCoVs among older patients. Dual or multiple respiratory virus infections have been reported by

some studies to mainly occur during winter season (75;579). Similar to these findings, almost all

the respiratory virus single and dual infections in this study were identified in the winter season.

SYBR Green one step RT-PCR assays were employed for detection of hCoV and hBoV and

positive results were confirmed in a second assay using amplicon specific TaqMan probes. The

success of primers and a PCR assay in general, depends on its ability not to give nonspecific

amplifications. Our primers for identification of coronaviruses did not give nonspecific

Outcome

n (%)

OR (95% CI)

p

Admitted to GW

SeasFlu A only 54/137 (39.4) base

SeasFlu A + hCoV 2/9 (22.2) 0.43 (0.08 - 2.16) 0.30

SeasFlu A + hBoV 7/12 (58.3) 2.19 (0.65 - 7.33) 0.21

SeasFlu A + Other RVIs 12/34 (35.3) 0.93 (0.42 - 2.06) 0.86

Admitted to ICU

SeasFlu A only 12/95 (12.6) base

SeasFlu A + hCoV 2/9 (22.2) 2.0 (0.33 – 10.70) 0.48

SeasFlu A + hBoV 1/6 (16.7) 1.40 (0.14 – 13.98) 0.77

SeasFlu A + Other RVIs 0/22 (0.0) NA

247

amplifications as indicated by the melting curves and our assays results compare equally with

those of a previously published SYBR green assay for coronaviruses (628). The importance of

developing a coronavirus RT-PCR assay that is able to identify both Alphacoronaviruses and

Betacoronaviruses which attack animals and humans, and Gammacoronaviruses which attack

birds, is that such assays could identify zoonotic coronaviruses associated with more severe

disease in humans (45;368;621;622).

The clinical usability of any PCR assay for the identification of all or most coronaviruses

depends on its ability to amplify a region which is common to all coronaviruses (both human and

human adapted animal viruses), and its sensitivity. The primers for coronavirus, we designed,

target the replicase ORF1b nsp15 (XendoU/NendoU) uridylate specific endonuclease gene,

which is highly conserved among all coronaviruses (104;105). In addition, the hCoV standard

curve experiments were able to pick up to 2 copies/µl of human coronavirus RNA template and

we confirmed our results with a TaqMan probe. We are therefore confident that it could be

applied for diagnosis of coronaviruses in clinical settings. Further experiments in statistically

significant number of patient samples is recommended, before se in clinical settings.

A limitation of the study is that only a part of the total number of samples that were positive for

influenza A virus between 24 June 2011 and 29 June 2012 in North West England were tested

for bocavirus and coronavirus. The 217 samples included in this study represent 36.4% of the

596 samples that were positive for influenza A virus and 5.7% of 3,804 samples tested for

respiratory virus infections during this period. Given the high co-infection rates for bocavirus

reported by other studies (87;88), probably more bocavirus infections could have been identified

if all the samples were tested. A possibility of finding more coronaviruses could also not be ruled

out.

In conclusion, this study found insignificant association between hCoV and hBoV co-infection

with seasonal influenza A viruses and risk of admission to ICU and general ward respectively,

more and larger studies are needed to confirm whether these viruses do increase disease severity

when they co-infect with influenza A viruses. Our RT-PCR protocol for the identification of

coronaviruses is sensitive, specific and reliable and could be used for diagnosis of coronaviruses.

248

Acknowledgements:

The authors would like to acknowledge the University of Manchester, the Manchester Academic

Health Science Centre, and the Central Manchester University Hospitals NHS Foundation Trust

and staff for their support in this research.

Funding:

This work was supported by the University of Manchester.

249


4.3.6.1. Optimization of primers and annealing temperature

Details of reagent concentrations used in experiments to optimize the primer and probe

concentration, are given in Table 4.12, whereas the variations in annealing temperature of the

primers are detailed in supplementary Figures S1 and S2. The cycling conditions have been

described in the main manuscript. Additional experiments to further explore and determine an

optimum the primer concentration involved varying the volume of the forward and reverse

primers (i.e. 2.5µl, 3.6µl, 5.0µl, 7.0µl, and 9.0µl in duplicates) and using the same concentration

of the positive control in all the tubes. The same designs of experiments were conducted to

optimize primers and template for hBoV. Wells containing the 20µM forward and reverse

primers (at volume of 2.5µl per well), 6.6µM of probe, and annealing temperature 57oC for

hCoV and 60oC for hBoV gave the best results (good Ct values and fluorescence signal - as

shown by the melting curve graphs). Therefore these concentrations were used in experiments to

test for hCoV and hBoV in patient samples.

Table 4.12: Details of primer optimization experiment set up

Wells

PCR

Master

mix (µl)

Forward

Primer

Reverse

Primer

Template

(µl)

RNAse

free

water

(µl)

Reverse

transcriptase

(µl)

Total

volume

(µl)

A1 10 20 µM 20 µM 3 2 1 20

A2 10 20 µM 20 µM 3 2 1 20

A3 10 10 µM 10 µM 3 2 1 20

A4 10 10 µM 10 µM 3 2 1 20

A5 10 7.5 µM 7.5 µM 3 2 1 20

A6 10 7.5 µM 7.5 µM 3 2 1 20

A7 10 5 µM 5 µM 3 2 1 20

A8 10 5 µM 5 µM 3 2 1 20

B1 10 10 µM 10 µM 0 5 1 20

B2 10 10 µM 10 µM 0 5 1 20

Notes: similar experiments were conducted to optimize probe concentrations. The concentration of the probes were

of 10µM, 6.6µM, 5µM, 3.3. µM, 0.3µM and 0.12µM. The 2 wells at the bottom were negative control wells with no

RNA added.

250

4.3.6.2. Standard curve experiments for hCoV using RNA from 229E and pEX-A vector

The StepOne and StepOne Plus software (Applied Biosystems) was used to write the PCR

protocol. Calculation of the number of copies in each well for RNA transcribed from the pEX-A

vector is shown in Table S1, as an example. Seven serial dilutions of the RNA were pipetted into

MicroAmp Fast Reaction PCR tubes and mounted on a 48 well plate. The reaction mixture

comprised: 10µl of Power SYBR Green RT-PCR Master Mix (2X), 2.5µl forward and reverse

primer (20µM), 1µl of Arrayscript RT Enzyme mix (125X), 2µl of RNAse free water and 3µl of

RNA template in positive control well or sample. Two wells were filled with RNase free was as

negative controls. Real-time PCR experiments were conducted on the StepOne and StepOne Plus

PCR machine (Applied Biosystems) with the following cycling parameters:

Step

Parameter

Reverse transcription 48oC for 30 minutes

Enzyme activation 95oC for 10 minutes

Denaturation 95oC for 15 seconds x 40 cycles

Annealing and extension 57oC for 1 minute

Melting curve 95oC for 15 seconds

57oC for 15 seconds

95oC for 15 seconds

4.3.6.3. RNA extraction from the live hCoV 229E virus

The QIAamp® Viral RNA Mini Kit Qiagen RNA Extraction Protocol was used to extract RNA

from the live hCoV 229E (Public Health England Culture Collection, Salisbury UK), as follows:

Preparation of reagents

Addition of carrier RNA to Buffer AVL

Add 1 ml Buffer AVL to the tube containing 310 µg lyophilized carrier RNA to obtain 5.6 µg of

carrier RNA per sample. Buffer AVL/carrier is stable at 4 °C.

Buffer AW1

Buffer AW1 is supplied as a concentrate. Before using for the first time, amount of ethanol (96–

100%) as indicated on the bottle was added.

251

Buffer AW2

Buffer AW2 is supplied as a concentrate. Before using for the first time, appropriate amount of

ethanol (96–100%) was added to Buffer AW2 concentrate as indicated on the bottle.

Resuspending QIAGEN Protease

To suspend the enzyme, 6.2 of carrier RNA-AVE was added to 220µl of buffer AL.

Procedure

1. Pipette 25µl QIAGEN Protease into a 1.5ml eppendorf tube.

2. Add 200µl of hCoV virus sample.

3. Add 200µl of Buffer AL (containing 28µg/ml of carrier RNA). Close lid and mix by

pulse-vortexing for 15 seconds.

4. Incubate at 56°C for 15 minutes in a heating block.

5. Briefly centrifuge the 1.5ml tube to remove drops from the inside lid.

6. Add 250µl of ethanol (96–100%) to the sample, close the lid and mix by pulse-vortexing

for 15 seconds. Incubate the lysate with the ethanol at room temperature (15-25°C) for 5

minutes.

7. Briefly centrifuge the 1.5ml tube to remove drops from the inside of the lid.

8. Carefully apply all of the lysate into the QIAamp MinElute column without wetting the

rim. Close the lid, and centrifuge at 8000 rpm for 1 minute. Place the QIAamp Mini

column into a clean 2ml collection tube, and discard the tube containing the filtrate.

9. Carefully open the QIAamp MinElute column, and add 500µl of Buffer AW1 without

wetting the rim. Close the lid, and centrifuge at 8000 rpm for 1 min. Place the QIAamp

Mini column in a clean 2ml collection tube (provided), and discard the tube containing the

filtrate.

10. Carefully open the QIAamp MinElute column, and add 500 µl of Buffer AW2. Close the

lid and centrifuge at 8000 rpm (full speed) for 1 minute. Place the QIAamp MinElute

column in a new 2 ml collection and discard the tube containing the filtrate.

11. Carefully open the QIAamp MinElute column, and add 500 µl of ethanol (96-100%).

Close the lid and centrifuge at 8,000 rpm for 1 minute. Place the QIAamp MinElute

column in a new 2ml collection tube, and discard the tube containing the filtrate.

12. Place the QIAamp MinElute column in new 2ml collection tube and centrifuge at 14,000

rpm for 3 minutes to dry the membrane completely.

13. Place the QIAamp MinElute column in new 2ml collection tube, open the lid and incubate

at 56°C for 3 minutes in a heating block.

252

14. Place the QIAamp MinElute column into a clean 1.5ml eppendorf, and discard the

collection tube with the filtrate. Carefully open the QIAamp MinElute column and add 20-

150µl of RNase free water to the centre of the membrane. Close the lid, and incubate at

room temperature for 1 minute. Centrifuge at 14,000 rpm for 1 min.

15. To increase yield, repeat step 14, reuse elute from step 14.

4.3.6.4. RNA Synthesis and associated protocols

Linearization of plasmid

First the plasmid was digested using the following procedure:

RNAse free water 20μl

NE Buffer # 3 3μl

Acetyle BSA 10μg/ μl 0.4μl

Plasmid 3μl

Mix by pipetting then add 0.5 μl of BAMHI restriction enzyme 10units/μl

Incubated at 37°C for 2 hours.

Purification of digested plasmid

The digested plasmid was then purified using the QIAGEN Miniprep Kit as below:

Procedure

1. Preheat TE Buffer (TE) to 65-70°C for elution. Heating is optional for eluting DNA of 1-30

kilobases (kb) but is recommended for eluting DNA of >30kb.

2. Place the spin column with supernatant from restriction digestion and centrifuge for 1

minute at 12,000 rpm. Discard flow through.

3. Add 700μl Wash Buffer (W9), with ethanol, to the column and centrifuge for 1 minute at

12,000 rpm. Discard flow through.

4. Centrifuge again for 1 minute at 12,000 rpm to discharge any residual Wash Buffer (W9).

Discard flow through.

5. Place spin column in a new 1.5ml eppendorf tube. Add 75μl of preheated Buffer TE to the

centre of the column.

6. Incubate column for 1 minute at room temperature.

7. Centrifuge for 2 minutes at 12,000 rpm.

253

RNA Synthesis protocol (adopted from T7 High Yield RNA synthesis manual (New

England BioLabs, Ipswich USA)

RNA was then synthesized from the hCoV insert into the plasmid using the following protocol:

Procedure

1. Prepared master mix of reagents by mixing 10X reaction buffer and four ribonucleotide

(NTP) solutions as follows.

Nuclease-free water 5μl

10X Reaction Buffer 2μl

ATP (100 mM) 2μl 10 mM final

GTP (100 mM) 2μl 10 mM final

UTP (100 mM) 2μl 10 mM final

CTP (100 mM) 2μl 10 mM final

Template DNA 3μl (of the stock sample of pEX-A vector)

T7 RNA Polymerase Mix 5μl

Total reaction volume 20 μl

2. Mixed thoroughly by pulse-spin in microfuge. Incubated at 37°C for 2 hours using a water

bath.

DNAse treatment of synthesized RNA

DNAse treatment was conducted to remove the DNA from the synthesized RNA. The QIAGEN

RNeasy Minikit, part 2 DNAse treatment protocol (below) as described in RNeasy® Mini

Handbook (QIAGEN) was used.

Procedure:

1. Add 350μl Buffer RW1 to the RNeasy spin column. Close the lid gently, and

centrifuge for 15 seconds at 10,000 rounds per minute (rpm) to wash the spin column

membrane. Discard the flow-through.

2. Add 10μl DNase I stock solution to 70μl of Buffer RDD. Mix well by gently inverting the

tube. Centrifuge briefly.

3. Add the DNase I mix (80μl, from 2 above) directly to the RNeasy column membrane and

place on bench (20-30°C) for 15 minutes.

4. Add 350μl Buffer RW1 to the RNeasy spin column. Close the lid gently, and centrifuge

for 15 seconds at 10,000 rpm. Discard flow through.

5. Add 500 μl Buffer RPE to the RNeasy spin column. Close the lid gently, and centrifuge

for 15 seconds at 10,000 rpm to wash the spin column membrane. Discard flow through.

254

6. Add 500 μl Buffer RPE to the RNeasy spin column. Close the lid gently, and centrifuge

for 2minutes at 10,000 rpm to wash the spin column membrane. Discard flow through.

7. Place the RNeasy spin column in a new 2ml collection tube. Close the lid and centrifuge

for 1 minute at 14,000 rpm (full speed).

8. Place the RNeasy spin column in a new 1.5ml collection tube. Add 30-50 RNeasy free

water directly to the spin column membrane. Close the lid, and centrifuge for 1 minute at

10,000 rpm.

9. Repeat step 8 to increase yield, reuse elute from step 8.

The amount of RNA yielded was measured using a nanodrop.

4.3.6.5. Purification of PCR Products

In addition to the above mentioned protocols, a few PCR products (both from standardization

experiments and tests on clinical samples) were run on precast 8% TBE polyacrylamide gel 1mm

15 well (Applied Biosystems). However before running the gel, the samples were purified using

the QIAquick PCR purification kit as below:

Procedure:

1. Transfer 10μl of the PCR product sample into 1.5 eppendorf tube.

2. Add 1.5 volume of PBI buffer (i.e. for 10μl of sample add 50μl PBI buffer).

3. Check colour to see if it is yellow or add10μl of sodium acetate.

4. Transfer all the contents into a 2ml QIAQuick spin column. Centrifuge for at 14,000 rpm

for 1 minute. Discard the flow through and place a new clean 2ml collection tube.

5. Add 750μl of Buffer PE. Centrifuge at 14,000 rpm for 1 minute. . Discard the flow

through and place a new clean 2ml collection tube.

6. Centrifuge at 14,000 rpm for 1 minute to dry the membrane.

7. Transfer the QiaQuick column into a clean 1.5 eppendorf tube. Add 50μl of RNase free

water.

8. Incubate at room temperature for 1 minute.

9. Centrifuge at 14,000 rmp for 1 minute. To increase yield, repeat this procedure reusing

elute.

255

Figure 4.3S1: Ct values for hCoV RT-PCR at different annealing temperatures. Notes: Ct

values in the 1 well with RNA template synthesized from pEX-A vector, 2 wells with hCoV 229E, and in the

negative control well (NTC). At annealing temperature of 57oC, the Ct values for pEX-A RNA template and hCoV

229E were lower (Ct 21-29) compared to that at annealing temperature of 63.4 oC (Ct 28 – 38) indicating the PCR

performed better at annealing temperature of 57 oC.

256

Figure 4.3S2: Strength of the fluorescence signal at different annealing temperatures

(hCoV RT-PCR). Notes: Fluorescence signal strength in pEX-A RNA template, hCoV 229E and NTC wells. In

all the samples, the fluorescence signal was highest at annealing temperature of 57oC.

257

Figure 4.3S3: Melting curves for the hCoV 229E, pEX-A vector RNA and the negative

control wells for the hCoV RT-PCR at annealing temperature of 57oC. Notes: Melting curve

plots for the RNA template generated from the pEX-A vector (pEX-A RNA), 2 hCoV 229E coronavirus samples,

and 2 negative control wells (NTCs) with only RNAse free water. Both the pEX-A RNA template and the

hCoV229E had their peaks at 76.63oC which lies within the Tm for coronaviruses (75.5 – 80.0oC), whereas the

primer dimers in negative control wells (formed due to use of SYBR Green dye) had Tm of 67.0oC

258

Figure 4.3S4: Amplification plots for the coronavirus RT-PCR in standard curve

experiment. Notes: Y axis gives the change in fluorescence signal, The X axis gives the Ct value (number of

cycles) corresponding to the number of copies in the well in 7 (1:4) serial dilutions of the RNA template

prepared in duplicates; 104 = 10,535.0 cRNA copies of the coronavirus template, 103 = 2,633.8 copies, 102(1) =

658.4, 102(2) = 164.6 copies, 101(1) = 41.2 copies, 101(2) = 10.3 copies, and 100 = 2.6 copies, NTC = Negative

control well.

259

Figure 4.3S5: Standard curve for the hCoV RT-PCR. Notes: Seven 1:4 serial dilutions of the

RNA template were prepared in duplicates; the first well had 10,500 cRNA copies of the coronavirus template.

The Y axis gives the Ct value corresponding to the number of copies in the well on the X axis. The plot is for the

same experiment in figure S4.

260

Figure 4.3S6: Amplification plot for probe confirmation experiment for a few hCoV

positive patient samples.

Notes: The red line is for the positive control and the Ct values for the samples are 27.93, 29.01, and 29.98. All

samples that were positive in SYBR green but could not be confirmed using a probe were regarded as negative.

261

Figure 4.3S7: PEX-A vector map

262

Notes to Figure S7:

PEX-A vector

name_of_sequence: COBOC

DNA_sequence of the insert into the PEX-A vector:

TAATACGACTCACTATAGGGAGATGAAGGCTCAGGAAGGTCTGCTCCTAATT

CCAGATCTACTTCGCGCACATCCAGCAGAGCCTCTAGTGCAGGATCGCGTAG

TAGACTAGGTTTCCGCCTGGCACGGTACTCCCTCAGGGTTACTATATTGAAG

GCTCAGGAAGGTCTTGGGGAGTAATGAACCCGGTAATGTCGGTGGTAATGA

TGCTCTGGCAACCTCCACTATCTTTACACAAAGCCGTGTTATTAGCTCTTTTA

CATGTGCTAGTTATTGCTCAGCGGTAATGACTGCAGACAACGCCTAGTTGTT

TGGTGGGAGGAGTGCTTAATGCACCAGGATTGGGTGGAACCTGCAAAGTGT

ATCTTGGGCGGGACAGAGCTCTGTAAGTACTATTACTTTCTTTAACACTTGG

CACGCACAGCCACGTGACGAAGATGAGCTCAGGGAATATGAAAGACAAGCA

TCGCTCCTACAAAAGAAAAGGGAGTCCAGAAAGAGGGGAGAGGAAGAGACA

CTGGCAGACAACTCATCACAGGAGCAGGAGCCGCAGCCCGATCCGACACAG

TGGGGAGAGAGGCTCGGGCTCATATCATCAGGAACACCCAATCAGCCACCT

ATCGTCTTGCACTGCTTCGAAGACCTCAGACCAAGTGATGAAGACGAGGGA

GAGTACATCGGGGAAAAAAGACAATAGAACAAATCCATACACTGTATTCAGT

CAACACAGAG

Coronavirus Pink = amplicon hCoV – primer pair 1 = 84 bp

Blue = amplicon hCoV – primer pair 2 = 63 bp

LRed = amplicon hCoV – primer pair 3 = 95 bp

Bocavirus Red = amplicon hBoVNS1 primer = 95 bp

Green = amplicon hBoVNP1 primer = 354 bp

Black = T7 promoter and T7 terminator (19 & 23 bp respectively)

Total # bp amplicons only = 691 bp

Total length of sequence (promoters inclusive) = 733 bp

NB: Primer pair1 and 2 of the hCoV did not work and were therefore not reported in the

manuscript.

263

Table 4.3S1: Number of RNA copies calculation for hCoV – T7 synthesized PEX-A vector

Initial

conc/µl C1

Volume transferred (µl)

V1

Volume of diluent

(µl)

Total Volume

V2

New Conc/ µl

C2

10,535.0 25 75 100 2,633.8

2,633.8 25 75 100 658.4

658.4 25 75 100 164.6

166.6 25 75 100 41..2

41.2 25 75 100 10.3

10.3 25 75 100 2.6

No of copies = Quantity * Avogadro

MW

Molecular weight double stranded DNA = 325 = 325 × 282

= 91,650

Concentration of RNA given by nanodrop = 0.016 µg/µl

No of molecules in 75 µl = 1/13th of 6.02 × 1023

= 6.02 × 1010

No of copies in 1µl stock solution = 0.016µg/91650µg

= 1.75 × 10-7× 6.02 × 1010

= 1.0509 × 104

Notes: RNA transcribed from the pEX-A vector was measured using nanodrop. A similar calculation was done for

RNA extracted from the live hCoV 229E, assuming the virus hCoV 229E has 27,374 nucleotides as in sequence

lengths of the same on GenBank. Formular for calculating the number of copies in serial dilutions used was C2 =

[C1*V1)/V2]

264

Table 4.3S2: Number of copies of hBoV PEX-A-COBOC plasmid in serial dilutions

Initial concentration

(per µ l)

C1

Volume

of plasmid (µ l)

V1

Volume

of diluent (µ l)

Final

volume (µ l)

V2

Final concentration

(per µ l)

C2

Stock 326947763.4

326947763.4 25 75 100 81736940.85

81736940.85 25 75 100 20434235.21

20434235.21 25 75 100 5108558.803

5108558.803 25 75 100 1277139.701

1277139.701 25 75 100 319284.9252

319284.9252 25 75 100 79821.2313

79821.2313 25 75 100 19955.30782

19955.30782 25 75 100 4988.826956

4988.826956 25 75 100 1247.206739

1247.206739 25 75 100 311.8016848

311.8016848 25 75 100 77.95042119

77.95042119 25 75 100 19.4876053

19.4876053 25 75 100 4.871901324

4.871901324 25 75 100 1.217975331

No of copies = Quantity * Avogadro

MW

Molecular weight double stranded DNA = 325*2 = 650 × (666+2,450)

= 650 × 3,116

= 202,540

Quantity of plasmid supplied = 1.1µg

No of molecules in 1000µl = 1/10th of 6.02 × 1023 = 6.02 × 1013

No of copies in 1µl Stock solution = 1.1µg/202,540 × (6.02 × 1013)

= 5.43103 × 106 × (6.02 × 1013)

= 326947763.4

Noted: To avoid contamination serial dilution 106 was used in the first well. Formular for calculating the number of

copies in serial dilutions used was C2 = [C1*V1)/V2]

265

Part V: Discussion and conclusion

Synopsis

This section present a summary of all the results presented in the previous sections of the thesis

and draws conclusions from the results. It also suggests directions for future research.

266

5.1. Discussion and conclusion

5.1.1. Age, gender and epidemiology of respiratory viruses

The role of age in the epidemiology of respiratory viruses is well documented (204;207-211).

In this study the majority of the identified viruses (51.8%) were in children ≤5 years old and the

incidence of ARI and influenza A viruses associated hospitalization was higher in children ≤5

years old and the elderly > 65 years old; age-specific incidence of ARI hospitalization; 4,726.3

per 100,000 in children ≤5 years old to 2,628.7 and 6,167.4 per 100,000 in patients 65 -84

years and the elderly >85 years old compared to 189.51/100,000 in 25-65 year olds. Pandemic

influenza A(H1N1)pdm09 and the seasonal influenza A viruses associated hospitalizations

ranged from 192.8 to, 86.9 per 100,000 and 23.6 to 99.8 per 100,000, respectively. In addition,

the majority of the respiratory virus co-infections 83.5% (1,014/1,214) were in under-five year

old children. Correspondingly, the majority (54.7%; 75/137) of pandemic influenza

A(H1N1)pdm09 virus co-infections and seasonal influenza A viruses co-infections (59.3%;

35/59) occurred in ≤5 year old children. This finding is in agreement with the reviewed

literature which showed that, in general, studies that recruited children <6 years old reported

higher co-infection rates (5.7 - 62.0%) than those that recruited both children and adults (5.0 –

14.0%). This study included both children and adults and this finding supports targeted

influenza vaccination of ≤5 year old children and the elderly ≥65 years old.

In this study, respiratory virus infections were significantly higher in males than in females. Of

the 11,236 PCR positive for any respiratory virus, that had information on their gender

available, the majority, 5,822 (51.8%) were males compared to 5,414 (48.2%) females and the

difference was statistically significant (p = 0.01). Historical evidence has been divided with

some studies finding high levels of infections in females (204;210) and others indicating

respiratory viruses are higher in males, especially young boys (64;208;324). The reason for the

“male flu” are not well understood, it could however be due to gender related immune

responses (28-30). Some studies have suggested that gender may predispose individuals to

susceptible genes (28), or sex specific hormones e.g. testosterone select men to poor immunity

phenotype (29;30). It could also be due to differences in service utilisation between males and

females. Public health educational programmes raising awareness on the subject in order to

improve health are recommended. More research to elucidate the gender related risk factors in

encouraged.

267

5.1.2. Burden of co-infection and associated hospitalizations and mortality

5.1.2.1. Influenza A viruses co-infections and disease outcome

Among influenza viruses, RSV had the highest number of co-infections (68), of which 43 were

with the pandemic virus and 25 with the seasonal virus, followed by RV (65/56; 9), Flu B

(35/18; 17), AdV (27/20; 7), hBoV (17), hCoV (12), hMPV (10/4; 6), and hPIV1-3 (5; 0). The

results of logistic regression models controlling for age, presented in the preceding chapters,

indicate that co-infection with Flu B, RSV, hCoV and hBoV increased risk of admission to ICU

or death: (OR: 22.0, 95% CI: 2.21 – 219.84, p = 0.008 for Seasflu A/Flu B, OR: 3.40, 95% CI:

0.75 – 15.15, p = 0.11 for Flu Apdm09/Flu B, OR: 1.48, 95% CI: 0.14 – 14.86, p = 0.74

SeasFlu A/RSV, OR: 2.0, 95% CI: 0.33 – 10.70, p = 0.48 SeasFlu A/hCoV and OR: 1.40, 95%

CI: 0.14 – 13.98, p = 0.77 for SeasFlu A/hBoV), although the other four associations were not

statistically significant. In addition, the results also suggest an association between Flu B, RSV,

AdV and hBoV co-infection and increased risk of admission to a general ward (OR: 2.20, 95%

CI: 0.85 – 5.74, p 0.12 for Flu Apdm09/RSV, OR: 4.64, 95% CI: 0.60 – 35.73, p = 0.14 for Flu

Apdm09/AdV, OR: 2.0, 95% CI: 0.24 – 15.0, p = 0.54 for SeasFlu A/Flu B, and OR: 2.19, 95%

CI: 0.65 – 7.33, p = 0.21 for SeasFlu A/hBoV) respectively but none of these associations was


To understand the public health significance of the various co-infections, a Population

Attributable Risk Percent (PAR%) analysis was conducted and results on the role of different

co-infections summarized in Table 5.1. A review of the co-infection patterns between influenza

and other respiratory viruses was conducted using a meta-analysis. Combining the data from

the primary study and the meta-analysis, the evidence suggest that Flu B virus co-infection

would be responsible for up to 12.2% of all influenza A virus associated hospitalizations and up

to 94.6% of admissions to ICU or deaths, whereas RSV and AdV would contribute to 31.1%

and 18. 4% of influenza virus A associated admissions to the ICU respectively. Since only a

small proportion (0.07%; 217/30,975) of the samples in this study were tested for hCoV and

hBoV, PAR% was not calculated for hCoV and hBoV despite the observed high Flu A/hBoV

co-infection rate (20.9%), in the meta-analysis, and similar rates in previous reviews (87;88).

Research on vaccines (e.g. AdV- serotype 5 based with Flu A/RSV inserts vaccine and drugs

targeting Flu A, RSV/AdV attachment proteins), combined drugs, and multi-target tests for

these viruses is recommended.

Influenza A and B viruses differ only in the their NP and M genes (130), therefore competition

between them is driven by, among other factors, positive selection (527;634;635). The positive

association between Flu A and RSV, AdV, hCoV and hBoV co-infections and severe or fatal

268

disease (though not statistically significant), suggests the viruses have evolved to an extent they

can co-infect and cause severe disease. However the mechanism behind these associations is

not well understood. Some researchers have suggested that it could be due to, among other

factors, interaction between these viruses at a molecular level (616),. Future biomedical

research exploring the nature of such interactions and the directions of their effect are

recommended.

Table 5.1: Population Attributable Fraction Percent (PAF% or PAR%) associated

with influenza A virus co-infections

Hospitalized to GW

n (%)

Total

Prevalence

of co-infection

Risk

Difference

PAR (%)

Single Flu A 2636 (79.2) 3330 base

Flu A + Flu B 20 (83.3) 24 4.5 4.1 12.2

Flu A + RSV 46 (83.6) 55 13.3 4.4 31.1

Flu A + RV 43 (76.8) 56 4.5 -2.4 -12.6

Flu A + AdV 9 (90.0) 19 2.3 10.8 18.4

Flu A + hMPV 17 (90.0) 10 1.6 10.8 13.4

Flu A + PIV1-3 4 (100) 4 0.3 20.8 5.6

Total* 2781 3508

ICU/Dead

n (%)

Single Flu A 255 (26.8) 949 base

Flu A + Flu B 8 (66.7) 12 4.5 39.9 94.6

Total* 273 1,000

Notes: PAR% = [Pe (RRe-1) / Pe (RRe – 1) + 1]*100 (636). Where Pe = prevalence of exposure in this case co-

infections, RRe is the risk difference (in hospitalization or mortality) between exposed and unexposed. Prevalence

of specific types of co-infections is the average prevalence rates from a meta-analysis on the patterns of co-

infections between influenza A and other respiratory viruses reported by different epidemiological studies. The

total* also includes; 12 patients who had influenza A and 2 or more other respiratory virus infections; 3 patients

who had Flu A + RSV co-infection of which 2 were admitted to ICU and 1 died; and 5 patients with Flu A + RSV

co-infection of which 2 were admitted to ICU and 3 died. Sufficient component causal model allows for the sum

of individual causes to be >100 i.e. since each outcome (hospitalization or death in this case) can be caused by

several necessary causes which individually may contribute to different percentages, the sum of all necessary

causes may be greater than 100 (637).

5.1.2.2. Co-infections among respiratory viruses, in general, and severity

Of the 11,715 respiratory virus infections, 89.0% (10,501) occurred as single infections and

10.4% (1,214) as dual or multiple infections. Among the 10,501 single infections, 1,738

(16.6%) were treated as outpatients, 8,009 (76.3%) were admitted to a general ward, 530

(5.1%) were admitted to the ICU and 224 (2.1%) died. Among the 1,214 mixed infections, 147

269

(12.1%) were outpatients, 992 (81.7%) were hospitalized to a general ward, 57 (4.7%) were

admitted to ICU and 18 (1.5%) died.

In a multivariate logistic regression, controlling for age and season, co-infection among

respiratory viruses, in general, was associated with a 43% increase in risk of admission to a

general ward (OR: 1.43, 95% CI: 1.20 – 1.71, p = <0.0001) and a 15% increase in risk of

admission to ICU or death (OR: 1.15, 95% CI: 0.86 – 1.55), although the association with ICU

was not statistically significant (p = 0.34). In the stratified analysis, Flu Apdm09/Flu B co-

infection increased the risk of admission to ICU 22-fold (OR: 22.0, 95% CI: 2.21 – 219.8, p =

0.008), yet the pandemic influenza A(H1N1)pdm09 virus caused mild illness as a single

infection (risk of admission to GW, OR: 0.62, 95% CI: 0.55 – 0.70, p = <0.0001, and

ICU/death OR: 0.89, 95% CI: 0.72 – 1.10, p = 0.21) . This result suggest that co-infection is a

significant predictor of respiratory virus illness, however, its true effect is obscured, or biases

towards the null, when crude analysis is applied. Future studies should endeavour to carry out

stratified analysis.

The risks of hospitalization and mortality identified by this study were substantial and fall

within the risks reported by the reviewed studies (range 18.8 – 3,046 per 100,000 population)

for hospitalization and for mortality (range 5.0 -220.9) (232;249;250;293-296). Vaccination

and early treatment with anti-viral drugs would produce economic gains. The World Health

Organisation called for concerted efforts in research to generate evidence on need for

development of vaccines and drugs for respiratory viruses, and R&D on vaccines and drugs

(98). The results of this study provide the much needed evidence for the same. As co-infection

has been found here to be a significant contributor to respiratory viruses’ disease severity,

multi-targeted testing, combined treatment and vaccination would be more cost-effective.

However, as described in the preceding paragraphs, this research found that Flu A co-infection

with Flu B, RSV, and AdV would more likely cause illnesses of public health significance,

therefore we reiterate our recommendations towards development of integrated drugs and

vaccines targeting these viruses. A number of potential vaccines and treatment protocols, are

under development, an AdV based vaccine with influenza and RSV inserts; and drugs targeting

the attachment proteins has been recommended because some researchers have already

described the limitations of serum antibody responses, hence antibody therapeutics to RNA

viruses (141-145;638).

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5.1.3. Possible interaction between RV and influenza A viruses

The logistic models suggested that rhinoviruses reduced severity of influenza A viruses (OR:

0.92, 95% CI: 0.48 – 1.74, p = 0.79 in pandemic virus analysis), and the PAR analysis here

attribute a 12.6% reduction of this risk. Aberle et al., (80) and Esper et al., (81) also found

reduction in disease severity among co-infections involving rhinoviruses. Some studies that

investigated interaction between rhinovirus and other respiratory viruses Greer et al., (541),

Linde et al.,(544) and Casalegno et al., (545) suggested that rhinovirus interfere with influenza

virus or other respiratory viruses infection. Evidence from this study indicates that influenza

virus co-circulated with rhinovirus, for example between November 2008 and March 2010

(Figure 5.1). However, the majority 97.0% (3,626/3740) of RV infections occurred in samples

that were negative for influenza A viruses, so did the majority of RSV (96.2%; 2,725/2,832),

AdV (95.4%; 725/760), hMPV (97.4%; 561/576), and hPIV1-3 (98.1%; 623/635). RV was the

most co-infecting virus, involved in among others; 367 exclusive dual-infections with RSV,

169 with AdV, 63 with hPIV-3, 61 with hMPV, 56 with pandemic influenza A(H1N1)pdm09

and 9 with seasonal influenza A viruses (Table 4.1). In addition, the majority of the RV co-

infections with other respiratory viruses e.g. 98.8% (413/418) of RV/RSV, 97.8% (222/227) of

RV/AdV, occurred in patients who were negative for influenza viruses, suggesting that RV

interferes with influenza virus.

Although there is a possibility that this outcome could be due to ecological fallacy, where

observations at ecological and individual level have no direct association. Nevertheless, these

results, highlight the need for further research investigating the virus-virus interactions between

influenza A and rhinovirus, or among other respiratory viruses, exploring the biomedical

processes involved and their association with virulence. Such as in vivo studies in knockout

mice [i.e. mice with mitochondrial antiviral signaling protein (MAVS), or toll like receptor

(TLR) and other genes involved in signalling of interferon innate immune response knocked

out], investigating the pathogenicity of influenza A and RV co-infection; histological changes

in the lungs, interferon produced, viral loads and survival of the mice maybe worthwhile.

Alternatively a selective of these outcomes could also be investigated in vitro experiments

using cell culture systems. Currently, most of vaccine research concentrates on use of

baculovirus or adenovirus expression vectors. Rhinovirus might offer good alternatives for

RNA virus-virus and virus-host interactions, the results of which could be used for research on

development of vaccines or drugs against influenza and other respiratory virus infections

(639;640).

271

Figure 5.1: Weekly activity of the pandemic Influenza A(H1N1)pdm09 and rhinovirus in

North West England between November 2008 and March 2010

5.1.4. Seasonality and epidemiology of respiratory viruses

During the study period, weekly incidence of ARI associated hospitalizations were peaking

between 39 and 12 and were highest during 2008/2009 and the 2010/2011 influenza seasons

(Figure 2.9A&B page 83). For specific viruses, influenza A and B, RSV and hMPV were

highly seasonal circulating mainly in winter seasons, RV and AdV circulated all year round,

whereas hPIV1-3 were predominantly found in summer (Table 4.1S2A&B, pages 211 and

212), and these findings agree with previous studies (9;219;641). A review on prevalence of

respiratory viruses presented in this study, show that globally, RSV is the most predominant

virus among under five children whereas RV and influenza A viruses affect all age groups

(Figures 2.3A&B pages 57 and 58). This study spanned an influenza pandemic period April

2009 – October 2011), therefore our results could be useful in planning for stockpiling of anti-

viral drugs, and planning for hospital beds during pandemics.

272

5.1.5. Limitations of this study

Table 5.2 gives a summary of the research questions and aims of this study and the specific

studies that were conducted to answer those questions. It also summarizes the limitations and

possible sources of bias in each study.

Table 5.2: Summary of research questions and findings

Research question

Study conducted

Main outcome

Remarks on possible sources of bias

What is the

incidence of influenza and

other respiratory virus infections &

hospitalization?

Narrative review of incidence of respiratory viruses & and a primary study on incidence of ARI and Flu A hospitalizations

Incidence of hospitalization was higher in children ≤5 years old and the elderly >65 years old

The data used in the primary study did not have information on patient profiles e.g. comorbidities, nutrition and behavioural factors prematurity and low birthweight and other factors known to increase disease severity. The hospitalization rates could be different if these factors were controlled for.

Did mutations that are known to increase severity

occur in influenza A

viruses during the study period?

A systematic review and meta-analysis on association between mutations on HA, PB2 & NS1 and severity

Mutation HA-D222G increased severe and fatal disease, influenza viruses that circulated during 2009 to 2011 did not have virulent marker mutations on PB2 and NS1 genes

Most of the included studies did not control for comorbidities and viral and bacterial infection. Also there is a possibility of sampling bias. None of the included studies sequenced all the identified viruses. Each study used different sampling methods to select representative viruses to sequence and there could have been bias introduced by these processes.

Which respiratory

viruses most likely co-infect

with influenza A viruses?

Systematic review and meta-analysis on patterns of co-infections between respiratory viruses and influenza A viruses

RSV (4.2 – 26%, RV (4.0 – 9%), AdV (1 – 11%), hBoV (8 – 21%), of co-infections

Most of the included studies did not control for patients vaccination status and predisposing factors such as chronic disease, pregnancy, immune status. The observed co-infection patterns might be different if all included studies controlled for these.

What is the disease outcome

in single and multiple

respiratory virus infections in

general?

Systematic review and meta-analysis and a primary study on the association between single and multiple infection and risk of hospitalization and mortality

Multiple infection, RSV and hPIV1-3, were associated with an increase in risk of hospitalization to a GW, ICU and death

Confounding factors such as patient’s comorbidities & viral and bacterial co-infection were not controlled for by all included studies in the review and not available in the dataset in the primary study, the results could be biased by these.

What is the disease outcome

in single and multiple

influenza A virus infections?

Primary study on association between influenza A virus and other respiratory viruses co-infections and risk of hospitalization and mortality

Co-infection by Flu B, RSV and AdV was associated with an increase in risk of hospitalisation to a GW or ICU/death although only the Flu B analysis was statistically significant

Confounding factors such as co-morbidities, were not available in the data set, the results could be biased by these.

Does hCoV and hBoV co-infect

with influenza A virus in

substantial numbers, if yes, is the disease in

such co-infection severe or mild?

Primary study on association between hCoV and hBoV co-infection with influenza A viruses and risk of hospitalization to GW/ICU

The RT-PCR assays were of adequate analytical sensitivity. Both hCoV & hBoV had a positive association with severe disease but none of the associations was statistically significant

Small sample size used in this study hamper our interpretation of the results. A larger study testing over 1,000 samples including some negative for influenza A viruses is needed to give a clear picture of the sensitivity of the assay, and association between co-infection and disease outcome.

Notes: GW – general ward, ICU – intensive care unit, RSV – respiratory syncytial virus, RV – rhinovirus, AdV –

adenovirus, hPIV1-3 – human parainfluenza virus types 1 to 3, HA – haemagglutinin gene of influenza A viruses,

PB2 polymerase basic 2 gene, NS1 – nonstructural protein. RT-PCR – real-time polymerase chain reaction

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5.1.5.1. Limitations in the systematic review and meta-analysis papers

In the review of the mutations associated with severity of the pandemic influenza

A(H1N1)pdm09 virus, the three major sources of bias included how was sampling of the

viruses that were sequenced done? Was the selection process good enough to achieve a

representative sample of all the influenza viruses circulating during the study period? Secondly,

did the studies control for demographic, comorbidities and bacteria and virus co-infections?

Lastly the number of genes sequenced in each study.

Most of the studies did not control for comorbidities; only 6/18 (33.3%) of the included studies

controlled for comorbidities. Further, only one study each investigated and reported bacterial

and respiratory virus co-infections. Patients with comorbidities such as chronic renal disease,

chronic heart disease, chronic liver disease, chronic neurological disease, atopic dermatitis for

example, have been reported to have more severe form of disease (severity increasing up to 35

fold) compared to those without these factors (9-13). Similarly, bacterial and respiratory virus

co-infections have been associated with more severe outcome (15;59;76;513-517). Therefore,

the observed risk differences in the meta-analysis could have been biased towards the null due

to the crude estimates we have used, higher risk differences could have been observed if

stratified analysis, controlling for these factors, was conducted and this should be born in mind

when interpreting our results.

In addition, the review on patterns of co-infections between influenza A and other respiratory

viruses, apart from comorbidities, factors such as immune status, pregnancy, malnutrition,

obesity, are known to increase risk of infection with respiratory viruses (10;11;13;23). Also

important is vaccination status. Some studies have indicated that influenza vaccination might

increase patients susceptibility to infection by other respiratory virus (561-563). The inclusion

criteria adopted in this review was for studies to have had controlled for at least 2 of these

confounding factors. However, there was a great variation in the number of factors controlled

for by included studies and this might, again, have biased the estimates towards the null, in

those cases where very few confounding factors were controlled for. Therefore this should be

born in mind when interpreting our co-infections estimates. Similar observation are made for

the review on association between co-infections and risk of hospitalization to a GW or the ICU,

risk of developing bronchiolitis, and pneumonia.

274

5.1.5.2. Limitations inherent in the four primary studies

A general observation is that the dataset used to write the 4 primary studies (listed in Table 5.2)

did not contain information on the variables known to affect susceptibility to respiratory virus

infections or affect disease severity. The dataset only contained age, sex, season/year, and

disease outcome (admission to GW, ICU or death). It did not contain factors such as: co-

morbidities [chronic respiratory disease such as asthma and chronic obstructive pulmonary

disease (COPD), diabetes, ischaemic heart disease, chronic renal disease, chronic heart disease,

chronic liver disease, chronic neurological disease], obesity, pregnancy, immune status,

influenza vaccination status and treatment regiments administered, bacterial co-infection,

prematurity, low birthweight, malnutrition, host genetics and mutations in human gene. In

addition, due to cost implications, we did not sequence the influenza or other respiratory

viruses identified in the patient samples included in this study.

Table 5.3 summarizes the prevalence of these factors in the United Kingdom. The most

important risk factors are: obesity (affecting 26.2% of adults aged ≥16 years old and 8.8 – 20%

of children <11 years old - UK); asthma (5.4 million - UK), COPD (3.8 million – England);

diabetes (2.5 million - England); and coronary heart disease (2.3 million - UK). Most of the at

risk individuals, i.e. children >6 months old and ≤5 years old, and the elderly >65 years old,

responded very well to influenza vaccination campaigns during the study period; Vaccine

uptakes reaching up to 74% among the elderly >65 years old in England in 2011/12 season.

However vaccine uptake among pregnant women was lower (51%).

It is, therefore more likely that some of the patients who had samples sent to MMPL for

screening for respiratory viruses, might have had one or two of these factors. Apart from

asthma, leukaemia, and low birthweight, most of these factors affect mainly adults ≥40 years

old. Figure 5.2 gives a plot of the ratio between the numbers of hospitalizations to deaths

among patients who had a sample tested for a respiratory virus at MMPL between 2007 and

2012. There was a positive correlation between age and risk of hospitalization and subsequent

death. For example, among patients positive for any respiratory virus, the hospitalization: death

ratio was 0.01 in ≤5 year olds, steadily increasing to 0.07 among patients ages 40-64 years old.

Similar results are observed for Flu Apdm09 and seasonal influenza A viruses (Figure 5.2).

Given the correlation between age and comorbidities, it would be plausible to conclude that the

deaths also correlate with comorbidities. As these factors are known to increase severity of

respiratory virus infections, and since we did not control for them in our analysis, our estimated

odds ratios, for the association between co-infections and risk of hospitalization and death,

might have been biased towards the null and could have been higher.

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Table 5.3: Prevalence of confounders associated with respiratory virus infections (UK)

Risk factor

Prevalence

Reference

Asthma

During the 2011-2012, a total of 5.4 million people in the UK, of which 1.1 million were children (1 in 11), and 4.3 million adults (1 in 12).

Asthma UK (2014)

(642)

Simpsons & Sheik (2010) (643)

Lung cancer Yearly incidence: 43,463 new cases in 2011 of which 23,770 (55%) were in men and 19,693 (45%) in women, crude incidence rate: 77/100,000 males, and 61/100,000 females, in the UK. 10 year prevalence 38,141 cases (Dec 2006)

Cancer Research UK (2014) (644)

COPD 3.4-3.8 million cases in England and wales (2006 estimate), of which 13.3% were aged ≥ 35 years, annual deaths 25 000

Health and Safety Executive UK (645)

Obesity In 2011, around 26.1% of adults ≥16 years and are obese in the UK; 9.7% of boys 8.8% ( 4-5 years), 20.4% of boys and 17.4% of girls aged 10-11 years were obese in England (BMI ≥30kg/m2)

Public Health England (2014a) (646)

Public Health England (2014b) (647)

Diabetes In 2012, a total of 2,566,436 (5.8%) of the population in England were diabetic

Diabetes UK (2013) (648)

Coronary heart disease

In 2010, there were a total of 2.3 million people living with heart disease in the UK

British heart Foundation (2010) (649)

Chronic renal disease

In 2008/09 there were 1,739,443 people with kidney disease in England with prevalence rates ranging from 1.3 - 5%, for different locations

National Health Service (2010) (650)

Chronic liver disease

in 2009 there were around 17,000 new cases of liver cirrhosis in the United Kingdom, 12 year prevalence 5,118 of which 57.9% were male

Ratib et al., (2014) (651)

Chronic neurological

disease

The rate of neurological disorders lies around 625 per 100,000 population annually, with lifetime prevalence rates of: 9/1,000 for completed stroke; 5/1,000 for transient ischaemic attacks; 4/1,000 for active epilepsy; 3/1,000 for congenital neurological deficit; 2/1,000 (for Parkinson's disease, multiple sclerosis, diabetic polyneuropathy, compressive mononeuropathies), and 1/1,000 for sub-arachnoid haemorrhage.

MacDonald et al., (2000) (652)

HIV In 2012 a total of 77,610 were living with HIV in the UK. Prevalence; 1.5 per 1000 for all age groups (2.1 per 1000 for men and 1.0 per 1000 for women)

National AIDS Trust UK (2014) (653)

Leukaemia In 2010, there were a total of 8,257 new cases of leukaemia in the UK, of which 4,816 (58%) were in men and 3,441 (42%) in women (annual incidence 6/100,000 and 11/100,000 in males and females respectively)

Cancer Research UK (2013) (654)

Flu vaccine uptake

At the end of 2011/12 season, 74.0% of people ≥65 years, 51.6% of those aged ≥6 month’s ≤65 years, and 50.8% of pregnant women had been vaccinated – in England (WHO target is 74%)

Public Health England (2013) (655)

Low birth weight The 2009 statistics indicated that around 7% of live births, in England and Wales, were below 2,500 grams at birth

Poverty.org UK (2014) (656)

Notes: Some of the samples used in three primary studies in this thesis, were from patients admitted in specialized

wards e.g. oncology ward, maternity ward etc. indicating they had cancer as an underlying condition, were

pregnant etc. However, we did not use this variable to assign comorbidities because such information was missing

among patients that were seen as outpatients. However, this alone confirms that some of the patients had these

factors.

276

Figure 5.2: Hospitalization to death ratio for patients who were positive for any respiratory

virus infection, and patients positive for pandemic influenza A(H1N1)pdm09 and seasonal

influenza A viruses in NW England (2007 – 2012).

Lastly, studies have shown that in addition to other factors, the virulence of influenza viruses is

also driven by changes on the HA, PB2 and NS1 genes (18-20;34;37). Since this study was

conducted during the pandemic period, and since during this time the seasonal influenza A

viruses that circulated are well characterised, a review was conducted on the epidemiological

evidence on occurrence of virulent conferring mutations in patients with severe or mild disease

in the UK and globally. Important mutations in the circulating viruses included the HA-

D222G/D222E, and Q310H the PB2-K340N and NS1-T215P and I123V (482;496;507;508).

However no virus quasispecies, with all the well characterised virulence mutations on influenza

HA, NS1 and PB2 genes, circulated and evidence from a UK study found these mutations both

in severe and mild cases (607). Therefore, although this study did not sequence the influenza A

viruses included, it is unlikely that the observed severity were due to influenza virus mutations

and could rather be attributed to co-infections within the discussed context. The mutations on

the other respiratory viruses, RSV, AdV, hPIV1-3, hMPV, hBoV and hCoV are not routinely

tested. Therefore, the impact of mutations, in these viruses, on viral virulence should be born in

mind when interpreting the results of this study.

277

5.1.5.3. Possible selection and diagnostic bias in the primary studies

In addition to the above, a considerably large number of samples that were received at the

MMPL during the study period; 35.9% (11,112 of the 30,975), were from children ≤5 years old

despite children comprising of only 6.1% of the general population. If this was a result of

preferential collection of samples in children by doctors, it would have caused selection bias. In

addition the age of included and excluded samples differed significantly and this could be a

source of diagnostic bias. However, the majority of samples 64.1% (19,863/30,975) were from

patients >5 years old despite the majority of positive cases 51.2% (6,065/11,715) being identified

in children ≤5 years. Evidence reviewed in this study in Section 2.2 (incidence of respiratory

virus infections as reported by community based and hospital based studies), indicate that

respiratory viruses are indeed higher in ≤5 year children. It is therefore unlikely that the results in

this study are due to selection or diagnostic bias.

Further, not all the samples that were received at the Manchester Microbiology Partnership

Laboratory were tested for all the respiratory viruses. Table 5.4 presents the number of samples

that were tested for influenza A viruses that were also tested for other respiratory virus

infections. The number of samples that were not tested for influenza B (112; 1,759) was

comparatively fewer than those that were not tested for RSV (4,952; 5,890) and much larger

figures for the other respiratory viruses (Table 5.4). This might have affected the observed

outcomes in risk of hospitalization to a general ward, admission to the intensive care unit, and

mortality associated with co-infection between influenza A and other respiratory virus infections.

Probably most of the odds ratios would have reached statistical significance if all the samples

were tested for all viruses.

Table 5.4: Number of samples tested for influenza A viruses that were also tested for other

respiratory viruses

Primary virus

Flu B

RSV

RV

AdV

hMPV

hPIV1-3

Seasflu A & other RVIs 25,853 21,013 18,195 20,900 17,849 8,610

Total tested Seasflu A 25,965 25,965 25,965 25,965 25,965 25,965

Not tested for other RVIs 112 4,952 7,770 5,065 8,116 17,355

Flu A pdm09 & other RVIs 19,821 15,690 12,575 15,527 12,165 2,225

Total tested Flu A pdm09 21,580 21,580 21,850 21,580 21,580 21,580

Not tested for other RVIs 1,759 5,890 9,275 6,053 9,415 19,355

Notes: Flu A pdm09 – pandemic influenza A(H1N1)pdm09 virus, SeasFlu A – seasonal influenza A

viruses, RSV - respiratory syncytial virus, RV - rhinovirus, hMPV – human metapnuemovirus, hCoV -

human coronavirus, Flu A/B - influenza A or B, AdV - adenovirus, PIV parainfluenza virus types 1 to 3.

278

HBoV have been reported to cause significant disease and to co-infect with other viruses (87;88),

and the role of hCoV and hBoV co-infections on disease outcome might not be clearly elucidated

by this study. Since human coronavirus and human bocaviruses were not routinely tested during

the study period, we designed pan-coronavirus primers and used previous designed primers for

hBoV to identify these viruses. Both the hCoV and hBoV PCRs had adequate analytical

sensitivity, and could therefore be used for diagnosis of these viruses. The fact that a positive

association between co-infection between these viruses and influenza A (though not statistically

significant), despite only a few samples being tested suggest their importance in influenza like

illnesses. Therefore routine testing for these two viruses should be piloted, or a larger study on

their co-infection with influenza A be conducted; as these would further explore this possible

association.

5.1.6. Summary of limitations in the study design and recommendations for future designs

The above mentioned complexities are an example of the shortfalls of observational studies

where bias arises due to lack of completeness in the information obtained from secondary

sources, as it was not primarily collected for use in studies. In addition, we employed a cross-

sectional study design to make inference on possible association between co-infection and

disease severity. Cross-sectional studies cannot establish temporal relationship between exposure

and effect, therefore it is difficult to make causal inference from them. For example in the

respiratory virus co-infections, it is difficult to ascertain whether influenza A virus infection

occurred first, then other respiratory viruses followed. Some of the patients must also have had

bacterial co-infection. It again is difficult to ascertain whether, in those patients, influenza/other

respiratory virus infection was the first to ensure then a bacterial infection followed. In the

absence of patients’ hospital records, it is difficult to ascertain whether there were other

competing causes of hospitalization or death, other than respiratory virus infection(s).

However, cross-sectional studies are useful for understanding disease aetiology and generation

of hypotheses of possible causal associations. They are useful for the assessment of possible risk

factors and to assess most outcomes [Rothman et al., (657)]. Therefore the associations observed

in our study should be interpreted as such. Future studies could employ longitudinal follow-up or

serial study design (on virus and bacterial colonization and disease outcome); collecting

information on patients’ comorbidities and other variables described in the preceding chapters.

Such studies could further the understanding on the association between co-infection between

respiratory viruses and influenza A viruses, or among respiratory viruses themselves, or with

bacteria, and disease outcome.

279

5.1.7. Summary of the major findings and recommendations

In conclusion, despite any shortfalls, this study has found that:

1. Flu B, RSV, RV, AdV and hBoV are the respiratory viruses most likely to co-infect with

influenza A viruses. Multiple testing for these respiratory viruses, in samples being tested

for influenza A viruses, is recommended. Multiplex real-time PCR or alternative multiplex

nucleic acid amplification procedures for these or all respiratory viruses, would be an

economical way of achieving this.

2. Influenza B, RSV and AdV are the co-infecting viruses most likely to, co-infect with

influenza A viruses to numbers, large enough to cause public health concern and, be

associated with increased risk of hospitalization and mortality. Research on vaccines (e.g.

AdV- serotype 5 based with Flu A/RSV inserts vaccine and drugs targeting Flu A,

RSV/AdV attachment proteins), combined drugs, and multi-target tests for these viruses is

recommended.

3. RV co-infection with influenza A viruses might interfere with severity of influenza A

viruses, further research is needed to understand the biomedical mechanisms in their co-

infection and the role of RV co-infection with other respiratory viruses on disease outcome.

Results of this research could aid in drug and vaccine development.

4. In general the greatest burden of infection and co-infection, was in children ≤5 years old

years old, whereas hospitalization was predominantly in children ≤5 years old and the

elderly ≥65. This result agrees with the policy of vaccinating these age groups.

5. This study found that males are more susceptible to respiratory virus infection than females.

Educational programmes aimed at raising awareness on this subject, with the aim of

improving public health, are recommended. More research aimed at understanding the

gender specific, environmental, biomedical, and other risk factors associated with this

difference is encouraged.

6. In NW England, UK, it is more likely to have high influenza A and B, RSV and hMPV

circulation during winter seasons; RV and AdV activity all year round and hPIV1-3

predominantly in summer. This result could be helpful in planning public health

interventions, and during epidemics and pandemics planning for hospital beds.

280

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List of appendices

Appendix I: List of papers included in this study

No

Title of the manuscript

Paper type

Journal submitted

Status

Date

submitted or published

1 The incidence of medically attended respiratory virus infections, hospitalization, and mortality in North West England, 2007 – 2012

Primary paper

Eurosurveillance Presented as a poster at the Society for Medical Microbiology Spring Conference, Manchester

Under review

30-Apr-14

2 Genetic mutations associated with pathogenicity of pandemic influenza A(H1N1)pdm09 virus: A review

Review Archives of virology Under review

25-Apr-14

3 Co-infection patterns between influenza and other: A systematic review and meta-analysis respiratory viruses and its effect on viral load and interferon production

Review Lancet Infectious Diseases Under Review

29-Apr-14

4 Single and multiple respiratory virus infections and severity of respiratory disease: A systematic review

Review Paediatric Respiratory Reviews Poster presented at the XV International Symposium on Respiratory Viral Infections. 14 – 17 March 2013, Rotterdam, the Netherlands - The Macrae Foundation

Published 16-Nov-13

5 Single, dual and multiple respiratory virus infections and risk of hospitalization and mortality

Primary paper

Epidemiology and Infection Poster presented at the University of Manchester’s Faculty of Medical and Human Sciences poster day, 26 January, 2012.

Published 28-Jan-14

6 Influenza A viruses dual and multiple infections with other respiratory viruses and risk of hospitalization and mortality

Primary paper

Influenza and other respiratory viruses

Published 22-Aug-12

7 Influenza A viruses co-infection with human coronavirus and bocavirus and risk of hospitalization: Use of SYBR Green RT-PCR assays for virus identification

Primary paper

Diagnostic Microbiology and Infectious Disease Journal

Under review

02- Mar-14

328

Appendix II: ICD-10 major diagnosis codes used to determine the number of medically

attended influenza like illnesses

ICD-10 Code

Major diagnosis

ICD-10 Code

Major diagnosis

J00.X

Acute nasopharyngitis [common cold]

J20.9

Acute bronchitis, unspecified

J02.9 Acute pharyngitis, unspecified J21.0 Acute bronchiolitis due to respiratory syncytial virus

J04.0 Acute laryngitis J21.8 Acute bronchiolitis due to other specified organisms

J04.1 Acute tracheitis J21.9 Acute bronchiolitis, unspecified

J04.2 Acute laryngotracheitis J22.X Unspecified acute lower respiratory infection

J05.0 Acute obstructive laryngitis [croup] J31.1 Chronic nasopharyngitis

J05.1 Acute epiglottitis J31.2 Chronic pharyngitis

J06.0 Acute laryngopharyngitis J37.0 Chronic laryngitis

J06.8 Other acute upper respiratory infections of multiple sites

J37.1 Chronic laryngotracheitis

J06.9 Acute upper respiratory infection, unspecified

J39.9 Disease of upper respiratory tract, unspecified

J10.0 Influenza with pneumonia, influenza virus identified

J40.X Bronchitis, not specified as acute or chronic

J10.1 Influenza with other resp manifest influenza virus identified

J41.0 Simple chronic bronchitis

J10.8 Influenza with other manifest influenza virus identified

J41.1 Mucopurulent chronic bronchitis

J11.0 Influenza with pneumonia, virus not identified

J41.8 Mixed simple and mucopurulent chronic bronchitis

J11.1 Influenza with oth resp manifestation virus not identified

J42.X Unspecified chronic bronchitis

J11.8 Influenza with other manifestations, virus not identified

J44.0 Chronic obstruct pulmonary dis with acute lower resp infec

J12.0 Adenoviral pneumonia J44.1 Chron obstruct pulmonary dis wth acute exacerbation, unspec

J12.1 Respiratory syncytial virus pneumonia J44.8 Other specified chronic obstructive pulmonary disease

J12.2 Parainfluenza virus pneumonia J44.9 Chronic obstructive pulmonary disease, unspecified

J12.8 Other viral pneumonia J47.X Bronchiectasis

J12.9 Viral pneumonia, unspecified J70.8 Resp conditions due to other specified external agents

J17.1 Pneumonia in viral diseases classified elsewhere

J70.9 Respiratory conditions due to unspecified external agent

J18.0 Bronchopneumonia, unspecified J80.X Adult respiratory distress syndrome

J18.1 Lobar pneumonia, unspecified J81.X Pulmonary oedema

J18.2 Hypostatic pneumonia, unspecified J82.X Pulmonary eosinophilia, not elsewhere classified

J18.8 Other pneumonia, organism unspecified J84.0 Alveolar and parietoalveolar conditions

J18.9 Pneumonia, unspecified J84.1 Other interstitial pulmonary diseases with fibrosis

J20.3 Acute bronchitis due to coxsackievirus J84.8 Other specified interstitial pulmonary diseases

J20.4 Acute bronchitis due to parainfluenza virus

J84.9 Interstitial pulmonary disease, unspecified

J20.5 Acute bronchitis due to respiratory syncytial virus

J96.0 Acute respiratory failure

J20.6 Acute bronchitis due to rhinovirus J96.1 Chronic respiratory failure

J20.8 Acute bronchitis due to other specified organisms

J96.9 Respiratory failure, unspecified

Notes: The week and month number were calculated using the date of admission. The admission could have been

initiated by the consultant responsible for the outpatient attendance or 01 = following an emergency admission, 02 =

following a domiciliary visit, 10 = following an accident and emergency attendance, or 11 = other (not initiated by the

consultant responsible for the outpatient attendance: 03 = referral from a general medical practitioner, 04 = referral

from an accident and emergency department, 05 = referral from a consultant, other than in an accident and emergency

department, 06 = self-referral, 07 = referral from prosthetics, 12 = referral from GP with special interest, 13 = referral

from a specialist nurse (secondary care), 14 = referral from an allied health professional, 15 = referral from an

optometrist, 16 = referral from a orthopaedists, 17 = referral from a national screening programme, 92 = general

dental practitioner, 93 = community dental service, 08 = other source of referral, 97 = other – not initiated.

329

Appendix III: Number of specific respiratory virus infections identified by different studies

globally

Paper

Sample

Size

Flu A/B

Flu A Flu B RSV RV AdV hMPV hCoV hBoV hPIV1_4

1 Martin et al., (82) 893

14.22

24.97

12.77

7.61

6.72 2 Oçallaghani-Gordo et al., (583) 333

15.32

12.31

3

25.83

14.11

6.61

4.8

3 Sutmoller et al., (307) 827

1.33

33.62

4 Singleton et al., (92) 1,073

2.42

11.18

31.13

18.55

8.85

2.8

8.39

5 Venter et al., (595) 610

2.95

21.31

33.44

4.43

4.75

3.44

5.41

6 Khor et al., (298) 10,269

2.89

2.27

0.62

18.63

1.37

3.48

7 Malekshahi et al.,(573) 202

5.45

4.95

0.5

16.83

14.36

0.5

8 Weigl et al., (658) 18,899

7.9

6.53

1.31

14.45

17.21

8.23

3.26

1.62

5.12

9 Ruutu et al., (659) 318

21.07

15.41

5.66

6.92

3.77

8.49

10 Ajayi-Obe et al., (231) 977

6.04

5.94

0.1

22.93

1.02

1.33

11 Brandit et al., (660) 484

1.86

0.41

1.45

4.13

1.03

3.51

12 Wake et al., (661) 919

19.48

22.85

26.01

6.96 8.6 7.94

13 Kristoffersen et al., (571) 452

7.3 31.42 20.13 9.73 12.17 15.04 12.17 9.29

14 Naghipour et al., (574) 151

7.28 25.83 24.5 13.91

15 de Vos et al., (575) 404

9.16 38.12 14.36 6.93 12.62

16 Ren et al., (564) 5,808

19.27

0.52

6.47

0.88

0.33

1.12

4.34

17 Tsai et al., (321) 6,986

8.09

5.5

2.59

1.72

3.97

1.96

18 Lin et al., (662) 910

12.97

6.26

6.7

0.33

9.12

0.77

19 Yu et al., (663) 416

16.11

17.79

11.3

2.16

11.78

0.96

20 Alonso et al., (664) 4,403

5.7

18.1

1.86

3.25

4.86

21 Sasaki et al., (665) 1,498

53.67

50.33

3.34

5.61

22 Kouni et al., (666) 611

9.17 36.82

11.95

5.07

2.45

0.33 4.09

19.31

23 Thiberville et al., (667) 660

19.7

5.15

0.45

0.15

0.91

1.06

24 Tsuchiya et al., (668) 1,348

5.49

3.93

1.56

20.4

0.96

6.53

25 Drews et al., (75) 4,336

10.89

7.01

3.87

3.39

10.98

2.88

1.18

3.18

26 Razanajatovo et al., (302) 313

14.38

12.78

1.6

17.25

12.14

4.15

0.96

3.83

0.64

27 Greer et al., (541) 1,247

2.73

5.69

26.54

2.49

5.37

4.33

8.1

2.89

28 Laguna Tores et al., (576) 1,756

10.08

7.4

2.68

6.95

3.59

0.17

3.25

29 Nisii et al., (577) 544

21.51

19.85

1.65

1.29

6.25

1.29

0.18

1.47

9.19

30 Druce et al., (582) 4,254

34.98

30.42

4.56

11.64

33.64

4.91

2.49

5.34

6.79

31 Chew et al., (297) 12,354

3.45

2.7

0.74

22.81

0.1

1.63

3.42

32 Tanner et al., (579) 4,821

9.91

9.73

0.19

17.69

14.85

4.48

5.72

4.9

33 Al-Turab et al., (619) 388

1.55

1.29 0.26 3.87 9.79 3.09 6.7 0.26

34 Renois et al., (578) 95

33.68

2.11

25.26

3.16

2.11

12.63

35 Lina et al., (669) 962

11.54

6.65

4.89

11.23

3.64

2.29

6.65

36 Esper et al., (81) 496

2.82

10.89

1.61

1.61

0.4

4.23

37 Wallace et al., (580) 240

55.83

1.25

4.58

38 Peci et al., (90) 1,018

44.4

11.79

19.06

0.59

0.39

0.88

4.52

39 Rhedin et al., (91) 502

16.53

2.39

28.09

5.78

0.4

3.39

5.78

6.57

40 Weissbrich et al., (559) 785

12.48

20.13

6.75

11.08

4.97

41 Echneque et al., (94) 1,192

26.34

3.94

20.64

3.36

1.68

330

Appendix III continued: Number of specific respiratory virus infections identified by

different studies globally

Paper

Sample

Size

Flu A/B

Flu A Flu B RSV RV AdV hMPV hCoV hBoV hPIV1_4

42 Raboni et al., (670) 171

48.54

47.95

0.58

9.94

36.26

4.09

7.02

2.92

5.26

43 Pretorious et al., (581) 8,173

8.6

5.87

2.73

14.3

24.89

13.25

3.71

0.56

44 Chanock et al., (203) 528

4.92

3.22

1.7

15.91

5.3

20.45

45 Aberle et al., (80) 772

3.37

21.89

24.61

3.11

4.4

46 Forgie et al., (671) 133

2.26

25.56

5.26

5.26

2.26

47 Madhi et al., (225) 2,715

2.69

8.84

1.18

4.64

1.92

48 Boivin et al., (566) 259

18.92

45.56

2.32

4.63

49 Richard et al., (76) 186

3.23

37.63

20.97

3.23

5.91

3.76

6.99

50 Huguenin et al., (593) 138

87.68

7.97

22.46

12.32

51 Calvo et al., (85) 749

0.67

26.03

8.54

3.74

1.74

0.53

5.61

1.6

52 Franz et al., (516) 404

2.72

2.23

0.5

39.6

21.53

6.93

3.96

1.73

6.93

4.95

53 Christensen et al., (672) 376

7.71

24.73

17.29

6.38

14.36

10.9

54 Portnoy et al., (79) 258

8.91

3.49

5.43

33.72

24.81

20.54

55 Tan et al., (673) 500

1.2

0.8

0.4

11.8

0.2

5.8

0.6

8.0

2.4

56 Park (674) 2,209

2.67

2.44

0.23

16.57

3.62

3.85

57 Suwanjutha et al., (675) 242

10.33

7.02

3.31

50

16.53

32.23

58 Jain et al., (676) 736

5.71

4.08

1.63

5.03

3.26

59 Kwofie et al., (677) 128

1.56

15.63

10.94

60 Khamis et al., (568) 259

5.79

21.62

5.41

10.42

1.16

0.39

3.09

7.34

61 Zhang et al., (678) 1,795

2.06

1.06

2.06

0.78

0.56

0.33

3.9

62 Banerji et al., (604) 220

3.64

28.64

7.73

8.18

0.45

0.45

63 Suryadevara et al., (679) 221

1.36

47.06

22.17

3.17

7.69

10.41

8.14

64 Canducci et al., (680) 322

27.95

14.29

8.7

2.48

65 Jennings et al., (565) 75

13.33

9.33

4.0

48

14.67

13.33

5.33

5.33

66 Mansbach et al., (681) 277

6.86

63.54

15.88

9.39

67 Chung et al., (682) 231

14.29

31.17

5.63

9.96

68 Midulla et al., (683) 182

0.55

41.21

8.79

1.65

0.55

12.09

1.65

69 Marguet et al., (684) 209

64.11

26.79

7.66

70 Wolf et al., (569) 516

14.92

19.57

2.13

6.98

71 Foulogne et al., (570) 589

3.06

28.01

3.06

9.0

72 Pierangeli et al., (685) 227

4.85

4.41

0.44

17.18

9.69

2.2

3.52

3.08

7.49

73 De Paulis et al., (560) 304

57.89

6.58

3.95

2.63

74 Miller et al., (686) 592

3.04

19.93

25.84

3.04

6.93

75 Garcia-Garcia et al., (687) 884

1.81

19.23

17.76

12.44

3.39

1.02

11.31

3.28

76 Cheuk et al., (614) 426

7.51

6.57

0.94

6.81

35.45

3.05

3.76

3.99

5.87

77 Peng et al., (612) 316

59.49

30.7

28.8

1.9

24.37

16.77

11.71

78 Do et al., (517) 309

16.5

27.18

3.56

4.85

6.8

7.77

16.18

6.15

79 Weigl et al., (688) 3,469

3.08

2.59

0.49

4.67

2.83

80 Kim et al., (689) 1,389

6.05

4.75

1.3

11.81

3.89

6.48

81 Yun et al., (690) 712

1.97

14.04

1.97

5.2

82 Stempel et al., (691) 180

1.11

95.56

26.67

15.56

13.89

7.78

331

Appendix IV: Accession numbers for the coronavirus sequences that were downloaded

from GenBank and used to design the pan-coronavirus primers

Virus name

Accession number

Virus name

Accession Number

Human coronavirus HKU1 DQ415903.1 Avian infectious bronchiolitis JF330899.1

Human coronavirus HKU1 DQ415904.1 Avian infectious bronchiolitis JF330898.1

Human coronavirus HKU1 DQ415905.1 Avian infectious bronchiolitis EU637854.1

Human coronavirus HKU1 DQ415906.1 Avian infectious bronchiolitis FJ904723.1


Human coronavirus HKU1 DQ415908.1 Avian infectious bronchiolitis GQ504725.1





Human coronavirus HKU1 DQ415913.1 Avian infectious bronchiolitis AY851295.1






Human coronavirus HKU1 DQ415900.1 Avian infectious bronchiolitis AY514485.1

Human coronavirus HKU1 DQ415901.1 Avian infectious bronchiolitis GU393331.1


Human coronavirus HKU1 AY884001.1 Avian infectious bronchiolitis GQ504722.1

Human coronavirus OC43 JN129834.1 Avian infectious bronchiolitis GQ504720.1

Human coronavirus OC43 JN129835.1 Avian infectious bronchiolitis GQ504721.1

Human coronavirus OC43 AY585228.1 Avian infectious bronchiolitis FJ888351.1

Human coronavirus OC43 AY903460.1 Avian infectious bronchiolitis FJ904715.1

Human coronavirus NL63 NC_005831.2 Avian infectious bronchiolitis DQ834384.1

Human coronavirus NL63 DQ445911.1 Avian infectious bronchiolitis AY692454.1

Human coronavirus 229E NC_002645.1 Avian infectious bronchiolitis EU418975.1

Scotophilus bat coronavirus NC_009657.1 Avian infectious bronchiolitis EU418976.1

Bat coronavirus 1B NC_010436.1 Avian infectious bronchiolitis NC_001451.1

Bat coronavirus 1A NC_010437.1 Avian infectious bronchiolitis FJ807652.1

Bat coronavirus HKU8 EU420139.1 Avian infectious bronchiolitis GU393335.1

Bat coronavirus HKU2 EF203067.1 Avian infectious bronchiolitis EU526388.1

Bat coronavirus HKU2 EF203065.1 Avian infectious bronchiolitis AJ311317.1

Bat coronavirus HKU2 EF203066.1 Duck coronavirus JF705860.1

Bat coronavirus HKU2 EF203064.1 Murine hepatitis NC_006852.1

Bat coronavirus HKU8 NC_010438.1 Murine hepatitis GU593319.1

Bat coronavirus HKU2 NC_009988.1 Murine hepatitis AY700211.1

Bat coronavirus HKU4-1 NC_009019.1 Murine hepatitis AF208067.1

Bat coronavirus HKU4-2 EF065506.1 Murine hepatitis AF208066.1


Bat coronavirus HKU4-4 EF065508.1 Murine hepatitis AB551247.1

Bat coronavirus HKU5-1 NC_009020.1 Murine hepatitis AF029248.1


Bat coronavirus HKU5-2 EF065510.1 Murine hepatitis NC_001846.1

332

Appendix IV Accession numbers for the coronavirus sequences that were downloaded

from GenBank and used to design the pan-coronavirus primers - continued

Virus name

Accession number

Virus name

Accession Number

Bat coronavirus HKU5-3 EF065511.1 Murine hepatitis FJ884686.1






Bat coronavirus HKU9-5-1 HM211098.1 Murine hepatitis FJ647223.1




Bat coronavirus DQ648857.1 Murine hepatitis FJ647227.1

Bat coronavirus DQ648794.1 Bovine coronavirus DQ811784.2

Bat coronavirus DQ648858.1 Bovine coronavirus AF220295.1

Bat coronavirus DQ648856.1 Bovine coronavirus FJ938063.1

Bat SARS Coronavirus HKU3-5 GQ153540.1 Bovine coronavirus U00735.2

Bat SARS Coronavirus HKU3-6 GQ153541.1 Bovine coronavirus AB354579.1

Bat SARS Coronavirus HKU3-7 GQ153542.1 Bovine coronavirus EF424615.1



Bat SARS Coronavirus HKU3-10 GQ153545.1 Bovine coronavirus FJ938064.1




Bat SARS Coronavirus HKU3-1 DQ022305.2 Sambar deer coronavirus FJ425189.1

Avian infectious bronchiolitis GU393338.1 Waterbuck coronavirus FJ425185.1



Avian infectious bronchiolitis GU393337.1 Equine coronavirus EF446615.1

Avian infectious bronchiolitis GU393333.1 Porcine haemagglutinating encephalomyelitis DQ011855.1

Avian infectious bronchiolitis JF828980.1 Porcine haemagglutinating encephalomyelitis NC_007732.1

Avian infectious bronchiolitis JF828981.1 Canine coronavirus GQ477367.1

Avian infectious bronchiolitis JF274479.1 Rat coronavirus Parker NC_012936.1

Notes: These viruses were downloaded from the GenBank website http://www.ncbi.nlm.nih.gov/

333

Appendix V: The consensus sequence that was used to BLAST hCoV primer pair

CRCTAAGAAGGTTAACCGGTAYTARTTGAAGTAAGTAAATGATYMMKKAMCSWCCWTWKMATKGGACTACRMYCGAG

AAGGGKCMMYCCGYGRAYARSGGCAAGAHYRYYAATSWRSRYSYTSRRAMRDTKDAAGAGRKTGTGATAWGWRATRD

WRYGTTAKWGAAAGTTGATTAAYTAKRTMYCAGAYGRTRTTGGAAGAGATAGAYAATAYYGTCGTTCTGTAGGTAAG

ACTATCTCTCACRGRATAGRGCTGAAARRRCKCTCCCCAATGGTGGYYYRCGGCTKWATYCYAAGTAAGAGWAATDY

RWWRWCTCMRYAGAAGGTTGGGTAGWHYRYDYYSCGGKAGYAGDTCCTMKMMKMRKTYDTYYGAHDMYAGGAGWCSY

CADTYDAGAGAYRARHDTWAMRCGACKYRYRSBYGWRAAYYCGHVAYKYTSYSARTDTRRWAGWWDAMSWDKAAMDR

VKCGGCAAMGGWVGWVDRVDVMDRRWGMGAMHSMRAMHCCDRADYYGCYKAKTVWWRKHVRBRDRRYDRDMGDMBWA

MDWSYATHCGYAARWTTAAGABHAAKTYKTGRWMGKAHYAHCMRAGYTTTGGWCYHTYWCAYRAYAGTTTRGAYTRD

CKYGGHMHTATYRYKTYRAGTWTKTGTASRAABMMYCCRMKYAGTYBYRDKRGTYWDYWWDAKACRTRAWAYYGGTT

RAGATYARAWCTAGGTTTCWWYTTNCGTGGWCSACAHCCHMGNKYRAGACGTTCTTAYCCYTWGACDCCBAGTRATC

AWGRWTSRAAATTGTAAAGDSSHRRHGGYTTTAARACTMAHYCHRGRRARRWDKKWKWYKTGAMRWMKTKWHVYRKD

WSRRRBAMHYCHSADRMSRRRMMCSCBCMDMWWAARWKTWWMAMNACRGAYWNRAGDAAMSRYVANAMKWRTCRWWR

VWCGRHNCCDRMWSRMCDTAGRAWYSGWYMVMRNSKGKBKWKYYMBNDTYRYWRVWNHWRBYKGTWYAKKCYAVARY

KWBRTSTBMVYCBNVVHSHKHHHRRYMYYDAYVDASWYVWNBNDCTHGSWMNTVWHCHVSSWGYHBDRMYYAYNNRS

NCTYSRHCYAKMYSWTMVKMBDYGKYKRSRMGKDYTCGRMMKTTRTMYBHYHKRRWSWVVVHHWHDRNHNVRHYHSM

VHHWNRMWSVKVYHKWDVRRWKWRNYGMYBANHMNRNDVVWHDBHNYKRHWKYHBNHHYYWWMWYNRNAVKBRNVYB

NVYRDWVDNHKDRWYHYSHNHKDNRRHKHHVDMMDNDRBHKNVHHVGDRDYBSYDBNYCVVDVSHDRKNWNBRKSRK

HTYYRWRBWVRVHYHNHHDTNVDNNDHVHYNHVNDDYADYHSRHWWDMYTHYSWBRYNSMMARVMMRRRHHNVRKBW

TNVVRMRWHVDVDDYNTVNHYNVBVAVGNVHNMRBYRBBYRTRNRYCVRWMNRBDVGWYRTDYVVNHDBNRGRRMDK

GVHVVWBNKWVVAARAHYNNMMTBRVRDBSHBHTRDWBBWHMYYAHNBYNWNDYRDNWDBBDNVWNNRBBDRCDMST

BWKBDNYKTYVNDYDRMVHBRRHHNNDVHVBWYRRRGATAAHGBNHRWDHHHNHNRHBYKWNYYDNRBHWGYHDKRY

HMDYYNDRDBHWYHVCWHHRSYWAWRSHHKYKBYMVRKVHHWHHDNNNNDDYKNHBRHBNNHRNHSDWNBVRBYMHY

RYWHKVHDTMSBMDTNHNRVDDHTMHWHYNRMBAVDHDWKHVRYYDBAGKYDYRRDCYYVDDNVWVNHNMWYKHSBN

NNHGVNRTYWAHDDBTKRDVHBNSMRDHRYWDDTDNMGDYBDNSNSRTHVNDVBRVNDDRDVYMSDHHHYWNBDHSN

NWMHDNDHRDHDHDNHHDVRMNNWDDWAHHDNHRHHDDDRHRNDHDHVBSYSRSWHHWHVWBBKNNDHHHNNHRNHB

RHNVHKBRHHDDNYDVNVYNNDNWHBBNBHHNNNNRVHDVDDNNHWBHVNHMNNHNVNHNVHDHHVBDMDNHRNHVN

BBVKRYBDNDNDDNNNDDVNDHHNYNWNVHYRHRNKDVNNNBAMHVHRNHRWBNHVYHVBNBHBDNDDNNDHDWDVN

YYWVDRHAYNDHBDHBDDVHDRNDDYNMDBDYBYHDWDNNNNVRVYRDDHHRNHNNHNDDVNNTRBHHNWHBBDHYD

HBHBDHNRNHYHHDHDHRHHRYVRNNNNYRNDYNDBYDDNMWYHWHDVNNDDHNNWVNWDNMANKHNHHBKDRYDHY

HVNKBDNNDHHYDSWDRRAKYNYNVHVBNDDDKNBHNHDMYDVYDNNNWYHNWDDYBHDMBNTRWBWHDHDVDBNNK

DWBDHNBRHNNHDHNNHNDDNDNBWHHBYVWRSWAVBNDRWWBHHBDVVHBNHBSRNVWBHMVNMBNHHDVDHNNNW

NYNDHNHDHBVHVNVVDHNHYVHHNDVRBVRVHDYYKBSDHNRHNHHNVNNVNDHNBHDVDWNRHBHNNDHHYNVNN

SNNVNHVHDBDMSDRMBWHWHBRDVBHHNNBHVRHRNDTNVDVDWNNTNHKDRWRYNNNYHBWVNHVWNNNDDNNNH

NBRHDRBBHNWNHRRNNDBBMVHHBHDDNDBHBNWVBHWYMDVYRDNHBDWNHDDYSNHDNKDVBMVHNNBDBYNDB

HHRNDYNNNDNBVVHNNHHBYNNBRNWYRVVNYRYWNRRBDHVDHHBVVHHVHHBNDDHHDYRVWDDVHNBWNDNND

NVRNKYDNNVVNHNNNVHWBHNVHYHYNNHVHNYYHVKNBWNVDDHBHHNHTRDHHDHDHVHNDKNNRNVNNVNNYC

NWBDDHDHVDNHNNHNNYDNHNHVDYNRHNDNYDHRDHYDHNNDWNHDNDDNNHHRVRSMBWRNYNNHVNWHNHYVH

BNNVMNVDWVBWNHNRMYSBDHNNHNHVMHSNDNHKHDDRNHNHHVNDDVDBHYVWWNDNBRNDDNNCGHCWACCSW

TATTAATTTRACYTTYGTTTTTAGAAGWTCMGCTYAYTGYGGTTTCAGDGCGCANGMCGCATYGGTHCGYAGTGAGT

GTWCGGTWTASAGGTACAYCGACMRCCVMNDNHNDNNNHHHBNDNWDBVRVYYHNVHNNDNNYNDNWKDWHHWVAYN

HYNDNHYSHRDYNHHRVHNDNNDWNHDDNHNHNDNDMHNNHNDNNBNNVHKNHHBDNWRNHWNNNRDNBMYNHWHNN

VNWNNNHWNDRNRNBDHHRRWHNYBHYRARHWYSMDKMTBNHDDHBDKAHBBWWHHYDDHMBYHMRNNDHRVBVNWR

VHMSHWHYDVBMHHHWWKSRHYWBMHHNNYYDBWRYNDHBDRHMBHDYDNYNRDDDDVHDYVVYWHNDTHMYBRHMM

HKWBYHNDHDWRNAMKYWWRYKDKAYKTRKWWGWTMCSRHYAYSGRTMRYMKKWACGWYSWWATKWWYHWBVYDNHN

HBHDHMRYVKGWBVHDWSDVRHBMBYRRHNNVHNYVRBHAYHKNWDABNDVVNRDWDHYRHYSMSDRBWYHWHBYTA

WDYDKASDVWVYKHMVHWHWWNWWANWBVMRBYHDBDBYNWHBHMKDYNVNBNBNDNNVNBDSBVNYNBDYWHHDNN

MHWAHACDKVNYVNBNNBNDNDNHBNRHDNNDWBVWWKBNNHHSDNKWNHDVNNDNDNDWDHBNDYYWNDHHVNDND

BDDWVHNRRNVHHNDNNHNDHNBHHHDHBKHHNDBDNYVTNNDDDTHRRWDDBKDBDNYHHVDHYWVBHNYYVBHDK

RYHBBBTKNVBKKNHRDYKBRHDHNDRDYWYHHDNNVHHDNKWWVNNRVYBYNDRHYRYNDNBNNWNWHDDBDNNND

HVNHRNYNNNNRDYWYWDNNYRDRYARMBKDDNHNDNNYVDNHBNWKDHRHNVDMHNKHHMRNDNHWHKNDHHBWYN

DNBDNVDMNRNNDNDWHBDNHDRHNNNNDDNNKRDBDNHBVDDHNNDVRVDRWDYAYHNVDHNHNNYHNDNNHMYNN

BDDHMHNHBNYNHNWHBWHNATNYHNHVVNMHNDNWYYDVHKRWVBHDYNNMDRVDYWDHDTHNBHBDBRWHMDHNB

HBDDYDNHWBHRANNHHNVKHHHNNYWNDBNDDSDWYRHHNRDDRRNDNDHRNBHVWHBWRHGRKHDBNNWNDHSNN

NVNNDWHNNYBWHDWRRNDHDWRHBDWSBHYVHDYNYNDNBNVHBRRTDYVNDNNNRDNMHHNTHHNHNHVHBSHND

VHDKNDHBNNNRHHWRHHWWHWNHDBRVNHHNNNBNRTDVANWNNNNVYDHNDVHDDHVHNYNNDYSRHHDHRDBBW

YWYYDDHDDNRHHYNVBNMRYYSTTHDHHBHVDKYRDBNRDDBNNNMVWNNBHBMKBRNNVHVDHSVHSDNHBDDHN

WVNBMNVDNNDYNDYDVDDVNHBNNHKBNDNHVHHHHYDYHAGNTHHHVHMNWYNDDNNVVKRAKDNDHBDRHBWNW

YMNRHDBBNWNDYVRMSDHNWVVNWGBTCNGHBYHNDHBWHAWNNVNDHYWNBNHNYRNDWNHNKKDNWHWVRWNWH

YNHNDNNMHNHNDHYDHBBDHNDWHNBWBVNNBDNVHVRNHKTRDBDBWWVYNNDDNYNWVRDNBNNYNWYDBYHWY

RWYCDWBNBHRNYNDDDWNNKHSDHDDYDKNBHTVNHRNRRHADYTNWVBTNVTMNWTNBWHRNRNVHYRRRWVYVV

NNMNNNBRDTCRHHNNMHBRYSRBBNDRBWDRHWAHKHTRBNHNHNVYNAHYBTHDBDDCGWHBDYDDVDHHNWVGD

334

BNHHNDVNNHYDYMHNBNBNHDVRBNKDBDNYMNNBDNDDBNDHDHHNHKRWBHBDVRVNHVANVDNHNVYYNWNWY

NNNNDHYNHDCTAAATVRRBDHNDNNNWBDYYNNDAHNHVYSDDWHHWNNWDVNRYBNNNDHNNYDDWVBNYATDHD

HHHDYWNNNHHVVNDNHBDHHNNDDNDRYDDNNHNWHNDMHDNNVWNHNYKTNKBRWNYHDVHVMNWDHDHYWMKCK

GGWHMDTSAMSWAAAYMCKKNNDHMWBBDHNNNVDYBNHWBHBWHNDNNBYKDHSNNHNNWVYHNHVHBKHHHAYKN

YWRNDDNBRBDVHDDDMNNKRNDNNNNNNVNHHBDHNHWNVAHYHNHANDKYDRNHNNDHRDHNNDHVHDMNDWKRW

YNYYVWHNNANHWRNVNDWHNYHHMNHDDNNBDHRHNVHDNHWDNBDBHNNHRNADNNNKBWHYBNVHHDVBAWHNN

WDNHNNDWDNBDNBDDNYNHDHVHNDNWYNWNYNBHRNYHNWHYNRTBDNNDHSWYDNNYAHVDYNDNRDNYHYHDB

DVNHWNDHBNRBBNDDVNMBDWHYVNYBDNKWHBVBNWNNDHNHHNBHHHNRMNDRNDNBHTVKTHGDNNDNDNDHB

DHRHWNBBWNDVWVANNDNBDNRMBTNNNDBYDNHYDHBBDNDVWNHVNDYNDMBDYHVRNNRBNNHBNDNBRDVBN

NNNNWDVHDHNCTTCTCCWYYWWBWYHNNNDHHDYNDYADBBKNNNYWNBDBHNHNRNDNNHNHNWNNWDHNNDYWN

NNNDBRHYNTNNNHNWNWNNHYDNHHDWMNYRDYBDHNVHRHBDNNDHHDTBNDBRDHDDRSKWNHHRRWKVHVNHC

NNNNNNVMNDBNNHDHHHNNNBHRKDHNHYNHNBVHNNNHDDDAWHDHWDANDCBWYNDHDBHDYKWHBNMKYCYWY

DBMRHNNBMVHWHNYVNHRNHDBBBNKHNDRHVRYWCBBNBBNHDNHDHSDDYNWHNDBDNRHHRDHBHDHNHWHYW

RNNVNHDHSDHNHNTNHDHHNDYRMHHBYNKYVNDKNNWBHDMNDHRNHYMVVBHWDRHHABYHDNHRNHDTDDNWV

NDNWHHHHVBKRVACYRVHVWWYRHBBHVKNDNHARHRTDDWNDRDRHBDNHVNWYNHNNNWDNDNNNRYDWHNBNN

NWDYNBYKNDHNRYNTYBYNVDHBNBKDYKDYNNKYDBVDWDVVHDNTNNBBYWRYNNBTDYWATCYRHBKWBDHNN

VDNVNVNNHNBRKKBDNDBDNDNBNGKTNVRNTDYHHHMRNHNHBNNDMNHHNTNVDNVWVDYNBNVHYRCYRWAYT

AYVYADNDWHSDNNDNDHHHNNDNNDVDWNDTVYVRDAYNWDDDVNHSYBRHNRHRBHNDWYVNRVDTVNHNYNNDD

VNHDHRNBHHYKTMKRDYYNHVBDNBHNNVDHNNHNNDNDYHHNHNKDWHYWNNNBVRNNBDNNHNNDNMWYMDTKW

MHNYKNDNDTBHRVHHNNDNYGNDYVTNVWNBBNWYHVNNNYHNNNDWNNDVRHNBYNYDNWHBHWBDNNHDNNVNV

HNVVWNNNNNBNDYHNWDNNHNDVNWNNNBNBNNHBHHNWWDBNTNHHDHNNNVNNNNNNRNADDDNNHYNDRYWWN

HDNMDNNNHYRNNHHKWHDHNHNNDVBWNKYWNYNNDHNHWNYWHHDNKVDWYYWBBNDBRVNKMCRVNNNBDDNHN

YNDNHBNDDBHMRAYDNBKDNBWNDDDHVRHWWHBRYBBDHNWDVHNWNHHBNDHYNDNWDBNDMDHWBKMRDNVNH

NWNNNDYWRMHVWHRYNHYNAYYDYNVHHVWDYDWVDHNNNRDDDHNVWHRHHDHDHHNBHDRVNYYTNNYRNDYWN

VMNBBVWNVRYDYYBHHSKYVDDNNNVNDNYRDYMYBVDRHNRTBTVRRYADNTRDHWHNVHVWYMVDWTHNVHNNA

HHRDNNYDNDNVVHYDNVRYVRNWNDRRYVWBDWWNNNWHNYHBDDHNBDHVTHRNYHHVNNYNDWYNSHHSVBKHY

AAYTTHHWYDDWHDNHYHDWNBWRNAHBHDRNVSDHMHNWHHNBDSYNYNNHSNNHNHHHMNHNNWHNNRWHNNDRN

NKHNWNHVDBRNNVBDYYWANNVHDDVDDNDVYHMWDNVHHNHNBNNVDYHDNNHNDNVRNDYNRNYHWWYGNWWRB

RKNHSHBDDVHHVNHYYWMVDBNDVDDHNHBADDHATNHYKRASDNNNBRVRVDVHBHDBHBWRNKDABHNBVDHVD

YYDHMDYVNNNNWYDYWRHVRKTAWHVVAHHANBHNWHNHNNKYDBHNHWVRMVNMNHWWHMRMASDTNBYNNNDYN

VNWMVKWDVKHWVKRHBRHNNNNNHNNNRAHKBHWYNRWHDYYHDYWHBVWVNNKYNHDNHBWYBRHNNWVYDKNDD

NWYDYDNDHNBDDNDHKRWHNHHYKNDNNAYDWDWNNYDDHHNNHNHNBYNDVDDNRHWNNBNWHYBWHDYBNWHWT

WNSRHVNNVNNWBDVYHRHRWBHWYRNKBHHDBNDYKVNHADNNKKHNBYVBRDHDBDHDYDHDVRDNMDVNHDYDD

WNRYNBBNBNWDVHRKKNHBDNNVNWBDKDNNNHYRNDDYYNDRHHVHBHDNHYRHDBDBDRKYADVVWNHHDHNDN

NHNDBDNNNNVHNHYRANNNWDHWBHDHMNRDNNDHHYVWHDHHRNDNNBWBWNYRVNAMADDHWHHVNKVDKKKYR

RGWSWCMWBDWHVNHDNHDBNANHBSRNNDYDWWMVDTNHVRTDYBYDHHHNNDHHVYHHNNRHNVNNVDNRYNRDY

DNVDHNNYHWNHNRNKWYDDNHNNNRNDNHVRHDDNHNDNNDVRNNNHDMDVDHYRDWMBYNKHYDKDHRDHRVHNW

YTRYYHDNNHNTDWHNHNHNHRHYDDDNWYSDDDDHHBNYDHVDYNHHDNNDNWWBNDHHNHNHHNGWDHANYVHHH

NNNHNNKDKNWNNNNNHDVHYWHNHHNKRNHRNHYNWMYDHWNYWNNDYWWWYBNVWNWRNBVHHRHYHBTDYNDHD

DGGACCWMDNNDDDHNYDVHDHNVHHDSTDDHBBWHRDDNDHDHSDHHRHBNNNYNYNHNVNNNNVRHHNYHDHNDN

HHBRATGGARWYYVRVDHBNNNNNHDHNWYBBDVAHDDYNNNKDDDNBNDDNSNDHHNDDBDHHHDBHNNHNHNDYN

WYNNDDNHBDTTAAACCAHRHYNNBRHHRAYVHDWKNVKKWHYHRHDNRWYBBNDNTDYBNHVNDWNDYNNNVWHVD

RHDRYWYCAAKTYVDNHDNDHBYHDHBHDVHNNWYVNWNNWVDBDNWNHBNDHYDHHHYDNNVDHHDWYDKANWVDH

NDHNNBYNWNHBWWDKNHNWHHRNYBWBDNDDDYNYHMWBNDNDBYVBDNHHDBHAYNNNWHNHYBBNRHNDDNBDH

NBNHRYNKBNNNDDYNDNNWBBDHNRYNBWMRHMNWDTDDNYHNDYNDBRNHBNRHNNDHYWBNDYVNDHDHVRDMB

NBBDBHHRBWVHVWBHNNNNHNNAHBDDRDWNNHDRNSDDDHYVNHWDWWDKHHHHDNRRWDRHNHDVWYWDNHDYS

WHHDWMNBRMYDHHWHYRNNNHDHNHHNYNDVNYRYCHVRWBVNCNNBDNHNNWVHNNDYWBNBDDNHNVNVWNVRB

DTDRNDWAAGTCCTACVYYYHVRWWDHHKRVWYVNDWNWBYDYNWNNHNNDMRNNBDNDBVNNDHNRWYYVHNVDRW

DSWNNNHDVHVDMYKNDHDDBDNKNNDVNTDHDHYRDNHBDWHNHWNDNVNYMVNTNWNNDHYBNNNDNNRHYNWND

NTVDNYNBVRRCCTAAGMMHHRKYNHHDNBNDNVHNNNDNNHRNHNHNHNNYNVDNDHHDNNWDYSKTVNYVRDDND

DNYHHHAHHNDNDVHBDNWYDTHRDDHWVNDDVHVNHVRNMYDHNDBNYNYBVHHRRKRHSMWWMMDVKNDNNWHDH

NBNDHHMNYHNWVNHNYHBDDVHHNYBHDHVDDHNDYNDHHNTBKRNWHDDHNNBDNNRWHDYVHHRDWVDNNVDND

BRRHVNNRVKNMBVNHHRNHNHHVNMHYHNBBHHWNYNNHNDHSTSRCCVWSRWNDWNNNNRHVNNVWNNBDNHNHN

HNRYYBDNNHBDNMNBBVDNDBHWYHDNDTDMVDHNBNDBNDHWWYHDHYVHVNNVNNHDDNVNNRNWRBRVBNHYR

DNTDNHVBVVHABNDNNDHHBRRWWYVDNNBKTRKRKDDDNVNYDWYVDWDNWHDYNNBHHNRHVDRHYDVVNHHNY

DDYBVDNDNWDWVVBDRHBNNVVHVHNHHHBDDNDDNNNWNHBDNVNBBNDHHNVDYMRNDNBKHTNNHBRYHDHDV

DTDBBHBDNYWDBNNHNNBHDHBBDNNNDVBHDHYNWWRTVMVNBDHHDWDVWNBDNNNNNHMHHNNHNNNVDDDHK

DTTHKNWKWDRDNVHNBNNNHHDHHRHHRWYDNNNHHAHNNNNRHNHNDVHKVYHRNNRDWHNHYBWWDVDYDNNNN

YWMNHNHAHNVWNKVNWVNWRYHRNHNNHBHHNNDDHNHNNNHDBNKVNNWYSDHHRHNRBNNHBWNBDNNHYNNNW

NANDNBHBYYDDHWWNVHMDHYDBNTRDMDKACCAAGGGCTCCAHHRDDYBDHBHHHKDWHWYNDDWNDWDNDDHND

HNHHNDBDNHNDNDBVKNHRDBDDBBKYRDDWNDWNNNHHVWHBNBHMHBVHDYDWHHNHDKWWRMDRRYWHNHBYW

NNRNHNHYYYNNDHNHHRRHRRDRNDWNYNNYVDBNHDTVNRRHNDDVWDHKDDVHHRYRBNDVBAVHVNWTBYMRH

VBHDRHNGWHNDRBHNRWBMVKRRHRDAGWYWHVWHSWHRWBRVBWWWKWNWRNVVHBHDVWBHVWBDVHHDYYDVH

HVYDNDHHNDDHDDRHBDNDWRKNYNBYNRDNNNDDHVMNDNYWHHYWDBNWHDNWMDDNMNNHHDHKVHKVDWNNW

335

VNBNVDRHYHNKVWYDBHVNMHNBDHBHYWWNWWBSWMTRMVWNWYDMANKBNHHNDHHNHNHHRHRBNHBWBNYNH

DNVDVNVDDDDHHDNDNNHDNHNDRKDHNHWYDMHHNVNHVBYWYKHHWVNNNBTHSDWHHVNRNYYDRAVNDBNHD

WNDHNNTBVDNDNRDDNVHNMYDWNDRHNDDDHNDDNDVHNVBWDDVDWHDHWDWBBNDVDWVHRRNWHYHRWBNYR

DDDDHDDWDBVNVHRVHCVBMRNDNBNHNNDYNWBMMHHBNSGKDDYNDDSNNVNVWMWDDWRDWTWWTGNRWVDWY

DTWHNDHNNWHVDWVDNNRNVYNWHWBNWSWMVDWVDDAHHDDWYRYVHHDHDNKDRYYVNRYNNHDNDDNVRDSNK

WWNHBDHYRDNWBHBDRDVDRNNHNYNHDRMTYWRMMDTBKRNNDHYNYNHNHDDRWNBHTHRBBDNHWYNHBYYVD

WHHHYYKBHMHNNNHDNNDHYMVNNBKDDYDNDHDNDYHYBBTRHVNBHVWNRSYAHWBTYHRWWDYHWRVYVDAWY

RHVWNWRDKHYVMHWRNSRTWBYKDDBDNDBDRVBRVNHDYVDNGMNWDNWHVTBRDMYKDNDANVVWBNDKHHVHN

NNNTRVWNNRRYVRNWDNASHRNDMVDRHKDWYHVMDKVNWSHBVDCRNVRWMNNHBRWDBDYNNNNWWRAMBVVDW

NBWBWBKNYNHVWYAHBMKNNKNNNHVNNDHDHNDMWYVWBMHDRMRRWTDHRWNYRYBRAHCRYACDWNNBRHRRH

GDHVRWKRWDHDHNNDDHVSHNRNHWYMRYVDDKYKDWBRWWTAHSRNKNDKBBTYNSVRADNVHNYYHYACGWNBV

BAVDYRHMDNBWNRHYWYBKDNYRVDBDBHDYDYKHDDDNDVDYRHBHNDDYMDDWHNRDDHDHAMGGTYBWHHHDA

VDCWHYDVMWNHRBCGTWNBSWHRRHRVBYHYSDDYHVRAHRNNRHWTDNYDTVNYVNHYHHWYWYAYWHYTDNVRY

HWSDMMBVDNWRYRAHMBHDVMWNHRBNRDBNHHHMRVDDAHWNWDDVTVVHAVHVWYWHSTATAYRGWCWHYWDKN

DDHYAVBDYNHHHRBDARDHHNNHHWDHMRDHRWBKWNRWRDNYNBHRHDHNYNKNDDHSMVYAKWVWKHRHHKHMV

HHNNYDWWSHARHDRWYRHYNNYVWYAWKRWCGWMMKKWDSDGACGGATTCTTAHVRNRBNVBDMMRTHVBDNRHDR

WWTYDWRWYNNBWHRRDMMHCRNTRWNGDRDNMRDVDYRRHMRHRSDWNYAMNWDNDBNVMVHAYAHTHSDHMHWSH

VNRWDRMAHMVHVANHWNWHHWKRDVTRWGDWHDAMHMAYATRWAHBRKWRHMAVTDRWWWDWHGKWSWYSMRWWDV

ADBDDSSDHRBSDYHDYWMDVHNTNYNWHBDWSMRRKRWWHNBBNDHRVRWYWHNDHRYAYHKHDDBWCCRTAYNNW

AWBRBYVHRHVDRWNRDYMDDMDDBDRAYDHHDRVWVMBDKHDKHMMNHNNWDNNNNDRDWBNARYDKDBKDYHRBM

NDWNVHNDWHVVNWMBHWHMRHWVHHWBKDNMRHWBHKDHRMYSWDAMYVWDBRWDDYVNNWNHDNDWYNBAMRWRD

ANYNDKVNMDNWNYTWHRRHRVDWYRATTDWHHABDHNBWDWMWYDWBTRWNVRMWHVWHSDWNBWRDBHRDMWBYW

KDVWBTNDYHHDDHBDNVHNYNHVRHYDBAKNHYNHNHCRVHNNWBDMKDRNHNRNHVRTYNHDHNDNHDWNVDDHD

DVDKHHHDRWHNBBWTVDYBDYWYGAAAYDKBTGGHGGWTNDHVDMVDMMMMDHTDWRRTRDBAWDAYSWYRBACNA

NHHYNVDHWTHMSRWNDHBKRDBAHVADDMVWTVYYKKWVBRDDWVDNKVWCAWHVRDTHYYWYWRDDNNRBDWNNH

RHSWHRHWSYRHNYYWYHDHNNNWDWWDDSMBGRDMYNYAYWHHWHHSRHBDHBVWSDVTVTVSHVDHDHKDKHDVH

YNHDNTKRVHBWAMHSNTMDHRNNBNDNNBHDHKRHWHDBHYRWRRHHWHYHHBRAVYSMRRDYMMRHNVGNHHHVH

CRVYWYMKAHYRKWKNMBDKNNHNMVADDVNWVKVDHHDDRDDBKVHVNBDKNNBHNHWVRVVBHRNVNBRDNTBDT

RWKKHDDYHHVNHNARHYDNGMVVMVNYYBRMRAWKRBWRRMWBKHHVANDANTDHHDWYDKBDBNDYYDHDKNWRB

BRYAVDNVHVNNNNDNMKYHHBNDRSHWHNHBYSDDRVHWRWRHHVRCCHWGHWYDBYHHRWHADYYHRWDMRYDRN

NNNHNWYHHWVWSANWTDNAACAYRYRTHYGDVDBDDNYMKHDHVHHNWBWYNNNNNVDABNVVHHDBRYTRRRRYD

WHNHSYWHDVRKRYHVNNHRHWBWYVTRVNWBBVNHRHNBBYTMDVNBDHNMNKHYYNHHVHHNRBKWRDVWNKHRM

ANCRHHKDWHVRRHWKNCGDYRHNWWBADYYNHYWMHDYHHAMYAAWHRWTKYTDNHBBHDHHRNHRBRVHRNYWDN

DMHHRBTDNDBAMMBRRHDNDBBRTGTWRWTRHYVWNDHSBVTHVARWKVDVDNNVNNRNNRHWKHNNNRVWVNNRA

HDDDWNYRAYHWNHNWBRHAMVWBDKHNVDRAHHKNBVRHHRRAKWYWYKHDBANWHSKTDYYHAAWVYHBBBRVHK

KNNANNMHMNHWBRVYBWDNMAHHYHSKDCKNMMNYRWBKDYVYRMYWYWVDDHDNYBYWKRVVDVNYAYVDNDMVD

HGYAYRVWWHHBVWHKRHCDHDBHRDWKWHVRHRDNRMHYRNBVYRWNVVHVRHSDNVNDDHHMYNDRKHHHMRHWD

DAMNVDDHDHRRDHDHKDNABNTRHNDNWRNKDHMBNBDHHKNNKWWTYWKHWWHBWNYMRYRWYVNAANTYHVHNN

SBKWHBRWVVNVVHNDTDYHMVHVVNYABARYMMYYWRYKTRYDWWYDWNDRHAWDWHHBDNDRNRDWHAWKDHGGW

HSNYRHHARDWTDHRHNDDBNDNDNKHDVAYARNHYCCTDRMHRBDWKKWNDYBYDHNVNNSYYMKDDDHMRDYDYD

WHSYBAWDGTNWWRYWYRWDDYNGDYARHHDYDYNNHBHRNAWBACNHVDNNHDWNHDTRYNDDDYHDHHRMRNHDH

HNBNHNHVWBRWNCHMHBHDNWGHAYHKTNRWBDKDAVHHDHVRDGRTSMWNRNHRHKDNNVWYVDVVRMMHHVYAM

HBKDWABMVHWYHKDDHDWVVYHNNRRHHWNBYNDVKWHNBRWAYBDNHKKNKHDDHVHRVRAYVHDNHDNKHBKDH

SDNNDHWAHYRHNDNWNDRDWHRHYKWTDNHKHHCCYAYNVRNWNHVDSGTHRDNTVYMDDAMYKDHTBHBNYARYR

HNWVHYRNWHHRYNKVHVRBKWHWRHMKDRRNBKHMDNYSHWDYACTDMRNRNVVDHDHNWVHRAYRTHHHDHNYAS

YRMYAWHNHNVNNKNHVWHRRHHNYAWHSRRRWDRCAMCHWMTWGDYDNHNWDHDCBYAMBRWDHNYHWYARWKWNB

DDWDHBDYAMNRHBDTBKHHBKDAWHHKDDVTVMWDVHSDTWRYHKNNNDSRNSDDBMNWNYHWYSNNDDHNNYGDK

NNHHVDWRDNVNRRHTNRAMWHVYYWYAWNSHVYHHNHNSWNWNDARNTYYRDHVNNBDVRWDYHDKDWTYVWVDKN

HKDYAVWYKDYNYRDBBDNWHYWNHASDMDNYDRHRAACDKDDTWWANGHWNMWSNNWDDTDDDNDKTNVHNAMNBK

NRWNVHDYHDYBHVVNKDNTHHMYNNABKWRVVRWSHVKHCYDSVHCBHNHDCDDNNWYDDHHNRRWYDHDDYRDYR

DDHDHVDNMRNHNNYKDDHNGGWHDNYVRMVBYKWTSBTTYRNDNRHKGWHHNBWHYYNSKWBDRWMDYYDMBHWBB

MRWYWYNKGHKRYRRYMDNRVNBHBBRHAYNBVHSDVDDDHRYRRBKWNDDDYNGRTHSRHSHNWSBDVHRMBTNHM

SDWKHVBDDHWMRWDHYWYDYHNVDHVHBVWNRSNMVKWVNVDBDDBDRDYNDVAAYTYTKWSGRWHBHYWYYDDAN

WBDRHDMNBRTHYMNDNDHDHVHHNRHVDAAGHHDNVRHKNHDYNBKYYGNTYHRRHVKHNNWYRVMRHHTVVHHMG

WVDDYBDYBRWWHHWDYWBHNNCGWDYYBRWYHHMYRHHBBDVHNNTHYBVRNRMHVRYGKWWVRWWTBDDWHBRRH

VDYBRKRNNRVDYNWDHNMWMWDYHNMVBKMNVVVNTDDVRHHRDDHHHNNTHNVRDYWYTWNMMHWYDCDDVBHWW

NTYDHRWHRDRGHNRVHSHRDHHKRYKKYYHNDABRVNRMBAWHMRNRNDBHHKMNYKHYRYAAHWKNKMBWMHHDY

MRNKBHYDYWSDHVHMWNDRDYDHSMNHVNBDYWWHYMDKVTWDBWDYBWYRRKWBHTRVRWYDMBHMWBWKDWBHM

RDMVHHDWWMDYBHKDDWYBWNNRHBVNYYRWBNKDBNATVRWMYVRTAAYDYNYMNBNBTAHYWNYVHKHSRWCMG

DTMKDRRHRVBRKNNNNMNBWHHNVDDNVTDWWWDDVDRWBBRDTNDANDYHNDMDNHNNAYARTYHYVHVVHSDHB

KWRWVHKWHSRNNDYRDDVWYSRTYACCKTWWHRVNRMHWNYDBMTAHYDDHRYYWYSYDWNNHDYRMWNRNVNHMD

NWDHVDCBNHDDYYRHHNRRDWDYATWYRDTKYYNMRNMYBYWSRNYRNWRNYRHYAVARNMDBVMHRHHKHNAWYK

WNVANVNDHHBYYHWRHAGHBMNRRNTDYBDYDDKTWHBMVRMHYHNWRYYNMYRRWHWYANDDCAKTDWYYDVNDT

HNYDWGRDHVYNHNYHDHNNYRHSMWCCWHKNYMDHBRMSWHSWCAABYWRWSGWTVBAYNWKKWNBAYRAMYHDNV

336

NDWDKRHKVNBWWNWWNTDHYWKWAAYVMRYRHHAAHSMBDHSTADSWNKVDWKYRBNTRNWDHTVHDAYADDBNKN

NNBDNMBNBYRKWNMRYDRWTRHCAYVWTTTDWVNABRVHHNNBBRHVAYMRYDRHYVDAMYKDNRRTVDYWDYBWB

RKNTDNSMBBRDRHNHNKYYHTRWGWATABSMNCAYGHNRDYARWYRHRWDWHHYKDMDNNNDYGDMAAMKYGTVKB

NCKHHRWYKWCSHMRYACNBNYVDBBBNYVBWTNWRNYRNGHDYRHVRWVNHHDHHYDYDDBKDYHNGGHYAHVBND

HBACAWANCTDWRDRSDNMHRRYMSNHCWRRVACVKRHMAYSDHNHNRHNWHVWAHRDDYRHDYDABYRTKWKCWHB

VNDVBDHYVADNSWDWNNDBWDDBMHGDNYRRWRAWDTWNBHYVMDMNYYDHRHWCACRKHTWBSRKSDKWKNHWRA

SRACNHNNBBDYNDMWWVSDYKDBNWYHWVHHHNNTSNMDHAMDCTNYARWYNTDHWTNTDBHDWBBWYVWYKNHYR

WAWMCADWYRHNDDHWHYBVDHYAWBVWBKKBNRHADHVKYRVNNBDRBWBDDRMNASTDKNYRHWBDARKSRDKNN

DSNHNRWDHBDYHDDNHDNRHYWNRYHWWYRKWMWYTYKYKWHYWDANRMYAHDBBNMSVHYDRWBYRAVAHBVHNH

VAVSGWHBHBTNBTNDWBRARNRHDHVHBNNWBBYVNMDNVMNYNNMVNSKDNWDARCRYDYNNHNMYRRAWWBHBN

VAKRWGHHANHRVMWNDVDMMRAKDCGNHRHKYNKYRRADYYHSHNTWVHNRWBDNDBBAMDMRDYHNHDYAWBMSN

RVNDDHHDHSWSSWWMRYBWBBWHBKWWVNRMYDWTTSTYHSNARHYRHANNNNNNHWRSDYDWVBDHHDVNRDDHR

YNWHDRATAYKTTTHKRDHMTGGDHRDHWDBKRTYDMGRNHYNDMTBDHNAHRMCDMAHAMBDVDNNKKWHHYHHYW

HHDYHYGRWDBNTYYRYDWHADWNHRHWSYRKWVRDWSTRRHDTHRVNVYDHNHVWYRYDDRHRCCWCTDMHVMWDT

DWBDDYBNKRYHHRAMHMCHMVHCRNNNDYDNYKBASHAKHATNVKHRTRWAYBWYGKDWHBKHNYSVDAHKVNWNY

RYBDRYRBYKHHSMNTYWHNWHAMACWBWWNDYKKHWGDTRYSWHBNHKRHBDYBNDDYRWWNYRVMDBVHNNVRDD

YWWRWAYRABTHRYRTYYBNVNDDVWVDDVDYWTDHAWNNDRKDMVHRDHHNHDNNBNNYRYDKTVDBWTYVDNHVD

WYBWRBRNDMNYVCTDNYYNMRNAVYAWBTHDMMVRAHTRDRWNHKDDYDWHWHRNNHYWYBDDDAWDRHYWHDRKN

VNHARNMBBRHNAHNHDDYBDYYYAWNHRYAVHTAHCRNWHKSDNWDYWWHRVDRDNDWNYNHYVBTRHNVNWKNYW

GAANHDNWDYABWMSDTRKWHYKDHRBHYGWSDDBWNTRNBMNBWDYSWAVWSVHAYWBHNTNNABBWDNMNNDRNR

RHRDNWRDDDNKDDWHGWAHMWYRHRRHNSDBSWYKTRAGHAHDCYHRDNDMNHDHHHBAVDMVRWSNYRRSKYSKR

WAYYBDHVDSNRYATWTBTRHRRTHWRWCHWTYHKVWYNHDNVVVRHTDKYBSTVWWDAYACTAARDYRWWRYHYNK

BNHHNVDBVRWHYDMDNMHHWMDABYSWNYKDTRNBDYHGYRRWVWNKHHWHGYRKYMHVHYWYYWMRAMDRHDHYK

WYDNDWRTTWWNRRWVDBBRYKTHCDNHNHVRKTKKNHHNDNNNDKMWWYBKKBMVNVNYAWDTYNRVDVHDWYDWY

NVVBRHWYRYRRDWBTHDHWRYRYAYWYMDMVYRMNHRDKDNYGWRVDBYNNWNBVDYRWWKDYWRHYDHBDDYVYC

RVRMHBDWTRBRWHWHYTRWBHHAAHCDYWTYSRWCAYBRWVRYRRKRSTBHBNRDHABKRWRSWYCDMRKYBTAYK

WDNRDHRSNVRWNKDWWTYKHBTTAVRRGHBDDKYKWKRMYNMGBKYRMKYAANVDKTHNRMKATHWWVRMBAKHSN

DNBCTCGRDCDTGWYRWNNDNHDYACRARDBHHSRDWBNTRRTHYBDDBNNYNNKWYRHDGTHDTHHHHNYMRWHWY

BWSHTADYGWVRWHSYKMDYHRSRTDKGRYADYHDWRWCCNDRNWGDTKYRRNMNDYMDYYBVMNVHRYTRTAYRRH

DNDKYNRRHKRHDYWYTRCAABYRHYANSRHHWDWVDWSYCVNYSYDRHKMNSATHVNYRHTABSWVRADWYKDHDW

RDAYKAHBHWDWMMRMHVRHSRKWMHMWWVNNBHNBHHYYBTRYDSHSDDYRWCAHGMNCGHBVWWDVRMKVKYBWW

DAYVGYWKRAMDBYTAMDYHVDCDHRDTHHHRNSYSHCYTRYAABRMDWMWCRNNWNMDHWTBWHWVVYRVRAYDHG

RASHWYHVWDWWNMDVRSYAYYAYNWYDRTVHCGRAWWSGWTTRARTCRAAAWTYVYMBRHHGKWCGHSARWRHMRD

WYHMAHHHWYKNDRNWRVMSHVHDYKNMVWTTRYKHNVRYKKNWRHAHYBWNAKBYWHRDNRHBHNNYVDYDDANDW

KHBDDTDVNHDYNDMHDVNHYRYMSNWNBTANBWYBBVDHYARYRWYKDMYRWWDYYWRWRAWYTHDHHHDTRWMRW

SRTAVTRSKMRRAYDSVYWWYTWYYKYAMVAMDYNHHVWTNHNDCHVWKRSRHVDRDHTYCMNTHBRDNVVNNWDDN

WYNYKDNARKWTBKYBRBRAVAYAATWKYHHHRKWVCRRAWWKWYRRWYBHHDVWDYDHRRCHSWWSDDKNHCKNBD

HTKNBWHYYTGKHBDWCWHRMDWHNAGDDBNKYNHRWNMBSDWYWDBDNYVHNNDYHMBYDHYMHWGHBRNHRKDKD

WHDKHNTNVRNWVADYHWVDDWHNVHHNDHBHHMHDHAYMDBWRYWRDWHHAWKKHKKDNTNWBACRWKHRWVNWAT

HTYRCHAWNSRYNNDVWNYRWTRHYBNCSDWKDGGYRRDYRYWTDKRDRVDDHYNBDWYDHMDRGTTTTBBNMANRV

NMYDHDWHVVYMHVDYHYHWYWYDYMRHYDYVWHHBYRDWMYNWYGWNBDYDWKDMBHDHDVDRNHGTBABNDNNVH

NWDBHYSAAYWCYABNHVKWHWVRRRWBMYBAMTYHBVWRAAHRHBHTNDBDNRNWNDWWBWNNRBDHMHWWWMGTH

YVAMNWBBWYNMRYDYNHNRVAYDHBVRDDVTDNKDHNTDNDDYRAVHNNBNYDWBRDRRKYBHHYGRWKDNDWWVH

VWVBWYBRNWHBWRHVCTRGTRYAVTAYMBWYKWHTHRTDYWNMDNBDNDDHHSDYARWRWGGKNDDVRHMCNYNNV

DDDNNHCWACVYWWYAKWKRYTWYARTRKBKTHACMNRRWWHNWYYAKRMHBWAYARWWHHMHNYHTDRYRKWYGDW

KHBRDHRNHVDDKDYDDNVHWYRVNBDHYHKHVYHRHVDNVADWBRTNHYKDNHWVVHBNHDDNMNHGDNKYNHDNM

DYDDBTDWRVYRYRKYWBAWVDHBHAAVABWKRHYYBABYDYRDYYDVDRAKDWWWGRMGTTTGSKRHNDYYHMHNH

KNMNHRVDDWBDKTNRHMWDHNHBBNYYWTRVVDNDWNYBNYNNHDYSTYMRDMMHNBYRKHYBHAHDSVDWNDCGB

WDYDDKKYYMKVYWRYANYMBNKVDHNCWWWHHNRDRMNRNHDVHYDYHRHVVHDTBHRMYDMDGDNRDDWRWWVBB

TDYRHKWCWWDARYRMNMRWHRDYNWHDDVRWBYHDDNVRWYNNDNSWDWATBNWNHNRWMSAVNRMNWVKWHDNRN

NNDHNYNATYACYGAGRHVHDDMRWRDYVBBRTDTYNDBVNHWRNKYDAHNRDBDYYMWHDVBARNWVYMVWHNTRV

RKRRWRDDHYWYBSMBBDNRWYHWBBNDRWDDHDRHVWNDKBNTHYKBHYNDBNWNRDVRTAWHRWWYNNARHKWNS

RNRVNWDWNNNWNBRVHYHYVBDNWNKVTDVBHVYDBSVMDWRNTMDKYBHSNADVYRTDMNBWKYVRDRHNBKRRY

AVRTTYHWBVWNNWYMRTBKYSDTCCACGTGGTCKHWDWSWRRMHYDDHNHYNNRRHCSAHHRVWTTCACWWWRDSW

NNNYGKNSDHRBHRDDKNKGWYKYNVVWSDKTRNVHYGWBWVBHRYRHDVNDYHBKKYWTNBNNKRTYYHWRDWNDN

DBVDNYDYYBNYRWGWKCMTRWYNWGKDBDNWYBBNRNHHYRMMYVWBDHDRBBWBNHWWAWWBKRMKTRNYVDDVY

BASNMBWYRHRMVCDNRHBRTKAYDWHYRYKWRHYGGWBYBHRBHVVHVWHYDYARVAMBVDYHNYHAHYBNRRNBV

NVMDNDWSRWTRHYRNWNKRRHVAYRRTMDTTRBBWDHNDVHBSWMDRWWYRTRWHRCAHNDDYDWCCWHNDVRHSK

DVBWMVNKRTHKWRRDKYHHDBMRHHRNHNDYYYMSWYRWSDWBTDSKHKTNHBNMWDTYDYNHVNHYKYVRNHBNH

DDWBRMRWSDDMBNHNNGDMWRHMDRAVRWHNBVKKDWYRNHDDSBWYDNNDYKVNTBWWRBNDNKVDGWNNVWKHH

VDNRMHNHDDRVWWNTRTTYNAWBWYBRWBWVKTRVDNYYNNVDDNNDNSDHRVAVNTVNNNWWWMDHYKTNCVNHH

HRDDKYNWRNTDHNDHDRNNNHRRHVKNHRDWBDWVDKNWTWDMWHYVHWNNARBTHYDNWMVDDNNNNYBWBYVDD

DWHBHMBDYRHTDNVDNRVDVRYRRDSNRKNAHMWYATVNNYYKTRWHNSDDVBDNNYDHWVHNVCACRNNVWARHB

BNDGDSDNWRNBDHYBRAVHHVDRWNYNDDSRBNBDYMSTADTHVARYADHWRDRYKTHWNRHBDYHDTSKNKSRWR

337

HRAYRNKKNYMRDBNBYKARDTTHMANYGRWADTBHBCKSKDYNNHNWHMWWRDDVHVNWHRDKWCWYMYWRWHHDK

NDABRDAYWTCKTMRWBNBAMNTKWHVDNRYKKKARTCTAHHNYARDDHBYNHVNNRVHTNDNMKBTHDRKDDDVRN

HKYRTNMVBYRWHYNDRTDDHDDDYRNBDDWMBHDBDTDYNDNNDHMTRRWSSDKRWYRHDNNHRNKBDNRYMDWND

HBNBHDHHDWCCNTRWYDNHDDTYHSYNBRHBDYBRDRDBKVHMVNHNKARNYAYNWNHYNHARNWBKVNDMBBHWY

AVDWHNDMDDHNWNGTRHVDWYYYWMRDWNBDNACTWVDNHBDNVWBKDHYBRWADBDDDHRRMWARWHDTBDNBHN

YKVWDVRAYAHKYWNHVHMVBYNHSDDNYNARHCKRWAHKVNRRNTHVMRNRHDHVHDGWSNNNHNWRNNVNARYWY

NHGWYKRMADSGHDRWRWBNDHCSNRMDRVNWBDMSDHRHYHDAAWVRWCDHBWTBGRYNWHYDHVNHRRYRAHDAY

YTYWDHHDDDNBYGDARNHBNWHBNNNWHVRWDYKHNNRRDKRWNMRYYDBWTHVWHBBRTRNRVWHDBKWDMNWVH

DVNDYBHSDDWYDBBHYVBBDYRVYRRNVNDBDBMWATMKYABBKNKWBNRRHVDNTNDDRWHDBHCYGTRMNNVAY

ASDHDWNNDBNHYHNNWNYRNKNNWHNBGDYNRYYNDHRMYYSRDTGHDVWDRVNHHHBKDHRHVDKYBYADNMDDS

WBYBNWVNKRDHDTWAYACDYRHDHNBKRYDYKRYDHBKRHNDDDRWNMTYDWDMKWHVHWVHRMKTHBRMDMHVWN

YDHNVNNBNDWYNWVHWBBHAHMVYRWDRANYNTAKKVRGKNWRWHVYRACHDDDHVWYKWYRHMVNKWBBDWVHBH

NHGRHRBWBDWVVDWHDTMNWMHWWHHWDYNKBDBWNNYRDHKBBWBBNNNRNRHHSMWSNHVVNTHNRRTANTHST

DDKYAYAYDRMNHTRRVNMNDYVHBWNYRDRWYWTHMRTWWNHRYRRNTWHCMNKWNRVYDYNTDBRRHAANAWHBK

YNVYBHHNTDNVKDDTYATKBHBVHHDDNBNDDHNSGWKWKBNVDNHAYHNDDHDNTBDHVDHNRHNDKDHRNNVNN

KDNWBDYRHDYHVHDYDHWRBDHBHVYTDBBKTDRBKDWRTSBBTDNVNDDNWHNNKDHNKHDRWHHHNDYNDYNHH

RHBRDNWVYDRKYNBDHRNNKHDYHDYHBNDBRNWNNBHDBHDKHDYDNNNNHDWRNDRRWRHNHYDHNYNHBVBWG

NBHDNDHDHHWHYHHNDHNHNWNYRDWNNBNYRDYDDNDYNYNDDDNNWKVDNTBNHDHVNBDBHWMWHAHVWYNMD

WHHVWNHDHKHNBRNBDDNDHHNWBHNWDDWNKDNYRRYAYTACCDHNWYSDRRYHDNYRRTBDYWBRYWSGWDMVD

TDDYDWBNYNGDWWNHBAYBHYSWVRDHHNSKHDYHNDHHBHKDHKRYKKHNRDAVNRWNRYDNBHYBWBVNBDHDY

RHHHNHVYDHNWHSYRYDNNBDYNWBRWYDTYMDYNDYDTHBRWNWMMDHCRTKDWNABYTWNRDHKRYRHAYDNRK

YDTKBTWNHRDKNNDNHYNHYDBKWYHNDYDWNYDKYNBDBDDHDDBHHDYRKYWVNYDDNVWHVYHHDWRKKWTDW

RYHVRBNMNNNYRDYHDHDWYDHWNWWVHVNDRDNKTBTNHHDHYWNHRAWYRTWHDRHNNYDKRYNNDWRHNVYWD

DNNWHTNKNNDDBNNHNRRANHDNBNDMWHCDDAHRTYVVAHYRDKKYNKNYNRVBDAWNTDWHYSWVDHNDHYMWS

RSTBDABKRDNVDHKNBNDYVWHDHHKRDTHHWWNMMHRNNSDNDVBBTNDNNNDNHNNHHDNVRWMDWAHYDHNBD

TYNTBDNVWRDKKDWNDNTKHSRNCRRHAWCKHVRDTYDHRYNDDWNBBKBHNNDRNSDDHDNKDDVNVBHNWWDKB

KADNMDDHNDYRRRYNHRYAWYWKHRBDVDDYVTNHNDDNNVHMVWVNHDRWTCGRYDWHNHHDTNNNKDDWDVRBK

WBRGAARWWRYRRDTHNYWHMSWCRHRHNTNKKWHYRDWDKTDTWAYWTGRWYDDHBNYVBHHHVDVHVWNNNHVDB

DDAYNBNHDRDMSDMBYAKDNRYWWHNVYSRHRWNWDBDDBSRNNRHKHBVRHSWWKDHYRYVBHNNDNRTVAHSVN

KHDDDNKHNAMYWYNYHDAWDBNYDBDRNDTKTCANNDNRKBDNDNRHBNNWDHWYHKRHYVBMTNRVYRYNYDDKV

YVDNYDYTKHBWNBRNVVHDHHMMDDNHRRHBWHRMVYDYHNNTVYVVBWYHYVHRGWHBWHSWNNBKKHRVYWKHB

KNVKNYNHYRNHMBAGATTGGTCTCGGGYGTTCCATTAGACCCCTCATTACTTGGGCCATTACAGCCACCATTACTA

CGAGACCGTTGGAKKBSDDAGWDRTSYDWHNYBDHRNDDHWWNHDWRNRRAWBYDSANVNDBNYDWWMNNWBHYDNY

RDMMDWHNNVDWYNHVDBDHKKNRVRWDDVVBNNTBNVTRBYRNVVNTYYBDNYNHSVHRRCTWNYDYWDYRWNDWY

YHHDWVVRHKDBHDTDHBRDKWDHYRHBHHVHNNHRWNDRDYVHNVHNDNYAGHNRNVDHHNDHHDBKHBHNDRWHM

DRMHVMYRWBHWYTNDHAYAVWRDDTNVKDNKBDSRTRWKWYDHDDNAHNRWVRTADHSWHTRYKMYTVNYRBSWNV

MDVRDTVDNDNVNDSDHSMHNHDHBVTVDAWVDVDWHYKRHYDRWACRYYNNDHVHWNDHVDBKRHHHHHDWVHNVM

NVCDMHBRTNYBNWVBAVHDRYRVTDRHRHNDMHMDTWYHNNNHNKYHWMBDAHNMVRYYDCRYNSBYRHBBKKYBN

RBDBHBRWHVRWMSDHRYDRNBRDNMBRSNBWBWYYVWNVHTHBRRMYVRTAWBVDDCKBHBDDWHNRNRDDYHDRR

NHKHVDDBRNWBWVHNYDYBHNAWNHRDKHNVDDVNTRBYVYDNVDWCAVHNDWHMBKRBDWNCRWVRHNVYVNDHD

HRRNRRTTBWYWBRARHHRHBDNRNHWDKNNDHDYDBKHYNDNNNVNYNBHVWDVDHBDRTKDKHNKYKYRWWRMYR

HWNWYNNDHNDBBNNBDBBDDBNNBNYWWNBHNBNYBNNBNBGWNDHNMHDYNDYHDYNNDNBDWNBRYYDHKNBDH

RVNNNDYDHKKWHTRDWNWNNNBWDWRBVHHSVNYDHNDNDNNVDNBDHWDHDWYMHBKNWBDBNNWNDDWDNBBDH

DNKHHYDNBNHHHYWDDRMKHHDWRWKDHWHDHNNTNHHDBVRWDVBKDKBNDBKDDDBYVHHVHYDBKDHDBDBRH

NDBYDYHHHMGWRRTKWDYRYRSRDHDBYRWANYANKBNHHNHKYDWKNDDKWHYVHYVKWNWYRWBKNTHNYNTBD

DDRTYNNHDNHNWRMNBKDYDDYHHHDDHBHYKYVDNTDHSBNNHHNDHYKNNDHVNHTBNYNNAMDWBNTNWTYHM

RDBWDYBHVRDVDWDSWSTYYNBHDDRRHYMRHNNNDDBDNRYDRDBMSYRDTWHKDYDYKKDTNVNNNDYHRYYYH

VNWTKHBWNWDYGKRMVDAVHYDNWNDDBHRWAHBWSDDKRVGVYGTYAWNVHNNRNMBBKHNSWNKNDTRHNDNRV

DWWYKRYDHNSNDNNHVVDKNDHRDHVDMMNBNNNVHKDBDNBRDHDDWNDHRDTYBHDYNDDBWYDDNNBRYWHHM

SNWVVWWHSDDVWBWWNYYDHHYCDHDNDMNKHRYWVKVDNHRNNDYHATTTTBHWWBDHNWRMDMMHVNWRDYRRN

BBDNNDVNDYBRKAYYWBRWNBNBYDNMHDYHDDNNVDHDVKBHWSDYMKKWTBVRHVDNVNDSHHHMNWVDBDHHB

NNNNHDHAKTCSBDDVDBRHVRNDDWVRYHDBBBHMRDHBDYNNHRTBBNWNNDHDWBHWVNWHNVNYRNHNTHDTD

BKKWHDYVBNDNVNDRHRRDYNNWDNNDBVHBDNYKHNDRDHBBDRNKDNNRWBBDBDNYRHDRYYVYRNHVRDDRK

WNCTNBRABRHHNWYHHHNNNBNNSVNHWNHMHMDDDKWRYVBHMDNDGGGGTTTTCCGCWDHYTHWHNNDDDTDHR

BRAWSNYKBYRYNHNHHNDMVDYBHNDDTDDHVRVMNBNBHBTRRNRHDMKDYHNWDDMWWWTDDMNYSWADDBDBH

VKTHNVMDBDNWRHBDYNRRYWHDKDNBDYHNNBNNHHTGAAANKRHDHHBDSRTRKNNNDHTRHKBDRYYDVDWBK

DDDHNDDBDDBNNYNDNDWYDNBDNHNNWHNDNDWNBDHNVHDDDBDDDDWDBWSDHKDNHSRHRNYDDDBNHRYNN

YNRHNRWYBDDRVMHDHRYMHWDWYBRDYHRHDDBNNWHNHHYVDWYNWWAWNKNBHDYRDWDHVNDDNNNDDDHHN

RHNHWHMDKBDHNDNWNNHHKDANDHKBVWRVNHVTYHRRTHDAYRMHWWYMBNYVAYRRYHNNHDRWVHYVWYVNV

RYNKTTCTCGDWYDWVBDYVWVHNSDVNKVVDHNWHMNWHBWSWRVNHNYMDWHKHHRDHNBYDYVRHHRYRVDDHN

BDTDWNVRNSNDNRVHDYHNBMMDVWYRWNVKWDDNVRBHNWDRNBNYVKNCDKMNVWRNYDBMWDBHBDDYNBNAV

YVRWYHHNDRVRNHNWNDDWNKWVMWYWDHNDYHBHNYNKWRWNRHTKYMGSWBRHVNHRNNDTHDKBDHSWHHVNW

HNHDNAVWNNWBNDBRYBNRYBNNABRWTVRWYHHHNHBRDHDBRNDNRYBDNHRHDWMRDHBYKNNHHWHHNYVDN

WNNVNNARDDBHWDYBHBWVHNHRYNDNHNHDHHDDKRNWWHHVNRRYDHNDNNHRDDVNVRWTCRTMTBYDDRNHN

338

DNVWNYDHCNNBDDYWDBBDMDDVWHDNHYDNHNDHHDWDNHNNRRDDKDTYNYBDHCDTHKDDDBWHDHRWNNTSD

NDRNWNHHDBRVNHRWDRHWRBBVDNRWNWNVDNDDHHDKYDNYDBWNDNDDWVNYHBTVYNRNRWKHDRWDBBYKD

VRHNDWNHDDHNDKNWNMRNBDWNDDYHDBMHMVWNDHWRBHRTDRHVBWNNBDYDHVDDDNHVHNMDNDNWDHBWD

YYHDAAAGAAVRWWDHWNNTDBRYYRVDYRVWYWRBDYVDNRNTRDYNHWNYMHHYDBHBNVKWHRHMMDHMDTRHM

NVSMNRWBYKMRNDDDNRRDVNNYNNWKNBNNYDHYRDRBHNVNMWDTMHTKNVMYVWHDNHMYNRDBNWHHDYRVY

GTWARDHNYBTHWHNDTNRHHWYNVANHNYSKHHWNVDHVWHRDYVBYAAWSDBHWDBRHWBBBNDWMWHVVWWVRY

DNRVWNRYWRHWRHYDNVVTKBDHVHHBDSVNWVHBHHBNHHRDDVVDRBHWHSDDHKDWNHDDHVDDYNHHDNNRT

RWNSRYDDBHVDYDYHHDDVBBDDDHNNDYWDNKVNAMYDDDKWHYDHNRBYDDHRHYDHHNDWHRDVDDNNNHDHD

DDBDVRHYNNYNDTDWDWYVVTADHKYHKDYBBDHDWHNRVWBDNNNRRDNDNVYDWYRDNNNNHDBNNYDNYYNDV

DHNVHHVDWWNRYSRDYHNDWVMMDYNWSDHHRHBSHNNNWTRTHDYBNDNNNYNRNRHSNVWWNTNHBNNNYRNHR

DHKDHYDNKYDDNBDNNKRDBHNDDNWNNYNHBVWNVNDWYRRYBDWYDNNDHVVNRRWNVHYDHDVBDNVDHNHSH

TNNNVNHWNVVHVRKNKNNNNRVDYHVHDWRBNDWHDRHHBNDYNRDNYNDRVHGYNBVBTWHVDHRRWRVWSYHWD

BNNTDRVVAYHDBSDNWDHVVNNHNWRNHNNRDHWNYVGTVWHTVNBRNHDDBNHBDWYYWCAYVRKRAWYWBRKRN

NHBDYNWRRHHDDDYDHDYNHDBHNNKBYNDHDDHNAHDNNHNNDNHVYDWWNDVHNABYNDBVWYRCHMNHHNKDD

NNWYDTYHDWNBDDBDWBRWNNNNNAHBNNYNNBDDYNHYNNBBHNDNNDNVHWNDDWNNTNYRWDDYDNANWTNBD

BHNHDDNYRKRHVAMHYNHMKRDHNVDNKDDDNDHRYBDDHDDNGCBRVANHDWYRHHNHVKRMDYYDNRNNNNNWN

NVWWHNKWDHNDNHHNYNDNHDHDVNHHSRWBBDBNDWHHWNYKNBWHKRNDDHNBNDHBWRDKDDYBDHVVNNWYY

DDRADHVDNVBDDDNDNVNWBSNHVHDDNWNNHDHHNDNRNWDBWDNRAWYNDNHRHNRHHDNRWHDHMBWYVWNRW

DDKHNDNHDNWWKVBNKDRHNYDYHCTTRRRDNVWTRWYDNBBDYDNDRHYDRNBNBVNBDNKNDMRDHHWNDNYNN

NDYKVBHDYVNDRVWDDBKDWHNNWNBDDDYDTVRDDYBWDWADDYNHHDHHDNDBYRNVWRHHRHDBDVHNBVNNN

BNWWVRNNNYVDTVAYKDWHRNWDWNRWDRNDNNWNVDMWYDNHHNVVDVDDNNWDDDNVRDBDDNNWRBNNHHAYW

RYRRTTRYCGAYTTKGTMWWCCGADAHBHWHBNDDVNDVNNHDYKGRMCKMGGWKKYGTMRTMGYBYYHGDDNHNRW

NDHNNBRBDDRHBRHMMWSSCSRKWSCMWYYTWWDNNNNYHNBRBVNBNNBBNKRRGMCMTGMGGSMAYYGWKYYTR

AYSKCGRCSKYSTHMRWKRVVDWNDBRBHSMHNWKDYNKHNBDWVDNNDKDNNGDVRHNHDNHNHNNNNNVBHDDBK

NHNWHYVWVHHVDBDDHMDWBDNYNDMNHRMNHHNHYDMNWHVDADDVHHDDHWBDRHHHDHNDKHBNWMHRBHWYD

HMWHDHDNDNDNDRDNNNHNDDHNDNBWVWHBRHTDANTHDDNNDCRVWBDWNDHDYAVNDBNNDNMSWBDDVHVBW

AGNHNNNANYRNNRYWYNVNDWVYDWNVVNHDTHDHNNDRVTBNWVNNDBRNHWHSHAABKYNDVNMRKNDNBVYND

BDDNWNDDKVDHDVBDDDWDWYVNYYCDDHNHBNRTDRHADHHNVNWNNRNDHNHVDDMHBHVDNHHDDWVWBDHYV

NNVDHVDVRNDBHDTVRTDDDYWYBRDVDNDVDDDNVNDVHDMBNMDNNNYHKDYBNDHNNNDMRDNDNNDDHRWYN

THKWMRRHYNDHKNYNNRNVDRYWDYDDBWDNDDHHDNHRNBNNRDYYNHVRRNHNHNWWNBNNNBNRNHMDNKHWR

CDNRVDNMHYRNVDDHDWNDDHVNDDNYRTNVWVBHHNDVKNDDHHVNDVHRDKNNNHNHNRYBTDDDBWDYHNNHD

NHDHRHNBVDYNKNVWDMRDNVNHVDKDKWRNWVHYVHVDHBVDBSWVBNMNDCVDYNNWVDDDHNRDHNDHNWHRH

HNBVWHKTYAARBVDKHHDDNBHWNBNDNBDWVVHNHDNGWKRYNWRNYHDNHHNRHNNNDWHNWNDWHVNNHDNVA

HHKNNTRNNNNHNYTNDDDHVNNNVHVRDBBWHRWVDBBBNDNDWRHNDTBNNWDDKNHKRYRAKTHNNDHKHNNVN

VKNVMDDNNYVKRRHRDHRRHNHHNNNHNNVBDWHNCWRNNYDYVNYGKDVDVNVWHHAKHBDNHVTKBBVNDNBCA

HHDWDDTDHNHDKWNBWHNRWBRNNBDHVBDYDDYNWHRWDRNYDHVRKNNBVNHNDDDVHYRDNNNNNDDHNHBNT

DNBBNRVWHNNMNNVNHVRNCABBNNNNHYDNRHNNNVDNMBRWBNNHNKHDWVDBBNDVNNVDTKRNHDKNNHNBH

NDNVNDBNHNNHNHYBNDNNBKDNKBNNDWNNNDYBHYAHHVVYDNWVNNVBDAGGTCWDNDNNNDYDWHNNDHNYN

VNNDYHBNNVHWRDHBNBDVRRKNDDNRHYHMNDDHYHNNVNAHNHYHHWNHDDBBDDNDVNRNHNNNNARBGAWHD

DDYHYRYGHNKKRDNDMHHVBMNDNDDWNNADNHDNNDHMHKDYNDDNDDHDDVRMNHDHHBNDRNMDHDDNVDNNN

WDDNHNNNNHBDDVVHNNNNDNVWNWRYNVNDRNNRWRRHDHBYWNDRNVNHDRDYHHBMDVMNNRDYHWNNHNVHD

VDNNDHNDWNNNDNKNYVDHNVHWKHWANWVYSRNRDDNNHVAHDHNDWNYVBRVDNNVVABRDDNDHBNRNYBBRD

NNWWRYWAVYHNNWNNDVNNYNVWNWVHNYDWBNWNBNVNBDHDBDNRHDDDDDNNDDDVDNVBSDBHRDNNDDNNY

DNDHDNNBWDRNVHDKWDNNDDHYWNRWNHDRWWNHHWVNDDYDWNKWHNDBDNNVNHNYDNNRDNRVHKDHWVBBN

BDHYDRDNRHNVNYBBNDBVDNWVDDDNNDSHBKBDYHDYHDWNNNHRHWGBWVNHNNHNDHVHDRDVVHDYNNHNY

YNNNDHDKDRHRRDWNDYDNNNHNNBHDNNRDNDBHRNHKNNDHHNNHDHNDHNDDWNNWHDNNDMVNNHWNRHHVR

VDDDDHWHTWMHHSDBWDDNDHHVNHNHSHNRDNHMMBDHVHHNYVDNBNHDNKNNDHDBNNDHRDNAWMDNDVDNR

DDRHVDNBRDBDNHMNVNNDANHNNWDNWNDNBHHDYRRNWVNWVHVDNWWNDVHWRYHNHHNYHKNNHVHNNVRNB

RDHNHDRNWBNWNNDHNHNDKNNNBNTNWDDNGATHNYVVDYRWVNNWYHWMHNNBHNVNDHKHNYNDBHNBWDHNN

DNHDNNMVYNWVVAHRACGWRDWYVNDHDVHNHKDRWWHVNVNDHNNNDTNNWBDNRHBYDNNYDNNAMNNDMNDDD

WVDNNNVNDHDWNRDHHNNSDWNDBNNWRDHNNHDNHNRHDWBAKSWWYTRHDHDKDVBHNDNNYNHRWMDHKHNDV

NRRWBRHNNHSBWNRDDKDVNDHHDHVKTWBYNRHRWHDHNDHYRNADNNWWNDDNDNNHVDNHNNNHRNNNNHDNN

DDKRNNDDNDVHNDBWVHBNHBDYGHDWAHNYNDYDNNNNDKMVYMWBVKNVVHDVHWDNWNNHNNMKNHRDNNNKR

KKNNRHDHBDHNDVVNYRNHRYNDHVDNDRYHVNNHWKHNDRWHDWHHDVVVHWNNBNBDNGKYDDVWHVMHHNRHB

HDRRYNBHYNDHHVWVDNHKWBHVWRDHDVVHHHDBRDDVDTNWBDDDDDDBDDDHVRKBDHGTNHBYDHNDYBWHH

NHDHNRVHDDRVWYDVNYDNDCVAAGGARBDRHVRDHNNYNDHBDDVNMBHWNDKHHDRBWRNWDWDVDDDBYRWVN

YMKDHRNDRYHNNNNNNNHDHNNVNHVNNDYHNDHDHVDDHBDHNBNNHDNDDWTRDNNDYNWBDKWHDHNNNYNHS

MBDDNHVNNDYWVDHVHHNNHHNHNHNDBNDYNDDNDWVRHSNVWVTYRWDYNKWHDYKNBRHNDBDVHNNYRKBDW

HDWCYTCGARWRNHRYDSNWKVHNGWHNDKNNVRDHYDVNNYHYNRNVDNRNWWHNVNHRNDKHHRNNNKHNNDHNN

DDNDDHHDVHNBNDYNNDNDNHDNVNNTDHNHATSDDNHHDHBVNHDDADYRRHRKDDYNVVBWNNDDHBVBVNNND

HNHNDNNNRBVHDDVDTNNNNNAVNBRHWNNDDNRBNRDYNWNDBDHNRWWNAHDYNDKDDDHBHHDVNVDBNWNBH

VDWHBWBVNWBHBNHNNBNNNNDNHHDMKMMBHDDNDWNNRBDBYRNNWNNHDTMYDBTNKBHAHNHRBNNDHNDYR

WHDDHYDNNBRKNDBWVKNNNDNVVRDDHWWHDDVWRAVAYMDRKDHDDSRWHBDKKYRTWBMKTKWHBVHBYNNDW

SNDRKVHDDNYRVDWATTTTTGATTTTMNHKDNMNVWNTYNYVNYWDNNBDNWDWBKBRMMRHDWWAAYWRWYACAK

339

TTATWRTRRAHSTRBHMKYGRBCATYRBYVWYAAACCAGTAGTAACAGTRGTAGTAACAKKRKNBNDNNYBDDWDHN

GWVBYARTMRYWRTKDHHDBNDHNDYNNDHDYDNHMRBDDYWRWNDWYNBNVYWNBNNNDVYHNHRBWHHBDDTNVK

VMWDKTTRKAGAAGTAGCAGTAACAGTGGTCATTGTTRKAGAAGTAGCARTAACAGTGGTCATTGTTGTAGAAGTAG

CAGKWVSRDTNVNBDNHVNNDHADWVNHDVBHRWMVYWGKDVYSWDWBYNDHNRWDNDHRBWVDHNNWBDNSHHNNN

NNHBWMNDHDHVDWDNHHHMVDKDDNBDWNNNNDNAHDHDNNDYNBTWMKDVKVBTCATTGWWBDWVVNVYRBHRNW

HDSARWVBBHNDDNBYDHHRNDRDHRVRBTHNBNKYDDHHHHNDDWVHHNVDDHHRHNNDHNNNBDRNWVVNWNHDB

DHVVHVHDKHVRHMRHAVBSRBHNYDNYKDDDNNNNYARVHKKAWNAKWDGTHWWYGWYRWHRHDKYMDMHDBVNBN

DNVDHBRHDNBNNBHNNHRHWNDVNHVNWVNHHNNNKNHHHYNNHVHVDNNNBHDYNVHHRNNDWDDHHVHDVWDKD

NKDHDDNRYDVDWDYRRDRRYYAHHKWDVYDMRRRYWVMHRYWWMRGTGGTCATTGTTGTACAAGTAGCAGTAACAK

KKRDSRWDGTYNNNDMNNYAGCAGTAATTMHVNNHNDVNHYBBKDBVDNRVTYVNBHWHGWWDWHBDHBWHBDVATG

ACTWKDKVWKAHRDHBDASRARWHMNYNWHDHSDYRDHVWVYHNNVDYHHBBDKNNYRNNDNDDYBRNVRNRBWHRW

HNNDNHHYNBBHNBRDVNDRKDHNNHNDNDDHVNBNDDWHNBNRNNHNDRNDNDNBBDWVDDRDHNYDYRWDDVHMB

NYDYNNNHHHBDHBVDNRDNBHDDHYDHWRHNNDNVDDDHNNNHNNYBDWBBDNDBDHNDDHRNNDNHDWHRDNVVA

NYYDDWDNNNHDDDHNDHMNNDNDNWBDBNNDHDNHNHHNNHDNNKNNNNVNHHNBNNYYDDNDDNNNHDVNAHBHD

NWHNDNNNDHVRVDDHDHDRDNDHBNDANNHNNHDNDYNHNNDKHNNNNVNVRDBRDNWNNYNHNNDVDDDHNBRNN

RDHDDYBNDVYYVNDNHNNHNMNDDBHNHDVNNVDDRNNNYBBHHDNHKDWNHRDYSDHNDNHVNNNDYRNRNNDDH

WRWMSBDTNTNRNNHYBHMNDRHNARHBDHHNDVDNCNNNYDDNTNDHVDNNRNDHNNNHNDHNNWYYHBDRMHYYK

NNDNVRWYRNBYBYDDHHKDNVHDDHNDWYNDHBDHNHBRDHDNDDNNVVBNYNNHHRHBYDVWRAMBDHNNDNDDN

VMKDNDDNDNDYMNNHVBBNDDHDHNWNHNBDYRNNHNNYVDWNAHNNNWHNHDDNRNYDKTHVRNVMVDDWYVNRN

WDTRKYNDNHHHNHYDNWHDNHNNKBWBDDDHNNDHNNHNRHHRDDNBHNDNDHHNNABVHDKHHDNKHHDNHHDHB

DDVNDWNBNDNRDNRYHNDDDWDDDVNBNNDYNNNNNNHHBDNNNNNVDBNHYDBHDNDNHYDNNHDVNYNNDNRNN

DDNNHYKHHTNVHNHVBNNHNNNDVNYDDYVNHRHHHDBNNNVDWVDYBWRNDBDHSNWYNDHNKBGNHNVDNHHKN

HYVBNNNNDTNMHNBNRNVNDYDNNNBNMNWDKWDNDKWVVHHVNDRMHVNVDBDWMHWVDRNDNHNDNYVHRRNKT

DNDDNNYHDHDVDNVHNBDYNNDRHNVWYDNNNNHNDHVDKDNDWBNNNHRNVRNHVNNDDHDNNNYVVDKHNHDNY

DNWNNNNNDWDHNNNRDHWDKNNNNBMDNNNDYBNMVHWBHBWHBNHMHHNDDYNNNDBNNHNNYHWMRRWYKDHVH

BNHSDNDNWBNDTHBWBHDWHBDNDNNNMASAYNYVNNHNRGTHVDNNNWNKWNWCNDBRNYBNBVDVNWBBYBWNH

RHBRNNDHYNHDNNYDNDRWWYDMVDHVYNNNKHNDNHNNHHTNHBNRVDTRYRVTCCDVNWDBNHNNDHNHVWYNB

BWBDRVWRNBNHWBBDDNNYHDDBNRHHRNBRNNHNKDDNNNBTBNCAWDDHDYBNNNVNNDVRVYNHBNTHBWNNB

DDNYDNHNNNVNSKWNHDYNVDDDNNWBDWYNNBDNNNNDDDBRBBVNHRHMWNWKVDHWNNDWRDNNVHHBKNHBD

HYNDHBHDKWHHHVBWNRNYBWDNHHNNWYDNBDMRNDNDHYHBHVVDHWNNKRHYNNHVVNWBDYDDVNHNBNNND

HNHVHRKHBVNDYDBVBKNNHVDNDBHNHNHDDWDBVDNNNNRNHDNNHHNNHBHHDVBNDDDRHHDNDNDBHWBRB

NVDHHYBNBDYRDHNNRRBNHKYVBNNNDNNYDBNVNNVHHDHYBNWNHDNHAVVNYDNSWRDHHHDVNNDNNNVRD

DNNYNNHDNVYHNRDHNYSNDBHNNNNYNHRHDBNDKDYRDHDRNYNDBRNDDNNKNBNHDVKNDWVNHNHYNBHHD

DBNKBNNNNRDDNDNNHDHHVDNHRHNNYDDHNNVVYSNWHDHNBYBYBKTKYNNVHRNVHNNMCDNYRHVDYHBWB

DBHBHVDHBVHBDNBNNNDHHBDVDVBRKHBNHNDNYKNNVHWHHVNRNBNNNHWNNHYNHNHBDNHWNHNHDNWHN

NDYDRBNRNNDHNCGTTGNWYNHANDYNHCNRVNNDNNYDHHDNHRDNDWRVWVNNDNNYNNHSNNDNVDNMHNRHH

NNBBNBVDWMVYKDNKNDRBWHVTNNBHNDNVWBWBNBNNYNRNNWDYNNHHNNDCNHHNVKDDBNNDDHSBWBDBY

HNBRBNDNVHBNNDRNNNHVVHBBHHDVHSYKVBRNDNVHVNBNYRHHDNNNDNNYNNRYVNNVNNNNNDBNNYDNH

SRWVTNHNDDBNBHBDBBDDNMDNHYNRNNVKHMWBRKHWRDRYBRRBNBBNNNDVVNNNWYBYCNNNVVHNHMNKV

YDDBBVHRTNDNVNHDBNDNNDHVYGTTADHBNVDDVWNNNHNHHNNVRHDVDDDVBNHNDNYNNHDHNYBKNNBND

YVNBVHBWDRDNVNTBDNVDWBBDDTVYBHVNVBDDNYNDDDDBNKBNHVNNRHBNDNYVNHNDHNDNNBNHBKDVD

WNHRNHKKYRNDRDDVHBBYNDBVKNBBNKDKNYYNHNNYNNDDVHNRNBNNBNDNRHDHDDYHRNHNNDHNVHHNH

VHNYDNYNKVYBDVDTNNBKNNHNDRHHNDVNNYHHNWHNDYRNDNNNNNNMNHYDNBWYHWNTHDDHNWHHHRNKR

BAMNNRYDDBKBNMDHKWNRVDRDYVWBHDYDYVHNHDDHDDYSYRRHYYTRWHNBSRHHHBRDNYNVNNDHWYMDD

HDKDHBSDBWBNRWMBVSKHBRKRBNTHYCKRYWHRBDNHBMHMBHRHHWYNRHYNYRHYDDNVBRVMBWGWMRWKH

SRDHRDHHWHMMYYBBRSNMKDWNKDVBAYHHCTDVNHNYWVDWVKWDHDWWDRDBHVYWDTHYNDHYDKASDWSTN

HBRBDMRDDYHNNWNVYKHVRYMSNTSDYRVNWRKNWKBKYRCBWRDWSBHHTBDHDHWRDHDNWBDHVVRHHDHVR

MVKSKTMRHHHCSBCRYWBDDBDYDKBHKKMBVMWDRVKNDYYYHRHYVRKYKKHDBMKKHDVHVBVRRVVWTYSKH

BKDNSYWYTMRRRHHNYGRSYNMHVWBVVKYYYTDRYRWHMWSDDVYDNRANNDRTRMNRMSDTGKMYHMCCWYYSD

DBVRCNBMBBHTNHBYYVTRYWSBKWSVGWBWHHBDMYWWTYVDHNHVWWBNHNDHTRYYWDYBVTNWYDHYNWWHW

KVDKYDRRYVYYYRWWYMWDYDHVTYYYWHRVCBRNRMHRHYWYHKVWHRYHWMMTVYHWHDHTDDHNKDHYBHVWW

BMHYHMSDNHNBYYNMCRKKYKHVCRDWHWKYKMRDDBKWRMAMTWYMAYKRTGKGTCAT

Notes: The region conserved in all coronaviruses is highlighted in red, and the primer positions have been

underligned. The positions here are in positive sense orientation, because of variations in the sizes of coronavirus

genomes, in bovine coronavirus, the amplicon region lies between nucleotide 20062 and 20156. Primers were

blasted from the GenBank website http://www.ncbi.nlm.nih.gov/ .

Influenza A viruses dual and multiple infections with other ...

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