Molecular Epidemiology of Malaria in

Papua-Indonesia

Zuleima Pava Imitola

(B.Sc, M.Sc)

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

April 2017

Global and Tropical Health Division

Menzies School of Health Research

Charles Darwin University

Darwin, Australia

DECLARATION

I hereby declare that the work herein, now submitted as a thesis for the degree of Doctor of

Philosophy of the Charles Darwin University, is the result of my own investigations, and all

references to ideas and work of other researchers have been specifically acknowledged. I hereby

certify that the work embodied in this thesis has not already been accepted in substance for any

degree, and is not being currently submitted in candidature for any other degree.

Zuleima Pava Imitola

April 2017

i

Table of Contents Abstract ......................................................................................................................... iv

List of Publications .......................................................................................................... vi

Acknowledgement ......................................................................................................... vii

1.1 General Introduction to Malaria ................................................................................. 1

1.1.1 The Malaria Life Cycle ......................................................................................... 1

1.1.2 Malaria Epidemiology.......................................................................................... 5

1.1.3 The Global Burden .............................................................................................. 7

1.1.4 The Burden in Southeast Asia: Challenges in Indonesia .......................................... 8

1.1.5 Malaria Control and Elimination Strategies ........................................................... 9

1.1.6 Surveillance and Response Systems for Malaria Control and Elimination ...............10

1.2 Challenges for Malaria Surveillance Systems and Response .........................................12

1.2.1 The Hidden Burden of Malaria ............................................................................12

1.2.1.1 Current strategies to target asymptomatic and low -density

pa rasi taemias in pre-e limina tio n a rea s ...........................................................17

1.2.2 The Threat of Antimalarial Resistance .................................................................19

1.2.2. 1 Definit ion ...............................................................................................19

1.2.2. 2 Dist ribut ion ............................................................................................19

1.2.2. 3 Ant imala ria l eff icacy surve il la nce .......................................................20

1.2.2. 4 Spec if ic C ha llenges of Survei lla nce of P. v ivax infect ions ................27

1.2.2. 5 Mo nitor ing CQ Resista nt P. v ivax ........................................................27

1.2.3 Imported malaria detection in pre-elimination areas............................................29

1.3 Molecular Approaches to Facilitate Parasite Surveillance ............................................29

1.3.1 Population Genetic and Genomic Studies for Improving Parasite Surveillance .......29

1.3.1. 1 Available Tools for Pa ras ite Genotyping ............................................30

1.3.1.2 Applicabi lity of Genotyping Tools for Characterizing Malaria

Tra nsmiss io n Dyna mic s .....................................................................................33

1.3.1.3 Applicabi lity of Genotyping Tools to Characterize Population

Different iat ion a nd I mported I nfec tio ns .........................................................37

1.3.2 Molecular Surveillance of Antimalarial Drug Resistance in P. vivax ........................40

1.3.2. 1 Ca ndida te Approac h .............................................................................40

1.3.2. 2 Ca ndida te-free Approac h .....................................................................42

1.4 Rationale and Aim of the Current Study .....................................................................43

1.4.1 Study Site and Context of the Projects.................................................................43

1.4.2 Rationale ...........................................................................................................44

ii

1.4.3 The Hidden Burden of Asymptomatic and Low-Density Plasmodium Infections......45

1.4.4 Molecular Epidemiology and Transmission of Malaria ..........................................46

1.4.5 The Impact of Active versus Passive Sampling Strategies on Surveillance ...............46

1.4.6 The Effect of Artemisinin-based Combination Therapy (ACT) on Parasite Population

Structure. ..................................................................................................................47

1.4.7 Elucidating the Role of the P. vivax Chloroquine Resistance Transporter (CRT) in

Chloroquine Resistance ..............................................................................................47

1.5 References ...............................................................................................................48

CHAPTER 2 – Submicroscopic and asymptomatic Plasmodium parasitaemia associated with

significant risk of anaemia in Papua, Indonesia ................................................................67

CHAPTER 3 - Genetic micro-epidemiology of malaria in Papua Indonesia: Extensive P. vivax

diversity and a distinct subpopulation of asymptomatic P. falciparum infections. ..............87

CHAPTER 4 – Passively versus Actively Detected Malaria: Similar Genetic Diversity but

Different Complexity of Infection. ................................................................................. 107

CHAPTER 5 - Longitudinal characterization of P. vivax and P. falciparum genetic diversity

and structure in Papua, Indonesia between 2004 and 2016 ............................................ 119

5.1 Abstract ................................................................................................................. 120

5.2 Introduction ........................................................................................................... 121

5.3 Materials and Methods........................................................................................... 123

5.3.1 Study site and sampling .................................................................................... 123

5.3.2 Microsatellite typing ........................................................................................ 124

5.3.3 Data Analysis ................................................................................................... 124

5.3.4 Ethics .............................................................................................................. 127

5.4 Results................................................................................................................... 127

5.4.1 Comparable genetic diversity amongst the Pre-ACT and Post-ACT years in P.

falciparum and P. vivax............................................................................................. 129

5.4.2 Comparable demographics between the pre-ACT and post-ACT patient groups in

both species............................................................................................................. 131

5.4.3 Significant decrease in complexity of infection in P. vivax after ACT implementation

............................................................................................................................... 132

5.4.4 No difference in population genetic diversity before and after ACT implementation

in P. falciparum and P. vivax ..................................................................................... 135

5.4.5 Evidence of increasing self-fertilization in the P. falciparum population after ACT

implementation ....................................................................................................... 135

5.4.6 Evidence of increasing genetic differentiation over time in P. falciparum ............ 138

5.5 Discussion .............................................................................................................. 141

5.6 Supplementary Material ......................................................................................... 148

5.7 Supplementary material ......................................................................................... 150

5.8 References ............................................................................................................. 153

iii

CHAPTER 6 –Expression of Plasmodium vivax crt-o is related to parasite stage but not ex

vivo Chloroquine susceptibility. .................................................................................... 159

CHAPTER 7 – General Discussion and Outlook ............................................................... 170

7.1 Introduction ........................................................................................................... 170

7.2 Characterization of Asymptomatic and Subpatent/Submicroscopic Malaria ............... 171

7.2.1 How deep do we need to look in the “malaria iceberg” to reduce transmission? . 171

7.2.2 What is the clinical impact of subpatent/submicroscopic malaria? ...................... 172

7.3 Molecular Epidemiology and Population Genetics .................................................... 173

7.3.1 Characterizing malaria transmission patterns .................................................... 173

7.3.2 Sampling strategies .......................................................................................... 175

7.3.3 Longitudinal surveys to monitor the efficacy of interventions ............................. 176

7.3.4 Surveillance of chloroquine efficacy in P. vivax .................................................. 177

7.4 Limitations of the Study .......................................................................................... 179

7.4.1 Political and logistical challenges ...................................................................... 179

7.4.2 Technical limitations ........................................................................................ 180

7.5 Outlook and Conclusive Remarks............................................................................. 181

7.6 References ............................................................................................................. 183

v

Abstract

Papua is the most easterly province of Indonesia and has one of the highest burdens of

malaria in Asia, with drug resistance documented for both P. falciparum and P. vivax.

Improved surveillance systems are needed to overcome specific challenges arising from the

declining transmission of malaria. This thesis aimed to investigate the benefits of

incorporating molecular tools into traditional surveillance. Principal findings include the

following:

Firstly asymptomatic and submicroscopic infections in this area constitute two thirds of

detectable parasitaemia and are associated with significant morbidity, highlighting the

importance of identifying and treating patients with low-level asymptomatic infection.

Secondly, co-endemic P. falciparum and P. vivax have contrasting micro-epidemiology.

Molecular analyses demonstrate intense transmission of P. vivax, but more focal and lower

transmission intensity in P. falciparum.

Thirdly, parasites from asymptomatic and symptomatic patients are from genetically similar

populations, demonstrating that passive sampling is appropriate for molecular parasite

surveillance.

Fourthly, the implementation of highly effective schizontocidal treatment with artemisinin-

based combination therapy (ACT) reduced the proportion of polyclonal infections in P.

falciparum and P. vivax, suggesting reduction in transmission of both species. However, the

reduction was greater in the P. falciparum population.

Finally, the expression of the P. vivax chloroquine resistance transporter orthologue (pvcrt-

o) is not a primary determinant of ex vivo response to chloroquine, and thus not an

appropriate molecular marker of drug resistant P. vivax.

vi

This dissertation provides evidence of the benefit of incorporating molecular tools into

malaria surveillance by characterising the asymptomatic reservoir and its impact on clinical

disease and transmission dynamics, evaluating the suitability of passive molecular

surveillance and its usefulness to measure the effect of malaria control interventions, and

contributing to the validation of molecular markers of chloroquine resistance in P. vivax.

vii

List of Publications

1. Pava, Z, Burdam, FH, Handayuni, I, Trianty, L, Utami, RA, Tirta, YK, Kenangalem, E, Lampah,

D, Kusuma, A, Wirjanata, G, Kho, S, Simpson, JA, Auburn, S, Douglas, NM, Noviyanti, R,

Anstey, NM, Poespoprodjo, JR, Marfurt, J & Price, RN 2016, 'Submicroscopic and

Asymptomatic Plasmodium Parasitaemia Associated with Significant Risk of Anaemia in

Papua, Indonesia', PLoS One, vol. 11, no. 10, p. e0165340.

This publication was included as “Chapter 2” in this thesis

2. Pava, Z, Noviyanti, R, Handayuni, I, Trimarsanto, H, Trianty, L, Burdam, FH, Kenangalem, E,

Utami, RAS, Tirta, YK, Coutrier, F, Poespoprodjo, JR, Price, RN, Marfurt, J & Auburn, S 2017,

'Genetic micro-epidemiology of malaria in Papua Indonesia: Extensive P. vivax diversity

and a distinct subpopulation of asymptomatic P. falciparum infections', PLoS One, vol. 12,

no. 5, p. e0177445.

This publication was included as “Chapter 3” in this thesis

3. Pava, Z., Handayuni, I., Trianty, L., Utami, R.A., Tirta, Y.K., Puspitasari, A.M., Burdam, F.,

Kenangalem, E., Wirjanata, G., Kho, S. and Trimarsanto, H., 2017. Passively versus Actively

Detected Malaria: Similar Genetic Diversity but Different Complexity of Infection,

tpmd170364.

This publication was included as “Chapter 4” in this thesis

4. Pava, Z, Handayuni, I, Wirjanata, G, To, S, Trianty, L, Noviyanti, R, Poespoprodjo, JR,

Auburn, S, Price, RN & Marfurt, J 2015, 'Expression of Plasmodium vivax crt-o Is Related to

Parasite Stage but Not Ex Vivo Chloroquine Susceptibility', Antimicrob Agents Chemother,

vol. 60, no. 1, pp. 361-367.

This publication was included as “Chapter 6” in this thesis

Other publications arising during the candidature:

Kho, S, Marfurt, J, Handayuni, I, Pava, Z, Noviyanti, R, Kusuma, A, Piera, KA, Burdam, FH,

Kenangalem, E, Lampah, DA, Engwerda, CR, Poespoprodjo, JR, Price, RN, Anstey, NM, Minigo,

G & Woodberry, T 2016, 'Characterization of blood dendritic and regulatory T cells in

asymptomatic adults with sub-microscopic Plasmodium falciparum or Plasmodium vivax

infection', Malar J, vol. 15, p. 328.

Trimarsanto, H., Benavente, E. D., Noviyanti, R., Utami, R. A. S., Trianty, L., Pava, Z., ... & Liu,

Y. (2017). VivaxGEN: ‘An open access platform for comparative analysis of short tandem

repeat genotyping data in Plasmodium vivax Populations’. PLoS neglected tropical diseases,

vol. 11, no. 3, e0005465.

viii

Acknowledgement

Mentioning people in a large list of thanks does not seem natural to me... I guess because I

was taught to thank people by giving them a strong handshake or a hug, but anyways here I

go...

I’m deeply grateful to have had the opportunity to reach this dream. I’m aware of the

tremendous privilege of being granted a scholarship in Colombia, and being accepted into

such a prestigious group at Menzies to undertake my studies. I tried my best to make it count

and I will keep trying harder to make it worthwhile.

At the end of this journey, I weigh the sacrifices made against all the things I learnt. And

maybe because I’m finishing, all I can remember are the positive things. This experience has

allowed me to grow personally and academically. And for that, I’ll be forever thankful and

forever indebted.

To my great team of supervisors, I’ve never seen a team that complement each other so well.

You all have integrity, brains and a sense of humour. And, I’m very grateful for your support

and all the things you taught me over the last four years. I hope one day you learn to dance

salsa.

To my friends, thank you for being there when the ship was sinking and when the sun was

shining. Thank you for sharing those moments that made life in Darwin unforgettable. Thanks

for encouraging me to try new things, and to “pull my ear” when I was being whingey.

Whether we’re physically close or not, I’ll always have you in my mind… and hopefully in

Facebook.

To my husband Ammar, you witnessed the whole thing and stood by my side, even in the

scariest moments when I was hangry. You are a very brave person! I can’t thank you enough

for all the things you have done.

ix

A mis padres, quienes han sufrido conmigo todos los sacrificios que conllevaron a esta meta.

Estoy aquí gracias a ustedes, estoy aquí por ustedes. Ustedes son mi orgullo y el ejemplo que

sigo: trabajo duro, corazón blandito. Los amo.

A mis hermanas/amigas gracias por mantenerme los pies en la tierra. Por recordarme

siempre lo afortunada que soy, y por estar tan pendiente de mí. Por recordarme no tomarme

las cosas tan enserio y hacerme reír cantidad. Finalmente a la vida, porque sigo sin saber para

donde voy pero disfrutando cada momento.

CHAPTER 1

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CHAPTER 1 – Introduction and Literature Review

1.1 General Introduction to Malaria

Malaria remains the most important human parasitic disease, with approximately 3.2 billion

people at risk globally (WHO 2016b). Each year, malaria accounts for more than half a

million deaths, predominantly caused by Plasmodium falciparum in young African children.

Outside of Africa, Plasmodium vivax has become the predominant cause of infection and,

whilst less pathogenic, it is also associated with significant morbidity and mortality (WHO

2016b). In addition to direct health consequences attributable to malaria, there is also a

substantial economic burden. In Africa alone, this has been estimated to reduce the gross

domestic product by more than 10% (Sachs & Malaney 2002). Malaria is a disease of

poverty and this has prompted global initiatives from major funding bodies and

philanthropists to renew efforts for global malaria elimination. In this introduction, I will

review the main attributes of malaria, and developments in molecular approaches to

improve malaria surveillance, which forms the focus of my thesis.

1.1.1 The Malaria Life Cycle

There are five Plasmodium species that cause malaria in humans: P. falciparum, P. vivax, P.

malariae, P. ovale (P. ovale wallikeri and P. ovale curtisi subspecies) and P. knowlesi. These

protozoan parasites are transmitted by female mosquitos of Anopheles spp. Human malaria

begins with the transmission of 8-15 Plasmodium sporozoites through the bite of an

infected female anopheline mosquito. The sporozoites reach the liver, invade hepatocytes,

and develop and divide into 10,000 to 30,000 merozoites. After 5-8 days, thousands of

merozoites rupture the hepatocytes to invade the bloodstream and start the asexual

erythrocytic cycle. P. vivax and P. ovale infections can form an intrahepatic stage

(hypnozoites) that remain dormant anywhere from two weeks to more than a year

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(depending on geographic origin), before they awaken and cause relapsing episodes of

clinical disease (White, NJ et al. 2014).

Inside the erythrocyte, the parasite develops and replicates (mitotic division) rupturing the

red blood cells (RBCs) and releasing merozoites that invade new RBCs. Some parasites

differentiate into male or female gametocytes, which will later infect a naïve Anopheles

mosquito during a blood meal. Gametogenesis (i.e., sexual maturation) starts in the

mosquito and lasts depending on the Plasmodium species: 3-4 days for P. vivax and P.

ovale, 6-8 days for P. malariae, and 8-10 days for P. falciparum (White, NJ et al. 2014).

The pathophysiological processes of malaria infection result from the rupture of

erythrocytes, subsequent release of merozoites and erythrocyte material into the

circulation, and the host reaction to these events (Miller, Good & Milon 1994). There is no

evidence that sexual blood stages (gametocytes) or hypnozoites cause organ dysfunction.

The symptomatic stage of infection usually begins between six to eight days after parasites

emerge from the liver (White, NJ et al. 2014). Fever usually precedes detectable

parasitaemia and can be present together with chills, headache, sweats, nausea and fatigue

(Carter & Mendis 2002). Malaria symptoms vary depending on the level of host immunity.

Typically, people living in highly endemic areas develop immunity and tolerate higher levels

of parasitaemia without symptoms. Children under 5 years of age and pregnant women are

most likely to be severely affected, since they have weaker immunity. In contrast, people

living in areas where low or seasonal transmission occurs, present symptomatic malaria at

all ages and with overall lower parasitaemias (Bronzan, McMorrow & Kachur 2008). In a

susceptible individual, the parasite density expands by between six and 20 times per cycle.

When parasite densities reach 20 to 50 parasites/μL of blood, they become detectable by

traditional diagnostic tools used at health-facilities (i.e., microscopy or rapid diagnostic

tests) (White, NJ et al. 2014).

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Traditional diagnostics tools aim to confirm quickly and accurately the presence and species

of Plasmodium in febrile individuals. This is important for effective clinical treatment and

for malaria surveillance. Effective treatment aims to eliminate the parasites rapidly and

completely from the patient’s body preventing: a) transmission of the infection (by

reducing the likelihood of infecting mosquitoes), b) the progression to severe disease and

death, and c) progression to chronic infection (i.e., P. vivax relapses) (White, NJ et al. 2014).

Details on the number of antimalarial drugs available, mechanism of action (MOA), main

use, contraindications, and drug resistance-conferring genes are summarized in Table 1.

Currently, Artemisinin-based Combination Therapies (ACTs) are the recommended

treatment for P. falciparum malaria, while CQ is recommended to treat blood stages of all

non-falciparum species and PQ to treat liver stages in P. vivax and P. ovale infections (WHO

2015a).

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Table 1. Current antimalarials. Adapted from Antony et al. (Antony & Parija 2016).

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1.1.2 Malaria Epidemiology

Endemicity levels of malaria may vary widely, even at local spatial scales. Malaria

transmission is, in part, determined by the seasonal fluctuation of mosquito populations

and conditions suitable for mosquito survival such as water deposits. Transmission is also

dependent upon the local Anopheles mosquitoes’ vector capacity (i.e., susceptibility to

infection, infection density, and longevity), and bionomics (i.e., larval habitats and biting

habits) (Brady et al. 2016). Approximately 70 anopheline species are able to transmit

human malaria parasites, but only 41 have been described as “potent vectors”, comprising

those which are present in high densities, breed easily, and preferentially bite humans (e.g.

Anopheles gambiae complex) (Sinka et al. 2012).

Characterizing local patterns of endemicity can be complex. The first method used to

quantify malaria endemicity involved determining the spleen rate (i.e., the proportion of

populations with palpable enlargement of the spleen, known as “splenomegaly”). The

consensus was to categorize transmission intensity according to the spleen rate in children

aged 2–9 years-old. A prevalence of >75% was termed holoendemic, 51-75%

hyperendemic, 11-50% mesoendemic, and <10% hypoendemic (Hay, Smith & Snow 2008).

In 1957, Macdonald proposed a classification between stable and unstable malaria

endemicity based on a deeper mathematical understanding of entomological determinants

of malaria transmission (Macdonald, George 1957). The mathematical model considered

malaria prevalence, inoculation rate, together with other vector biology parameters, such

as biting habits and vector capacity. This model revealed that the stability of malaria

transmission was determined by the average number of feeds that a mosquito takes on a

human during its life. Whereas a vector-based index of more than 2.5 represents stable

malaria not affected by natural or man-made perturbations, an index of less than 0.50

represents unstable malaria sensitive to climate and control measures; indices between

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these extremes are regarded as of intermediate stability (Macdonald, G. 1952). Despite the

high sensitivity of this model, the logistical complexities to obtain entomological data led to

the stable-unstable concept being rarely implemented and used (Hay, Smith & Snow 2008).

Over the last few decades, different malaria metrics have been used to measure malaria

transmission and guide control and elimination programs (Figure1). For example, the

numbers of reported malaria cases per year and their threshold values have been used to

define transitions between activities of malaria control programmes. The same metrics

have also been used to establish a common nomenclature for describing malaria risk and

the appropriate level of caution when travelling (Hay, Smith & Snow 2008).

To date, the most frequently used parameters to evaluate risk associated with malaria and

the effectiveness of an intervention are founded on host-based assessments of malaria

prevalence (WHO 1971). Measuring malaria incidence requires every suspected malaria

case to be identified by active case detection. The results are expressed as annual parasite

incidence (API) per 1,000 of the population of the administrative area investigated. This

index is only considered valid if the proportion of the target population examined exceeds

10% (Black 1968; Hay, Smith & Snow 2008).

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Figure 1. Malaria endemicity and classification, and timelines for action phases of the

Global Malaria Eradication Programme – reproduced from Hay et al. (Hay, Smith & Snow

2008)

1.1.3 The Global Burden

Between 2000 and 2015, malaria incidence was reduced by 41% among populations at risk

(WHO 2016c). Malaria-related death rates fell by 62% globally across all age groups, and by

65% and 71% among children under 5 worldwide and in Africa, respectively (WHO 2016b).

Nevertheless, the estimated burden remains extensive, with 212 million new cases of

malaria (range 149–303 million) and approximately 429,000 malaria deaths (range

235,000–639,000) reported worldwide in 2015 (WHO 2016c). Most global malaria cases are

reported from Africa (90%) followed by Southeast Asia (7%) and the Eastern Mediterranean

Region (2%). The majority of deaths occur in Africa (92%), followed by South-East Asia (6%)

and the Eastern Mediterranean Region (2%). However, the disease burden is often

underestimated due to inaccurate estimates from populations such as India and Indonesia,

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whose combined population accounts for 20% of the world's population at risk (WHO

2016c).

1.1.4 The Burden in Southeast Asia: Challenges in Indonesia

According to the World Health Organization (WHO) estimates, there were 1.4 billion people

living at risk of malaria in South-East Asia in 2015. In this region, Indonesia is one of the

most affected countries with nearly 227 million people living in malaria-endemic areas

(Supplementary Figure 3, Chapter 3). According to the World Health Organization, 217,025

confirmed malaria cases were reported in Indonesia in 2015, but the true burden was

estimated at closer to 1,300,000 cases (range 990,000–1,600,000) (WHO 2016c). This large

discrepancy reflects a number of factors including infrequent treatment-seeking behaviour,

with only 20% of symptomatic patients seeking treatment, and the infrequent practice of

laboratory case confirmation (Elyazar, Hay & Baird 2011).

The highly heterogeneous malaria epidemiology across the approximately 6,000 inhabited

islands renders a major challenge to malaria control and elimination in Indonesia. Socio-

economic differences, varying ecological patterns, and varying prevalence of the five

human-infecting Plasmodium species and more than 20 Anopheles species, all contribute to

the extensive heterogeneity (Elyazar, Hay & Baird 2011).

Indonesia is also known for being a hotspot for resistance to antimalarial therapeutics.

Heterogeneity in antimalarial resistance profiles presents a further challenge to control and

elimination efforts. The distribution of resistant parasites varies greatly depending on the

drug, the species of parasite, and the geographic location (Elyazar, Hay & Baird 2011).

Elyazar et al., assembled the records of treatment efficacy studies (TES) and in vitro

susceptibility tests performed in 452 localities in Indonesia since 1953, the majority

evaluating chloroquine (CQ) and sulphadoxine-pyrimethamine (SP). Reports of resistance

were found in 3 of the 4 species circulating in the country. TES revealed resistance to CQ in

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9

52% (1,539/2,967) of P. falciparum cases and 48% (331/687) of P. vivax cases evaluated

across Indonesia (Elyazar, Hay & Baird 2011). Marked variation was observed in the

geographical distribution of P. falciparum resistance to CQ (higher in east Indonesia than in

west Indonesia), but not SP (Elyazar, Hay & Baird 2011).

1.1.5 Malaria Control and Elimination Strategies

After intense vector control and massive case treatment with CQ until 1955, regional and

international efforts succeeded in eliminating the disease in Europe, North America, the

Caribbean, and parts of Asia and South-Central America (Najera, Gonzalez-Silva & Alonso

2011). Unfortunately, the lack of integrating local health services and communities, the

inability to adjust to regional challenges, and the emergence and spread of insecticide and

antimalarial resistance led to the initiative being abandoned in 1969.

In turn, there was a significant increase of malaria incidence worldwide until the creation of

the Global Fund to Fight AIDS, Tuberculosis and Malaria, and other financing mechanisms in

the early 2000’s. The WHO Global Strategy Consolidate in 1993 established new objectives

aimed to prevent mortality and reduce morbidity and socio-economic loss resulting from

the disease. The strategy means to improve and strengthen local and national malaria

control programmes and presented four basic technical elements (Najera, Gonzalez-Silva &

Alonso 2011; WHO 1993):

i. To provide early diagnosis and prompt treatment,

ii. To plan and implement selective and sustainable preventive measures, including

vector control,

iii. To detect early, contain, or prevent epidemics, and

iv. To strengthen local capacities in basic and applied research to permit and promote

the regular assessment of a country’s malaria situation; particularly the ecological,

social, and economic determinants of the disease.

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In contrast to highly prescriptive and centralized control programs used in the past, the new

approach placed emphasis on flexible, cost-effective, and sustainable programs adapted to

local conditions and needs.

As countries approach malaria elimination, improved surveillance systems play a vital role

in helping to ensure every infection is detected, treated, and reported. For instance, in

some countries approaching elimination, a high proportion of malaria cases are found

among visitors and mobile populations. Identification of those cases relies heavily on strong

surveillance and response systems.

1.1.6 Surveillance and Response Systems for Malaria Control and Elimination

Robust malaria surveillance requires a range of approaches including tools, procedures,

people, and structures that generate information on malaria cases and deaths. This

information can be used for planning, monitoring, and evaluating malaria control

programmes. An effective information-gathering system allows:

i. The identification of areas and/or populations groups at higher risk of malaria (i.e.,

“hotspots” and “hotpops”),

ii. The identification of temporal trends in cases and deaths requiring additional

interventions (e.g. outbreaks),

iii. The assessment of the impact of control measures (e.g. antimalarial efficacy).

The approaches used for malaria surveillance systems vary across countries. Depending on

the level of transmission and a nation’s capacity, malaria programmes are encouraged to

adapt the guidelines proposed by the WHO to local needs and capabilities (WHO 2012).

Appropriate surveillance can reduce the time spent towards malaria elimination by

enabling adequate investment of resources, providing prompt and effective reaction to

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epidemics or ineffective interventions. Malaria surveillance systems vary according the level

of malaria transmission and the resources available (Table 2).

Table 2. Malaria surveillance in different transmission settings and phases of control

(Reproduced from (WHO 2012)).

Given the high frequency of malaria cases in high to moderate transmission settings,

malaria surveillance is often integrated into a comprehensive system of communicable

diseases. At health facility level, case-based surveillance aims to decrease malaria-

associated deaths by focusing on clinical disease that has potential to become severe and

fatal. WHO guidelines recommend that at district and national level, cases and deaths

should be summarised monthly into 5 control charts: 1) malaria incidence and mortality

rates, 2) proportional malaria incidence and mortality rates, 3) general patient attendance

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rates, 4) diagnostic activity (i.e., annual blood examination rate), and 5) quality of diagnosis

and health facility reporting (WHO 2012).

In contrast, case-based surveillance in low-endemic settings aims to identify populations at

higher malaria risk. Severe cases and deaths are recorded to investigate and address

weaknesses in the program. At health facility level, cases are reported daily or weekly to

identify trends, whilst at a district level, the same five control charts outlined above are

prepared weekly or monthly. In pre-elimination areas, each confirmed case is notified to

district and higher levels immediately prior to a thorough investigation to determine

whether the case was: a) imported (i.e., infected outside the area where it was diagnosed),

b) induced (i.e., infected by blood transfusion), or c) locally acquired by mosquito-borne

transmission.

1.2 Challenges for Malaria Surveillance Systems and Response

There are three specific challenges for the current passive-surveillance malaria system: i)

defining the relevance of asymptomatic infections, ii) the need for better tools for

monitoring antimalarial drug resistance, particularly in P. vivax, and iii) defining imported

malaria cases in pre-elimination areas.

1.2.1 The Hidden Burden of Malaria

Asymptomatic Plasmodium infections are routinely missed by fever-based surveillance

systems. During community surveys, more than 75% of infections that are detected by

microscopy are asymptomatic. While some of these infections progress to symptomatic

malaria in days or weeks after diagnosis, many will often persist for months at fluctuating

levels of parasitaemia (Bousema et al. 2014).

Estimates of the size of the asymptomatic reservoir rely heavily on the detection limit of

the technique used. Light microscopy and rapid diagnostic tests (RDT) have comparable

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detection limits. In reference laboratories, this limit could be as low as 10 parasites per µL

of blood. In the field, this limit could go up to 100–500 parasites per µl of blood, depending

on the quality of the equipment, staining, and microscopy training. However, given that a

great proportion of asymptomatic infections are at low-density and often submicroscopic,

the real extent of symptomless infections can only be revealed with PCR-based diagnostic

tools.

The most widely used PCR-based protocols for parasite presence/absence determination

include assays targeting the 18S ribosomal DNA (18S rDNA) (Singh et al. 1999) (Imwong et

al. 2014; Padley et al. 2003). Other, less frequently, used assays target the mitochondrial

cytochrome c oxidase III (cox3), mitochondrial cytochrome b gene (cytb), and the 28S rRNA

gene (Isozumi et al. 2015; Pakalapati et al. 2013; Steenkeste et al. 2009). Comparison

between seven different assays targeting 18S rDNA and cytb revealed that the detection

limit, either as nested PCR or real-time PCR, is on average in the range of 0.1 to 1 parasites

per µL of blood (Alemayehu et al. 2013).

In recent years, large efforts have been made to develop sensitive, quantifiable, high-

throughput, and field-friendly diagnostic tools for malaria. Since the 1990s, when the first

polymerase chain reaction (PCR) was described for detecting Plasmodium spp., more

specialized and sensitive techniques have been developed. Among these, PCR-based

molecular assays have been modified to increase assay sensitivity using adaptations

including: amplification of expressed nucleic acid sequence (i.e., RNA templates), increasing

the volume of the examined whole blood, and pre-concentration of infected red blood cells

(Hofmann et al. 2015; Imwong et al. 2014).

Quantitative PCR (qPCR) is the most common technique used for quantification of low-

density parasitaemia, given its potential to be adapted for high throughput (i.e., allowing

the evaluation of up to 700 samples per week) (Imwong et al. 2014). However, the

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14

requirement of a standard curve from either a sample with known parasitaemia or plasmids

with known target concentration complicates the comparison between laboratories.

Recently, a new approach has been developed to quantify ultra-low parasitaemias without

a standard curve. The droplet digital PCR technique evaluates multiple replicates (1,500

droplets) from the same sample (Koepfli et al. 2016). Analysis between the replicates

replaces the use of a standard curve, allowing more accurate quantitation and

comparability between laboratories.

The development of techniques such as large volume ultra-sensitive PCR has improved the

characterisation of asymptomatic disease and its potential contribution to transmission

(Falq et al. 2016; Tripura et al. 2016; Vallejo et al. 2016). However, in the context of malaria

surveillance, these approaches present some limitations which include, large blood volumes

requiring venipuncture, which could be logistically challenging in community surveys, and

the costs associated with implementation, training, and maintenance of these techniques in

local health facilities.

The distribution and characteristics of the asymptomatic reservoir vary according to

transmission intensity. In high-transmission areas, continuous exposure to infections leads

to partial immunity (sometimes referred to as “premunition”) (Chen et al. 2016).

Premunition allows hosts to harbour higher parasite densities without presenting

symptoms. Thus, asymptomatic infections in high transmission settings are mostly patent

(i.e., detectable by microscopy). In contrast, in low-endemic settings, most asymptomatic

infections tend to be submicroscopic (Galatas, Bassat & Mayor 2016; Moreira et al. 2015).

In areas where control strategies have reduced transmission from high to low intensity,

residual immunity is believed to account for the large proportion of submicroscopic

infections. While in historically low-transmission areas, transmission hotspots may be

responsible for development of partial immunity, another explanation is the presence of an

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15

effective immune mechanism acquired from low-level parasite exposure. The low number

of superinfections and genetic diversity, together with the long persistence of parasite

clones described in low-transmission areas support this hypothesis for falciparum malaria

(Bousema et al. 2014).

Few studies have been performed on submicroscopic P. vivax and P. falciparum infections

in co-endemic settings, although a higher proportion of submicroscopic P. vivax infections

have been observed compared to P. falciparum (Cheng, Cunningham & Gatton 2015). P.

vivax elicits a higher inflammatory response than P. falciparum (Anstey et al. 2009) and has

an overall higher genetic diversity; hence, the determinants for submicroscopic infection in

the two species are likely to be different (Jennison et al. 2015). There is little Information on

P. vivax virulence factors, but frequent relapses (i.e., every 3-4 weeks) in high transmission

areas have been shown to result in significant cumulative morbidity and mortality (Douglas,

Lampah, et al. 2013), and at the same time, in malaria tolerance (Boutlis, Yeo & Anstey

2006; Mitchell et al. 2011).

The characterisation of the contribution of asymptomatic disease to malaria transmission

has been improved by the development of qPCR targeting gametocyte-specific antigens

such as pfs25 (for P. falciparum) and pvs25 (for P. vivax) (Wampfler et al. 2013). As with

asexual parasitaemia, microscopy lacks sensitivity to characterise the presence and

persistence of gametocyte carriage. Although an overall positive correlation has been

described between gametocyte density (detected by PCR) and mosquito infection rate, the

strength of the association is greatly affected by the maturity of the gametocytes and the

male/female gametocyte ratio (Bousema, Dinglasan, et al. 2012; Churcher et al. 2013;

Vallejo et al. 2016).

The relevance of asymptomatic reservoirs has only been well established in moderate to

high transmission settings where patent carriage is common (Bousema & Drakeley 2011). In

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16

these areas, it has been shown that asymptomatic patients (i.e., absence of fever) and

children are at higher risk of gametocyte carriage. In a study undertaken in Kenya,

submicroscopic infections demonstrated a similar contribution to malaria transmission

relative to smear-positive infections (Schneider et al. 2007).

The impact of asymptomatic and submicroscopic infections is more difficult to quantify in

low endemic areas. The high prevalence of submicroscopic infections, but different reports

on presence and/or persistence of gametocytes, has led to hypotheses that the

asymptomatic reservoir is more heterogeneous than in high transmission areas (i.e., risk

associated with age varies between locations) (Camargo, Alves & Pereira da Silva 1999;

Vallejo et al. 2016). Additionally, studies estimating the transmittable capacity of

submicroscopic infections have been variable and contrasting (Lin, Saunders & Meshnick

2014). However, the large size of the submicroscopic reservoir in low endemic settings is

likely to compensate for any reduced rate of infected mosquitoes (Lindblade et al. 2013).

Thus, in low-endemic settings, the size and persistence of the reservoir more than the

‘transmissibility characteristics’ may be a key determinant of malaria transmission.

In P. vivax infections, the early presence of gametocytes and the presence of dormant

hypnozoites are also factors increasing infection longevity, and subsequent opportunities

for transmission (Cheng, Cunningham & Gatton 2015).

Asymptomatic and submicroscopic infection contribution to malaria morbidity and

mortality. The partial immunity conferred by asymptomatic and low-density parasitaemia

has been described as a protective mechanism against the development of life-threatening

acute severe disease (Doolan, Dobano & Baird 2009). However, more evidence is emerging

on the long-term deleterious effect of such infections. Bacterial co-infection, cognitive

impairment, and anaemia are some of the pathologies associated with asymptomatic

carriage (Chen et al. 2016). Of these, anaemia is one of the most frequently reported

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17

among asymptomatic patent individuals, especially among pregnant women and children

(Cottrell et al. 2015; Michon et al. 2007; Mohammed et al. 2013).

Anaemia in acute malaria is caused by removal of parasitized and non-parasitized red blood

cells through a combination of splenic filtration, macrophage phagocytosis, complement-

mediated haemolysis, and increased free radical-mediated cell damage (Wickramasinghe &

Abdalla 2000). In chronic infections, the parasites and host cytokines inhibit the production

of functional red blood cells in the bone marrow by dysregulating erythropoiesis and iron

metabolism. As a result, anaemia can be found associated with raised levels of

inflammatory proteins, low iron levels, and reduced iron-binding capacity, transferrin levels

and reticulocyte counts (Wickramasinghe & Abdalla 2000). In summary, the degree of

haemolysis is correlated positively with the duration of infection. The longer the

parasitaemia lasts as an asymptomatic infection, the greater the reduction in red cell

production.

1.2.1.1 Current strategies to target asymptomatic and low-density

parasitaemias in pre-elimination areas

As malaria incidence decreases, malaria infections tend to cluster in “hotspots”, which are

areas maintaining higher transmission throughout the year such as areas close to large

mosquito reservoirs, or occupational risk areas such as plantations (Bousema, Griffin, et al.

2012). Likewise, infections also cluster in certain groups of people (i.e., hotpops”) sharing a

high-risk demographic factor such as occupational or socio-economic risks. As a result, case-

detection strategies need to be adapted and refined in order to improve surveillance in pre-

elimination and elimination areas (Sturrock et al. 2013). Active Case Detection (ACD)

constitutes the systematic search for infections in high-risk communities by health-workers.

Considering the specific limitations of the low pre-test probability of malaria, malaria-

eliminating countries are increasingly using different approaches to ACD, including:

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18

- Reactive case detection (RACD): In which health-workers respond to a single case or a

threshold of cases detected at the health facility by screening and treating house-hold

members and neighbours. RACD capitalises on the fact that parasite carriage tends to be

spatially and temporally clustered.

- Proactive case detection (PACD): In this approach, high-risk groups and geographical

areas are screened and treated without the trigger of a passively detected case (i.e., mass

screen and treat campaigns or blood surveys). PACD has been used extensively to reduce

transmission. Field and modelling studies suggest that this strategy is most effective when

combined with a highly sensitive diagnostic tool.

The administration of antimalarial drugs in an entire population, i.e., mass drug

administration (MDA), was a strategy performed during malaria elimination programmes in

the 1950s and is currently being reconsidered as a malaria elimination tool (Newby et al.

2015). In MDA programs, all people living in a specific area are treated on a synchronized

day. This procedure is repeated at intervals once or twice thereafter. This approach should

gradually reduce the parasite reservoir and eventually eliminate it in the given area.

If performed together with vector control and socio-economic interventions such as

improved housing and sanitation, MDA could decimate malaria prevalence. However, it is

only likely to be successful if the vectorial capacity is diminished during the implementation

period; otherwise, malaria endemicity in the area will eventually return to its original levels.

Furthermore, if MDA is maintained long enough for people to lose herd-immunity against

malaria, a potential malaria rebound could result in greater morbidity and mortality (WHO

2015b).

Mass screening and treatment (MSAT) is another approach proposed for malaria

elimination, in which whole populations are screened and treated regardless of the

presence of malaria symptoms. This approach requires a large amount of resources and is

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19

logistically challenging given the lack of field-adaptable, high-throughput diagnostic tests

that are sensitive enough to detect low density parasitaemias (Newby et al. 2015).

However, if screening defined areas, (i.e., Focal screening and treatment (FSAT)) and

treatment is provided to those tested positive only, the approach becomes operationally

more feasible than MSAT. However, since treatment is not delivered simultaneously, little

to no effect in cutting malaria transmission has been observed (WHO 2015b).

1.2.2 The Threat of Antimalarial Resistance

1.2.2.1 Definition

Antimalarial drug resistance is defined as the ability of a parasite strain to survive and/or

multiply regardless of the proven administration, absorption, and access of the drug to the

infected red blood cells (Bruce-Chwatt 1986). Antimalarial resistance is the result of

changes on genomic or transcriptomic levels that confer reduced sensitivity to drugs that

confer reduced sensitivity (White, N 1999). The wide-spread use of antimalarials has

potential to select parasites with advantageous resistance-conferring mutations. Malaria

infections may have an extensive range of antimalarial susceptibility within a single

infection and even more within a given geographical area (Thaithong, S 1983). After the

drug pressure is removed, resistance can be fixed in the population, or can return to

susceptible levels (Kublin et al. 2003).

1.2.2.2 Distribution

Antimalarial resistance has been reported in both of the predominant parasite species, P.

falciparum and P. vivax. P. falciparum has developed resistance to every antimalarial used

as national policy to date (Petersen, Ines, Eastman, Richard & Lanzer, Michael 2011). After

the eradication campaign in the mid and late 20th century, CQ resistant (CQR) P. falciparum

was reported in every continent where malaria was endemic (Petersen, Ines, Eastman,

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20

Richard & Lanzer, Michael 2011). At that time, the next alternative was SP. However, SP

induced drug resistant parasites within a year of its implementation (Sibley & Price 2012).

It was evident that monotherapies were a big contributor to the rapid development of

antimalarial-resistance in P. falciparum. Thus, in 2006, the WHO recommended the used of

artemisinin-based combination therapies (ACTs) to ensure high cure rates of P. falciparum

malaria and to reduce the spread of drug resistance. However, after thirty years of intense

use in the region, artemisinin resistance was reported on the Thai–Cambodian border close

to the epicentre where CQ resistance was first reported (Dondorp et al. 2009; Noedl et al.

2008). Nonetheless, most patients who have delayed parasite clearance following

treatment with an ACT are still able to clear their infections in the absence of concomitant

resistance to the partner drug. Antimalarial drug resistance is an important cause of failure

to clear parasitaemia (i.e., treatment failure). However, other host, parasite, and drug

factors can also be responsible for treatment failure including, inadequate dosing, high

parasite biomass, and poor patient adherence to complete the course of treatment (Ashley

et al. 2014; WHO 2016a).

In 2016, clinical resistance to ACT had been confirmed in five countries in the Greater

Mekong Subregion (GMS): Cambodia, the Lao People’s Democratic Republic, Myanmar,

Thailand, and Vietnam (Ashley et al. 2014). Despite the delayed response to artemisinin in

some areas of the GMS, ACTs remain the most effective available treatment for

uncomplicated falciparum malaria.

1.2.2.3 Antimalarial efficacy surveillance

Most malaria control programs and malaria researchers rely on protocols based on the

WHO guidelines for monitoring antimalarial therapeutic efficacy (WHO 2009). Early

detection and continuous surveillance is vital to monitor changes in parasite susceptibility

and ensure public health interventions to reduce the spread of resistance and its clinical

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21

consequences. The combination of in vivo drug efficacy studies, in vitro drug susceptibility

testing, and monitoring of molecular markers have proven to be useful tools for the

surveillance of drug resistant P. falciparum isolates (Sibley & Price 2012).

Therapeutic Efficacy Studies

The gold standard for determining treatment responses in malaria is through in vivo

therapeutic efficacy studies (TES). These studies involve treating a group of symptomatic

and parasitaemic individuals with known doses of drug, and the subsequent monitoring of

the parasitological and clinical response. In vivo surveys provide a valid assessment of the

efficacy of an antimalarial treatment regimen under close to actual operational conditions

(i.e., what would be expected to occur on a program level if provider and patient

compliance is high) (WHO 2009).

Limitations of the TES approach include host confounding factors such as immunity and

individual variations in drug absorption and metabolism, as well as logistical challenges

associated with the long follow-up periods.

Parasite Clearance Time: The revised WHO-standardized 28-day protocol requires

microscopy at days 2 and 3 in order to assess parasite clearance rates, a measure for the

early detection of declining efficacy of a drug. Parasite clearance rate is the rate at which

malaria parasite levels decline in a patient’s blood after treatment (Stepniewska et al.

2010).

The WHO defines artemisinin resistance as “delayed parasite clearance” (i.e., parasite

clearance half-life of >5 hours), although in practice, it represents a partial resistance

affecting only ring-stage parasites. Hence, they are ideally combined with in vitro/ex vivo

drug susceptibility and molecular studies of the parasite (Ashley et al. 2014).

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22

Parasite ex vivo/in vitro studies

During the last few decades, in vitro techniques have been successfully applied to the

monitoring of P. falciparum drug susceptibility. These techniques are mainly used to detect

early indicators of changes in parasite drug susceptibility, and to evaluate novel

compounds. The major advantage of these techniques is that they evaluate the intrinsic

susceptibility of the parasite without confounding factors from the host.

The availability of a continuous in vitro culture system for P. falciparum has allowed several

methods to be developed for quantifying and evaluating in vitro drug susceptibility in this

species (WHO 2009).

Drug-sensitivity tests can be classified into three groups.

i. Those that measure the parasite’s ability to grow in the presence of antimalarial

drugs (i.e., mature from ring to schizont stage).

ii. Those that measure the parasite’s ability to grow and invade uninfected red blood

cells in presence of antimalarials, and

iii. Those that measures the parasites’ ability to develop from early ring (0-3 hours old)

to trophozoite stage after being exposed to high antimalarial bolus doses (i.e., 700

nM artemisinin compound for 6 hours)(Witkowski et al. 2013).

Although the most commonly applied method is measurement of tritiated hypoxanthine

incorporation as an indicator of parasite growth, there are other methods to quantify

parasites’ viability (WHO 2009). Some of these techniques are based on morphology

examination (i.e., schizont maturation test) (WHO 2009), detection of parasite’s biomarkers

(e.g. HRP2-ELISA) (Noedl et al. 2002), or the parasite’s DNA (e.g. isotopic assay)(Webster et

al. 1985). The results are generally reported as the concentration of drug at which 50% of

parasite growth is inhibited compared with the drug-free control (i.e., the half maximal

inhibitory concentration IC50). Although different methods can produce different IC50 values,

CHAPTER 1

23

they are generally comparable (Bacon et al. 2007). The selection of the quantification

method is most often based on the level of laboratory logistics and infrastructure, the

mechanisms of action of the antimalarial under investigation, and the drug’s half-life and

metabolism. Some of the limitations of these assays are the varied methodologies used by

different groups, difficulties in interpreting results in polyclonal infections, and uncertain

correlations between in vitro findings and clinical efficacy (Cui et al. 2015).

Molecular Monitoring of Parasite Resistance

Compared to the in vivo and ex vivo approaches, molecular markers of resistance (MMR)

offer the possibility of more time-efficient and higher throughput surveillance, in particular

in remote low-resource settings.

Understanding the genetic basis of antimalarial drug resistance has three major benefits.

Firstly, molecular markers of resistance can be used for tracking the dissemination of

resistant parasites. If molecular determinants of resistance are known, then simple, PCR-

based screening tests could be used to measure the distribution and rate of spread of

resistant parasites and, ultimately, inform decisions on local treatment policies. Secondly, a

better understanding of the molecular basis of resistance may offer insights into the design

of effective new antimalarial drugs or modification of existing drugs. Lastly, identifying the

genetic background of resistance would provide a better understanding of its evolution and

inform on novel strategies to curtail resistance in the future.

Mechanisms of antimalarial resistance have been mainly and widely studied in P.

falciparum. In particular, there is a good understanding of the resistance mechanisms used

by P. falciparum against CQ, SP, atovaquone, and more recently, against artemisinin (Cui et

al. 2015).

Resistance to quinoline-based drugs The P. falciparum genome encodes multiple predicted

transporters (Mu et al. 2003). Several parasite transporter proteins appear to be used by

CHAPTER 1

24

different antimalarials, and polymorphisms in these proteins have a direct impact on the

drugs’ performance (Picot et al. 2009). The most studied genes in P. falciparum are the CQ

resistance transporter gene (pfcrt), the multidrug resistance transporter 1 gene (pfmdr1),

with polymorphisms also detected in the plasmepsin 2-3 genes and an exonuclease gene

(exo-E415G).

The pfcrt gene is located on chromosome 7, encoding a 424 amino acid protein with 10

predicted transmembrane helices located at the digestive vacuole membrane. Pfcrt

expression starts at ring stage in pre-digestive vacuole compartments, reaching its peak at

the trophozoite stage and maximal haemoglobin digestion (Ehlgen et al. 2012). This protein

is essential for the parasite and disruption of the gene leads to a non-viable organism (Ecker

et al. 2012). Phylogenetic analyses predict PfCRT to be a member of the drug/metabolite

transporter superfamily. Analysis of a genetic cross between the CQ sensitive strain HB3

and CQ resistant Dd2 strain allowed the identification of pfcrt as the primary determinant

of CQ resistance (Fidock et al. 2000). The change of lysine 76 for threonine, an uncharged

amino acid, is the most common replacement found in CQR isolates, although asparagine or

isoleucine have also been reported (Chaijaroenkul et al. 2011). It has been proposed that

K76T increases the expulsion of CQ out of the digestive vacuole. The low accumulation of

CQ in resistant strains in comparison with sensitive ones supports this hypothesis. However,

evidence that PfCRT-mediated mechanisms are equally active in all stages contrasts with

the schizonts being unable to digest haemoglobin (Gligorijevic et al. 2008). This suggests

that in CQR strains, either the primary mechanism of action of CQ is not inhibition of heme-

detoxification, or CQR strains are more tolerant to the build-up of toxic heme and heme-CQ

complexes. A large number of field studies across the globe have demonstrated the

sensitivity of K76T as a molecular marker of treatment failure, although with moderate

specificity (Ecker et al. 2012).

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25

The pfcrt gene also modulates resistance to other antimalarials such as lumefantrine,

piperaquine, and amodiaquine (Muangnoicharoen et al. 2009; Mwai et al. 2009). Two

major codon 72-76 haplotypes have been frequently found in endemic areas (CVIET and

SVMNT) (Awasthi, Prasad & Das 2011). Whereas the SVMNT haplotype is highly resistant to

amodiaquine (AQ), but only moderately resistant to CQ, a negative correlation between

parasites carrying the mutant 76T and median IC50 have been described for lumefantrine

(Eyase et al. 2013). A change from cysteine to a phenylalanine at position 101(C101F) of the

PfCRT protein, together with a copy number variation on chromosome 5 has also been

linked to piperaquine (PIP) resistance (Eastman et al. 2011).

The pfmdr1 gene encodes a P-glycoprotein homolog, a transmembrane protein transporter

of unknown function located in the parasite’s digestive vacuole (Cowman et al. 1991).

Polymorphisms of this gene include increased gene copy number and single nucleotide

mutations (Cui et al. 2015) (Table 1). Point mutations such as N86Y and D1246Y are linked

to decreased sensitivity to CQ and AQ, and they are frequently found in Africa (Duraisingh,

Jones, et al. 2000; Duraisingh, Roper, et al. 2000; Mwai et al. 2009; Reed et al. 2000;

Tumwebaze et al. 2015). In contrast, copy number variation in pfmdr1 is more frequently

found in Asia, and associated with mefloquine (MQ) resistance (Price et al. 2004).

Nonetheless, correlation studies between polymorphisms and treatment efficacy suggest

that pfmdr1 is not the main CQ resistance determinant, but plays a critical role in MQ

resistance (Price et al. 2004; Venkatesan et al. 2014).

Multicopy plasmepsin 2 and 3 genes have been associated with higher risk of treatment

failure to piperaquine -a long half-life drug used in combination with artemisinin

compounds to treat P. falciparum infections- (Witkowski et al. 2017; Amato et al. 2016). In

addition, Amato et al. genome-wide association study on 297 isolates from Cambodia

identified a SNP encoding a Glu415Gly substitution in an exonuclease gene (exo-E415G)

associated with parasite recrudescence following treatment with dihydroartemisinin-

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26

piperaquine treatment (Amato et al. 2016). However, since plasmepsins genes encode

proteases involved in haemoglobin degradation and haem detoxification processes in the

parasite it is believed that plasmepsin 2-3 mediates piperaquine resistance.

Resistance to antifolates: Antifolate antimalarials include the dihydrofolate reductase

enzyme (DHFR) inhibitors pyrimethamine and proguanil, and the dihydropteroate synthase

enzyme (DHPS) inhibitors sulfadoxine and dapsone. These drugs are used for both

chemotherapy and chemoprophylaxis (Table 1). Their main MOA is disrupting these

enzymes involved in the folate pathway of the parasite (Petersen, I., Eastman, R. & Lanzer,

M. 2011). Point mutations in the genes encoding the enzymes, pfdhfr and pfdhps, are

implicated in resistance to SP. Resistance to pyrimethamine is strongly associated with

polymorphisms at 108D and 164L in pfdhfr, with 51I and 59R modulating the strength of

resistance. Whereas polymorphisms at 437G and 581G in pfdhps are determinants of

sulfadoxine resistance with 436A, 540E and 613S enhancing the effect (Thaithong, S. et al.

2001; Urdaneta et al. 1999; Wernsdorfer & Noedl 2003).

Resistance to artemisinins: Genome-wide association studies with field samples identified

regions on chromosome 13 linked to delayed parasite clearance (Cheeseman et al. 2012;

Takala-Harrison et al. 2013). Mutations in the propeller domain of the P. falciparum kelch

gene (K13) were found to be associated with delayed parasite clearance after artemisinin

therapy by using a combined resistance selection and genomic approach (Ariey et al. 2014).

Parasites harbouring K13 mutations were more likely to survive a pulse of

dihydroartemisinin (DHA), confirming the advantage of these mutations to resist DHA

action (Straimer et al. 2015). Furthermore, the transcriptomes of resistant parasites

showed increased expression of unfolded protein response pathways and prolonged ring-

stage development. These mutations have been found in Cambodia, Thailand, Myanmar,

and Vietnam. Although different polymorphisms in the K13 gene are present in African

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27

parasites, none of them appear to be related with delayed parasite clearance time (Conrad

et al. 2014; Kamau et al. 2015; Mok et al. 2015; Taylor et al. 2015).

Resistance to atovaquone: Atovaquone targets the cytochrome b (cytb) complex by

inhibiting electron transport. Monotherapy leads to rapid development of resistance by

single-point mutations in the pfcytb gene. Although pfcytb mutations 268S and 268N are

associated with treatment failure, treatment failure has been reported in the absence of

these mutations (Cui et al. 2015).

1.2.2.4 Specific Challenges of Surveillance of P. vivax infections

In regions where the two species co-exist, control efforts have led to a shift towards P.

vivax predominance, while P. falciparum has been successfully controlled (Oliveira-Ferreira

et al. 2010; Rodriguez et al. 2011). Several key biological differences appear to explain the

relative resilience of P. vivax to interventions. Low-density parasitaemias are common for P.

vivax infections, challenging diagnosis and increasing the risk that infections are left

untreated (Wangdi et al. 2011). A higher proportion of P. vivax infections are

submicroscopic compared to co-endemic P. falciparum (Cheng, Cunningham & Gatton

2015). Furthermore, P. vivax infections often present gametocytes before the onset of

symptoms, thus favouring transmission (Douglas, Simpson, et al. 2013). Lastly, P. vivax

hypnozoites may persist in the liver for weeks to months after the primary infection. If

primaquine (the only antimalarial targeting hypnozoites) is unavailable, P. vivax

hypnozoites can cause relapses.

Unfortunately, P. vivax specificity for invading reticulocytes has hampered the development

of a continuous culture system, hence hampering the development of new drugs.

Consequently, little is known about P. vivax resistance mechanisms to CQ, the first-line

treatment for P. vivax asexual stages in most endemic countries (Ferreira & de Oliveira

2015).

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28

1.2.2.5 Monitoring CQ Resistant P. vivax

Therapeutic efficacy studies demonstrate rising levels of CQ resistant (CQR) P. vivax

infections in endemic regions across the globe (Price et al. 2014). The first case of CQR P.

vivax was documented in Australian soldiers returning from Papua New Guinea in 1989

(Rieckmann, Davis & Hutton 1989). There are also reports of CQ resistance from South

America and Africa, although the highest prevalence and severity of CQ resistance is found

in New Guinea (Papua, Indonesia and Papua New Guinea) (Price, Douglas & Anstey 2009).

Acknowledging the challenges in diagnosing resistance in P. vivax, it is likely that the

current distribution of CQ resistance may be underestimated (Marfurt et al. 2008).

The inability to differentiate between relapse and recrudescence complicates the

interpretation of treatment efficacy studies. Current interpretation of clinical efficacy is

based on genotyping comparison of paired day 0 and recurrent infections and, in some

endemic settings, integrating temporal dynamics of relapses (White, NJ 2011).

Nevertheless, there is unequivocal evidence supporting the rise of CQR P. vivax on the

island of New Guinea and Indonesia (Price et al. 2014). In Indonesia, CQR P. vivax has been

associated with severe malaria and severe anaemia (Tjitra et al. 2008).

Early detection of changes in ex vivo/in vitro CQ susceptibility in P. vivax is possible by using

a modified schizont-maturation test that has successfully discriminated populations with

different levels of CQ resistance associated with clinical outcome (Russell, BM et al. 2003;

Suwanarusk et al. 2007). However, the assay is vulnerable to the stage of the parasite and

duration of the incubation, requiring samples with ≥70% ring stages, and ≥35 hours assay

duration, which restricts the isolates amenable to susceptibility testing (Russell, B et al.

2008). This could potentially lead to a bias in the estimation of antimalarial resistance in the

community. Investigation is needed into the representativeness of the samples meeting the

ex vivo assay requirements of the parasite population circulating in the broader community.

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29

In contrast to P. falciparum, the molecular mechanism of P. vivax resistance to CQ remains

elusive, impeding surveillance using molecular markers (Table 1). Polymorphisms in

orthologue genes associated with P. falciparum resistance have been implicated with no

conclusive outcome (Lu et al. 2011; Price et al. 2012; Rungsihirunrat et al. 2015;

Suwanarusk et al. 2007).

1.2.3 Imported malaria detection in pre-elimination areas

In many malaria-endemic settings, imported malaria is a key challenge in reaching and

maintaining elimination. The risk is highest for countries neighboring high-endemic areas. In

most settings, imported cases are recognized through basic epidemiological investigation.

In view of the increasing movement of people around the globe, several strategies to

strengthen public health surveillance systems have been implemented (Sturrock et al.

2015). However, despite stringent border control between neighboring countries,

transmission can be sustained in areas along and across international borders. Knowledge

of the dynamics of population migration, both domestically and internationally, and cross-

border malaria transmission is crucial for the development of appropriate surveillance and

response systems in such areas.

1.3 Molecular Approaches to Facilitate Parasite Surveillance

1.3.1 Population Genetic and Genomic Studies for Improving Parasite Surveillance

Parasite population genetics and genomics can complement traditional surveillance

methods. This is supported by multiple studies revealing underlying patterns of

transmission, population migration, and evolution that cannot be defined by traditional

surveillance techniques (Auburn & Barry 2016; Barry et al. 2015; Daniels, RF, Rice, et al.

2015; Ferreira & de Oliveira 2015; Kwiatkowski 2015). These studies highlight potentially

powerful tools that can provide information on outbreak dynamics, spread of infections

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within and across borders, as well as reservoirs of infection. The techniques used to gather

this information have evolved from typing polymorphic antigen-encoding genes (e.g. genes

of the merozoite surface protein (msp) family such as pfmsp1, pfmsp2, or pvmsp-3α, to

neutral short tandem repeat markers such as microsatellites, and single nucleotide

polymorphism (SNPs).

1.3.1.1 Available Tools for Parasite G enotyping

Antigen loci are highly polymorphic surface protein antigens, which have been studied for

vaccine development and parasite genotyping. Present in P. falciparum and P. vivax,

Merozoite Surface Proteins (MSP) and Circumsporozoite Protein (CSP) are the most

commonly used antigen loci in genotyping studies. Genes encoding MSP are often used as

markers to determine the Multiplicity of Infection (MOI), or to finger-print infections, for

example, to distinguish between recrudescence and re-infection events. SNPs and tandem

repeat variations can be found in MSP genes, resulting in size variations distinguishable by

gel or capillary electrophoresis alone, or in combination with restriction enzyme digestion

(Barry et al. 2015). CSP is encoded by a single-copy gene. The central domain of csp

contains tandemly repeated sequences flanked by non-repeating conserved sequences. The

repetitive region varies in sequence and length among Plasmodium spp. In P. vivax,

analyses of csp have allowed the discrimination of two variants named VK210 and VK247

and their associated geographical distribution. Other antigens used in both species for

clone differentiation include the Apical Membrane Antigen 1 (AMA1) and the glutamate-

rich protein (GLURP) in P. falciparum (Barry et al. 2015). The highly polymorphic nature of

antigen-encoding genes makes them good markers for measuring MOI or finger-printing

infections in both species. However, their usefulness for measuring underlying patterns of

gene flow between populations is limited because the genetic diversity of these loci may be

shaped by host immune pressure.

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Microsatellites (MS) are short (typically 1 to 5 bp long) tandemly repeated sequences of

nucleotides. Variation in the total number of repeats (regardless of repeat type) causes size

polymorphisms that can be used to differentiate clones in population diversity studies. MS

polymorphisms are generated and maintained by replication and/or slip-strand mismatch

repair, and do not tend to be under selective pressure. MS have become a popular

alternative to polymorphic antigens in malaria studies due to their often highly polymorphic

nature, putative neutrality, and ubiquity throughout the genome (Sutton 2013).

MS markers have been used in dozens of parasite population genetic studies to

characterise local and regional patterns of malaria transmission in P. falciparum and P.

vivax isolates from across the globe (Auburn & Barry 2016). Often between 9 and 14

markers are applied, with size variation determined by capillary electrophoresis. Selecting

the appropriate panel relies on the degree of polymorphism and mode of evolution. Most

population genetic studies in P. falciparum have used a set of neutral MS described by

Anderson et al. in 1999, thus facilitating comparisons between parasite populations

investigated in different studies (Anderson, TJ et al. 1999). In P. vivax, less agreement has

been achieved with multiple panels published in the past decade. The most frequently

applied marker sets include panels proposed by Imwong et al. in 2007 and Karunaweera et

al. in 2008 (Imwong et al. 2007; Karunaweera et al. 2008). In 2011, the Vivax Working

Group of the Asia Pacific Malaria Elimination Network established a consensus set of 9 MS

markers to strengthen data sharing amongst partner countries (Vivax Working 2015).

Barcode/single nucleotide polymorphisms (SNPs). Whole genome sequencing studies have

allowed the development of panels of SNPs for population genetic studies in P. falciparum

and P. vivax. SNPs have slower mutation rates than MS and, depending on the platform, are

generally less prone to artefacts and easier to compare between studies. The first 24-SNP

barcode to study P. falciparum infections globally was developed by Daniels et al. in 2008

(Daniels, R et al. 2008). TaqMan genotyping assays were developed for each SNP, with as

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32

little as 5 ng DNA (parasite and human combined) required for reproducible genotype calls.

This barcode has been used to define transmission patterns in multiple countries including

Senegal (Daniels, RF, Schaffner, et al. 2015), Malawi (Sisya et al. 2015), Zambia

(Mharakurwa et al. 2014), Cambodia (Sluydts et al. 2014), and Panama (Obaldia et al. 2015)

by determining Complexity of Infection (COI) and genetic diversity.

A similar SNP barcode was developed for P. vivax in 2015 (Baniecki et al. 2015). Using

available sequence data from 4 different countries, a set of 42 SNPs were identified. The

SNP panel includes finger-printing variants for distinguishing individual infections, as well as

a set of variants for determining the geographic origin of infections at large spatial scales

(Baniecki et al. 2015). The genotyping assays for each SNP were designed and standardised

using high resolution melt curve analysis, and demonstrated reproducible genotype calls

with small quantities of DNA (i.e., limit of detection of 0.3 ng/µL parasite and human DNA

combined). As the barcode was only recently published, there are not yet many field

studies reporting on its efficacy.

Whilst SNPs have clear potential for surveillance, there are limitations of these markers.

One limitation is the comparably fewer variants (up to 4 alleles per marker) relative to MS

(up to 50 alleles reported per marker), which may require larger SNP panels to detect

subtle changes in complexity of infection. Another potential limitation is that, in contrast to

MS, which tend to be diverse in multiple endemic settings, SNPs may not be polymorphic in

all populations and thus, the utility of a given SNP may vary between populations (Escalante

et al. 2015).

Mithocondrial and apicoplast markers. In addition to the nuclear genome, malaria

parasites have two organellar genomes, a ~6 Kb mitochondrial and a ~35 Kb apicoplast

genome. Reconstructing haplotypes is simpler using the organellar genomes, which are

uniparentally inherited, as opposed to using loci from the nuclear genome, which

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33

recombines during sexual replication (Conway et al. 2000). Additionally, the organellar

genomes evolve slower than MS and nuclear SNPs and thus, changes in their haplotypes are

very useful to determine global patterns in the spread of the parasite from many thousands

of years ago, and for mapping the geographic origin of infections (Preston et al. 2014;

Rodrigues et al. 2014). Conversely, MS and nuclear SNPs are more effective than organellar

loci for detecting more recent changes in parasite transmission patterns.

Whole genome analysis. Evaluation of SNPs from whole genome sequencing (WGS) can

provide information about the effect that external pressures (e.g. changes in transmission

patterns or prevalence of infection) can induce on the population of interest. This

information is also useful to identify the specific areas in the genome changing as a result of

an intervention that is associated to a specific phenotype such as drug resistance (Carlton

et al. 2015). Moreover, in addition to SNPs, WGS can detect other types of polymorphisms

including insertions, deletions, and copy number variants. However, this approach currently

requires high quality samples (i.e., high parasite and low human DNA yield), which

complicate its applicability for routine surveillance (Auburn & Barry 2016). Although new

sample processing methodologies such as selective Whole Genome Amplification (sWGA)

are being developed to overcome the challenges of sample quality, these methods have

some drawbacks such as the loss of clones in polyclonal infections and reduced ability to

detect copy number variants (Cowell et al. 2017). Furthermore, the economic costs and

data handling complexities currently render WGS an infeasible approach for routine

surveillance in many endemic settings (Barry et al. 2015; Daniels, RF, Rice, et al. 2015).

1.3.1.2 Applicability of Genotyping Tools for Characterizing Malaria

Transmission Dynamics

Malaria control and elimination campaigns require robust and timely measures of parasite

transmission to assess the efficacy of interventions, and prioritise appropriate intervention

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34

strategies. For example, where transmission is high and stable, affecting a large proportion

of the host population, interventions need to achieve broad coverage across all reservoirs

of infection. As parasite populations approach elimination, they may become more

fragmented and inbred, and targeted interventions to eliminate the major reservoirs of

infection may be more efficient (Carlton et al. 2015). This section describes the application

of several of the techniques described in section 1.3.1.1 to inform on transmission

dynamics in P. vivax and P. falciparum.

Characterizing transmission patterns using measures of polyclonality, population diversity

and structure. Given that meiotic recombination takes place in the vector as part of the

Plasmodium life cycle, the rate of genetic recombination and the effective population size

are both expected to be linked to the transmission intensity (i.e., the higher the intensity of

transmission, the higher the number of parasite clones circulating and recombining). Thus,

it is expected that as transmission decreases, so will the overall genetic diversity, as

opportunities for meiotic crossover events between clones with different genetic

background in the mosquito become unlikely. With fewer parasite types in the population,

crossover events will often occur between identical or almost identical individuals,

increasing the level of inbreeding in the population. Consequently, the chances of finding

genetically different asexual parasites within a single infection (i.e., MOI>1) will drop, as

well (Carlton et al. 2015).

Irrespective of the genotyping method applied, population genetic studies of P. falciparum

have shown a wide range of genetic diversities, which tends to correlate with transmission

levels in the region. High-transmission areas generally present a higher number of clones

within a single infection, and the populations tend to be panmictic with high genetic

diversity and no linkage disequilibrium (Anderson, TJ et al. 2000; Campino et al. 2011;

Manske et al. 2012; Mobegi et al. 2012; Rebaudet et al. 2010). Conversely, in low

transmission settings (common in many parts of South America and some parts of South-

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35

East Asia), P. falciparum tends to present lower numbers of polyclonal infections, more

structured and lower genetic diversity, and moderate to high linkage disequilibrium

(Anderson, TJ et al. 2000; Anthony et al. 2005; Campino et al. 2011; Gray et al. 2013;

Noviyanti et al. 2015; Pumpaibool et al. 2009).

In contrast, the relationship between the level of parasite diversity and transmission

intensity is not as clear for P. vivax. For instance, many low-transmission settings present

high population diversity (Gunawardena et al. 2014; Liu et al. 2014; Wangchuk et al. 2016).

In fact, findings from MS-based studies and, more recently, whole genome sequencing

studies in co-endemic P. falciparum and P. vivax areas showed that P. vivax tends to

present an overall higher genetic diversity than P. falciparum (Ferreira et al. 2007; Fola et

al. 2017; Hupalo et al. 2016; Neafsey et al. 2012; Noviyanti et al. 2015; Orjuela-Sanchez et

al. 2013). Multiple factors related to the biology of P. vivax are likely to be involved. For

instance, the presence of dormant liver stages and the early appearance of sexual stages in

P. vivax may enhance the chances of multiple clones to coexist in a single infection and to

be taken up by a mosquito, promoting the opportunity for transmission and recombination.

Additionally, the longer persistence of asymptomatic P. vivax infections (Tripura et al.

2016)- in some areas up to 11 months - could also contribute to a higher recombination

rate between different clones, by allowing an individual to be inoculated multiple times by

different mosquitoes before developing symptoms. Nevertheless, clonal expansion, high

linkage disequilibrium and population structure have provided evidence for interrupted P.

vivax transmission in pre-elimination areas such as Sabah (Malaysian Borneo) and Iran

(Abdullah et al. 2013; Hamedi et al. 2016). Furthermore, although the trend has not been

observed in all studies, a study of P. vivax in four islands in the Indonesian archipelago

demonstrated a positive correlation between the local endemicity and the proportion of

polyclonal infections (Noviyanti et al. 2015).

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Longitudinal evaluations of polyclonality, population diversity and structure. A limited

number of studies have directly investigated the association between malaria prevalence

and parasite diversity over time. Fundamental questions for monitoring malaria

intervention efficacy are: i) whether genetic changes would be large enough and rapid

enough to be detectable over a short period with a reasonable sample size, and ii) if

passively collected samples are representative of the parasite population circulating in the

community.

A MS-based longitudinal study of the genetic diversity and structure of P. falciparum in the

northwestern region of Colombia, one of the areas with highest malaria incidence in South-

America, revealed a steady decline in population diversity, demonstrating that ongoing

interventions have successfully reduced local transmission (Chenet et al. 2015). In a SNP-

based study on the Thai-Burma border region, after 10 years of strong deployment of

malaria control strategies, there was no significant difference in population diversity, but a

significant decline was observed in the complexity of P. falciparum infections (Nkhoma et

al. 2013).

Fewer studies have addressed the impact of interventions in P. vivax population diversity

over time (Abdullah et al. 2013; Chenet et al. 2012; Iwagami et al. 2013; Kim, JY et al. 2011)

and none has yet addresses the effect of unified-treatment policy against both species.

With the currently widespread use of MS markers in declining transmission settings, it is

likely that future studies will shed more light on the resolution of this approach to detect

changes in parasite population dynamics resulting from control measures. These

longitudinal studies should also provide insight on the utility of molecular surveillance

approaches to identify clinically important changes in the parasite population such as the

early detection of outbreaks resulting from clonal expansions, possibly reflecting imported

strains, or newly emerging drug resistant populations.

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1.3.1.3 Applicability of Genotyping Tools to Characterize Population

Differentiation and Imported Infections

Despite stringent border controls between neighboring countries, transmission can be

sustained in areas along and across international borders (Dharmawardena et al. 2015;

Wangchuk et al. 2016; Zhou et al. 2016). The introduction of new parasite strains

constitutes a risk for non-immune individuals, enhancing the chances of severe disease and

local outbreaks. The risk is greater in P. vivax infections due to the parasite’s ability to keep

latent and virtually undetectable for up to several weeks or months before relapsing

(Ferreira & de Oliveira 2015). Given the rising prevalence of this species in low endemic

areas, knowledge of the dynamics of population migration, both domestically and

internationally, and cross-border is crucial for the development of appropriate surveillance

and response systems. Several studies of both P. vivax and P. falciparum have

demonstrated the application of the techniques described in section 1.3.1.1 to differentiate

local from imported infections and, in some cases, to map the geographic origin of an

infection at a regional spatial scale (Baniecki et al. 2015; Choi et al. 2011; Daniels, R et al.

2008; Delgado-Ratto et al. 2016; Griffing et al. 2011; Obaldia et al. 2015; Preston et al.

2014; Saenz et al. 2015; Wangchuk et al. 2016).

Global and regional patterns of population differentiation. In a seminal study using 12 MS

loci, Anderson et al. described dramatic geographic differentiation between continents

using 465 P. falciparum infections collected from 9 locations worldwide (Anderson, TJ et al.

2000). Striking differences were observed in the geographic differentiation within each of

the continents, with a negative correlation between endemicity and differentiation. Low

differentiation was observed between the highly endemic African populations (Uganda,

Congo and Zimbabwe), moderate differentiation between Thailand and Papua New Guinea,

and strikingly high differentiation between the South American populations (Brazil, Bolivia,

and Colombia), where endemicity was lowest. Differences in local transmission dynamics

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38

between the regions may partly explain these patterns, but population history may have

also shaped the genetic make-up of the parasite populations. In a similar MS-based study

assessing global patterns of P. vivax diversity and structure using data generated at 11 loci

in 841 isolates, Koepfli et al., revealed that geographical distance and isolation are

important determinants of population structure in this species (Koepfli et al. 2015). South

American populations were separated from the African, Southeast-Asian, and South Pacific

populations, with samples from Central America, Indonesia, and India grouping with

Southeast-Asia. Similar patterns of geographic differentiation shaped by features including

distance, diversity, and isolation have been found for P. falciparum (Campino et al. 2011)

and P. vivax (Baniecki et al. 2015) using panels of 24-300 SNPs, and with genomic data from

P. falciparum (Manske et al. 2012) and P. vivax isolates (Hupalo et al. 2016; Pearson et al.

2016).

Local patterns of population differentiation. MS typing proved to be useful to characterize

local patterns of gene flow in several low transmission areas. In an MS-based study

conducted at different sites in and around the Peruvian Amazon capital Iquitos, patterns of

population differentiation revealed that the city was the main reservoir of P. vivax

infections for surrounding regions (Delgado-Ratto et al. 2016). Another MS-based study in

Ecuador demonstrated that the local P. falciparum population was maintained by multiple

introductions from the Peruvian coastal region, characterized in another study by Griffing et

al. (Griffing et al. 2011; Saenz et al. 2015). The imported infections led to frequent clonal

expansions and unstable transmission in Ecuador, highlighting the importance of identifying

and containing these infections early. In Bhutan, an area in the pre-elimination stage, MS

typing also unveiled the magnitude of imported cases from India as a major reservoir of

diverse infections (Wangchuk et al. 2016). In Panama, a similar endemic malaria setting,

genotyping at a 24-SNP barcode revealed imported P. falciparum cases from the Colombian

Darien Amazon (Obaldia et al. 2015).

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In summation, genotyping, with MS or SNP-based platforms, can be useful to distinguish

between geographically distinct parasite populations at a regional level, or potentially

higher spatial resolution, in low endemic settings or highly isolated populations. However,

in the context of malaria surveillance for imported infections, additional markers may be

needed to confirm the geographic origin of a given infection.

Geographic markers. Antigen-encoding genes were used to map imported infections in

South Korea (Choi et al. 2011). However, as the indigenous P. vivax population in South

Korea has low genetic diversity, it remains uncertain whether antigen-encoding genes

would be efficacious in higher transmission settings.

Other studies have used markers in the mitochondrial and apicoplast genomes to map the

geographic origin of P. vivax and P. falciparum infections. Analyzing more than 348

mitochondrial genomes, Rodrigues et al. tracked the origin of 69 P. vivax cases imported to

the United States between 2004 and 2008 (Rodrigues et al. 2014). Most of these infections

appeared to originate from the Americas, Southeast-Asia, East Asia, and Melanesia.

However, the mitochondrial markers had limited resolution to map P. vivax infections from

Africa, South Asia, Central Asia, and the Middle East, owing in part to limited data from

these regions. More recently, Preston et al. analysed the mitochondrial and apicoplast

genomes of 711 P. falciparum isolates from 14 countries to create a 23-SNP geographic

barcode. The majority of informative markers were derived from the apicoplast. The

evaluation of 187 additional samples from different areas revealed an overall predicting

accuracy of the geographic origin of 93% (174/187) (Preston et al. 2014). Whilst the 23-SNP

barcode is highly predictive of regional level geographic origin, further loci may be required

to achieve higher resolution at the national and sub-national level. Investigation of

apicoplast markers still needs to be undertaken in P. vivax.

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Lastly, Baniecki and colleagues have assessed the potential efficacy of the 42-SNP P. vivax

barcode to map infections at a regional level in this species (Baniecki et al. 2015). The assay

correctly assigned 87 clinical samples from South America (Brazil, French Guiana), Africa

(Ethiopia) and Asia (Sri Lanka) to their country of origin, but higher spatial resolutions need

to be investigated (Baniecki et al. 2015).

1.3.2 Molecular Surveillance of Antimalarial Drug Resistance in P. vivax

Two approaches are being used to discover molecular markers of CQ resistance in P. vivax:

1.3.2.1 Candidate Approach

The candidate approach entails examination of orthologues involved in resistance

mechanisms in other species (e.g. P. falciparum, Table 1). This approach proved useful to

identify mutations associated with treatment failure to SP in P. vivax (Marfurt et al. 2008).

However, studies on the role of pvcrt-o and pvmdr1 in CQ resistance have failed to show a

strong correlation (Antony & Parija 2016).

P. vivax chloroquine resistance transporter orthologue (pvcrt-o). Synteny between pfcrt

and the orthologue in P. vivax (pvcrt-o) has been confirmed (Nomura et al. 2001), and

PvCRT-o involvement in CQ transport/accumulation has been demonstrated (Sa et al.

2006). Supportive evidence shows that PvCRT-o modified to express either lysine or

threonine at position 76, decreased the CQ accumulation in a Dictyostelium discoideum

system to approximately 60% of that in control cells. Further studies using Saccharomyces

cerevisiae systems proved that pvcrt-o wild-type modulates CQ transport more efficiently

than resistant pfcrt variants CIET (Dd2) and SNNT (7G8) (Baro, Pooput & Roepe 2011).

A pvcrt-o K10 insertion described by Suwanarusk et al. in Indonesian and Thai samples has

been found in Korea and Myanmar, but with no correlation with the CQR phenotype (Lu et

al. 2011; Suwanarusk et al. 2007). Similarly, other studies have not found an association

between sequence variations in pvcrt-o and the CQR phenotype (Price et al. 2012;

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Rungsihirunrat et al. 2015). Few studies have attempted to investigate the role of pvcrt-o

expression in CQ resistance. A 20-fold increase in pvcrt-o expression was found in a severe

P. vivax case in Brazil, assessed against three patients with non-severe P. vivax infection

(Fernandez-Becerra et al. 2009). A further study in 44 subjects from Brazil revealed a

correlation between high expression of pvcrt-o and poor response to treatment (Melo et al.

2014). Nonetheless, no other studies have replicated these findings.

P. vivax multidrug resistance gene 1 (pvmdr1): Amplification and mutations of pvmdr1

have been associated with resistance against multiple antimalarials in P. vivax. While point

mutations have been linked to decreased susceptibility to CQ, amplification may be

involved in MQ resistance (Valderramos & Fidock 2006). Like pfmdr1, CQ and MQ appear to

apply a competitive evolutionary pressure on pvmdr1 (Suwanarusk et al. 2008).

Amino acid substitutions Y976F and F1076L in pvmdr1 have been described to be

associated with CQ resistance in P. vivax (Brega et al. 2005). These two mutations have

been found in Brazil (Gama et al. 2009), Papua Indonesia (Suwanarusk et al. 2007),

Madagascar (Barnadas et al. 2008), Mauritania (Mint Lekweiry et al. 2012), and Thailand

(Imwong et al. 2008), and CQ resistance was suggested to be the result of a two-step

mutation pathway, in which the F1076L mutation is followed by the Y976F mutation

However, F1076L is currently proposed as a marker of drug pressure in the population,

rather than a determinant of CQ resistance (Kim, YK et al. 2011). Although an association

with CQ resistance has been found in multiple studies (Lin et al. 2013; Melo et al. 2014;

Rungsihirunrat et al. 2015), the finding of parasites with wild-type pvmdr1 alleles that are

CQR suggests that other polymorphisms may be involved (Marfurt et al. 2008; Sa et al.

2006; Suwanarusk et al. 2008).

Although MQ is rarely used to treat vivax malaria, P. vivax isolates from Thailand have

shown an increased copy number of pvmdr1, potentially as a result of MQ pressure in the

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population (Auburn et al. 2016; Imwong et al. 2008). Supportive evidence has shown

pvmdr1 amplification associated with significant decrease of ex vivo susceptibility of P.

vivax to MQ (Suwanarusk et al. 2008) and a significant decline in the prevalence of pvmdr1

amplifications after discontinuation of MQ regimens in western Thailand (Auburn et al.

2016).

1.3.2.2 Candidate-free Approach

Candidate-free approaches such as genome-wide association studies (GWAS) and detection

of signatures of selection have been used to identify novel determinants of antimalarial

resistance (Anderson, T et al. 2011). The GWAS approach utilises genome-wide scans to

identify loci with significant differences in allele frequency between, for example, parasites

with CQ sensitive versus CQ resistant ex vivo profiles (Mu et al. 2010). Haplotype-based

approaches to detect signatures of positive directional selection aim to identify the rapid

spread of an advantageous mutation, such as a drug resistance-conferring mutation,

through a population (Anderson, T et al. 2011). These signatures include reduced genetic

variation and increased linkage disequilibrium (LD) at flanking loci. Proof of principle for this

method has been demonstrated in several P. falciparum and P. vivax studies, confirming

signals in known drug resistance-conferring genes including pfcrt, pfdhfr, pfmdr1, pvdhfr,

pvdhps and pvmrp1 (Auburn & Barry 2016).

Signals of natural selection in the P. vivax genome found by Hupalo et al. (Hupalo et al.

2016) suggest that this species evolved in response to antimalarial drugs and adapted to

regional differences in the human host and the mosquito vector. Another genomic study

performed on more than 200 clinical samples collected across the Asia-Pacific region also

found strong evidence of recent selection on chromosomes 2, 5, 10, 13, and 14 (Pearson et

al. 2016). The signals found on chromosome 5 and 14 correspond to western Thai isolates

with known resistance genes for pyrimethamine (pvdhfr) and sulfadoxine (pvdhps). In

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Papua-Indonesian isolates, signals potentially associated with CQ resistance were found in

regions encompassing 22 genes on chromosome 14, and 29 genes on chromosome 10, the

latter lying close to pvmdr1 (Pearson et al. 2016). Neither of the genomic studies found

evidence of signatures of selection in the region encompassing pvcrt-o.

1.4 Rationale and Aim of the Current Study

1.4.1 Study Site and Context of the Projects

Indonesia has one of the highest burdens of malaria in Asia, with nearly 257 million people

living at risk of infection (WHO 2016c). The Ministry of Health (MoH) is endeavouring to

achieve nation-wide malaria elimination by 2030. The Papua Province carries the highest

malaria burden in the country with an API of 10% in comparison to a national API of 1%. In

this setting, malaria severely affects pregnant women and infants (Poespoprodjo et al.

2009; Poespoprodjo et al. 2008). In addition to extensive heterogeneity in malaria

epidemiology across the archipelago, this region presents several unique conditions that

complicate malaria control, such as a highly mobile population, remoteness, and constant

conflicts between indigenous groups.

Treatment efficacy studies (TES) performed in 2004 revealed a high prevalence of CQ and

SP resistant P. falciparum and P. vivax in the region (Ratcliff, Siswantoro, Kenangalem,

Wuwung, et al. 2007). In turn, TES comparing alternative treatment regimens, including

ACTs, were conducted between 2004 and 2006, whereby dihydroartemisinin-piperaquine

(DHP) was found to be the most effective treatment against P. vivax and P. falciparum

(Ratcliff, Siswantoro, Kenangalem, Maristela, et al. 2007). In 2006, Indonesia became the

first country in the world to implement ACT as first-line treatment against all malaria

species.

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Since 2011, close clinical, ex vivo, and molecular drug resistance monitoring has been

performed at the Rumah Sakit Mitra Masyarakat (RSMM) hospital in Timika, the capital city.

In 2013, a collaborative study between the Menzies School of Health Research, the Eijkman

Institute for Molecular Biology, the University of Gadjah Mada, and the Health Authorities

in Mimika District, Papua-Indonesia, undertook a cross-sectional study to assess the impact

of ACT implementation post-2006.

This thesis was conducted as part of the Malaria Operational Research Program in Mimika

District, and evaluated samples collected in the framework of these different clinical, ex

vivo and community studies (Figure 2). Details of the inclusion criteria, sample size, and

demographic characteristics are given in the respective chapters.

Figure 2. Type of study and timeframe of the surveys involved in each Chapter

1.4.2 Rationale

Enormous gains have been made in reducing malaria-associated morbidity and mortality in

the last 15 years (WHO 2016b). One of the key components of previous efforts to eradicate

the disease is the strong emphasis on strengthening malaria surveillance and response

systems (WHO 1993). Whereas in areas aiming to control malaria, surveillance plays an

important role in determining the effectiveness of interventions, in areas close to

elimination, surveillance becomes the pillar for informed decision-making on directing

efforts and resources efficiently (WHO 2012).

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The information gathered by traditional epidemiological tools can be complemented by

that obtained through molecular tools and population genetic studies. Specifically,

molecular tools have the potential to expand our knowledge of the characteristics of

symptomless infections and the emergence and spread of drug resistant parasites. The

contribution of population genetic surveys to traditional surveillance systems relies on the

ability to obtain information inaccessible through conventional methods. This includes high-

resolution characterisation of transmission dynamics, including transmission intensity,

stability and foci, as well as the identification of likely imported infections, and the

evaluation of the impact of ongoing interventions.

The overarching aim of this study was to develop and investigate molecular tools for the

surveillance of Plasmodium parasite populations. In order to provide evidence of the

relevant role of molecular surveillance tools to achieve sustainable elimination, the

following objectives were addressed in this thesis.

1.4.3 The Hidden Burden of Asymptomatic and Low-Density Plasmodium Infections

Until recently, the detection and treatment of asymptomatic infections has been a low

priority in malaria control and elimination efforts, primarily due to a poor understanding of

the contribution of asymptomatic infections to malaria morbidity and mortality, including

the underlying pathophysiology associated with disease presentation. Clearly more

evidence is required to determine the significance of asymptomatic/submicroscopic

infections to clinical disease and ongoing transmission. This information will contribute to

an improved assessment in order to rationalise public health interventions as cost-effective

as possible. Capitalizing on a unique P. falciparum and P. vivax co-endemic scenario, the

study presented in Chapter 2 characterizes the extent and risk of clinical disease associated

with asymptomatic carriage of low density infections in southern Papua, Indonesia.

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1.4.4 Molecular Epidemiology and Transmission of Malaria

Epidemiologic surveillance systems have contributed to a better understanding of many

aspects of malaria transmission such as seasonal fluctuations of malaria incidence,

geographical delimitation of malaria risk, and identification of outbreaks by means of case

reporting or entomological surveys (WHO 2012). However, malaria prevalence has limited

utility to elucidate transmission dynamics at higher resolution. For example, imported

infections are a major threat to achieving malaria elimination. Currently, an infection is

presumed to be imported based on travel history gathered by qualitative questionnaires.

Since malaria has a 9-14 day window between sporozoite inoculation and presentation of

clinical symptoms, and, in P. vivax infections, there can be several months between an

acute infection and a relapsing disease episode, discerning an autochthonous from an

introduced infection is often difficult. The study presented in Chapter 3 highlights the

contribution and utility of molecular investigations on parasite population structure to

characterize and understand malaria micro-epidemiology and transmission in an area co-

endemic for P. falciparum and P. vivax.

1.4.5 The Impact of Active versus Passive Sampling Strategies on Surveillance

The high feasibility of sampling at health facilities means that malaria surveillance tends to

be conducted on symptomatic individuals seeking treatment. However, as malaria

incidence decreases, so does the need increase to gain a more in-depth knowledge of the

parasite reservoir associated with asymptomatic infections. Comparison between the

population of parasites circulating in the community and those causing symptomatic

disease has the potential to indicate whether parasites causing symptomatic infections are

representative of the total parasite population. For instance, if parasites causing

symptomatic illness had a unique genetic background conferring their drug susceptibility

phenotype, the results of drug resistance surveillance studies might not reflect the true

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47

prevalence of resistant parasites in the entire population. Chapter 4 characterises and

compares the genetic background of parasite populations collected by active and passive

surveillance, using different sampling methods, and assesses the appropriateness and

representativeness of these strategies.

1.4.6 The Effect of Artemisinin-based Combination Therapy (ACT) on Parasite Population

Structure.

Genetic parameters can reflect transmission dynamics in a more sensitive fashion than

traditional tools. Several studies have reported a fall in genetic diversity and multiplicity of

infection (MOI) concomitant with reductions in transmission intensity. Chapter 5 compares

the population structure of P. vivax and P. falciparum before and after ACT implementation

in the study area, with the ultimate aim to gain a better understanding of the genetic

changes associated with decreasing transmission.

1.4.7 Elucidating the Role of the P. vivax Chloroquine Resistance Transporter (CRT) in

Chloroquine Resistance

The rise and spread of P. vivax chloroquine (CQ) resistance is a threat for sustainable

malaria control and elimination (Price et al. 2014). Contrary to P. falciparum, for which key

determinants of CQ resistance are single nucleotide polymorphisms (SNPs) in pfcrt (Fidock

et al. 2000), molecular markers of CQ resistance in P. vivax remain elusive. Although several

studies have suggested that the CQ resistance-conferring mechanisms are different

between P. vivax and P. falciparum (Baro, Pooput & Roepe 2011; Nomura et al. 2001),

some studies found an association between expression levels of pvcrt-o and disease

severity and treatment failure with CQ (Fernandez-Becerra et al. 2009; Melo et al. 2014).

The study presented in Chapter 6 investigates the potential of pvcrt-o expression levels for

the molecular surveillance of CQR P. vivax, by assessing the relationship between pvcrt-o

transcription levels and ex vivo susceptibility to CQ and other standard antimalarials.

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1.5 References

Abdullah, NR, Barber, BE, William, T, Norahmad, NA, Satsu, UR, Muniandy, PK, Ismail, Z, Grigg, MJ, Jelip, J, Piera, K, von Seidlein, L, Yeo, TW, Anstey, NM, Price, RN & Auburn, S 2013, 'Plasmodium vivax population structure and transmission dynamics in Sabah Malaysia', PLoS One, vol. 8, no. 12, p. e82553.

Alemayehu, S, Feghali, KC, Cowden, J, Komisar, J, Ockenhouse, CF & Kamau, E 2013, 'Comparative evaluation of published real-time PCR assays for the detection of malaria following MIQE guidelines', Malar J, vol. 12, p. 277.

Amato, R, Lim, P, Miotto, O, Amaratunga, C, Dek, D, Pearson, RD, Almagro-Garcia, J, Neal, AT, Sreng, S, Suon, S, Drury, E, Jyothi, D, Stalker, J, Kwiatkowski, DP & Fairhurst, RM 2016, 'Genetic markers associated with dihydroartemisinin-piperaquine failure in Plasmodium falciparum malaria in Cambodia: a genotype-phenotype association study', Lancet Infect Dis, vol.17, no.2, pp.164-173.

Anderson, T, Nkhoma, S, Ecker, A & Fidock, D 2011, 'How can we identify parasite genes that underlie antimalarial drug resistance?', Pharmacogenomics, vol. 12, no. 1, pp. 59-85.

Anderson, TJ, Haubold, B, Williams, JT, Estrada-Franco, JG, Richardson, L, Mollinedo, R, Bockarie, M, Mokili, J, Mharakurwa, S, French, N, Whitworth, J, Velez, ID, Brockman, AH, Nosten, F, Ferreira, MU & Day, KP 2000, 'Microsatellite markers reveal a spectrum of population structures in the malaria parasite Plasmodium falciparum', Mol Biol Evol, vol. 17, no. 10, pp. 1467-1482.

Anderson, TJ, Su, XZ, Bockarie, M, Lagog, M & Day, KP 1999, 'Twelve microsatellite markers for characterization of Plasmodium falciparum from finger-prick blood samples', Parasitology, vol. 119 ( Pt 2), pp. 113-125.

Anstey, NM, Russell, B, Yeo, TW & Price, RN 2009, 'The pathophysiology of vivax malaria', Trends Parasitol, vol. 25, no. 5, pp. 220-227.

Anthony, TG, Conway, DJ, Cox-Singh, J, Matusop, A, Ratnam, S, Shamsul, S & Singh, B 2005, 'Fragmented population structure of plasmodium falciparum in a region of declining endemicity', J Infect Dis, vol. 191, no. 9, pp. 1558-1564.

Antony, HA & Parija, SC 2016, 'Antimalarial drug resistance: An overview', Trop Parasitol, vol. 6, no. 1, pp. 30-41.

Ariey, F, Witkowski, B, Amaratunga, C, Beghain, J, Langlois, AC, Khim, N, Kim, S, Duru, V, Bouchier, C, Ma, L, Lim, P, Leang, R, Duong, S, Sreng, S, Suon, S, Chuor, CM, Bout, DM, Menard, S, Rogers, WO, Genton, B, Fandeur, T, Miotto, O, Ringwald, P, Le Bras, J, Berry, A, Barale, JC, Fairhurst, RM, Benoit-Vical, F, Mercereau-Puijalon, O & Menard, D 2014, 'A molecular marker of artemisinin-resistant Plasmodium falciparum malaria', Nature, vol. 505, no. 7481, pp. 50-55.

Ashley, EA, Dhorda, M, Fairhurst, RM, Amaratunga, C, Lim, P, Suon, S, Sreng, S, Anderson, JM, Mao, S, Sam, B, Sopha, C, Chuor, CM, Nguon, C, Sovannaroth, S, Pukrittayakamee, S,

CHAPTER 1

49

Jittamala, P, Chotivanich, K, Chutasmit, K, Suchatsoonthorn, C, Runcharoen, R, Hien, TT, Thuy-Nhien, NT, Thanh, NV, Phu, NH, Htut, Y, Han, KT, Aye, KH, Mokuolu, OA, Olaosebikan, RR, Folaranmi, OO, Mayxay, M, Khanthavong, M, Hongvanthong, B, Newton, PN, Onyamboko, MA, Fanello, CI, Tshefu, AK, Mishra, N, Valecha, N, Phyo, AP, Nosten, F, Yi, P, Tripura, R, Borrmann, S, Bashraheil, M, Peshu, J, Faiz, MA, Ghose, A, Hossain, MA, Samad, R, Rahman, MR, Hasan, MM, Islam, A, Miotto, O, Amato, R, MacInnis, B, Stalker, J, Kwiatkowski, DP, Bozdech, Z, Jeeyapant, A, Cheah, PY, Sakulthaew, T, Chalk, J, Intharabut, B, Silamut, K, Lee, SJ, Vihokhern, B, Kunasol, C, Imwong, M, Tarning, J, Taylor, WJ, Yeung, S, Woodrow, CJ, Flegg, JA, Das, D, Smith, J, Venkatesan, M, Plowe, CV, Stepniewska, K, Guerin, PJ, Dondorp, AM, Day, NP, White, NJ & Tracking Resistance to Artemisinin, C 2014, 'Spread of artemisinin resistance in Plasmodium falciparum malaria', N Engl J Med, vol. 371, no. 5, pp. 411-423.

Auburn, S & Barry, AE 2016, 'Dissecting malaria biology and epidemiology using population genetics and genomics', Int J Parasitol, vol. 47, no. 2-3, pp. 77-85.

Auburn, S, Serre, D, Pearson, RD, Amato, R, Sriprawat, K, To, S, Handayuni, I, Suwanarusk, R, Russell, B, Drury, E, Stalker, J, Miotto, O, Kwiatkowski, DP, Nosten, F & Price, RN 2016, 'Genomic Analysis Reveals a Common Breakpoint in Amplifications of the Plasmodium vivax Multidrug Resistance 1 Locus in Thailand', J Infect Dis, vol. 214, no. 8, pp. 1235-1242.

Awasthi, G, Prasad, GB & Das, A 2011, 'Population genetic analyses of Plasmodium falciparum chloroquine receptor transporter gene haplotypes reveal the evolutionary history of chloroquine-resistant malaria in India', Int J Parasitol, vol. 41, no. 7, pp. 705-709.

Bacon, DJ, Latour, C, Lucas, C, Colina, O, Ringwald, P & Picot, S 2007, 'Comparison of a SYBR green I-based assay with a histidine-rich protein II enzyme-linked immunosorbent assay for in vitro antimalarial drug efficacy testing and application to clinical isolates', Antimicrob Agents Chemother, vol. 51, no. 4, pp. 1172-1178.

Baniecki, ML, Faust, AL, Schaffner, SF, Park, DJ, Galinsky, K, Daniels, RF, Hamilton, E, Ferreira, MU, Karunaweera, ND, Serre, D, Zimmerman, PA, Sa, JM, Wellems, TE, Musset, L, Legrand, E, Melnikov, A, Neafsey, DE, Volkman, SK, Wirth, DF & Sabeti, PC 2015, 'Development of a single nucleotide polymorphism barcode to genotype Plasmodium vivax infections', PLoS Negl Trop Dis, vol. 9, no. 3, p. e0003539.

Barnadas, C, Ratsimbasoa, A, Tichit, M, Bouchier, C, Jahevitra, M, Picot, S & Menard, D 2008, 'Plasmodium vivax resistance to chloroquine in Madagascar: clinical efficacy and polymorphisms in pvmdr1 and pvcrt-o genes', Antimicrob Agents Chemother, vol. 52, no. 12, pp. 4233-4240.

Baro, NK, Pooput, C & Roepe, PD 2011, 'Analysis of chloroquine resistance transporter (CRT) isoforms and orthologues in S. cerevisiae yeast', Biochemistry, vol. 50, no. 31, pp. 6701-6710.

Barry, AE, Waltmann, A, Koepfli, C, Barnadas, C & Mueller, I 2015, 'Uncovering the transmission dynamics of Plasmodium vivax using population genetics', Pathog Glob Health, vol. 109, no. 3, pp. 142-152.

Black, RH & World Health Organization 1968, Manual of epidemiology and epidemiological services in malaria programmes. Geneve: World Health Organization.

CHAPTER 1

50

Bousema, T, Dinglasan, RR, Morlais, I, Gouagna, LC, van Warmerdam, T, Awono-Ambene, PH, Bonnet, S, Diallo, M, Coulibaly, M, Tchuinkam, T, Mulder, B, Targett, G, Drakeley, C, Sutherland, C, Robert, V, Doumbo, O, Toure, Y, Graves, PM, Roeffen, W, Sauerwein, R, Birkett, A, Locke, E, Morin, M, Wu, Y & Churcher, TS 2012, 'Mosquito feeding assays to determine the infectiousness of naturally infected Plasmodium falciparum gametocyte carriers', PLoS One, vol. 7, no. 8, p. e42821.

Bousema, T & Drakeley, C 2011, 'Epidemiology and infectivity of Plasmodium falciparum and Plasmodium vivax gametocytes in relation to malaria control and elimination', Clin Microbiol Rev, vol. 24, no. 2, pp. 377-410.

Bousema, T, Griffin, JT, Sauerwein, RW, Smith, DL, Churcher, TS, Takken, W, Ghani, A, Drakeley, C & Gosling, R 2012, 'Hitting hotspots: spatial targeting of malaria for control and elimination', PLoS Med, vol. 9, no. 1, p. e1001165.

Bousema, T, Okell, L, Felger, I & Drakeley, C 2014, 'Asymptomatic malaria infections: detectability, transmissibility and public health relevance', Nat Rev Microbiol, vol. 12, no. 12, pp. 833-840.

Boutlis, CS, Yeo, TW & Anstey, NM 2006, 'Malaria tolerance--for whom the cell tolls?', Trends Parasitol, vol. 22, no. 8, pp. 371-377.

Brady, OJ, Godfray, HC, Tatem, AJ, Gething, PW, Cohen, JM, McKenzie, FE, Perkins, TA, Reiner, RC, Jr., Tusting, LS, Sinka, ME, Moyes, CL, Eckhoff, PA, Scott, TW, Lindsay, SW, Hay, SI & Smith, DL 2016, 'Vectorial capacity and vector control: reconsidering sensitivity to parameters for malaria elimination', Trans R Soc Trop Med Hyg, vol. 110, no. 2, pp. 107-117.

Brega, S, Meslin, B, de Monbrison, F, Severini, C, Gradoni, L, Udomsangpetch, R, Sutanto, I, Peyron, F & Picot, S 2005, 'Identification of the Plasmodium vivax mdr-like gene (pvmdr1) and analysis of single-nucleotide polymorphisms among isolates from different areas of endemicity', J Infect Dis, vol. 191, no. 2, pp. 272-277.

Bronzan, RN, McMorrow, ML & Kachur, SP 2008, 'Diagnosis of malaria: challenges for clinicians in endemic and non-endemic regions', Mol Diagn Ther, vol. 12, no. 5, pp. 299-306.

Bruce-Chwatt, LJB, Robert Hughes Canfield, Craig J Clyde, David F Peters, W Wernsdorfer, Walther H 1986, Chemotherapy of malaria, http://www.who.int/iris/handle/10665/38605.

Camargo, EP, Alves, F & Pereira da Silva, LH 1999, 'Symptomless Plasmodium vivax infections in native Amazonians', Lancet, vol. 353, no. 9162, pp. 1415-1416.

Campino, S, Auburn, S, Kivinen, K, Zongo, I, Ouedraogo, JB, Mangano, V, Djimde, A, Doumbo, OK, Kiara, SM, Nzila, A, Borrmann, S, Marsh, K, Michon, P, Mueller, I, Siba, P, Jiang, H, Su, XZ, Amaratunga, C, Socheat, D, Fairhurst, RM, Imwong, M, Anderson, T, Nosten, F, White, NJ, Gwilliam, R, Deloukas, P, MacInnis, B, Newbold, CI, Rockett, K, Clark, TG & Kwiatkowski, DP 2011, 'Population genetic analysis of Plasmodium falciparum parasites using a customized Illumina GoldenGate genotyping assay', PLoS One, vol. 6, no. 6, p. e20251.

CHAPTER 1

51

Carlton, JM, Volkman, SK, Uplekar, S, Hupalo, DN, Pereira Alves, JM, Cui, L, Donnelly, M, Roos, DS, Harb, OS, Acosta, M, Read, A, Ribolla, PE, Singh, OP, Valecha, N, Wassmer, SC, Ferreira, M & Escalante, AA 2015, 'Population Genetics, Evolutionary Genomics, and Genome-Wide Studies of Malaria: A View Across the International Centers of Excellence for Malaria Research', Am J Trop Med Hyg, vol. 93, no. 3 Suppl, pp. 87-98.

Carter, R & Mendis, KN 2002, 'Evolutionary and historical aspects of the burden of malaria', Clin Microbiol Rev, vol. 15, no. 4, pp. 564-594.

Chaijaroenkul, W, Ward, SA, Mungthin, M, Johnson, D, Owen, A, Bray, PG & Na-Bangchang, K 2011, 'Sequence and gene expression of chloroquine resistance transporter (pfcrt) in the association of in vitro drugs resistance of Plasmodium falciparum', Malar J, vol. 10, p. 42.

Cheeseman, IH, Miller, BA, Nair, S, Nkhoma, S, Tan, A, Tan, JC, Al Saai, S, Phyo, AP, Moo, CL, Lwin, KM, McGready, R, Ashley, E, Imwong, M, Stepniewska, K, Yi, P, Dondorp, AM, Mayxay, M, Newton, PN, White, NJ, Nosten, F, Ferdig, MT & Anderson, TJ 2012, 'A major genome region underlying artemisinin resistance in malaria', Science, vol. 336, no. 6077, pp. 79-82.

Chen, I, Clarke, SE, Gosling, R, Hamainza, B, Killeen, G, Magill, A, O'Meara, W, Price, RN & Riley, EM 2016, '"Asymptomatic" Malaria: A Chronic and Debilitating Infection That Should Be Treated', PLoS Med, vol. 13, no. 1, p. e1001942.

Chenet, SM, Schneider, KA, Villegas, L & Escalante, AA 2012, 'Local population structure of Plasmodium: impact on malaria control and elimination', Malar J, vol. 11, p. 412.

Chenet, SM, Taylor, JE, Blair, S, Zuluaga, L & Escalante, AA 2015, 'Longitudinal analysis of Plasmodium falciparum genetic variation in Turbo, Colombia: implications for malaria control and elimination', Malar J, vol. 14, no. 1, p. 363.

Cheng, Q, Cunningham, J & Gatton, ML 2015, 'Systematic review of sub-microscopic P. vivax infections: prevalence and determining factors', PLoS Negl Trop Dis, vol. 9, no. 1, p. e3413.

Choi, KM, Choi, YK, Kang, YA, Seo, SY, Lee, HW, Cho, SH, Lee, WJ, Rhie, HG, Lee, HS & Kim, JY 2011, 'Study of the genetic discrimination between imported and autochthonous cases of malaria in South Korea', J Travel Med, vol. 18, no. 1, pp. 63-66.

Churcher, TS, Bousema, T, Walker, M, Drakeley, C, Schneider, P, Ouedraogo, AL & Basanez, MG 2013, 'Predicting mosquito infection from Plasmodium falciparum gametocyte density and estimating the reservoir of infection', Elife, vol. 2, p. e00626.

Conrad, MD, Bigira, V, Kapisi, J, Muhindo, M, Kamya, MR, Havlir, DV, Dorsey, G & Rosenthal, PJ 2014, 'Polymorphisms in K13 and falcipain-2 associated with artemisinin resistance are not prevalent in Plasmodium falciparum isolated from Ugandan children', PLoS One, vol. 9, no. 8, p. e105690.

Conway, DJ, Fanello, C, Lloyd, JM, Al-Joubori, BM, Baloch, AH, Somanath, SD, Roper, C, Oduola, AM, Mulder, B, Povoa, MM, Singh, B & Thomas, AW 2000, 'Origin of Plasmodium falciparum malaria is traced by mitochondrial DNA', Mol Biochem Parasitol, vol. 111, no. 1, pp. 163-171.

CHAPTER 1

52

Cottrell, G, Moussiliou, A, Luty, AJ, Cot, M, Fievet, N, Massougbodji, A, Deloron, P & Tuikue Ndam, N 2015, 'Submicroscopic Plasmodium falciparum Infections Are Associated With Maternal Anemia, Premature Births, and Low Birth Weight', Clin Infect Dis, vol. 60, no. 10, pp. 1481-1488.

Cowell, AN, Loy, DE, Sundararaman, SA, Valdivia, H, Fisch, K, Lescano, AG, Baldeviano, GC, Durand, S, Gerbasi, V, Sutherland, CJ, Nolder, D, Vinetz, JM, Hahn, BH & Winzeler, EA 2017, 'Selective Whole-Genome Amplification Is a Robust Method That Enables Scalable Whole-Genome Sequencing of Plasmodium vivax from Unprocessed Clinical Samples', MBio, vol. 8, no. 1.

Cowman, AF, Karcz, S, Galatis, D & Culvenor, JG 1991, 'A P-glycoprotein homologue of Plasmodium falciparum is localized on the digestive vacuole', J Cell Biol, vol. 113, no. 5, pp. 1033-1042.

Cui, L, Mharakurwa, S, Ndiaye, D, Rathod, PK & Rosenthal, PJ 2015, 'Antimalarial Drug Resistance: Literature Review and Activities and Findings of the ICEMR Network', Am J Trop Med Hyg, vol. 93, no. 3 Suppl, pp. 57-68.

Daniels, R, Volkman, SK, Milner, DA, Mahesh, N, Neafsey, DE, Park, DJ, Rosen, D, Angelino, E, Sabeti, PC, Wirth, DF & Wiegand, RC 2008, 'A general SNP-based molecular barcode for Plasmodium falciparum identification and tracking', Malar J, vol. 7, p. 223.

Daniels, RF, Rice, BL, Daniels, NM, Volkman, SK & Hartl, DL 2015, 'The utility of genomic data for Plasmodium vivax population surveillance', Pathog Glob Health, vol. 109, no. 3, pp. 153-161.

Daniels, RF, Schaffner, SF, Wenger, EA, Proctor, JL, Chang, HH, Wong, W, Baro, N, Ndiaye, D, Fall, FB, Ndiop, M, Ba, M, Milner, DA, Jr., Taylor, TE, Neafsey, DE, Volkman, SK, Eckhoff, PA, Hartl, DL & Wirth, DF 2015, 'Modeling malaria genomics reveals transmission decline and rebound in Senegal', Proc Natl Acad Sci U S A, vol. 112, no. 22, pp. 7067-7072.

Delgado-Ratto, C, Gamboa, D, Soto-Calle, VE, Van den Eede, P, Torres, E, Sanchez-Martinez, L, Contreras-Mancilla, J, Rosanas-Urgell, A, Rodriguez Ferrucci, H, Llanos-Cuentas, A, Erhart, A, Van Geertruyden, JP & D'Alessandro, U 2016, 'Population Genetics of Plasmodium vivax in the Peruvian Amazon', PLoS Negl Trop Dis, vol. 10, no. 1, p. e0004376.

Dharmawardena, P, Premaratne, RG, Gunasekera, WM, Hewawitarane, M, Mendis, K & Fernando, D 2015, 'Characterization of imported malaria, the largest threat to sustained malaria elimination from Sri Lanka', Malar J, vol. 14, p. 177.

Dondorp, AM, Nosten, F, Yi, P, Das, D, Phyo, AP, Tarning, J, Lwin, KM, Ariey, F, Hanpithakpong, W, Lee, SJ, Ringwald, P, Silamut, K, Imwong, M, Chotivanich, K, Lim, P, Herdman, T, An, SS, Yeung, S, Singhasivanon, P, Day, NP, Lindegardh, N, Socheat, D & White, NJ 2009, 'Artemisinin resistance in Plasmodium falciparum malaria', N Engl J Med, vol. 361, no. 5, pp. 455-467.

Doolan, DL, Dobano, C & Baird, JK 2009, 'Acquired immunity to malaria', Clin Microbiol Rev, vol. 22, no. 1, pp. 13-36, Table of Contents.

CHAPTER 1

53

Douglas, NM, Lampah, DA, Kenangalem, E, Simpson, JA, Poespoprodjo, JR, Sugiarto, P, Anstey, NM & Price, RN 2013, 'Major burden of severe anemia from non-falciparum malaria species in southern papua: a hospital-based surveillance study', PLoS Med, vol. 10, no. 12, p. e1001575.

Douglas, NM, Simpson, JA, Phyo, AP, Siswantoro, H, Hasugian, AR, Kenangalem, E, Poespoprodjo, JR, Singhasivanon, P, Anstey, NM, White, NJ, Tjitra, E, Nosten, F & Price, RN 2013, 'Gametocyte dynamics and the role of drugs in reducing the transmission potential of Plasmodium vivax', J Infect Dis, vol. 208, no. 5, pp. 801-812.

Duraisingh, MT, Jones, P, Sambou, I, von Seidlein, L, Pinder, M & Warhurst, DC 2000, 'The tyrosine-86 allele of the pfmdr1 gene of Plasmodium falciparum is associated with increased sensitivity to the anti-malarials mefloquine and artemisinin', Mol Biochem Parasitol, vol. 108, no. 1, pp. 13-23.

Duraisingh, MT, Roper, C, Walliker, D & Warhurst, DC 2000, 'Increased sensitivity to the antimalarials mefloquine and artemisinin is conferred by mutations in the pfmdr1 gene of Plasmodium falciparum', Mol Microbiol, vol. 36, no. 4, pp. 955-961.

Eastman, RT, Dharia, NV, Winzeler, EA & Fidock, DA 2011, 'Piperaquine resistance is associated with a copy number variation on chromosome 5 in drug-pressured Plasmodium falciparum parasites', Antimicrob Agents Chemother, vol. 55, no. 8, pp. 3908-3916.

Ecker, A, Lehane, AM, Clain, J & Fidock, DA 2012, 'PfCRT and its role in antimalarial drug resistance', Trends Parasitol, vol. 28, no. 11, pp. 504-514.

Ehlgen, F, Pham, JS, de Koning-Ward, T, Cowman, AF & Ralph, SA 2012, 'Investigation of the Plasmodium falciparum food vacuole through inducible expression of the chloroquine resistance transporter (PfCRT)', PLoS One, vol. 7, no. 6, p. e38781.

Elyazar, IR, Hay, SI & Baird, JK 2011, 'Malaria distribution, prevalence, drug resistance and control in Indonesia', Adv Parasitol, vol. 74, pp. 41-175.

Escalante, AA, Ferreira, MU, Vinetz, JM, Volkman, SK, Cui, L, Gamboa, D, Krogstad, DJ, Barry, AE, Carlton, JM, van Eijk, AM, Pradhan, K, Mueller, I, Greenhouse, B, Pacheco, MA, Vallejo, AF, Herrera, S & Felger, I 2015, 'Malaria Molecular Epidemiology: Lessons from the International Centers of Excellence for Malaria Research Network', Am J Trop Med Hyg, vol. 93, no. 3 Suppl, pp. 79-86.

Eyase, FL, Akala, HM, Ingasia, L, Cheruiyot, A, Omondi, A, Okudo, C, Juma, D, Yeda, R, Andagalu, B, Wanja, E, Kamau, E, Schnabel, D, Bulimo, W, Waters, NC, Walsh, DS & Johnson, JD 2013, 'The role of Pfmdr1 and Pfcrt in changing chloroquine, amodiaquine, mefloquine and lumefantrine susceptibility in western-Kenya P. falciparum samples during 2008-2011', PLoS One, vol. 8, no. 5, p. e64299.

Falq, G, Van Den Bergh, R, De Smet, M, Etienne, W, Nguon, C, Rekol, H, Imwong, M, Dondorp, A & Kindermans, JM 2016, 'Assessing the asymptomatic reservoir and dihydroartemisinin-piperaquine effectiveness in a low transmission setting threatened by artemisinin resistant Plasmodium falciparum', Malar J, vol. 15, no. 1, p. 446.

CHAPTER 1

54

Fernandez-Becerra, C, Pinazo, MJ, Gonzalez, A, Alonso, PL, del Portillo, HA & Gascon, J 2009, 'Increased expression levels of the pvcrt-o and pvmdr1 genes in a patient with severe Plasmodium vivax malaria', Malar J, vol. 8, p. 55.

Ferreira, MU & de Oliveira, TC 2015, 'Challenges for Plasmodium vivax malaria elimination in the genomics era', Pathog Glob Health, vol. 109, no. 3, pp. 89-90.

Ferreira, MU, Karunaweera, ND, da Silva-Nunes, M, da Silva, NS, Wirth, DF & Hartl, DL 2007, 'Population structure and transmission dynamics of Plasmodium vivax in rural Amazonia', J Infect Dis, vol. 195, no. 8, pp. 1218-1226.

Fidock, DA, Nomura, T, Talley, AK, Cooper, RA, Dzekunov, SM, Ferdig, MT, Ursos, LM, Sidhu, AB, Naude, B, Deitsch, KW, Su, XZ, Wootton, JC, Roepe, PD & Wellems, TE 2000, 'Mutations in the P. falciparum digestive vacuole transmembrane protein PfCRT and evidence for their role in chloroquine resistance', Mol Cell, vol. 6, no. 4, pp. 861-871.

Fola, AA, Abby Harrison, GL, Hazairin, MH, Barnadas, C, Hetzel, MW, Iga, J, Siba, PM, Mueller, I & Barry, AE 2017, 'Higher Complexity of Infection and Genetic Diversity of Plasmodium vivax Than Plasmodium falciparum across all Malaria Transmission Zones of Papua New Guinea', Am J Trop Med Hyg.

Galatas, B, Bassat, Q & Mayor, A 2016, 'Malaria Parasites in the Asymptomatic: Looking for the Hay in the Haystack', Trends Parasitol, vol. 32, no. 4, pp. 296-308.

Gama, BE, Oliveira, NK, Souza, JM, Daniel-Ribeiro, CT & Ferreira-da-Cruz Mde, F 2009, 'Characterisation of pvmdr1 and pvdhfr genes associated with chemoresistance in Brazilian Plasmodium vivax isolates', Mem Inst Oswaldo Cruz, vol. 104, no. 7, pp. 1009-1011.

Gligorijevic, B, Purdy, K, Elliott, DA, Cooper, RA & Roepe, PD 2008, 'Stage independent chloroquine resistance and chloroquine toxicity revealed via spinning disk confocal microscopy', Mol Biochem Parasitol, vol. 159, no. 1, pp. 7-23.

Gray, KA, Dowd, S, Bain, L, Bobogare, A, Wini, L, Shanks, GD & Cheng, Q 2013, 'Population genetics of Plasmodium falciparum and Plasmodium vivax and asymptomatic malaria in Temotu Province, Solomon Islands', Malar J, vol. 12, p. 429.

Griffing, SM, Mixson-Hayden, T, Sridaran, S, Alam, MT, McCollum, AM, Cabezas, C, Marquino Quezada, W, Barnwell, JW, De Oliveira, AM, Lucas, C, Arrospide, N, Escalante, AA, Bacon, DJ & Udhayakumar, V 2011, 'South American Plasmodium falciparum after the malaria eradication era: clonal population expansion and survival of the fittest hybrids', PLoS One, vol. 6, no. 9, p. e23486.

Gunawardena, S, Ferreira, MU, Kapilananda, GM, Wirth, DF & Karunaweera, ND 2014, 'The Sri Lankan paradox: high genetic diversity in Plasmodium vivax populations despite decreasing levels of malaria transmission', Parasitology, vol. 141, no. 7, pp. 880-890.

Hamedi, Y, Sharifi-Sarasiabi, K, Dehghan, F, Safari, R, To, S, Handayuni, I, Trimarsanto, H, Price, RN & Auburn, S 2016, 'Molecular Epidemiology of P. vivax in Iran: High Diversity and Complex Sub-Structure Using Neutral Markers, but No Evidence of Y976F Mutation at pvmdr1', PLoS One, vol. 11, no. 11, p. e0166124.

CHAPTER 1

55

Hay, SI, Smith, DL & Snow, RW 2008, 'Measuring malaria endemicity from intense to interrupted transmission', Lancet Infect Dis, vol. 8, no. 6, pp. 369-378.

Hofmann, N, Mwingira, F, Shekalaghe, S, Robinson, LJ, Mueller, I & Felger, I 2015, 'Ultra-sensitive detection of Plasmodium falciparum by amplification of multi-copy subtelomeric targets', PLoS Med, vol. 12, no. 3, p. e1001788.

Hupalo, DN, Luo, Z, Melnikov, A, Sutton, PL, Rogov, P, Escalante, A, Vallejo, AF, Herrera, S, Arevalo-Herrera, M, Fan, Q, Wang, Y, Cui, L, Lucas, CM, Durand, S, Sanchez, JF, Baldeviano, GC, Lescano, AG, Laman, M, Barnadas, C, Barry, A, Mueller, I, Kazura, JW, Eapen, A, Kanagaraj, D, Valecha, N, Ferreira, MU, Roobsoong, W, Nguitragool, W, Sattabonkot, J, Gamboa, D, Kosek, M, Vinetz, JM, Gonzalez-Ceron, L, Birren, BW, Neafsey, DE & Carlton, JM 2016, 'Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax', Nat Genet, vol. 48, no. 8, pp. 953-958.

Imwong, M, Hanchana, S, Malleret, B, Renia, L, Day, NP, Dondorp, A, Nosten, F, Snounou, G & White, NJ 2014, 'High-throughput ultrasensitive molecular techniques for quantifying low-density malaria parasitemias', J Clin Microbiol, vol. 52, no. 9, pp. 3303-3309.

Imwong, M, Nair, S, Pukrittayakamee, S, Sudimack, D, Williams, JT, Mayxay, M, Newton, PN, Kim, JR, Nandy, A, Osorio, L, Carlton, JM, White, NJ, Day, NP & Anderson, TJ 2007, 'Contrasting genetic structure in Plasmodium vivax populations from Asia and South America', Int J Parasitol, vol. 37, no. 8-9, pp. 1013-1022.

Imwong, M, Pukrittayakamee, S, Pongtavornpinyo, W, Nakeesathit, S, Nair, S, Newton, P, Nosten, F, Anderson, TJ, Dondorp, A, Day, NP & White, NJ 2008, 'Gene amplification of the multidrug resistance 1 gene of Plasmodium vivax isolates from Thailand, Laos, and Myanmar', Antimicrob Agents Chemother, vol. 52, no. 7, pp. 2657-2659.

Isozumi, R, Fukui, M, Kaneko, A, Chan, CW, Kawamoto, F & Kimura, M 2015, 'Improved detection of malaria cases in island settings of Vanuatu and Kenya by PCR that targets the Plasmodium mitochondrial cytochrome c oxidase III (cox3) gene', Parasitol Int, vol. 64, no. 3, pp. 304-308.

Iwagami, M, Hwang, SY, Kim, SH, Park, SJ, Lee, GY, Matsumoto-Takahashi, EL, Kho, WG & Kano, S 2013, 'Microsatellite DNA analysis revealed a drastic genetic change of Plasmodium vivax population in the Republic of Korea during 2002 and 2003', PLoS Negl Trop Dis, vol. 7, no. 10, p. e2522.

Jennison, C, Arnott, A, Tessier, N, Tavul, L, Koepfli, C, Felger, I, Siba, PM, Reeder, JC, Bahlo, M, Mueller, I & Barry, AE 2015, 'Plasmodium vivax populations are more genetically diverse and less structured than sympatric Plasmodium falciparum populations', PLoS Negl Trop Dis, vol. 9, no. 4, p. e0003634.

Kamau, E, Campino, S, Amenga-Etego, L, Drury, E, Ishengoma, D, Johnson, K, Mumba, D, Kekre, M, Yavo, W, Mead, D, Bouyou-Akotet, M, Apinjoh, T, Golassa, L, Randrianarivelojosia, M, Andagalu, B, Maiga-Ascofare, O, Amambua-Ngwa, A, Tindana, P, Ghansah, A, MacInnis, B, Kwiatkowski, D & Djimde, AA 2015, 'K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa', J Infect Dis, vol. 211, no. 8, pp. 1352-1355.

CHAPTER 1

56

Karunaweera, ND, Ferreira, MU, Munasinghe, A, Barnwell, JW, Collins, WE, King, CL, Kawamoto, F, Hartl, DL & Wirth, DF 2008, 'Extensive microsatellite diversity in the human malaria parasite Plasmodium vivax', Gene, vol. 410, no. 1, pp. 105-112.

Kim, JY, Suh, EJ, Yu, HS, Jung, HS, Park, IH, Choi, YK, Choi, KM, Cho, SH & Lee, WJ 2011, 'Longitudinal and Cross-Sectional Genetic Diversity in the Korean Peninsula Based on the P vivax Merozoite Surface Protein Gene', Osong Public Health Res Perspect, vol. 2, no. 3, pp. 158-163.

Kim, YK, Kim, C, Park, I, Kim, HY, Choi, JY & Kim, JM 2011, 'Therapeutic efficacy of chloroquine in Plasmodium vivax and the pvmdr1 polymorphisms in the Republic of Korea under mass chemoprophylaxis', Am J Trop Med Hyg, vol. 84, no. 4, pp. 532-534.

Koepfli, C, Nguitragool, W, Hofmann, NE, Robinson, LJ, Ome-Kaius, M, Sattabongkot, J, Felger, I & Mueller, I 2016, 'Sensitive and accurate quantification of human malaria parasites using droplet digital PCR (ddPCR)', Sci Rep, vol. 6, p. 39183.

Koepfli, C, Rodrigues, PT, Antao, T, Orjuela-Sanchez, P, Van den Eede, P, Gamboa, D, van Hong, N, Bendezu, J, Erhart, A, Barnadas, C, Ratsimbasoa, A, Menard, D, Severini, C, Menegon, M, Nour, BY, Karunaweera, N, Mueller, I, Ferreira, MU & Felger, I 2015, 'Plasmodium vivax Diversity and Population Structure across Four Continents', PLoS Negl Trop Dis, vol. 9, no. 6, p. e0003872.

Kublin, JG, Cortese, JF, Njunju, EM, Mukadam, RA, Wirima, JJ, Kazembe, PN, Djimde, AA, Kouriba, B, Taylor, TE & Plowe, CV 2003, 'Reemergence of chloroquine-sensitive Plasmodium falciparum malaria after cessation of chloroquine use in Malawi', J Infect Dis, vol. 187, no. 12, pp. 1870-1875.

Kwiatkowski, D 2015, 'Malaria genomics: tracking a diverse and evolving parasite population', Int Health, vol. 7, no. 2, pp. 82-84.

Lin, JT, Patel, JC, Kharabora, O, Sattabongkot, J, Muth, S, Ubalee, R, Schuster, AL, Rogers, WO, Wongsrichanalai, C & Juliano, JJ 2013, 'Plasmodium vivax isolates from Cambodia and Thailand show high genetic complexity and distinct patterns of P. vivax multidrug resistance gene 1 (pvmdr1) polymorphisms', Am J Trop Med Hyg, vol. 88, no. 6, pp. 1116-1123.

Lin, JT, Saunders, DL & Meshnick, SR 2014, 'The role of submicroscopic parasitemia in malaria transmission: what is the evidence?', Trends Parasitol, vol. 30, no. 4, pp. 183-190.

Lindblade, KA, Steinhardt, L, Samuels, A, Kachur, SP & Slutsker, L 2013, 'The silent threat: asymptomatic parasitemia and malaria transmission', Expert Rev Anti Infect Ther, vol. 11, no. 6, pp. 623-639.

Liu, Y, Auburn, S, Cao, J, Trimarsanto, H, Zhou, H, Gray, KA, Clark, TG, Price, RN, Cheng, Q, Huang, R & Gao, Q 2014, 'Genetic diversity and population structure of Plasmodium vivax in Central China', Malar J, vol. 13, p. 262.

Lu, F, Lim, CS, Nam, DH, Kim, K, Lin, K, Kim, TS, Lee, HW, Chen, JH, Wang, Y, Sattabongkot, J & Han, ET 2011, 'Genetic polymorphism in pvmdr1 and pvcrt-o genes in relation to in vitro

CHAPTER 1

57

drug susceptibility of Plasmodium vivax isolates from malaria-endemic countries', Acta Trop, vol. 117, no. 2, pp. 69-75.

Macdonald, G 1952, 'The analysis of equilibrium in malaria', Trop Dis Bull, vol. 49, no. 9, pp. 813-829.

Macdonald, G 1957, 'The epidemiology and control of malaria', The Epidemiology and Control of Malaria.

Manske, M, Miotto, O, Campino, S, Auburn, S, Almagro-Garcia, J, Maslen, G, O'Brien, J, Djimde, A, Doumbo, O, Zongo, I, Ouedraogo, JB, Michon, P, Mueller, I, Siba, P, Nzila, A, Borrmann, S, Kiara, SM, Marsh, K, Jiang, H, Su, XZ, Amaratunga, C, Fairhurst, R, Socheat, D, Nosten, F, Imwong, M, White, NJ, Sanders, M, Anastasi, E, Alcock, D, Drury, E, Oyola, S, Quail, MA, Turner, DJ, Ruano-Rubio, V, Jyothi, D, Amenga-Etego, L, Hubbart, C, Jeffreys, A, Rowlands, K, Sutherland, C, Roper, C, Mangano, V, Modiano, D, Tan, JC, Ferdig, MT, Amambua-Ngwa, A, Conway, DJ, Takala-Harrison, S, Plowe, CV, Rayner, JC, Rockett, KA, Clark, TG, Newbold, CI, Berriman, M, MacInnis, B & Kwiatkowski, DP 2012, 'Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing', Nature, vol. 487, no. 7407, pp. 375-379.

Marfurt, J, de Monbrison, F, Brega, S, Barbollat, L, Muller, I, Sie, A, Goroti, M, Reeder, JC, Beck, HP, Picot, S & Genton, B 2008, 'Molecular markers of in vivo Plasmodium vivax resistance to amodiaquine plus sulfadoxine-pyrimethamine: mutations in pvdhfr and pvmdr1', J Infect Dis, vol. 198, no. 3, pp. 409-417.

Melo, GC, Monteiro, WM, Siqueira, AM, Silva, SR, Magalhaes, BM, Alencar, AC, Kuehn, A, del Portillo, HA, Fernandez-Becerra, C & Lacerda, MV 2014, 'Expression levels of pvcrt-o and pvmdr-1 are associated with chloroquine resistance and severe Plasmodium vivax malaria in patients of the Brazilian Amazon', PLoS One, vol. 9, no. 8, p. e105922.

Mharakurwa, S, Daniels, R, Scott, A, Wirth, DF, Thuma, P & Volkman, SK 2014, 'Pre-amplification methods for tracking low-grade Plasmodium falciparum populations during scaled-up interventions in Southern Zambia', Malar J, vol. 13, p. 89.

Michon, P, Cole-Tobian, JL, Dabod, E, Schoepflin, S, Igu, J, Susapu, M, Tarongka, N, Zimmerman, PA, Reeder, JC, Beeson, JG, Schofield, L, King, CL & Mueller, I 2007, 'The risk of malarial infections and disease in Papua New Guinean children', Am J Trop Med Hyg, vol. 76, no. 6, pp. 997-1008.

Miller, LH, Good, MF & Milon, G 1994, 'Malaria pathogenesis', Science, vol. 264, no. 5167, pp. 1878-1883.

Mint Lekweiry, K, Ould Mohamed Salem Boukhary, A, Gaillard, T, Wurtz, N, Bogreau, H, Hafid, JE, Trape, JF, Bouchiba, H, Ould Ahmedou Salem, MS, Pradines, B, Rogier, C, Basco, LK & Briolant, S 2012, 'Molecular surveillance of drug-resistant Plasmodium vivax using pvdhfr, pvdhps and pvmdr1 markers in Nouakchott, Mauritania', J Antimicrob Chemother, vol. 67, no. 2, pp. 367-374.

Mitchell, SA, Jacobsohn, D, Thormann Powers, KE, Carpenter, PA, Flowers, ME, Cowen, EW, Schubert, M, Turner, ML, Lee, SJ, Martin, P, Bishop, MR, Baird, K, Bolanos-Meade, J, Boyd, K, Fall-Dickson, JM, Gerber, LH, Guadagnini, JP, Imanguli, M, Krumlauf, MC, Lawley, L, Li, L,

CHAPTER 1

58

Reeve, BB, Clayton, JA, Vogelsang, GB & Pavletic, SZ 2011, 'A multicenter pilot evaluation of the National Institutes of Health chronic graft-versus-host disease (cGVHD) therapeutic response measures: feasibility, interrater reliability, and minimum detectable change', Biol Blood Marrow Transplant, vol. 17, no. 11, pp. 1619-1629.

Mobegi, VA, Loua, KM, Ahouidi, AD, Satoguina, J, Nwakanma, DC, Amambua-Ngwa, A & Conway, DJ 2012, 'Population genetic structure of Plasmodium falciparum across a region of diverse endemicity in West Africa', Malar J, vol. 11, p. 223.

Mohammed, AH, Salih, MM, Elhassan, EM, Mohmmed, AA, Elzaki, SE, El-Sayed, BB & Adam, I 2013, 'Submicroscopic Plasmodium falciparum malaria and low birth weight in an area of unstable malaria transmission in Central Sudan', Malar J, vol. 12, p. 172.

Mok, S, Ashley, EA, Ferreira, PE, Zhu, L, Lin, Z, Yeo, T, Chotivanich, K, Imwong, M, Pukrittayakamee, S, Dhorda, M, Nguon, C, Lim, P, Amaratunga, C, Suon, S, Hien, TT, Htut, Y, Faiz, MA, Onyamboko, MA, Mayxay, M, Newton, PN, Tripura, R, Woodrow, CJ, Miotto, O, Kwiatkowski, DP, Nosten, F, Day, NP, Preiser, PR, White, NJ, Dondorp, AM, Fairhurst, RM & Bozdech, Z 2015, 'Drug resistance. Population transcriptomics of human malaria parasites reveals the mechanism of artemisinin resistance', Science, vol. 347, no. 6220, pp. 431-435.

Moreira, CM, Abo-Shehada, M, Price, RN & Drakeley, CJ 2015, 'A systematic review of sub-microscopic Plasmodium vivax infection', Malar J, vol. 14, no. 1, p. 360.

Mu, J, Ferdig, MT, Feng, X, Joy, DA, Duan, J, Furuya, T, Subramanian, G, Aravind, L, Cooper, RA, Wootton, JC, Xiong, M & Su, XZ 2003, 'Multiple transporters associated with malaria parasite responses to chloroquine and quinine', Mol Microbiol, vol. 49, no. 4, pp. 977-989.

Mu, J, Myers, RA, Jiang, H, Liu, S, Ricklefs, S, Waisberg, M, Chotivanich, K, Wilairatana, P, Krudsood, S, White, NJ, Udomsangpetch, R, Cui, L, Ho, M, Ou, F, Li, H, Song, J, Li, G, Wang, X, Seila, S, Sokunthea, S, Socheat, D, Sturdevant, DE, Porcella, SF, Fairhurst, RM, Wellems, TE, Awadalla, P & Su, XZ 2010, 'Plasmodium falciparum genome-wide scans for positive selection, recombination hot spots and resistance to antimalarial drugs', Nat Genet, vol. 42, no. 3, pp. 268-271.

Muangnoicharoen, S, Johnson, DJ, Looareesuwan, S, Krudsood, S & Ward, SA 2009, 'Role of known molecular markers of resistance in the antimalarial potency of piperaquine and dihydroartemisinin in vitro', Antimicrob Agents Chemother, vol. 53, no. 4, pp. 1362-1366.

Mwai, L, Kiara, SM, Abdirahman, A, Pole, L, Rippert, A, Diriye, A, Bull, P, Marsh, K, Borrmann, S & Nzila, A 2009, 'In vitro activities of piperaquine, lumefantrine, and dihydroartemisinin in Kenyan Plasmodium falciparum isolates and polymorphisms in pfcrt and pfmdr1', Antimicrob Agents Chemother, vol. 53, no. 12, pp. 5069-5073.

Najera, JA, Gonzalez-Silva, M & Alonso, PL 2011, 'Some lessons for the future from the Global Malaria Eradication Programme (1955-1969)', PLoS Med, vol. 8, no. 1, p. e1000412.

Neafsey, DE, Galinsky, K, Jiang, RH, Young, L, Sykes, SM, Saif, S, Gujja, S, Goldberg, JM, Young, S, Zeng, Q, Chapman, SB, Dash, AP, Anvikar, AR, Sutton, PL, Birren, BW, Escalante, AA, Barnwell, JW & Carlton, JM 2012, 'The malaria parasite Plasmodium vivax exhibits greater genetic diversity than Plasmodium falciparum', Nat Genet, vol. 44, no. 9, pp. 1046-1050.

CHAPTER 1

59

Newby, G, Hwang, J, Koita, K, Chen, I, Greenwood, B, von Seidlein, L, Shanks, GD, Slutsker, L, Kachur, SP, Wegbreit, J, Ippolito, MM, Poirot, E & Gosling, R 2015, 'Review of mass drug administration for malaria and its operational challenges', Am J Trop Med Hyg, vol. 93, no. 1, pp. 125-134.

Nkhoma, SC, Nair, S, Al-Saai, S, Ashley, E, McGready, R, Phyo, AP, Nosten, F & Anderson, TJ 2013, 'Population genetic correlates of declining transmission in a human pathogen', Mol Ecol, vol. 22, no. 2, pp. 273-285.

Noedl, H, Se, Y, Schaecher, K, Smith, BL, Socheat, D, Fukuda, MM & Artemisinin Resistance in Cambodia 1 Study, C 2008, 'Evidence of artemisinin-resistant malaria in western Cambodia', N Engl J Med, vol. 359, no. 24, pp. 2619-2620.

Noedl, H, Wernsdorfer, WH, Miller, RS & Wongsrichanalai, C 2002, 'Histidine-rich protein II: a novel approach to malaria drug sensitivity testing', Antimicrob Agents Chemother, vol. 46, no. 6, pp. 1658-1664.

Nomura, T, Carlton, JM, Baird, JK, del Portillo, HA, Fryauff, DJ, Rathore, D, Fidock, DA, Su, X, Collins, WE, McCutchan, TF, Wootton, JC & Wellems, TE 2001, 'Evidence for different mechanisms of chloroquine resistance in 2 Plasmodium species that cause human malaria', J Infect Dis, vol. 183, no. 11, pp. 1653-1661.

Noviyanti, R, Coutrier, F, Utami, RA, Trimarsanto, H, Tirta, YK, Trianty, L, Kusuma, A, Sutanto, I, Kosasih, A, Kusriastuti, R, Hawley, WA, Laihad, F, Lobo, N, Marfurt, J, Clark, TG, Price, RN & Auburn, S 2015, 'Contrasting Transmission Dynamics of Co-endemic Plasmodium vivax and P. falciparum: Implications for Malaria Control and Elimination', PLoS Negl Trop Dis, vol. 9, no. 5, p. e0003739.

Obaldia, N, 3rd, Baro, NK, Calzada, JE, Santamaria, AM, Daniels, R, Wong, W, Chang, HH, Hamilton, EJ, Arevalo-Herrera, M, Herrera, S, Wirth, DF, Hartl, DL, Marti, M & Volkman, SK 2015, 'Clonal outbreak of Plasmodium falciparum infection in eastern Panama', J Infect Dis, vol. 211, no. 7, pp. 1087-1096.

Oliveira-Ferreira, J, Lacerda, MV, Brasil, P, Ladislau, JL, Tauil, PL & Daniel-Ribeiro, CT 2010, 'Malaria in Brazil: an overview', Malar J, vol. 9, p. 115.

Orjuela-Sanchez, P, Sa, JM, Brandi, MC, Rodrigues, PT, Bastos, MS, Amaratunga, C, Duong, S, Fairhurst, RM & Ferreira, MU 2013, 'Higher microsatellite diversity in Plasmodium vivax than in sympatric Plasmodium falciparum populations in Pursat, Western Cambodia', Exp Parasitol, vol. 134, no. 3, pp. 318-326.

Padley, D, Moody, AH, Chiodini, PL & Saldanha, J 2003, 'Use of a rapid, single-round, multiplex PCR to detect malarial parasites and identify the species present', Ann Trop Med Parasitol, vol. 97, no. 2, pp. 131-137.

Pakalapati, D, Garg, S, Middha, S, Acharya, J, Subudhi, AK, Boopathi, AP, Saxena, V, Kochar, SK, Kochar, DK & Das, A 2013, 'Development and evaluation of a 28S rRNA gene-based nested PCR assay for P. falciparum and P. vivax', Pathog Glob Health, vol. 107, no. 4, pp. 180-188.

CHAPTER 1

60

Pearson, RD, Amato, R, Auburn, S, Miotto, O, Almagro-Garcia, J, Amaratunga, C, Suon, S, Mao, S, Noviyanti, R, Trimarsanto, H, Marfurt, J, Anstey, NM, William, T, Boni, MF, Dolecek, C, Tran, HT, White, NJ, Michon, P, Siba, P, Tavul, L, Harrison, G, Barry, A, Mueller, I, Ferreira, MU, Karunaweera, N, Randrianarivelojosia, M, Gao, Q, Hubbart, C, Hart, L, Jeffery, B, Drury, E, Mead, D, Kekre, M, Campino, S, Manske, M, Cornelius, VJ, MacInnis, B, Rockett, KA, Miles, A, Rayner, JC, Fairhurst, RM, Nosten, F, Price, RN & Kwiatkowski, DP 2016, 'Genomic analysis of local variation and recent evolution in Plasmodium vivax', Nat Genet, vol. 48, no. 8, pp. 959-964.

Petersen, I, Eastman, R & Lanzer, M 2011, 'Drug-resistant malaria: molecular mechanisms and implications for public health', FEBS Lett, vol. 585, no. 11, pp. 1551-1562.

Petersen, I, Eastman, R & Lanzer, M 2011, 'Drug‐resistant malaria: Molecular mechanisms and implications for public health', FEBS letters, vol. 585, no. 11, pp. 1551-1562 %@ 1873-3468.

Picot, S, Olliaro, P, de Monbrison, F, Bienvenu, AL, Price, RN & Ringwald, P 2009, 'A systematic review and meta-analysis of evidence for correlation between molecular markers of parasite resistance and treatment outcome in falciparum malaria', Malar J, vol. 8, p. 89.

Poespoprodjo, JR, Fobia, W, Kenangalem, E, Lampah, DA, Hasanuddin, A, Warikar, N, Sugiarto, P, Tjitra, E, Anstey, NM & Price, RN 2009, 'Vivax malaria: a major cause of morbidity in early infancy', Clin Infect Dis, vol. 48, no. 12, pp. 1704-1712.

Poespoprodjo, JR, Fobia, W, Kenangalem, E, Lampah, DA, Warikar, N, Seal, A, McGready, R, Sugiarto, P, Tjitra, E, Anstey, NM & Price, RN 2008, 'Adverse pregnancy outcomes in an area where multidrug-resistant plasmodium vivax and Plasmodium falciparum infections are endemic', Clin Infect Dis, vol. 46, no. 9, pp. 1374-1381.

Preston, MD, Campino, S, Assefa, SA, Echeverry, DF, Ocholla, H, Amambua-Ngwa, A, Stewart, LB, Conway, DJ, Borrmann, S, Michon, P, Zongo, I, Ouedraogo, JB, Djimde, AA, Doumbo, OK, Nosten, F, Pain, A, Bousema, T, Drakeley, CJ, Fairhurst, RM, Sutherland, CJ, Roper, C & Clark, TG 2014, 'A barcode of organellar genome polymorphisms identifies the geographic origin of Plasmodium falciparum strains', Nat Commun, vol. 5, p. 4052.

Price, RN, Auburn, S, Marfurt, J & Cheng, Q 2012, 'Phenotypic and genotypic characterisation of drug-resistant Plasmodium vivax', Trends Parasitol, vol. 28, no. 11, pp. 522-529.

Price, RN, Douglas, NM & Anstey, NM 2009, 'New developments in Plasmodium vivax malaria: severe disease and the rise of chloroquine resistance', Curr Opin Infect Dis, vol. 22, no. 5, pp. 430-435.

Price, RN, Uhlemann, AC, Brockman, A, McGready, R, Ashley, E, Phaipun, L, Patel, R, Laing, K, Looareesuwan, S, White, NJ, Nosten, F & Krishna, S 2004, 'Mefloquine resistance in Plasmodium falciparum and increased pfmdr1 gene copy number', Lancet, vol. 364, no. 9432, pp. 438-447.

CHAPTER 1

61

Price, RN, von Seidlein, L, Valecha, N, Nosten, F, Baird, JK & White, NJ 2014, 'Global extent of chloroquine-resistant Plasmodium vivax: a systematic review and meta-analysis', Lancet Infect Dis, vol. 14, no. 10, pp. 982-991.

Pumpaibool, T, Arnathau, C, Durand, P, Kanchanakhan, N, Siripoon, N, Suegorn, A, Sitthi-Amorn, C, Renaud, F & Harnyuttanakorn, P 2009, 'Genetic diversity and population structure of Plasmodium falciparum in Thailand, a low transmission country', Malar J, vol. 8, p. 155.

Ratcliff, A, Siswantoro, H, Kenangalem, E, Maristela, R, Wuwung, RM, Laihad, F, Ebsworth, EP, Anstey, NM, Tjitra, E & Price, RN 2007, 'Two fixed-dose artemisinin combinations for drug-resistant falciparum and vivax malaria in Papua, Indonesia: an open-label randomised comparison', Lancet, vol. 369, no. 9563, pp. 757-765.

Ratcliff, A, Siswantoro, H, Kenangalem, E, Wuwung, M, Brockman, A, Edstein, MD, Laihad, F, Ebsworth, EP, Anstey, NM, Tjitra, E & Price, RN 2007, 'Therapeutic response of multidrug-resistant Plasmodium falciparum and P. vivax to chloroquine and sulfadoxine-pyrimethamine in southern Papua, Indonesia', Trans R Soc Trop Med Hyg, vol. 101, no. 4, pp. 351-359.

Rebaudet, S, Bogreau, H, Silai, R, Lepere, JF, Bertaux, L, Pradines, B, Delmont, J, Gautret, P, Parola, P & Rogier, C 2010, 'Genetic structure of Plasmodium falciparum and elimination of malaria, Comoros archipelago', Emerg Infect Dis, vol. 16, no. 11, pp. 1686-1694.

Reed, MB, Saliba, KJ, Caruana, SR, Kirk, K & Cowman, AF 2000, 'Pgh1 modulates sensitivity and resistance to multiple antimalarials in Plasmodium falciparum', Nature, vol. 403, no. 6772, pp. 906-909.

Rieckmann, KH, Davis, DR & Hutton, DC 1989, 'Plasmodium vivax resistance to chloroquine?', Lancet, vol. 2, no. 8673, pp. 1183-1184.

Rodrigues, PT, Alves, JM, Santamaria, AM, Calzada, JE, Xayavong, M, Parise, M, da Silva, AJ & Ferreira, MU 2014, 'Using mitochondrial genome sequences to track the origin of imported Plasmodium vivax infections diagnosed in the United States', Am J Trop Med Hyg, vol. 90, no. 6, pp. 1102-1108.

Rodriguez, JC, Uribe, GA, Araujo, RM, Narvaez, PC & Valencia, SH 2011, 'Epidemiology and control of malaria in Colombia', Mem Inst Oswaldo Cruz, vol. 106 Suppl 1, pp. 114-122.

Rungsihirunrat, K, Muhamad, P, Chaijaroenkul, W, Kuesap, J & Na-Bangchang, K 2015, 'Plasmodium vivax drug resistance genes; Pvmdr1 and Pvcrt-o polymorphisms in relation to chloroquine sensitivity from a malaria endemic area of Thailand', Korean J Parasitol, vol. 53, no. 1, pp. 43-49.

Russell, B, Chalfein, F, Prasetyorini, B, Kenangalem, E, Piera, K, Suwanarusk, R, Brockman, A, Prayoga, P, Sugiarto, P, Cheng, Q, Tjitra, E, Anstey, NM & Price, RN 2008, 'Determinants of in vitro drug susceptibility testing of Plasmodium vivax', Antimicrob Agents Chemother, vol. 52, no. 3, pp. 1040-1045.

Russell, BM, Udomsangpetch, R, Rieckmann, KH, Kotecka, BM, Coleman, RE & Sattabongkot, J 2003, 'Simple in vitro assay for determining the sensitivity of Plasmodium

CHAPTER 1

62

vivax isolates from fresh human blood to antimalarials in areas where P. vivax is endemic', Antimicrob Agents Chemother, vol. 47, no. 1, pp. 170-173.

Sa, JM, Yamamoto, MM, Fernandez-Becerra, C, de Azevedo, MF, Papakrivos, J, Naude, B, Wellems, TE & Del Portillo, HA 2006, 'Expression and function of pvcrt-o, a Plasmodium vivax ortholog of pfcrt, in Plasmodium falciparum and Dictyostelium discoideum', Mol Biochem Parasitol, vol. 150, no. 2, pp. 219-228.

Sachs, J & Malaney, P 2002, 'The economic and social burden of malaria', Nature, vol. 415, no. 6872, pp. 680-685.

Saenz, FE, Morton, LC, Okoth, SA, Valenzuela, G, Vera-Arias, CA, Velez-Alvarez, E, Lucchi, NW, Castro, LE & Udhayakumar, V 2015, 'Clonal population expansion in an outbreak of Plasmodium falciparum on the northwest coast of Ecuador', Malar J, vol. 13 Suppl 1, p. 497.

Schneider, P, Bousema, JT, Gouagna, LC, Otieno, S, van de Vegte-Bolmer, M, Omar, SA & Sauerwein, RW 2007, 'Submicroscopic Plasmodium falciparum gametocyte densities frequently result in mosquito infection', Am J Trop Med Hyg, vol. 76, no. 3, pp. 470-474.

Sibley, CH & Price, RN 2012, 'Monitoring antimalarial drug resistance: Applying lessons learned from the past in a fast-moving present', Int J Parasitol Drugs Drug Resist, vol. 2, pp. 126-133.

Singh, B, Bobogare, A, Cox-Singh, J, Snounou, G, Abdullah, MS & Rahman, HA 1999, 'A genus- and species-specific nested polymerase chain reaction malaria detection assay for epidemiologic studies', Am J Trop Med Hyg, vol. 60, no. 4, pp. 687-692.

Sinka, ME, Bangs, MJ, Manguin, S, Rubio-Palis, Y, Chareonviriyaphap, T, Coetzee, M, Mbogo, CM, Hemingway, J, Patil, AP & Temperley, WH 2012, 'A global map of dominant malaria vectors', Parasites & vectors, vol. 5, no. 1, pp. 1 %@ 1756-3305.

Sisya, TJ, Kamn'gona, RM, Vareta, JA, Fulakeza, JM, Mukaka, MF, Seydel, KB, Laufer, MK, Taylor, TE & Nkhoma, SC 2015, 'Subtle changes in Plasmodium falciparum infection complexity following enhanced intervention in Malawi', Acta Trop, vol. 142, pp. 108-114.

Sluydts, V, Heng, S, Coosemans, M, Van Roey, K, Gryseels, C, Canier, L, Kim, S, Khim, N, Siv, S, Mean, V, Uk, S, Grietens, KP, Tho, S, Menard, D & Durnez, L 2014, 'Spatial clustering and risk factors of malaria infections in Ratanakiri Province, Cambodia', Malar J, vol. 13, p. 387.

Steenkeste, N, Incardona, S, Chy, S, Duval, L, Ekala, MT, Lim, P, Hewitt, S, Sochantha, T, Socheat, D, Rogier, C, Mercereau-Puijalon, O, Fandeur, T & Ariey, F 2009, 'Towards high-throughput molecular detection of Plasmodium: new approaches and molecular markers', Malar J, vol. 8, p. 86.

Stepniewska, K, Ashley, E, Lee, SJ, Anstey, N, Barnes, KI, Binh, TQ, D'Alessandro, U, Day, NP, de Vries, PJ, Dorsey, G, Guthmann, JP, Mayxay, M, Newton, PN, Olliaro, P, Osorio, L, Price, RN, Rowland, M, Smithuis, F, Taylor, WR, Nosten, F & White, NJ 2010, 'In vivo parasitological measures of artemisinin susceptibility', J Infect Dis, vol. 201, no. 4, pp. 570-579.

CHAPTER 1

63

Straimer, J, Gnadig, NF, Witkowski, B, Amaratunga, C, Duru, V, Ramadani, AP, Dacheux, M, Khim, N, Zhang, L, Lam, S, Gregory, PD, Urnov, FD, Mercereau-Puijalon, O, Benoit-Vical, F, Fairhurst, RM, Menard, D & Fidock, DA 2015, 'Drug resistance. K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates', Science, vol. 347, no. 6220, pp. 428-431.

Sturrock, HJ, Hsiang, MS, Cohen, JM, Smith, DL, Greenhouse, B, Bousema, T & Gosling, RD 2013, 'Targeting asymptomatic malaria infections: active surveillance in control and elimination', PLoS Med, vol. 10, no. 6, p. e1001467.

Sturrock, HJ, Roberts, KW, Wegbreit, J, Ohrt, C & Gosling, RD 2015, 'Tackling imported malaria: an elimination endgame', Am J Trop Med Hyg, vol. 93, no. 1, pp. 139-144.

Sutton, PL 2013, 'A call to arms: on refining Plasmodium vivax microsatellite marker panels for comparing global diversity', Malar J, vol. 12, p. 447.

Suwanarusk, R, Chavchich, M, Russell, B, Jaidee, A, Chalfein, F, Barends, M, Prasetyorini, B, Kenangalem, E, Piera, KA, Lek-Uthai, U, Anstey, NM, Tjitra, E, Nosten, F, Cheng, Q & Price, RN 2008, 'Amplification of pvmdr1 associated with multidrug-resistant Plasmodium vivax', J Infect Dis, vol. 198, no. 10, pp. 1558-1564.

Suwanarusk, R, Russell, B, Chavchich, M, Chalfein, F, Kenangalem, E, Kosaisavee, V, Prasetyorini, B, Piera, KA, Barends, M, Brockman, A, Lek-Uthai, U, Anstey, NM, Tjitra, E, Nosten, F, Cheng, Q & Price, RN 2007, 'Chloroquine resistant Plasmodium vivax: in vitro characterisation and association with molecular polymorphisms', PLoS One, vol. 2, no. 10, p. e1089.

Takala-Harrison, S, Clark, TG, Jacob, CG, Cummings, MP, Miotto, O, Dondorp, AM, Fukuda, MM, Nosten, F, Noedl, H, Imwong, M, Bethell, D, Se, Y, Lon, C, Tyner, SD, Saunders, DL, Socheat, D, Ariey, F, Phyo, AP, Starzengruber, P, Fuehrer, HP, Swoboda, P, Stepniewska, K, Flegg, J, Arze, C, Cerqueira, GC, Silva, JC, Ricklefs, SM, Porcella, SF, Stephens, RM, Adams, M, Kenefic, LJ, Campino, S, Auburn, S, MacInnis, B, Kwiatkowski, DP, Su, XZ, White, NJ, Ringwald, P & Plowe, CV 2013, 'Genetic loci associated with delayed clearance of Plasmodium falciparum following artemisinin treatment in Southeast Asia', Proc Natl Acad Sci U S A, vol. 110, no. 1, pp. 240-245.

Taylor, SM, Parobek, CM, DeConti, DK, Kayentao, K, Coulibaly, SO, Greenwood, BM, Tagbor, H, Williams, J, Bojang, K, Njie, F, Desai, M, Kariuki, S, Gutman, J, Mathanga, DP, Martensson, A, Ngasala, B, Conrad, MD, Rosenthal, PJ, Tshefu, AK, Moormann, AM, Vulule, JM, Doumbo, OK, Ter Kuile, FO, Meshnick, SR, Bailey, JA & Juliano, JJ 2015, 'Absence of putative artemisinin resistance mutations among Plasmodium falciparum in Sub-Saharan Africa: a molecular epidemiologic study', J Infect Dis, vol. 211, no. 5, pp. 680-688.

Thaithong, S 1983, Clones of different sensitivities in drugresistant isolates of Plasmodium falciparum. Bulletin of the World Health Organization, 61 no .4, p.709.

Thaithong, S, Ranford-Cartwright, LC, Siripoon, N, Harnyuttanakorn, P, Kanchanakhan, NS, Seugorn, A, Rungsihirunrat, K, Cravo, PV & Beale, GH 2001, 'Plasmodium falciparum: gene mutations and amplification of dihydrofolate reductase genes in parasites grown in vitro in presence of pyrimethamine', Exp Parasitol, vol. 98, no. 2, pp. 59-70.

CHAPTER 1

64

Tjitra, E, Anstey, NM, Sugiarto, P, Warikar, N, Kenangalem, E, Karyana, M, Lampah, DA & Price, RN 2008, 'Multidrug-resistant Plasmodium vivax associated with severe and fatal malaria: a prospective study in Papua, Indonesia', PLoS Med, vol. 5, no. 6, p. e128.

Tripura, R, Peto, TJ, Chalk, J, Lee, SJ, Sirithiranont, P, Nguon, C, Dhorda, M, von Seidlein, L, Maude, RJ, Day, NP, Imwong, M, White, NJ & Dondorp, AM 2016, 'Persistent Plasmodium falciparum and Plasmodium vivax infections in a western Cambodian population: implications for prevention, treatment and elimination strategies', Malar J, vol. 15, p. 181.

Tumwebaze, P, Conrad, MD, Walakira, A, LeClair, N, Byaruhanga, O, Nakazibwe, C, Kozak, B, Bloome, J, Okiring, J, Kakuru, A, Bigira, V, Kapisi, J, Legac, J, Gut, J, Cooper, RA, Kamya, MR, Havlir, DV, Dorsey, G, Greenhouse, B, Nsobya, SL & Rosenthal, PJ 2015, 'Impact of antimalarial treatment and chemoprevention on the drug sensitivity of malaria parasites isolated from ugandan children', Antimicrob Agents Chemother, vol. 59, no. 6, pp. 3018-3030.

Urdaneta, L, Plowe, C, Goldman, I & Lal, AA 1999, 'Point mutations in dihydrofolate reductase and dihydropteroate synthase genes of Plasmodium falciparum isolates from Venezuela', Am J Trop Med Hyg, vol. 61, no. 3, pp. 457-462.

Valderramos, SG & Fidock, DA 2006, 'Transporters involved in resistance to antimalarial drugs', Trends Pharmacol Sci, vol. 27, no. 11, pp. 594-601.

Vallejo, AF, Garcia, J, Amado-Garavito, AB, Arevalo-Herrera, M & Herrera, S 2016, 'Plasmodium vivax gametocyte infectivity in sub-microscopic infections', Malar J, vol. 15, p. 48.

Venkatesan, M, Gadalla, NB, Stepniewska, K, Dahal, P, Nsanzabana, C, Moriera, C, Price, RN, Martensson, A, Rosenthal, PJ, Dorsey, G, Sutherland, CJ, Guerin, P, Davis, TM, Menard, D, Adam, I, Ademowo, G, Arze, C, Baliraine, FN, Berens-Riha, N, Bjorkman, A, Borrmann, S, Checchi, F, Desai, M, Dhorda, M, Djimde, AA, El-Sayed, BB, Eshetu, T, Eyase, F, Falade, C, Faucher, JF, Froberg, G, Grivoyannis, A, Hamour, S, Houze, S, Johnson, J, Kamugisha, E, Kariuki, S, Kiechel, JR, Kironde, F, Kofoed, PE, LeBras, J, Malmberg, M, Mwai, L, Ngasala, B, Nosten, F, Nsobya, SL, Nzila, A, Oguike, M, Otienoburu, SD, Ogutu, B, Ouedraogo, JB, Piola, P, Rombo, L, Schramm, B, Some, AF, Thwing, J, Ursing, J, Wong, RP, Zeynudin, A, Zongo, I, Plowe, CV, Sibley, CH, Group, AMMS & Wwarn, AL 2014, 'Polymorphisms in Plasmodium falciparum chloroquine resistance transporter and multidrug resistance 1 genes: parasite risk factors that affect treatment outcomes for P. falciparum malaria after artemether-lumefantrine and artesunate-amodiaquine', Am J Trop Med Hyg, vol. 91, no. 4, pp. 833-843.

Vivax Working, G 2015, 'Targeting vivax malaria in the Asia Pacific: The Asia Pacific Malaria Elimination Network Vivax Working Group', Malar J, vol. 14, p. 484.

Wampfler, R, Mwingira, F, Javati, S, Robinson, L, Betuela, I, Siba, P, Beck, HP, Mueller, I & Felger, I 2013, 'Strategies for detection of Plasmodium species gametocytes', PLoS One, vol. 8, no. 9, p. e76316.

Wangchuk, S, Drukpa, T, Penjor, K, Peldon, T, Dorjey, Y, Dorji, K, Chhetri, V, Trimarsanto, H, To, S, Murphy, A, von Seidlein, L, Price, RN, Thriemer, K & Auburn, S 2016, 'Where chloroquine still works: the genetic make-up and susceptibility of Plasmodium vivax to chloroquine plus primaquine in Bhutan', Malar J, vol. 15, no. 1, p. 277.

CHAPTER 1

65

Wangdi, K, Kaewkungwal, J, Singhasivanon, P, Silawan, T, Lawpoolsri, S & White, NJ 2011, 'Spatio-temporal patterns of malaria infection in Bhutan: a country embarking on malaria elimination', Malar J, vol. 10, p. 89.

Webster, HK, Boudreau, EF, Pavanand, K, Yongvanitchit, K & Pang, LW 1985, 'Antimalarial drug susceptibility testing of Plasmodium falciparum in Thailand using a microdilution radioisotope method', Am J Trop Med Hyg, vol. 34, no. 2, pp. 228-235.

Wernsdorfer, WH & Noedl, H 2003, 'Molecular markers for drug resistance in malaria: use in treatment, diagnosis and epidemiology', Curr Opin Infect Dis, vol. 16, no. 6, pp. 553-558.

White, N 1999, 'Antimalarial drug resistance and combination chemotherapy', Philos Trans R Soc Lond B Biol Sci, vol. 354, no. 1384, pp. 739-749.

White, NJ 2011, 'Determinants of relapse periodicity in Plasmodium vivax malaria', Malar J, vol. 10, p. 297.

White, NJ, Pukrittayakamee, S, Hien, TT, Faiz, MA, Mokuolu, OA & Dondorp, AM 2014, 'Malaria', Lancet, vol. 383, no. 9918, pp. 723-735.

Witkowski, B, Duru, V, Khim, N, Ross, LS, Saintpierre, B, Beghain, J, Chy, S, Kim, S, Ke, S, Kloeung, N, Eam, R, Khean, C, Ken, M, Loch, K, Bouillon, A, Domergue, A, Ma, L, Bouchier, C, Leang, R, Huy, R, Nuel, G, Barale, JC, Legrand, E, Ringwald, P, Fidock, DA, Mercereau-Puijalon, O, Ariey, F & Menard, D 2017, 'A surrogate marker of piperaquine-resistant Plasmodium falciparum malaria: a phenotype-genotype association study', Lancet Infect Dis, vol. 17, no. 2, pp. 174-183.

WHO 1971, Technical guide for a system of malaria surveillance. Retrieved from http://www.who.int/iris/handle/10665/218454

WHO 1993, A global strategy for malaria control'. Retrieved from http://apps.who.int/iris/bitstream/10665/41785/1/9241561610.pdf

WHO 2009, Methods for surveillance of antimalarial drug efficacy. Retrieved from http://whqlibdoc.who.int/publications/2009/9789241597531_eng.pdf.

WHO 2012, Disease surveillance for malaria control: an operational manual. Retrieved from http://whqlibdoc.who.int/publications/2012/9789241503334_eng.pdf.

WHO 2015a, Guidelines for the treatment of malaria. Retrieved from http://www.who.int/malaria/publications/atoz/9789241549127/en/.

WHO 2015b, The role of mass drug administration, mass screening and treatment, and focal screening and treatment for malaria. Retrieved from http://www.who.int/malaria/publications/atoz/role-of-mda-for-malaria.pdf.

WHO 2016a, Artemisinin and artemisinin-based combination therapy resistance. Retrieved fromhttp://apps.who.int/iris/bitstream/10665/250294/1/WHO-HTM-GMP-2016.11-eng.pdf

CHAPTER 1

66

WHO 2016b, 'Eliminating malaria'. Retrieved from http://apps.who.int/iris/bitstream/10665/205565/1/WHO_HTM_GMP_2016.3_eng.pdf

WHO 2016c, World malaria report 2016, G WHO, Geneve: World Health Organization, http://apps.who.int/iris/bitstream/10665/252038/1/9789241511711-eng.pdf.

Wickramasinghe, SN & Abdalla, SH 2000, 'Blood and bone marrow changes in malaria', Baillieres Best Pract Res Clin Haematol, vol. 13, no. 2, pp. 277-299.

Witkowski, B, Amaratunga, C, Khim, N, Sreng, S, Chim, P, Kim, S, Lim, P, Mao, S, Sopha, C, Sam, B, Anderson, JM, Duong, S, Chuor, CM, Taylor, WR, Suon, S, Mercereau-Puijalon, O, Fairhurst, RM & Menard, D 2013, 'Novel phenotypic assays for the detection of artemisinin-resistant Plasmodium falciparum malaria in Cambodia: in-vitro and ex-vivo drug-response studies', Lancet Infect Dis, vol. 13, no. 12, pp. 1043-1049.

Zhou, S, Li, Z, Cotter, C, Zheng, C, Zhang, Q, Li, H, Zhou, S, Zhou, X, Yu, H & Yang, W 2016, 'Trends of imported malaria in China 2010-2014: analysis of surveillance data', Malar J, vol. 15, p. 39.

CHAPTER 2 – Submicroscopic and asymptomatic Plasmodium

parasitaemia associated with significant risk of anaemia in

Papua, Indonesia

67

RESEARCH ARTICLE

Submicroscopic and Asymptomatic Plas-

modium Parasitaemia Associated with Signi-

ficant Risk of Anaemia in Papua, Indonesia

Zuleima Pava1☯, Faustina H. Burdam2,3,4☯, Irene Handayuni1, Leily Trianty5

Retno A. S. Utami5, Yusrifar Kharisma Tirta5, Enny Kenangalem2,3, Daniel Lampah3,

Andreas Kusuma5, Grennady Wirjanata1, Steven Kho1, Julie A. Simpson6,

Sarah Auburn1, Nicholas M. Douglas1,7, Rintis Noviyanti5, Nicholas M. Anstey1,

Jeanne R. Poespoprodjo2,3,4, Jutta Marfurt1, Ric N. Price1,8*

1 Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University,

Darwin, Australia, 2 Mimika District Health Authority, Timika, Papua, Indonesia, 3 Timika Malaria Research

Programme, Papuan Health and Community Development Foundation, Timika, Papua, Indonesia,

4 Maternal and Child Health and Reproductive Health, Department of Public Health, Faculty of Medicine,

Universitas Gadjah Mada, Yogyakarta, Indonesia, 5 Eijkman Institute for Molecular Biology, Jakarta,

Indonesia, 6 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health,

The University of Melbourne, Melbourne, Australia, 7 Division of Medicine, Christchurch Hospital,

Christchurch, New Zealand, 8 Centre for Tropical Medicine and Global Health, Nuffield Department of

Clinical Medicine, University of Oxford, Oxford, United Kingdom

☯ These authors contributed equally to this work.

* rprice@menzies.edu.au

Abstract

Submicroscopic Plasmodium infections are an important parasite reservoir, but their clinical

relevance is poorly defined. A cross-sectional household survey was conducted in southern

Papua, Indonesia, using cluster random sampling. Data were recorded using a standardized

questionnaire. Blood samples were collected for haemoglobin measurement. Plasmodium

parasitaemia was determined by blood film microscopy and PCR. Between April and July

2013, 800 households and 2,830 individuals were surveyed. Peripheral parasitaemia was

detected in 37.7% (968/2,567) of individuals, 36.8% (357) of whom were identified by blood

film examination. Overall the prevalence of P. falciparum parasitaemia was 15.4% (396/2567)

and that of P. vivax 18.3% (471/2567). In parasitaemic individuals, submicroscopic infection

was significantly more likely in adults (adjusted odds ratio (AOR): 3.82 [95%CI: 2.49–5.86],

p<0.001) compared to children, females (AOR = 1.41 [1.07–1.86], p = 0.013), individuals not

sleeping under a bednet (AOR = 1.4 [1.0–1.8], p = 0.035), and being afebrile (AOR = 3.2

[1.49–6.93], p = 0.003). The risk of anaemia (according to WHO guidelines) was 32.8% and

significantly increased in those with asymptomatic parasitaemia (AOR 2.9 [95% 2.1–4.0],

p = 0.007), and submicroscopic P. falciparum infections (AOR 2.5 [95% 1.7–3.6], p = 0.002).

Asymptomatic and submicroscopic infections in this area co-endemic for P. falciparum and

P. vivax constitute two thirds of detectable parasitaemia and are associated with a high risk of

anaemia. Novel public health strategies are needed to detect and eliminate these parasite res-

ervoirs, for the benefit both of the patient and the community.

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 1 / 17

a11111

OPENACCESS

Citation: Pava Z, Burdam FH, Handayuni I, Trianty

L, Utami RAS, Tirta YK, et al. (2016)

Submicroscopic and Asymptomatic Plasmodium

Parasitaemia Associated with Significant Risk of

Anaemia in Papua, Indonesia. PLoS ONE 11(10):

e0165340. doi:10.1371/journal.pone.0165340

Editor: Takafumi Tsuboi, Ehime Daigaku, JAPAN

Received: July 26, 2016

Accepted: October 10, 2016

Published: October 27, 2016

Copyright: © 2016 Pava et al. This is an open

access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: All relevant data are

within the paper and its Supporting Information

files. The data used in this paper were collected

from a household survey, with IP owned by the

coauthors of this paper affiliated to the Papuan

Health and Community Development Foundation

and Menzies School of Health Research. Further

details can be obtained from the corresponding

author at rprice@menzies.edu.au. The authors

confirm that the data included within the paper and

its Supporting Information files constitute the

minimal dataset necessary to replicate the study’s

findings.

Introduction

Significant gains have beenmade over the last decade in the control and elimination of malaria.Early diagnosis, highly effective treatment and intense vector control have led to reductions inthe number of malaria cases, as well as severe disease and associatedmortality [1]. In 2014, 18countries in the Asia-Pacific committed to eliminate malaria from the region by 2030; however,the challenges to achieve this goal are immense [2]. The final stages of malaria elimination areoften prolonged, with low-grade transmission sustained by a parasite reservoir present inasymptomatic individuals whomay not readily engage or attend healthcare facilities to betreated. Novel strategies are needed to identify and eliminate these asymptomatic parasite res-ervoirs [3].

The established standard for malaria diagnosis in the field remains blood film examination.Lower limits of detection range from 10–20 parasites/μL in research settings to more than 100/μL in clinical practice [4]. Recent advances in molecular techniques have allowed the develop-ment of highly sensitive methods for detecting peripheral parasitaemia at submicroscopic levels(0.1–10 parasites/μL) [5–8]. The application of molecular approaches in surveillance surveyshas resulted in an inevitable increase in the prevalence of detectable parasitaemia, often 2–10fold higher than the prevalence obtained by conventional methods [9]. Althoughmost of theavailable data on subpatency have been derived from the studies of P. falciparum, similarobservations have beenmade for P. vivax [10, 11].

Transmission can occur from individuals with asymptomatic microscopic parasitaemia,many of whom are semi-immune and maintain low levels of parasitaemia and gametocytaemiafor sustained periods of time [12]. Submicroscopic parasitaemia can also be transmitted to themosquito vector (albeit less efficiently) and given the higher prevalence, contributes signifi-cantly to the overall infectious reservoir [13, 14].

The clinical relevance of submicroscopic infections is less clear. In cross-sectional surveys,low-level parasitaemia may represent early detection of an expanding parasite biomass thatwill ultimately result in clinical disease. However, repeated exposure and acquisition of partialimmunity can result in persistent low-grade infection, and although these individuals may nothave fever, chronic carriage can result in anaemia, bacterial co-infection and cognitiveimpairment [15]. In pregnancy, asymptomatic parasitaemia has also been shown to increasethe risk of maternal anaemia and the delivery of low birth weight babies [16, 17]. Thus, carriageof asymptomatic parasitaemia potentially has profound implications for the health of individu-als and for the control and elimination of malaria [18–20].

We conducted a large cross-sectional survey in southern Papua, Indonesia, an area co-endemic for P. falciparum and P. vivax, to identify the risk factors for microscopic and submi-croscopic carriage for both species, and thereby populations at greatest risk of harbouringundetected parasitaemia by conventional microscopic surveys.We also aimed to quantify thedegree of anaemia associated with asymptomatic and submicroscopic parasitaemia, to gaugethe clinical relevance of these low level infections.

Materials and Methods

Study Site

Timika, the main city of the Mimika District, is located in southern Papua province, Indonesia.It has a population of 202,350 composed of different ethnicities broadly including HighlandPapuans and Lowland Papuans, and Non-Papuans. Nearly 90% of the population lives in low-land areas where unstable malaria transmission occurs. In 2013, the annual incidence of parasi-taemia was 450/1000, with P. falciparum and P. vivax causing 60% and 40% of cases,

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 2 / 17

Funding: The study was supported by the

Wellcome Trust (Senior Fellowship in Clinical

Science awarded to RNP - 091625) and

Fellowships to: JRP (Wellcome Trust - 099875),

JM (The Swiss National Science Foundation - -

PA00P3-139723/1), ZP (Instituto Colombiano para

el Desarrollo de la Ciencia y la Tecnologıa Francisco

Jose de Caldas, Colciencias - 512-2010) and NMA

(National Health and Medical Research Council

(NHMRC) of Australia – 1042072). The Timika

Research Facility and Papuan Health and

Community Development Foundation were

supported by DFAT (Australian Department of

Foreign Affairs and Trade) and the NHMRC

(Program Grant 1037304).

Competing Interests: The authors have declared

that no competing interests exist.

respectively [21], without significant seasonal fluctuation in incidence. Patients with uncompli-cated malaria due to any Plasmodium species are treated with dihydroartemisinin-piperaquine(DHP) as the first-line treatment regimen.

Cross-Sectional Survey Methods

Sample selection. Households were chosen by cluster random sampling. First, the fourlargest subdistrictswere chosen purposively. Consecutively, the numbers of cluster per districtwas calculated according to their respective relative population. Finally, 25 houses were chosenrandomly within each cluster, followingWHO recommendations. Clusters constituted wholeexisting villages, except in Mimika Baru subdistrict where large populated villages had to bedivided into census blocks.

Data and sample collection. People living in the house, eating from the same kitchen, andresiding in the study area for at least 6 months were defined as household members and wereincluded in the study. Socio-demographic information and medical history including fever epi-sodes in the last 24 hours and in the last month were recorded from all the members through astandardized questionnaire. Physical examination including weight, height, mid-upper armcircumference, axillary temperature and blood pressure were assessed immediately after com-pletion of the questionnaire. Venous blood from one adult and 200μl capillary blood collectedin a Microtainer™ from the remaining members of each household were collected and used forblood film examination, haemoglobinmeasurement, G6PD testing, and molecular analysis.Participants with a positive blood smear for Plasmodium spp. were treated with DHP. Thosewith anaemia were given iron supplementation according to local protocols.

Laboratory Methods

Parasite species was assessed from Giemsa-stained thick blood films. Peripheral parasitaemiawas determined from the number of parasites per 200 white blood cells, assuming a white cellcount of 7,300 cells/μL. All positive films and 10% of the negative slides were cross-checked bya secondmicroscopist at the Eijkman Institute for Molecular Biology in Jakarta and discrepan-cies reviewed by two expert microscopists for final assessment. Haemoglobin levels were deter-mined using a calibrated portable HemoCue1 machine (Ångelholm, Sweden). G6PD statuswas assessed using the G-6-PDHDeficiency Screen by Spot Test Kit1 (Trinity Biotech).

Genomic DNA (gDNA) was extracted from�50 μL packed red blood cell (RBC) pelletsusing the QIAamp 96 DNA BloodKit (Qiagen). Plasmodium species confirmation was under-taken in duplicate with 2 μL gDNA template using a nested PCR protocol as described else-where [22]. P. falciparum, P. vivax, P. malariae, and P. ovale small-subunit rRNA DNA clones(MRA-177, MRA-178, MRA-179, and MRA-180; ATCC, Manassas, VA) were used as posi-tive-control materials. The limit of detection (LOD) of the assay was 0.2–2 parasites/μL, asevaluated by using well characterized samples of P. falciparum and P. vivax (Piera et al. manu-script in preparation). If a duplicate result was discrepant, the assay was repeated. A samplewas positive when 2/4 replicates were positive. In case of a 1/4 positivity rate, the assay wasrepeated a third time and the sample categorized as positive if 2/6 total replicates were positive,but negative if 1/6 replicates were positive.

Definitions used in the study

An individual was diagnosed as aparasitaemic if both the microscopy and PCR result werenegative. Microscopic (patent) parasitaemia was defined in those participants with positivemicroscopy irrespective of PCR confirmation, assuming that these patients would be detectedat a clinical encounter. Submicroscopic (subpatent) parasitaemia was defined in participants

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 3 / 17

who were microscopy negative, but positive PCR result. Where there was discrepancy betweenthe species determined by microscopy and PCR, the diagnosis was classified according to thePCR result. Symptomatic malaria was defined as a positive malaria result by microscopy orPCR associated with a fever or history of fever in the last 24 hours.

Anaemia was diagnosed according toWHO guidelines, defined as a haemoglobin (Hb) con-centration less than 11 g/dL in children under 5 years old or pregnant women, less than 11.5 g/dL in children between 5–11 years, less than 12 g/dL in children between 12–14 years old oradult women (>15years) and less than 13 g/dL in adult men. Moderate and severe anaemiawas diagnosedwhen the Hb concentration was less than 10g/dL and 7g/dL in children under 5years old or pregnant women and less than 11g/dL and 8g/dL respectively for the remaininggroups. The remaining cases of anaemia were classified as mild [23].

Statistical Analyses

Questionnaire and laboratory data were entered into EpiData 3.02 software (EpiData Associa-tion, Odense, Denmark). Data were analysed using SPSS v.20.0 for windows software (IBMSPSS Statistics) and STATA 14.1 software (Stata Corp, College Station, TX). Normally distrib-uted data were compared by Student’s t-test and data not conforming to a normal distributioncompared by the Mann-Whitney U test. Categorical data were compared by chi-squared testwith Yates' correction, or by Fisher's exact test. A multivariable logistic regression model (withrobust standard errors to account for clustering by the 16 villages and within household) wasused to determine adjusted odds ratios for risk factors associated with anaemia and subpatency.To identify risk factors for the multivariable logistic regression model, variables associated sig-nificantly with anaemia or subpatency in a univariable analysis were entered into the equationand the model constructed by a forward stepwise analysis. Analyses for the outcome subpa-tency were restricted to those individuals with detectable parasitaemia.Multiple fractionalpolynomial models were used to illustrate the nonlinear association between age and the logodds of detectable parasitaemia, subpatency and anaemia.

Ethics

All biological samples and patient data were collectedwith written, informed consent from thepatient or a parent or guardian. Ethical approval for this study and the process of informedconsent, was obtained from the Eijkman Institute Research Ethics Commission, Eijkman Insti-tute for Molecular Biology, Jakarta, Indonesia, (Ref: KE/FK/763/EC) and the Human ResearchEthics Committee of the Northern Territory (NT) Department of Health & Families and Men-zies School of Health Research, Darwin,Australia (Ref: HREC-2010-1434).

Results

BetweenApril and July 2013, 800 households were surveyed comprising 4,010 individuals ofwhom 2,830 (70.6%) were present at the time of interview. Absentee individuals (n = 1180)were more likely to be adult males 52.3%, (617) or school age children 5–15 years 25.7%, (304).In total, 2,796 of 2,830 (99.8%) participants underwent blood film examination (S1 Fig).Twenty one infants under 5 year old, 2 children aged 5 to 15 years, 3 adults and another withunknown age declined finger prick sampling. The characteristics of household individualspresent and sampled during the survey and those absent from the household are presented inS1 Table.

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 4 / 17

Microscopy and PCR Confirmation of Parasitaemia

The age distribution of the 2,796 participants peaked in infancy and early adulthood (Fig 1A),with 59.2% (1654) of the individuals females and 45.6% (1,275) indigenous Papuans (19.2%(536) Highlanders and 26.4% (739) Lowlanders).

Peripheral asexual parasitaemia was diagnosed by microscopy in 12.2% (340) of partici-pants, with P. falciparum mono-infection present in 145 (5.2%), P. vivax in 160 (5.7%),

Fig 1. Age stratified prevalence of malaria diagnosed by microscopy and PCR. Microscopy (A) and

microscopy and PCR (B).

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P. malariae in 24 (0.9%) and mixed species infections in 11 (0.4%; Table 1). Gametocytes werepresent in 2.2% (61/2,796) of individuals; with 88.9% (8/9) of P. vivax gametocytes presentwith asexual stages, compared to only 66.7% (32/48) of P. falciparum gametocytes (p<0.001).The geometricmean asexual parasitaemia was 738 parasites/μL [95% CI: 543–1,001] for P. fal-ciparum and 459 parasites/μL [95% CI: 360–586] for P. vivax. Fever during the last month wasreported in 5.5% (8/145) of those with P. falciparum, 5.0% (8/160) of those with P. vivax, and3.8% (94/2,456) of those negative by microscopy (Table 1).

In total, 12.8% (357/2,796) of individuals had parasitaemia (either asexual or sexualstages) detectable by microscopy; Table 1 and S1 Fig. PCR analysis was available in 88.6%(2,476/2,796) of cases, although 91 of the remaining individuals were positive by microscopyand their microscopic parasitaemia categorized accordingly. Species was confirmed by PCRin 88.7% (236/266) individuals with microscopic parasitaemia. In the remaining 30 cases, 11P. falciparum infections were re-categorized as P. vivax by PCR, 10 P. vivax as P. falciparum,4 P. malariae as P. falciparum, 2 mixed infections as P. falciparum mono-infections, and 2mixed infections as P. vivax mono-infections. The remaining individual had P. malariaedetected by microscopy, but was negative by PCR and was categorized as having microscopicP. malariae with a false-negative PCR result. The characteristics of the 2,567 participantswho could be classified by both microscopy and PCR are presented in Table 1 and the agedistribution in Fig 1B.

Table 1. Malaria diagnosis and demographic features of survey participantsa

Sex Pregnancy Status b Age-Group (Years) Ethnicity All

n (%) Male Female Non-

Pregnant

Pregnant <1 1–5 5–15 >15 HighlandPapuan Lowland

Papuan

Non-

Papuan

Microscopic Parasitaemia (Blood Film Positive) c

P.

falciparum

78 (7.6) 88 (5.7) 48 (4.9) 3 (6.7) 2 (4.3) 19

(5.4)

57

(9.5)

88 (5.6) 40 (8.1) 74 (10.7) 52 (3.8) 166

(6.5)

P. vivax 76 (7.4) 88 (5.7) 38 (3.9) 2 (4.4) 4 (8.7) 43

(12.2)

53

(8.8)

64 (4.1) 26 (5.3) 51 (7.4) 87 (6.3) 164

(6.4)

P. malariae 13 (1.3) 7 (0.5) 5 (0.5) 0 (0.0) 0 (0.0) 1 (0.3) 3 (0.5) 16 (1.0) 8 (1.6) 6 (0.9) 6 (0.4) 20 (0.8)

Mixed

species

3 (0.3) 4 (0.3) 1 (0.1) 0 (0.0) 0 (0.0) 2 (0.6) 3 (0.5) 2 (0.1) 3 (0.6) 4 (0.6) 0 (0.0) 7 (0.3)

Submicroscopic Parasitaemia (Blood Film Negative and PCR Positive)

P.

falciparum

92 (8.9) 138 (9.0) 91 (9.3) 6 (13.3) 0 (0.0) 11

(3.1)

55

(9.2)

164

(10.5)

74 (14.9) 75 (10.9) 81 (5.9) 230

(9.0)

P. vivax 114

(11)

193

(12.6)

129 (13.2) 2 (4.4) 3 (6.5) 25

(7.1)

89

(14.8)

190

(12.1)

55 (11.1) 98 (14.2) 154

(11.1)

307

(12.0)

P. ovale 0 (0.00) 1 (0.07) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.1) 0 (0.0) 1 (0.2) 0 (0.0) 0 (0.0) 1 (0.04)

P. malariae 4 (0.4) 12 (0.8) 11 (1.1) 0 (0.0) 0 (0.0) 0 0.0) 3 (0.5) 13 (0.8) 9 (1.8) 4 (0.6) 3 (0.2) 16 (0.6)

Mixed

species

25 (2.4) 32 (2.1) 22 (2.2) 0 (0.0) 0 (0.0) 3 (0.8) 14

(2.3)

40 (2.6) 12 (2.4) 30 (4.4) 15 (1.1) 57 (2.2)

Blood Film Negative and PCR Negative

628

(60.8)

971

(63.3)

633 (64.7) 32 (71.1) 37

(80.4)

249

(70.5)

322

(53.7)

991

(63.2)

267 (53.9) 347 (50.4) 985

(71.2)

1,599

(62.3)

Total 1,033

(40.2)

1,534

(59.8)

978 (38.1) 45 (1.8) 46

(1.8)

353

(13.8)

600

(23.4)

1,568

(61.1)

495(19.3) 689 (26.8) 1,383

(53.9)

2,567

(100)

n = number.a All survey participants with a blood sampleb 22 adult women without negative PCR resultc Following reclassification of species with PCR result and inclusion of 17 infections with only sexual stages present.

doi:10.1371/journal.pone.0165340.t001

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In total, 37.7% (968/2,567) individuals had detectable parasitaemia by microscopy or PCR,the prevalence being greatest in children aged 5–15 years (46.3%, 278/600) compared to 28.3%(113/399) in children<5 years, and 36.8% (577/1,568) in adults (p<0.001); Table 1, Figs 1 and2A. The odds of detectable parasitaemia was greater in Papuans compared to Non-Papuans(OR = 2.30 [95%CI: 1.95–2.70]; p<0.001). There was no overall difference in the risk of parasi-taemia between individuals according to sex, pregnancy, fever in the last month, or bednetownership. Detectable parasitaemia was significantly less prevalent in the 66 (2.6%) individualswith severe or intermediate G6PD deficiency compared to those who were G6PD normal(OR = 0.44 [95%CI: 0.24–0.80]; p = 0.005). The protective effect of G6PD deficiencywas moreapparent in P. vivax infection (OR = 0.32 [95%CI: 0.13–0.81]; p = 0.010) than in P. falciparum(OR = 0.46 [95%CI: 0.20–1.08]; p = 0.067).

Risk factors for submicroscopic infections

Risk factors for subpatency were assessed in 968 individuals with detectable parasitaemia, ofwhom 611 (63%) had submicroscopic infection (Table 1). P. vivax parasitaemia was more likelyto be submicroscopic (65.2%, 307/471) compared to P. falciparum (58%, 230/396; (OR = 1.35[95%CI: 1.03–1.78], p = 0.038). P. malariae parasitaemia was submicroscopic in 44% (16/36) ofparticipants.

Overall the risk of submicroscopic infection in those with detectable parasitaemia increasedsteadily with age (37% (42/113) in children<5, 58% (162/278) in children 5–15 years, and 71%(407/577) in adults; p<0.001, Table 2). However, the association between subpatency and agewas significant for P. falciparum (χ2 test for trend, p<0.001), but not P. vivax (p = 0.524; Fig2B). There was a higher risk of submicroscopic infections in females compared to males(OR = 1.5 [95% CI: 1.1–1.9]; p = 0.006) and in those who did not own a bednet compared tothose who did (OR = 1.4 [95% CI: 1.0–1.8, p = 0.025]; Table 2).

Fever during the precedingmonth was reported by in 4% (103/2567) of participants. Only3.3% (32/968) of individuals with detectable parasitaemia reported fever in the preceding 24hours, of whom the parasitaemia was microscopic in 65.6% (21/32) and submicroscopic in34.4% (11/32) (p = 0.001). Those with a fever or history of fever in the preceding 24 hours weremore likely to be parasitaemic than those without parasitaemia (OR: 2.8 [95%CI: 1.6–5.0];p<0.001); Table 2.

Risk factors for anaemia

In total haemoglobin concentration was measured in 2757 individuals, of whom 2564 (93.0%)also had documentation of both microcscopic and submicroscopic parasitaemia. In 193 casesno sample was collected for PCR. Haemoglobin (Hb) concentrations varied significantly withgender, age, ethnicity, G6PD status, and malaria species (Table 3 and Fig 3). Anaemia was pres-ent in 32.8% (904/2757) of the participants and categorized as mild in 13.1% (365), moderatein 15.8% (443) and severe in 3.4% (96) (S2 Table). The risk of anaemia was greater in childrencompared to adults (OR = 1.4 [95% CI: 1.2–1.7]; p<0.001), Papuans compared to Non-Papu-ans (OR = 4.05 [95% CI: 3.4–4.8]; p<0.001), and females compared to males (OR = 1.18 [95%CI: 1.0–1.4]; p = 0.039) (Table 3).

Individuals with detectable parasitaemia were more likely to be anaemic than aparasitaemicindividuals (OR = 2.7 [95% CI: 2.1–3.4]; p<0.001). In those with microscopic infection, theperipheral parasitaemia of any species was negatively correlated with the haemoglobin concen-tration (r = -0.184; p = 0.001), with a geometricmean parasitaemia of 739 μl-1 [95%CI 560–974]) in anaemic individuals compared to 407μl-1 [95%CI 327–507] in non-anaemic individu-als; p = 0.001. Combiningmicroscopic and submicroscopic infections, P. falciparum was

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PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 7 / 17

associated with a significantly lower mean Hb concentration (mean = 11.9 g/dL) compared toP. vivax (mean = 12.6 g/dL); p = 0.003), but similar to P. malariae (mean = 11.9 g/dL) andmixed infections (mean = 11.9 g/dL); Table 3.

Fig 2. Multiple fractional polynomial curves showing the probability of P. falciparum and P. vivax

parasitaemia. Probability of parasitaemia by age (A) and probability that detectable P. falciparum and P.

vivax parasitaemia is submicroscopic by age (B).

doi:10.1371/journal.pone.0165340.g002

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After controlling for age, sex and ethnicity, individuals with either microscopic or submicro-scopic P. falciparum parasitaemia had a greater risk of anaemia compared to aparasitaemicindividuals (AOR = 2.9 [95% CI: 1.8–4.6]; p<0.001) and 1.9 [95% CI: 1.4–2.7]; p<0.001,respectively; Table 4 and Fig 4). However, in those with P. vivax infections, only those withmicroscopic parasitaemia had an increased risk of anaemia (AOR = 1.8 [95% CI: 1.1–3.0]; p =0.016). These trends were similar when restricting the analysis to severe or moderate anaemia(S3 Table). The adjusted risks were also similar after excluding 50 individuals with history offever within the preceding 24 hours (S4 Table).

Discussion

Our study highlights that microscopic blood film examination alone detected only 37% (357/968) of individuals with parasitaemia in this cross sectional survey. Whilst the overall parasiteprevalence was greatest in children and Papuan highlanders, the proportion of submicroscopicinfections was greatest in older participants, females and those who did not own a bednet. Only3.3% of participants reported a fever, but asymptomatic microscopic parasitaemia with either

Table 2. Risk factors for submicroscopic infection in 968 individuals with detectable parasitaemia.

Characteristics Submicroscopic% (n/total) Univariable Analysis Multivariable Analysis

Crude OR [95% CI] p AOR [95% CI] p

Age

<5 years 37.2 (42/113) Reference Reference

5 to 15 years 58.3 (162/278) 2.36 [1.51–3.70] <0.001 2.35 [1.6–3.5] <0.001

>15 years 70.5 (407/577) 4.04 [2.65–6.17] <0.001 3.82 [2.2–6.7] <0.001

Gender

Male 58 (235/405) Reference Reference

Female 66.8 (376/563) 1.5 [1.1–1.9] 0.006 1.41 [1.1–1.8] 0.002

Ethnicity

Non-Papuan 63.6 (253/398) Reference

Highland Papuan 66.2 (151/228) 0.9 [0.6–1.2] 0.544

Lowland Papuan 60.5 (207/342) 1.1 [0.8–1.5] 0.404

Bednet use

Yes 58.3 (197/338) Reference Reference

No 65.7 (414/630) 1.4 [1.0–1.8] 0.025 1.4[0.9–2.1] 0.199

G6PD status a

Normal 63.3 (602/951) Reference

Deficient 64.3 (9/14) 1.0 [0.3–2.9] 1

Fever in the last 24hr

Yes 34.4 (11/32) Reference Reference

No 64.1 (600/936) 3.4 [1.6–7.1] 0.001 3.2 [1.6–6.5] 0.001

Fever in last month

Yes 52.6% (20/38) Reference

No 63.5% (591/930) 1.6 [0.8–3.0] 0.117

Pregnancy b

Non Pregnant 73.3 (253/345) Reference

Pregnant 61.5 (8/13) 0.58 [0.2–1.8] 0.35

Abbreviations: CI = confidence interval; OR = odds ratio; AOR = adjusted odds ratio; n = number.a Data missing on 3 casesb In 358 adult women.

doi:10.1371/journal.pone.0165340.t002

Submicroscopic Plasmodium Parasitaemia

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P. falciparum or P. vivax was associated with a three-fold increased risk of anaemia comparedto aparasitaemic individuals. Similar to previous studies of both clinical malaria and asymp-tomatic infection, the degree of parasitaemia was correlated with anaemia [15, 24]. Whilstthese findings are being increasingly recognised and reported, our study strengthens this rela-tionship by demonstrating that the risk of anaemia, including that of moderate severe anaemia,extends to those with submicroscopic parasitaemia, for P. falciparum but not for P. vivax.

Plasmodium density can be controlled by acquired host immunity; hence, individuals whoare repeatedly exposed to malaria have lower parasite densities compared to less-exposed indi-viduals from the same region[25]. In our survey, older participants were almost 4 times more

Table 3. Haemoglobin concentration of 2757 individuals according to risk groups.

Risk factors Total Mean Hb g/dl Anaemic % (n) Univariable Analysis p

(SD) Crude OR [95% CI]

Age (years)

<5 497 11.2(2.0) 42.5 (211) Reference

5-15y 661 12.1(2.17) 33.3 (220) 0.7 [0.5–0.9] 0.002

>15 1599 13.2(2.62) 29.6 (473) 0.6 [0.5–0.7] <0.001

Gender

Male 1123 12.9(2.7) 30.5 (343) Reference 0.039

Female 1634 12.3(2.42) 34.3 (561) 1.18 [1.0–1.4]

Ethnicity

Non-Papuan 1501 13.3(2.2) 19.2 (288) Reference

Highland Papuan 529 11.6(3.0) 52.7 (279) 4.70[3.8–5.8] <0.001

Lowland Papuan 727 11.9(2.5) 46.4 (337) 3.63[3.0–4.4] <0.001

G6PD status

Normal 2685 12.6(2.55) 33 (887) Reference 0.099

Deficient 72 13.5(2.4) 23.6 (17) 0.63 [0.4–1.1]

Bednet used

Yes 956 12.5(2.6) 35.0 (335) Reference 0.067

No 1801 12.6(2.5) 31.6 (569) 1.17 [1.0–1.4]

Fever in the last 24hr

Yes 54 12.4(3.6) 42.6 (23) Reference 0.143

No 2703 12.6(2.5) 32.6 (881) 1.5 [0.9–2.6]

Fever in last month

Yes 112 12.3(2.6) 42.0 (47) Reference 0.040

No 2645 12.6(2.5) 32.4 (857) 1.5 [1.0–2.2]

Pregnancy

Non Pregnant 1000 12.7(2.5) 32.1 (321) Reference 0.103

Pregnant 45 11.1(2.31) 44.4 (20) 1.69 [0.9–3.1]

Plasmodium species *

Negative 1599 12.8 (2.4) 27.6 (442) Reference

P. falciparum 394 11.9 (3.0) 51 (201) 2.7 [2.2–3.4] <0.001

P. vivax 470 12.6 (2.6) 32.3 (152) 1.3 [1–1.6] 0.049

P. malariae 36 11.9 (2.9) 47.2 (17) 2.3 [1.2–4.5] 0.014

P. ovale 1 9.1 100 (1) - -

Mixed infections 64 11.9 (3.3) 48.4 (31) (6) 2.5 [1.5–4.1] 0.001

Abbreviations: n = number; Hb = haemoglobin; SD = standard deviation; OR = Odds ratio.

* 193 cases did not have microscopic or PCR diagnosis.

doi:10.1371/journal.pone.0165340.t003

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 10 / 17

likely to harbor submicroscopic infections than young children who had had less exposure toinfection. The odds of submicroscopy was also significantly, albeit marginally, higher in thosewith P. vivax parasitaemia versus P. falciparum infection (OR = 1.35). Similar observationshave been reported by other studies in areas of low transmission in the Solomon Islands, Thai-land and Myanmar [20, 26] and likely reflect the earlier development of immunity followingrecurrent P. vivax infection compared to P. falciparum [27], as well as the lower peripheralparasitaemias associated with P. vivax [28]. An increasing body of work emphasizes that theproportion of detectable submicroscopic infections varies hugely, dependent upon the sensitiv-ity of the underlyingmethodology [5]. Critical factors include the sample volume, collectionand storage method, amplification method, and abundance of the target genetic material [20,29]. Our assay was able to detect peripheral parasitaemias as low as 0.2 parasites/μL, similar tothe sensitivity reported from previous studies using 200μl of capillary blood [20]. Individualswith asymptomatic and submicroscopic infections at these levels have been shown to be capa-ble of infectingmosquitoes [9]. Hence, if elimination is to be achieved in a timely manner, suit-able strategies are needed to actively identify and treat all infected individuals or populations,rather than passively waiting for symptomatic patients to present to healthcare facilities. Sev-eral public health measures have been proposed.Mass screen and test (MST) relies on rapidmicroscopic diagnosis or rapid diagnostic tests (RDTs) to identify parasitaemic individuals inneed of treatment, including both symptomatic and asymptomatic infections [30]. However, inview of the high proportion of submicroscopic infections,MST will eliminate only a fraction ofthe parasite reservoir; thus its impact on transmission is likely to be limited [31]. Althoughdiagnosis can be improved using more sensitive field-adapted diagnostics such as LAMP [32],

Fig 3. Multiple fractional polynomial curves showing the haemoglobin concentration according to

microscopic and submicroscopic parasitaemia and age.

doi:10.1371/journal.pone.0165340.g003

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 11 / 17

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3.4

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0165340.t004

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 12 / 17

the sensitivity of these methods will also still miss the majority of individuals with low levelparasitaemia[20].

Alternative strategies are needed to identify individuals and populations at greatest risk ofinfection using highly sensitive diagnostics so that community-based interventions can then beapplied. Interventions such as mass drug administration (MDA) or targeted malaria elimina-tion (TME) have been proposed [31], but these are extremely challenging since they usuallyrequire three days of antimalarial treatment administered to individuals who are eitherunaware that they are infected, or are not infected at all. This has cost implications, as well asexposing apparently well individuals to potential drug-related adverse effects. Rather than per-sonal gain, the individual incentive to comply is, therefore, either altruistic (i.e., for the benefitof the community), or to gain some protection against malaria in the future. Our study pro-vides evidence to mitigate this dilemma, by demonstrating that low-levels infections were asso-ciated with a significantmorbidity, a three-fold increased risk of anaemia. Individuals withsubmicroscopic P. falciparum infectionwere also at greater risk of anaemia. Although in ourstudy, anaemia was not associated with submicroscopic P. vivax parasitaemia, this was beenreported previously from Brazil [33]. Treatment of patients with asymptomatic microscopicand submicroscopic parasitaemia, therefore, provides potential benefit for both the individualand the community.

Our observations could not be explained by recent symptomatic illness since fever in theprecedingmonth was reported in only 4% (103/2567) of individuals. Rather the anaemia inparasitaemic individuals is likely to have arisen from a combination of low-grade chronichemolysis, dyserythropoiesis, and reduced iron absorption [34–37]. Interestingly, detectableparasitaemia was significantly less prevalent in individuals with severe or intermediate G6PD

Fig 4. Multiple fractional polynomial curves showing the probability of anaemia by microscopic and

submicroscopic parasitaemia and age.

doi:10.1371/journal.pone.0165340.g004

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 13 / 17

deficiency (OR = 0.44), and overall the risk of moderate anaemia was significantly higher inG6PD normal individuals (OR = 6.7 [1.6–28]; p = 0.001). Hence, whilst G6PD deficiencyincreases the risk of oxidative stress and drug-inducedhaemolysis, this appears to be counter-balanced by its protective effect against malaria-attributable anaemia.

The degree of P. falciparum associated anaemia is a function of peripheral parasitaemia [24]and this was apparent in our Papuan study with anaemia increasing with higher peripheralparasitaemia. In P. vivax infections, the level of parasitaemia is significantly lower than thatwith P. falciparum and anaemia appears to be related more closely to recurrent and chronicinfection [34, 35]. Although we were able to quantify the level of microscopic parasitaemia bymicroscopy, we did not use a semi-quantitative PCR assay to determine the degree of submi-croscopic parasitaemia [26, 38, 39]. It is plausible that in submicroscopic P. vivax infections thelevel of parasitaemia, whilst detectable by PCR, was at levels too low to cause significant hae-molysis. Our study was also limited by being restricted to a single cross sectional observationwith almost 30% of household members absent at the time of the survey. Prospective longitudi-nal studies will be needed to define better the persistence of submicroscopic infections overtime and the associated of with recurrent parasitaemia. Since those individuals absent at thetime of the surveywere more likely to be adult males with a greater risk of submicroscopicinfection, our estimates of parasite prevalence and the proportion of parasitaemia undetectedby microscopy are likely to be conservative.

In conclusion, the prevalence of parasitaemia determined by PCR was approximately twicethat detected by microscopy. Althoughmost participants in the study were asymptomatic,those with any parasitaemia, including submicroscopic infections, were at significant risk ofanaemia. Our analysis identified groups at significantly greater risk of submicroscopic infec-tion, who should be targeted in surveys utilising ultrasensitive diagnostics aiming to quantifyhidden reservoirs of infection. The magnitude of submicroscopic infections detected by ourstudies and others highlights the urgent need to develop new public health strategies andappropriate community-based interventions for the elimination of malaria.

Supporting Information

S1 Data.(CSV)

S1 Fig. Study profile.(TIF)

S1 Table. Characteristicsof household individuals present and sampled during the surveyand those absent from the household.(DOCX)

S2 Table. The distribution and severity of anaemia.(DOCX)

S3 Table. Adjusted odds ratios for severe or moderate anaemia in symptomatic and asymp-tomatic malaria.(DOCX)

S4 Table. Adjusted odds ratios for severe or moderate anaemia in asymptomatic malariastratified by age group.(DOCX)

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 14 / 17

Acknowledgments

We are grateful to Mimika District Government and Lembaga Pengembangan MasyarakatAmungme Kamoro. We would also like to thank the study participants for their significantcontribution to the study. We thankMr. Abdul Muis (The Regent of Mimika District),Mr.Erens Meokbun (The Head of District Health Authority), Professor Adi Utarini and Dr. YodiMahendradhata from University Gadjah Mada, Yogyakarta for their excellent support to thisstudy.

Author Contributions

Conceptualization:RNP JRP JM RN SA.

Formal analysis:ZP RNP NMD JS JM SA.

Investigation: ZP IH RAU YKT AK SK GW.

Writing – original draft:ZP RNP JM SA.

Writing – review& editing: ZP FHB IH LT RAU YKT EK DL AK GW SK JAS SA NMDRNNMA JRP JM RNP.

References1. World Health Organisation. World Malaria Report: 2014. Geneva: WHO. 2015.

2. Vivax Working G. Targeting vivax malaria in the Asia Pacific: The Asia Pacific Malaria Elimination Net-

work Vivax Working Group. Malaria journal. 2015; 14(1):484. doi: 10.1186/s12936-015-0958-y PMID:

26627892; PubMed Central PMCID: PMC4667409.

3. Cotter C, Sturrock HJ, Hsiang MS, Liu J, Phillips AA, Hwang J, et al. The changing epidemiology of

malaria elimination: new strategies for new challenges. Lancet. 2013; 382(9895):900–11. doi: 10.

1016/S0140-6736(13)60310-4 PMID: 23594387.

4. Wongsrichanalai C, Barcus MJ, Muth S, Sutamihardja A, Wernsdorfer WH. A review of malaria diag-

nostic tools: microscopy and rapid diagnostic test (RDT). The American journal of tropical medicine

and hygiene. 2007; 77(6 Suppl):119–27. PMID: 18165483.

5. Bousema T, Okell L, Felger I, Drakeley C. Asymptomatic malaria infections: detectability, transmissibil-

ity and public health relevance. Nature reviews Microbiology. 2014; 12(12):833–40. doi: 10.1038/

nrmicro3364 PMID: 25329408.

6. Hofmann N, Mwingira F, Shekalaghe S, Robinson LJ, Mueller I, Felger I. Ultra-sensitive detection of

Plasmodium falciparum by amplification of multi-copy subtelomeric targets. PLOS medicine. 2015; 12

(3):e1001788. doi: 10.1371/journal.pmed.1001788 PMID: 25734259; PubMed Central PMCID:

PMC4348198.

7. Imwong M, Hanchana S, Malleret B, Renia L, Day NP, Dondorp A, et al. High-throughput ultrasensitive

molecular techniques for quantifying low-density malaria parasitemias. Journal of clinical microbiology.

2014; 52(9):3303–9. doi: 10.1128/JCM.01057-14 PMID: 24989601; PubMed Central PMCID:

PMC4313154.

8. Adams M, Joshi SN, Mbambo G, Mu AZ, Roemmich SM, Shrestha B, et al. An ultrasensitive reverse

transcription polymerase chain reaction assay to detect asymptomatic low-density Plasmodium falcip-

arum and Plasmodium vivax infections in small volume blood samples. Malaria journal. 2015; 14

(1):520. doi: 10.1186/s12936-015-1038-z PMID: 26701778; PubMed Central PMCID: PMC4690410.

9. Okell LC, Bousema T, Griffin JT, Ouedraogo AL, Ghani AC, Drakeley CJ. Factors determining the

occurrence of submicroscopic malaria infections and their relevance for control. Nature communica-

tions. 2012; 3:1237. doi: 10.1038/ncomms2241 PMID: 23212366; PubMed Central PMCID:

PMC3535331.

10. Moreira CM, Abo-Shehada M, Price RN, Drakeley CJ. A systematic review of sub-microscopic Plas-

modium vivax infection. Malaria journal. 2015; 14(1):360. doi: 10.1186/s12936-015-0884-z PMID:

26390924; PubMed Central PMCID: PMC4578340.

11. Cheng Q, Cunningham J, Gatton ML. Systematic review of sub-microscopic P. vivax infections: preva-

lence and determining factors. PLOS neglected tropical diseases. 2015; 9(1):e3413. doi: 10.1371/

journal.pntd.0003413 PMID: 25569135; PubMed Central PMCID: PMC4288718.

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 15 / 17

12. Roper C, Elhassan IM, Hviid L, Giha H, Richardson W, Babiker H, et al. Detection of very low level

Plasmodium falciparum infections using the nested polymerase chain reaction and a reassessment of

the epidemiology of unstable malaria in Sudan. The American journal of tropical medicine and hygiene.

1996; 54(4):325–31. PMID: 8615441.

13. Coleman RE, Kumpitak C, Ponlawat A, Maneechai N, Phunkitchar V, Rachapaew N, et al. Infectivity of

asymptomatic Plasmodium-infected human populations to Anopheles dirus mosquitoes in western

Thailand. Journal of medical entomology. 2004; 41(2):201–8. PMID: 15061279.

14. Nwakanma D, Kheir A, Sowa M, Dunyo S, Jawara M, Pinder M, et al. High gametocyte complexity and

mosquito infectivity of Plasmodium falciparum in the Gambia. International journal for parasitology.

2008; 38(2):219–27. doi: 10.1016/j.ijpara.2007.07.003 PMID: 17709108.

15. Chen I, Clarke SE, Gosling R, Hamainza B, Killeen G, Magill A, et al. "Asymptomatic" Malaria: A

Chronic and Debilitating Infection That Should Be Treated. PLOS medicine. 2016; 13(1):e1001942.

doi: 10.1371/journal.pmed.1001942 PMID: 26783752; PubMed Central PMCID: PMC4718522.

16. Cottrell G, Moussiliou A, Luty AJ, Cot M, Fievet N, Massougbodji A, et al. Submicroscopic Plasmodium

falciparum Infections Are Associated With Maternal Anemia, Premature Births, and Low Birth Weight.

Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2015;

60(10):1481–8. doi: 10.1093/cid/civ122 PMID: 25694651.

17. Mohammed AH, Salih MM, Elhassan EM, Mohmmed AA, Elzaki SE, El-Sayed BB, et al. Submicro-

scopic Plasmodium falciparum malaria and low birth weight in an area of unstable malaria transmission

in Central Sudan. Malaria journal. 2013; 12:172. doi: 10.1186/1475-2875-12-172 PMID: 23714259;

PubMed Central PMCID: PMC3671167.

18. Okell LC, Griffin JT, Kleinschmidt I, Hollingsworth TD, Churcher TS, White MJ, et al. The potential con-

tribution of mass treatment to the control of Plasmodium falciparum malaria. PLOS one. 2011; 6(5):

e20179. doi: 10.1371/journal.pone.0020179 PMID: 21629651; PubMed Central PMCID:

PMC3101232.

19. Mosha JF, Sturrock HJ, Greenhouse B, Greenwood B, Sutherland CJ, Gadalla N, et al. Epidemiology

of subpatent Plasmodium falciparum infection: implications for detection of hotspots with imperfect

diagnostics. Malaria journal. 2013; 12:221. doi: 10.1186/1475-2875-12-221 PMID: 23815811; PubMed

Central PMCID: PMC3701503.

20. Imwong M, Nguyen TN, Tripura R, Peto TJ, Lee SJ, Lwin KM, et al. The epidemiology of subclinical

malaria infections in South-East Asia: findings from cross-sectional surveys in Thailand-Myanmar bor-

der areas, Cambodia, and Vietnam. Malaria journal. 2015; 14:381. doi: 10.1186/s12936-015-0906-x

PMID: 26424000; PubMed Central PMCID: PMC4590703.

21. Statistics M. Mimika Regency in Figures. 2014.

22. Singh B, Bobogare A, Cox-Singh J, Snounou G, Abdullah MS, Rahman HA. A genus- and species-

specific nested polymerase chain reaction malaria detection assay for epidemiologic studies. The

American journal of tropical medicine and hygiene. 1999; 60(4):687–92. PMID: 10348249.

23. WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. In: Sys-

tem VaMNI, editor. Geneva,: World Health Organization; 2011. p. 6.

24. Price RN, Simpson JA, Nosten F, Luxemburger C, Hkirjaroen L, ter Kuile F, et al. Factors contributing

to anemia after uncomplicated falciparum malaria. The American journal of tropical medicine and

hygiene. 2001; 65(5):614–22. PMID: 11716124.

25. Doolan DL, Dobano C, Baird JK. Acquired immunity to malaria. Clinical microbiology reviews. 2009; 22

(1):13–36, Table of Contents. doi: 10.1128/CMR.00025-08 PMID: 19136431; PubMed Central

PMCID: PMC2620631.

26. Waltmann A, Darcy AW, Harris I, Koepfli C, Lodo J, Vahi V, et al. High Rates of Asymptomatic, Sub-

microscopic Plasmodium vivax Infection and Disappearing Plasmodium falciparum Malaria in an Area

of Low Transmission in Solomon Islands. PLOS neglected tropical diseases. 2015; 9(5):e0003758.

doi: 10.1371/journal.pntd.0003758 PMID: 25996619; PubMed Central PMCID: PMCPMC4440702.

27. Michon P, Cole-Tobian JL, Dabod E, Schoepflin S, Igu J, Susapu M, et al. The risk of malarial infec-

tions and disease in Papua New Guinean children. The American journal of tropical medicine and

hygiene. 2007; 76(6):997–1008. PMID: 17556601.

28. Anstey NM, Douglas NM, Poespoprodjo JR, Price RN. Plasmodium vivax: Clinical Spectrum, Risk Fac-

tors and Pathogenesis. Advances in parasitology. 2012; 80:151–201. doi: 10.1016/B978-0-12-

397900-1.00003–7 PMID: 23199488.

29. Wampfler R, Mwingira F, Javati S, Robinson L, Betuela I, Siba P, et al. Strategies for detection of Plas-

modium species gametocytes. PLOS one. 2013; 8(9):e76316. doi: 10.1371/journal.pone.0076316

PMID: 24312682; PubMed Central PMCID: PMC3848260.

30. Tiono AB, Ouedraogo A, Ogutu B, Diarra A, Coulibaly S, Gansane A, et al. A controlled, parallel, clus-

ter-randomized trial of community-wide screening and treatment of asymptomatic carriers of

Submicroscopic Plasmodium Parasitaemia

PLOS ONE | DOI:10.1371/journal.pone.0165340 October 27, 2016 16 / 17

Plasmodium falciparum in Burkina Faso. Malaria journal. 2013; 12:79. doi: 10.1186/1475-2875-12-79

PMID: 23442748; PubMed Central PMCID: PMC3599538.

31. von Seidlein L, Dondorp A. Fighting fire with fire: mass antimalarial drug administrations in an era of

antimalarial resistance. Expert review of anti-infective therapy. 2015; 13(6):715–30. doi: 10.1586/

14787210.2015.1031744 PMID: 25831482.

32. Britton S, Cheng Q, Sutherland CJ, McCarthy JS. A simple, high-throughput, colourimetric, field appli-

cable loop-mediated isothermal amplification (HtLAMP) assay for malaria elimination. Malaria journal.

2015; 14:335. doi: 10.1186/s12936-015-0848-3 PMID: 26315027; PubMed Central PMCID:

PMC4552465.

33. Ladeia-Andrade S, Ferreira MU, de Carvalho ME, Curado I, Coura JR. Age-dependent acquisition of

protective immunity to malaria in riverine populations of the Amazon Basin of Brazil. The American

journal of tropical medicine and hygiene. 2009; 80(3):452–9. PMID: 19270298.

34. Douglas NM, Anstey NM, Buffet PA, Poespoprodjo JR, Yeo TW, White NJ, et al. The anaemia of Plas-

modium vivax malaria. Malaria journal. 2012; 11(1):135. Epub 2012/05/01. 1475-2875-11-135 [pii] doi:

10.1186/1475-2875-11-135 PMID: 22540175.

35. Douglas NM, Lampah DA, Kenangalem E, Simpson JA, Poespoprodjo JR, Sugiarto P, et al. Major bur-

den of severe anemia from non-falciparum malaria species in southern papua: a hospital-based sur-

veillance study. PLOS medicine. 2013; 10(12):e1001575. doi: 10.1371/journal.pmed.1001575 PMID:

24358031; PubMed Central PMCID: PMC3866090.

36. Howard CT, McKakpo US, Quakyi IA, Bosompem KM, Addison EA, Sun K, et al. Relationship of hepci-

din with parasitemia and anemia among patients with uncomplicated Plasmodium falciparum malaria

in Ghana. The American journal of tropical medicine and hygiene. 2007; 77(4):623–6. PMID:

17978060.

37. Wickramasinghe SN, Abdalla SH. Blood and bone marrow changes in malaria. Bailliere’s best practice

& research Clinical haematology. 2000; 13(2):277–99. doi: 10.1053/beha.1999.0072 PMID:

10942626.

38. Robinson LJ, Wampfler R, Betuela I, Karl S, White MT, Li Wai Suen CS, et al. Strategies for Under-

standing and Reducing the Plasmodium vivax and Plasmodium ovale Hypnozoite Reservoir in Papua

New Guinean Children: A Randomised Placebo-Controlled Trial and Mathematical Model. PLOS medi-

cine. 2015; 12(10):e1001891. doi: 10.1371/journal.pmed.1001891 PMID: 26505753.

39. Barbosa S, Gozze AB, Lima NF, Batista CL, Bastos Mda S, Nicolete VC, et al. Epidemiology of disap-

pearing Plasmodium vivax malaria: a case study in rural Amazonia. PLOS neglected tropical diseases.

2014; 8(8):e3109. doi: 10.1371/journal.pntd.0003109 PMID: 25166263; PubMed Central PMCID:

PMC4148206.

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Supplementary Material

S1 Table. Characteristics of household individuals present and sampled during the survey and those

absent from the household

Risk factors Present and assessed Absent and not assessed

% (n) % (n) P value

Age#

<5 years 19.8%(559) 6.0% (70)

<0.001 5-15 years 23.5% (666) 25.9% (304)

>15 years 56.7% (1604) 68.1% (800)

Gender

Male 41.1% (1162) 68.9% (813) <0.001

Female 58.9% (1668) 31.1% (367)

Ethnicity

Non-Papuan 54.4% (1540) 59.2% (698)

Highland Papuan 19.0% (539) 16.4% (194) 0.019

Lowland Papuan 26.5% (751) 24.4% (288)

Fever in the last month

No 95.9% (2715) 98.6% (1164) <0.001

Yes 4.1% (115) 1.4% (16)

Total 2,830 1,180 #7 participants did not have their age documented (1 present and 6 absent at the time of the survey)

S2 Table. The distribution and severity of anaemia

Risk factors Non anaemic Mild Anaemia Moderate Anaemia

Severe Anaemia

% (n/N) % (n/N) % (n/N) % (n/N)

Age

<5 years 15.4 (286/1853) 24.4 (89/365) 23.9 (106/443) 16.7 (16/96)

5-15 years 23.8 (441/1853) 15.1 (55/365) 31.4 (139/443) 27.1 (26/96)

>15 years 60.8 (1126/1853) 60.5 (221/365) 44.7 (198/443) 56.3 (54/96)

Gender

Male 42.1 (780/1853) 38.1 (139/365) 37.7 (167/443) 38.5 (37/96)

Female 57.9 (1073/1853) 61.9 (226/365) 62.3 (276/443) 61.5 (59/96)

Ethnicity

Non-Papuan 13.5 (1213/1853) 18.1 (169/365) 37.5 (108/443) 49 (11/96)

Highland Papuan 21 (250/1853) 35.6 (66/365) 38.1 (166/443) 39.6 (47/96)

Lowland Papuan 65.5 (390/1853) 46.3 (130/365) 24.4 (169/443) 11.5 (38/96)

G6PD status 1

Normal 97 (1798/1853) 95.9 (350/365) 99.5 (441/443) 100 (96/96)

Deficient 3 (55/1853) 4.1 (15/365) 0.5 (2/443) 0 (0/96)

Plasmodium species

Negative 67.3 (1157/1853) 61.9 (208/365) 47.2 (197/443) 40.7 (37/96)

P. falciparum 11.2 (193/1853) 16.7 (56/365) 28.8 (120/443) 27.5 (25/96)

P. vivax 18.5 (318/1853) 17.6 (59/365) 18 (75/443) 19.8 (18/96)

P. ovale 0 (0/1853) 0 (0/365) 0.2 (1/443) 0 (0/96)

P. malariae 1.1 (19/1853) 0.9 (3/365) 2.4 (10/443) 4.4 (4/96)

Mixed infections 1.9 (33/1853) 3 (10/365) 3.4 (14/443) 7.7 (7/96)

Pregnancy2

Non Pregnant 96.4 (679/704) 93.8 (135/144) 94.1 (144/153) 95.5 (42/44)

Pregnant 3.6 (25/704) 6.3 (9/144) 5.9 (9/153) 4.5 (2/44)

Anaemia was categorized according to the WHO classification n=number with category; N= number assessed 1 Intermediate deficiency (n=36) and deficient (n= 36) were pooled together

2 Adult females only (n = 1045)

S3 Table. Adjusted odds ratios for severe or moderate anaemia in symptomatic and asymptomatic malaria.

n AOR 95% CI p

All Symptomatic Parasitaemia 17 2.7 1.5 - 4.7 0.001

Symptomatic P. falciparum 18 5.0 2.0 - 13 0.005

Symptomatic P. vivax 10 0.9 0.4 - 2.3 0.867

Symptomatic P. malariae Symptomatic Mixed Species

2 - - -

2 - - -

All Asymptomatic Parasitaemia 911 1.5 1.1 - 1.8 0.003

Asymptomatic P. falciparum 376 2.0 1.5 - 2.7 <0.001

Asymptomatic P. vivax 460 1.1 0.8 - 1.5 0.619

Asymptomatic P. ovale 1 - - -

Asymptomatic P. malariae 34 1.4 0.6 - 3.1 0.386

Asymptomatic Mixed Species 62 1.8 0.8 - 3.9 0.150

n=number of participants in the model; AOR = adjusted odds ratio; CI = confidence interval

Multivariate models included: sex, ethnicity and age group.

S4 Table. Adjusted odds ratios for severe or moderate anaemia in asymptomatic malaria stratified by age group.

Age Group (Years) Overall

<5 5 to 15 >15

n AOR 95% CI p n AOR 95% CI p n AOR 95% CI p n AOR 95% CI p

Any Microscopic 68 4.9 2.4-10 <0.001 116 3.1 1.7-5.6 <0.001 170 1.8 1.0-3.1 0.037 354 2.6 1.8-3.8 <0.001

Microscopic P. falciparum 19 10.3 2.9-36 <0.001 57 4.2 1.9-9.3 <0.001 88 1.9 0.9-4.0 0.106 164 3 1.6-5.3 <0.001

Microscopic P. vivax 46 3.8 1.7-8.6 0.001 53 2 1.0-4.0 0.051 64 1.1 0.4-2.8 0.91 163 2 1.2-3.3 0.007

Microscopic P. malariae - - - - - - - - 2 - - - 20 4.1 1.7-10 0.002

Microscopic mixed species 2 1.8 0.1-22 0.659 3 7.8 2.6-24 <0.001 - - - - 7 6.1 3.4-10.7 <0.001

Any Submicroscopic 42 1 0.4-2.5 0.987 162 2.3 1.2-4.3 0.013 407 1.3 0.9-1.8 0.135 611 1.5 1.0-2.1 0.029

Submicroscopic P. falciparum 11 5.2 1.5-18 0.01 55 3.3 1.3-8.4 0.015 164 2 1.2-3.4 0.007 230 2.4 1.5-3.9 <0.001

Submicroscopic P. vivax 28 0.3 0.1-1.1 0.068 89 1.7 0.9-3.0 0.075 190 0.7 0.5-1.1 0.123 307 0.9 0.6-1.4 0.632

Submicroscopic P. malariae 0 - - - 3 - - - 12 - - - 16 1.5 0.6-4.1 0.415

Submicroscopic mixed species 3 1.5 0.2-10 0.652 14 1.5 0.4-6.1 0.597 40 1.9 0.8-4.2 0.14 57 1.7 0.7-3.8 0.223

Negative 286 Ref 322 Ref 991 Ref 1,599 Ref

Abbreviations: n=number; AOR= adjusted odds ratio; CI=confidence interval

Models control for sex and ethnicity with robust standard errors to account for clustering within households. For the overall models age group was also included.

S1 Fig. Study Profile.

87

CHAPTER 3 - Genetic micro-epidemiology of malaria in Papua

Indonesia: Extensive P. vivax diversity and a distinct subpopulation

of asymptomatic P. falciparum infections

RESEARCH ARTICLE

Genetic micro-epidemiology of malaria in

Papua Indonesia: Extensive P. vivax diversity

and a distinct subpopulation of asymptomatic

P. falciparum infections

Zuleima Pava1, Rintis Noviyanti2, Irene Handayuni1, Hidayat Trimarsanto3,4, Leily Trianty2,

Faustina H. Burdam5,6,7, Enny Kenangalem5,6, Retno A. S. Utami2, Yusrifar K. Tirta2,

Farah Coutrier2, Jeanne R. Poespoprodjo5,6,7, Ric N. Price1,8, Jutta Marfurt1,

Sarah Auburn1*

1 Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University,

Darwin, Northern Territory, Australia, 2 Malaria Pathogenesis Unit, Eijkman Institute for Molecular Biology,

Jakarta, Indonesia, 3 Bioinformatics Laboratory, Eijkman Institute for Molecular Biology, Jakarta, Indonesia,

4 Agency for Assessment and Application of Technology, Jakarta, Indonesia, 5 Mimika District Health

Authority, Timika, Papua, Indonesia, 6 Timika Malaria Research Programme, Papuan Health and Community

Development Foundation, Timika, Papua, Indonesia, 7 Pediatric Research Office, Department of Child

Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia, 8 Centre for Tropical

Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom

* sarah.auburn@menzies.edu.au

Abstract

Background

Genetic analyses of Plasmodium have potential to inform on transmission dynamics, but

few studies have evaluated this on a local spatial scale. We used microsatellite genotyping

to characterise the micro-epidemiology of P. vivax and P. falciparum diversity to inform

malaria control strategies in Timika, Papua Indonesia.

Methods

Genotyping was undertaken on 713 sympatric P. falciparum and P. vivax isolates from a

cross-sectional household survey and clinical studies conducted in Timika. Standard popu-

lation genetic measures were applied, and the data was compared to published data from

Kalimantan, Bangka, Sumba and West Timor.

Results

Higher diversity (HE = 0.847 vs 0.625; p = 0.017) and polyclonality (46.2% vs 16.5%,

p<0.001) were observed in P. vivax versus P. falciparum. Distinct P. falciparum substructure

was observed, with two subpopulations, K1 and K2. K1 was comprised solely of asymptom-

atic infections and displayed greater relatedness to isolates from Sumba than to K2, possi-

bly reflecting imported infections.

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 1 / 13

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OPENACCESS

Citation: Pava Z, Noviyanti R, Handayuni I,

Trimarsanto H, Trianty L, Burdam FH, et al. (2017)

Genetic micro-epidemiology of malaria in Papua

Indonesia: Extensive P. vivax diversity and a

distinct subpopulation of asymptomatic P.

falciparum infections. PLoS ONE 12(5): e0177445.

https://doi.org/10.1371/journal.pone.0177445

Editor: Photini Sinnis, Johns Hopkins Bloomberg

School of Public Health, UNITED STATES

Received: January 10, 2017

Accepted: April 27, 2017

Published: May 12, 2017

Copyright: © 2017 Pava et al. This is an open

access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: All relevant data are

within the paper and its Supporting Information

files.

Funding: The study was supported by the

Wellcome Trust (a Senior Fellowship in Clinical

Science awarded to RNP - 200909) and

Fellowships to: JRP (Wellcome Trust - 099875)

and ZP (Instituto Colombiano para el Desarrollo de

la Ciencia y la Tecnologıa Francisco Jose de Caldas,

Colciencias - 512-2010). The Timika Research

Conclusions

The results demonstrate the greater refractoriness of P. vivax versus P. falciparum to con-

trol measures, and risk of distinct parasite subpopulations persisting in the community unde-

tected by passive surveillance. These findings highlight the need for complimentary new

surveillance strategies to identify transmission patterns that cannot be detected with tradi-

tional malariometric methods.

Introduction

Indonesia has one of the highest burdens of malaria in Southeast Asia. Despite recent progress

in reducing case incidence, more than a quarter of a million clinical cases were reported in

2013 with 230 million people at risk of infection [1]. The high heterogeneity of malaria epide-

miology across approximately 6,000 inhabited islands is a huge challenge to aspirations for

malaria control and its ultimate elimination. Across the archipelago, marked differences are

observed in malaria incidence, Anopheles vector distributions, and ethnic and socio-economic

features [2]. There is a continual flux in parasite populations in response to the changing envi-

ronment, combined with a highly mobile human population, resulting in a dynamic malaria

epidemiology at any site.

Further complicating the picture, all five species of Plasmodium that commonly infect

humans are present in Indonesia [2]. Differences in the respective transmission of these spe-

cies confound prioritization of malaria control activities. Although P. falciparum has histori-

cally been the predominant species, in recent years the proportion of P. vivax cases has risen

[3]. This trend is especially concerning due to the occurrence of high-grade multidrug-resis-

tance in the historically neglected P. vivax species, particularly in Papua Province [4, 5].

Population genetic approaches can define P. vivax and P. falciparum diversity and structure,

and inform associated patterns of transmission intensity, foci and stability, thus complement-

ing traditional malariometric surveillance [6–9]. A recent nationwide Indonesian study

applied parasite genotyping to contrast patterns of P. vivax and P. falciparum transmission

between four sites in Indonesia with varying endemicity, highlighting regional heterogeneity

within and between species [10]. However, control and elimination activities are generally

implemented at district-level spatial scales [11], and little is known about the heterogeneity in

parasite populations at this administrative level.

The aim of this study was to describe the population diversity and structure of co-endemic

P. falciparum and P. vivax circulating in Timika, the capital of Mimika district, Papua Prov-

ince. The region is an epicentre for antimalarial resistance [4, 5] and thus, with the threat of

resistance arising against artemisinin-based combination therapies (ACTs), effective strategies

are needed to interrupt local malaria transmission as quickly and cost-effectively as possible.

We describe the implications of our findings in regards to the local transmission dynamics

and appropriate malaria control and elimination strategies.

Materials and methods

Study site and sampling

The study was conducted in Timika, Papua Province in eastern Indonesia, which has the great-

est burden of malaria in the country. Timika is located within Mimika Baru, one of the largest

sub-districts in Mimika district. The population of 202,350 people is increasing in size by an

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 2 / 13

Facility and Papuan Health and Community

Development Foundation were supported by DFAT

(Australian Department of Foreign Affairs and

Trade) and the NHMRC (Program Grant 1037304).

The funders had no role in study design, data

collection and analysis, decision to publish, or

preparation of the manuscript.

Competing interests: The authors have declared

that no competing interests exist.

estimated 16% per year, mainly due to economic migration, and is composed of different eth-

nic groups including Highland Papuans, Lowland Papuans, and Non-Papuans. Unstable

malaria transmission occurs mainly in the lowland areas, where approximately 90% of the

population lives. In 2013, malaria prevalence by microscopy was estimated to be 12.2% (5.7%

due to P. vivax and 5.2% due to P. falciparum) [2, 3].

The Rumah Sakit Mitra Masyarakat (RSMM) is the main hospital in the district, serving a

population of approximately 150,000 people. Located in the lowland area, RSMM provides

medical healthcare to approximately 300 patients per day. Since 2006, local antimalarial guide-

lines have recommended that all patients with uncomplicated malaria due to any Plasmodiumspecies are treated with dihydroartemisinin-piperaquine (DHP).

Parasite DNA was collected within the framework of two studies. The first was an ongoing

ex-vivo drug susceptibility surveillance study conducted between March 2011 and August

2014. The second was a cross-sectional study conducted between April and July 2013. Patient

and parasitological details were recorded for each case. For the cross-sectional study, GPS

coordinates were recorded of the residence of all individuals assessed. The majority of houses

were located in one of 16 villages, within the 3 subdistricts Mimika Baru, Mimika Timur, and

Kuala Kencana. Geospatial mapping of residences with Plasmodium cases was performed

using ArcGIS software (version 10.2.1).

DNA extraction and species confirmation

For parasitaemia detected by microscopy, species and parasite density were determined by

examination of Giemsa-stained thick blood films. Genomic DNA (gDNA) was extracted from

50 μL packed red blood cell (RBC) pellets using the QIAamp 96 DNA Blood Kit (Qiagen) for

the cross-sectional study and from 2 mL of packed RBCs using the QIAamp DNA Mini Kit

(Qiagen) for the ex-vivo surveillance study cases. Confirmation of Plasmodium species was

undertaken using a nested PCR protocol with 2 μL gDNA template in duplicate [12]. P. falcipa-rum, P. vivax, P. malariae, and P. ovale small-subunit rRNA DNA clones (MRA-177, MRA-

178, MRA-179, and MRA-180; ATCC, Manassas, VA) were used as positive controls.

Genotyping and data analysis

P. falciparum and P. vivax genotyping was performed using short tandem repeat (STR) mark-

ers. Nine STR markers (ARAII, PfPK2, Poly-alpha, TA1, TA42, TA60, TA81, TA87 and

TA109) were amplified in P. falciparum infections using previously described methods [13].

For P. vivax, nine STR markers that have been defined as a consensus panel within the Asia

Pacific Malaria Elimination Network (APMEN) (Pv3.27, MS16, MS1, MS5, MS10, MS12,

MS20 and msp1F3) were amplified using the APMEN protocol [14–16]. Capillary electropho-

resis on an ABI 3100 Genetic Analyser with GeneScan LIZ-600 (Applied Biosystems) was used

to separate the fluorescently labelled PCR products. The resulting electrophoretograms were

analysed using GeneMapper version 4.0 software (Applied Biosystems) and verified manually.

To reduce artefact peaks, an arbitrary fluorescent intensity threshold of 50 RFU was applied,

and minor alleles were only scored when their height was at least 33% of the main allele’s

height. Only samples with a minimum of 50% successful genotype calls across the loci investi-

gated were included in downstream population genetic analyses.

Samples with more than one allele at any locus were defined as polyclonal infections. The

multiplicity of infection (MOI) for each sample was defined as the maximum number of alleles

at any locus. Population diversity was measured using the expected heterozygosity (HE), popu-

lation differentiation was measured using the pairwise FST metric. Both HE and FST were calcu-

lated using Arlequin software (version 3.5) [17, 18]. For P. vivax, measures of the HE, genetic

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 3 / 13

differentiation and LD (described below) were undertaken on the full marker set and on a sub-

set of five markers (MS1, MS5, MS10, MS12, MS20) defined as exhibiting balanced diversity in

a recent study [19].

STRUCTURE software version 2.3.3 was used to assess population structure [20]. The sim-

ulation was run using 20 replicates, with 100,000 burn-in and 100,000 post burn-in iterations

for each estimate of K (number of sub-populations), ranging from 1–10. The model parame-

ters included admixture with correlated allele frequencies. The most probable K was derived

by applying the delta K method [21]. STRUCTURE results were displayed using bar plots pre-

pared with Distruct software version 1.1. [22]. Multi-locus genotypes (MLGs) were recon-

structed from the predominant allele at each locus in isolates with no missing data. Using the

MLGs, multi-locus linkage disequilibrium (LD) was measured by the standardised index of

association (IAS) using the web-based LIAN 3.5 software [23]. The significance of the estimates

was assessed using 10,000 permutations of the data. Given that the measures of LD and other

MLG-based analyses could be affected by incorrect MLG reconstruction in complex polyclonal

infections, the analysis was also performed using low complexity infections (maximum of 1

multi-allelic locus) with each unique MLG represented just once. The proportion of alleles

shared between MLGs (ps) was calculated using 1-ps as a measure of genetic relatedness [24].

Unrooted neighbour-joining trees were generated with the ape package in R [25]. Mantel’s r-

test was used to assess the correlation between genetic and either temporal or spatial distances

using the ade4 package in R [26].

Statistical tests

Statistical analysis was conducted using SPSS v.20.0 for windows software (IBM SPSS Statis-

tics), with a significance threshold of 0.05. Data on patient gender, ethnicity and proportion of

polyclonal infections was compared between subgroups using Pearson’s Chi-square test. The

significance of difference between patient age, parasite density, MOI and HE was undertaken

using the Mann-Whitney U test and Kruskal-Wallis test.

Ethics

Ethical approval for this study was obtained from the Eijkman Institute Research Ethics Com-

mission, Eijkman Institute for Molecular Biology, Jakarta, Indonesia (EIREC no. 47/2010 and

EIREC no. 67/2013), Faculty of Medicine, University of Gadjah Mada (Ref: KE/FK/763/EC),

and the Human Research Ethics Committee of the Northern Territory Department of Health

& Families and Menzies School of Health Research, Darwin, Australia (Ref: HREC-2010-

1434).

Results

Sampling and data quality

Between March 2011 and August 2014, 108 P. vivax and 94 P. falciparum cases were collected

from the surveillance study, with genotyping successfully completed in 96% (104) of P. vivaxand all P. falciparum cases. The cross-sectional household survey, undertaken between April

and July 2013, assessed 2,476 participants by blood film examination and PCR analysis, of

whom 877 (35.4%) had peripheral parasitaemia detected by PCR. P. vivax and P. falciparumaccounted for 749 (85.4%) malaria cases. Genotyping was successful in 60% (240/397) of P.

vivax and 80% (280/352) of P. falciparum cases. A total of 73 (30.4%) P. vivax and 21 (7.5%)

P. falciparum samples failed to amplify any of the markers; all of these had submicroscopic

parasitaemia.

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 4 / 13

Demographic and parasitological details on the individuals with P. falciparum or P. vivaxparasitaemia are presented in Table 1. There were no significant differences in individual’s

age, gender, proportion of patent or symptomatic infections, or parasite density between the

species. The geographical distribution of P. vivax and P. falciparum cases did not reveal any

local high prevalence clusters for either species in the cross-sectional study S1 Fig.

The diversity and genotyping success rate for each of the P. falciparum and P. vivax markers

are summarised in S1 Table. Although diversity was moderately low in two P. falciparummarkers (TA42 HE = 0.315 and TA109 HE = 0.176), all markers exhibited a minimum 5%

minor allele frequency. With the exception of TA42 (20.6%) in P. falciparum populations, all

other markers exhibited >80% genotyping success in both species. The MS8 assay presented

artefacts that affected the reliability of the allele calling and therefore, this locus was excluded

from further analysis.

Higher genetic diversity in co-endemic P. vivax versus P. falciparum

P. vivax exhibited higher genetic diversity than P. falciparum (HE = 0.847 vs 0.625; p = 0.017)

and a higher proportion of polyclonal infections (46.2% vs 16.5%, p<0.001). The mean MOI

was higher for P. vivax (1.6) than P. falciparum (1.2) (p<0.001) (Table 2). In contrast to P. fal-ciparum, there was no or low LD detected for P. vivax (Table 3). Analysis of all 8 P. vivax mark-

ers revealed no significant LD (IAS = -0.0002, p>0.05), even after excluding complex infections

(IAS = -0.0008, p>0.05). There was no evidence of LD after restricting to the 5 balanced mark-

ers (IAS = 0.0036, p> 0.05), although LD became significant after removing complex samples

(IAS = 0.006, 0.01�p< 0.05; Table 3) [19]. Further MLG-based analyses restricted to the low

complexity samples corroborated that the polyclonal samples were not affecting MLG recon-

struction and subsequent results. Significant LD was observed in P. falciparum, with consis-

tently high IAS values varying from 0.115–0.103 (all p<0.001). LD analysis using the unique

haplotypes found no evidence of epidemic transmission in either species (Table 3).

Low level sharing of identical P. falciparum infections amongst

household members

Thirty three P. falciparum MLGs (106 infections) were observed in more than one individual

(range 2–13). Only 22 MLGs (68 cases) had GPS metadata available. Amongst these, 3 pairs of

Table 1. Demographic information.

Species Age1, n (%) Male patients, n

(%)

Subpatent2, n

(%)

Symptomatic infections, n

(%)

Parasite density, median

(range)<5 5–15 y >15

P.

falciparum

27 (7.4) 87

(23.7)

253

(68.9)

157 (42.8) 159 (43.1) 105 (28.6) 5,475 (28–361,728)

P. vivax 45

(13.2)

92

(27.0)

204

(59.8)

147 (43.0) 161 (46.8) 107 (31.3) 6,735 (38–120,576)

Superscript indicates number of individuals with missing data.

https://doi.org/10.1371/journal.pone.0177445.t001

Table 2. Within-host and population diversity.

Species and subgroup (K) n MOI, mean (range) Polyclonality, % HE, mean ± SD

P. vivax 344 1.6 (1–4) 46.2 0.849 ± 0.07

P. falciparum 369 1.2 (1–3) 16.5 0.656 ± 0.20

P. falciparum K1 39 1.5 (1–3) 43.6 0.587 ± 0.27

P. falciparum K2 236 1.1 (1–2) 14.4 0.556 ± 0.29

https://doi.org/10.1371/journal.pone.0177445.t002

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 5 / 13

infections (8.8% (6/68)), were shared between members of the same household. Seven pairs of

infections (35.3%, (24/68)) were less than 1 Km apart. The median distance between infections

was 5.3 Km (range 2 meters to 19.5 Km) (S2 Fig).

Distinct subpopulations of P. falciparum in Timika

In contrast to P. falciparum, no substructure was found in the P. vivax population. The

neighbour-joining analysis (Fig 1A) illustrates that the P. vivax isolates exhibited low

Table 3. Linkage disequilibrium.

Species and subgroup (K) All infections, n IAS Low complexity infections, n IA

S Unique MLGs, n IAS

P. vivax8 223 -0.0002NS 145 -0.0008NS 223 -0.0002NS

P. vivax5 250 0.0036NS 186 0.006* 245 0.0021NS

P. falciparum 262 0.1150** 237 0.1136** 188 0.1031**

P. falciparum K1 28 0.0405** 24 0.0249** 23 0.0121NS

P. falciparum K2 234 0.0323** 213 0.0334** 165 0.0133**

Only samples with no missing data were included in the analyses. All nine loci were used in P. falciparum. Superscript indicates the number of loci used for

the analysis in the P. vivax populations.

* p <0.05

** p< 0.01

*** p<0.001

NS = Not significant

https://doi.org/10.1371/journal.pone.0177445.t003

Fig 1. Neighbour-joining and STRUCTURE plots illustrating the genetic diversity and structure in the P. vivax and P. falciparum populations in

Timika. Panels A) and B) present unrooted neighbour-joining trees illustrating the genetic relatedness amongst P. vivax and P. falciparum isolates,

respectively. Panel C) presents a bar plot illustrating the substructure in the P. falciparum population at K = 2. Each vertical bar represents an individual

sample and each colour represents one of the K clusters (sub-populations) defined by STRUCTURE.

https://doi.org/10.1371/journal.pone.0177445.g001

Genetic micro-epidemiology of malaria in Papua Indonesia

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relatedness, with no distinct clustering. STRUCTURE analysis also failed to identify any evi-

dence of sub-structure in P. vivax. In the P. falciparum population, the delta K method dem-

onstrated K = 2 as the greatest likelihood, with 10.6% of isolates showing predominant

ancestry (>80%) to K1 and the remaining 89.4% of isolates demonstrating predominant

ancestry to K2 (Fig 1C). Neighbour-joining analysis confirmed the substructure and illus-

trated that neither subpopulation was the result of clonal expansion (Fig 1B). Assessment of

the differentiation between K1 and K2 further confirmed local substructure (FST = 0.389;

p<0.001 and F’ST = 0.885; p<0.001).

Expected heterozygosity analysis revealed moderate diversity in the two P. falciparumsubpopulations (K1HE = 0.587 vs K2HE = 0.556; p = 0.818) (Table 2). However, K1 had

more polyclonal infections than K2 (43.6% vs 14.4%; p<0.001) (Table 2). LD analysis

revealed an IAS of 0.041 (p<0.01) for K1 and 0.033 (p<0.01) for K2 (Table 3); the markedly

lower IAS scores relative to the pooled dataset suggesting admixture in the P. falciparum

population.

Investigation of the demographic profiles of the K1 and K2 clusters did not find any signifi-

cant difference in terms of age (p = 0.084), sex (p = 0.734), ethnicity (p = 0.972), or occupation

(p = 0.096) (Table 4). Interestingly, all (100%) isolates with predominant ancestry to K1 were

asymptomatic and mostly submicroscopic (71.8%) compared to 36.5% asymptomatic cases in

K2 (p<0.001) (Table 4). No significant correlation was observed between the distance in sam-

pling date and the proportion of alleles shared between infections (Mantel r-test, r = -0.03,

p = 0.762). The geographical distribution of both subpopulations is shown in S3 Fig. No associ-

ation was found between genetic distance and geographical distance (Mantel r-test, r = -0.03,

p = 0.851).

The P. falciparum K1 subpopulation in Timika is related to P. falciparum

parasites from other Indonesian islands

To investigate the epidemiological factor(s) underlying the sub-structure in the P. falciparumpopulation, the Timika genotypes (Papua) were compared with published data on 168 P. fal-ciparum isolates collected from 4 surveys in other Indonesian islands (Bangka, Kalimantan,

Sumba and West Timor; S4 Fig) [10]. Delta K evaluation of the STRUCTURE results across

the 5 sites revealed that the greatest likelihood was at K = 2. Distinct separation was observed

between K2 versus K1, Bangka, Kalimantan, Sumba and West Timor (Fig 2A). Principal

coordinate analysis (PCoA) showed the same pattern, illustrating that K1 shared more

genetic background with the non-Timika samples than with K2 (Fig 2A). Further investiga-

tion with PCoA, STRUCTURE and FST analysis on a restricted dataset with K1 and the other

four islands showed that the K1 samples were most similar to those from Sumba and West

Timor (Fig 2B; Table 5). In contrast, P. vivax differentiation was limited across the five

islands (S5 Fig).

Table 4. P. falciparum subpopulation 1 and 2 demographic data.

Subgroup

(K)

Age, n (%) Male, n

(%)

Ethnicity, n (%) Submicroscopic, n

(%)

Occupation, n (%) Total

<5 5–15 y >15 Highland Lowland Non-

Papuan

Student None House

wife

Miner Farmer

K1 3

(7.7)

19

(48.7)

17

(43.6)

18

(46.2)

11 (28.2) 15 (38.5) 13 (33.3) 28 (71.8) 17

(43.6)

13

(33.3)

7 (17.9) 1

(2.6)

1 (2.6) 39

K2 24

(7.3)

68

(20.1)

236

(72.0)

139

(42.4)

145

(44.3)

94 (28.7) 88 (26.9) 120 (36.5) 41

(12.5)

77

(23.5)

55

(16.8)

3

(0.9)

24

(7.3)

328

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Fig 2. Principal coordinate analysis (PCoA) and STRUCTURE plots illustrating the genetic differentiation between P. falciparum isolates from

Timika relative to Kalimantan, Bangka, Sumba and West Timor. Panels A) and B) present PCoA and STRUCTURE bar plots, respectively,

illustrating the similarity between the Timika K1 subpopulation and the other four islands. The STRUCTURE bar plot presents the results for K = 2.

Panels C) and D) present PCoA and STRUCTURE bar plots, respectively, for all five P. falciparum populations, with the exclusion of the Timika K2

subpopulation, illustrating the greater relatedness between K1 and Sumba relative to the other islands. The STRUCTURE bar plot presents the results

for K = 2, separating K1, Sumba and West Timor in the east from Kalimantan and Bangka in the west.

https://doi.org/10.1371/journal.pone.0177445.g002

Table 5. Pair-wise differentiation between sites.

Site K1 Timika K2 Timika West Timor Sumba Kalimantan Bangka

K1 Timika - 0.885 0.481 0.355 0.557 0.540

K2 Timika 0.389* - 0.884 0.853 0.94 0.956

West Timor 0.226* 0.398* - 0.241 0.719 0.653

Sumba 0.143* 0.343* 0.100* - 0.555 0.561

Kalimantan 0.286* 0.453* 0.382* 0.248* - 0.328

Bangka 0.293* 0.476* 0.361* 0.277* 0.199* -

FST in lower left triangle. F’ST in upper right triangle

*p < 1 x 10−5

**p = 0.011

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Genetic micro-epidemiology of malaria in Papua Indonesia

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Discussion

This study utilised a comprehensive collection of clinical and community samples to describe

the genetic microepidemiology of P. falciparum and P. vivax populations circulating in Timika,

Papua Indonesia. At this high spatial resolution, we demonstrate contrasting transmission pat-

terns of both co-existing species, and reveal admixture of two divergent P. falciparum subpop-

ulations, one comprised of submicroscopic infections with high genetic relatedness to isolates

from other Indonesian islands.

Similarly to previous reports, we found higher population and within-host diversity in co-

endemic P. vivax compared to P. falciparum populations [10, 27–30]. In contrast to P. falcipa-rum, there was no evidence of substructure in P. vivax. LD was also markedly lower in P. vivaxthan P. falciparum, even after accounting for admixture in the latter. These patterns are consis-

tent with more intense and stable transmission in the local P. vivax population relative to P. fal-ciparum. The patterns of diversity in the P. falciparum population were similar to those found

in low to meso-endemic regions in Southeast Asia, but markedly less diverse than in hyper-

holoendemic regions in Africa [28, 31]. Although genetic diversity estimates do not always

scale linearly with endemicity in P. vivax, the patterns of P. vivax diversity in Timika were

most comparable to those reported in other hypo-mesoendemic settings in South-East Asia

and the Pacific [32]. Several factors may have contributed to the higher intensity and stability

of P. vivax compared to P. falciparum transmission in this location, including recurrences

from relapsing infections, the earlier appearance of the transmissible sexual stages in P. vivax,

and the larger reservoir of submicroscopic infections in this species [3, 33].

Although the P. falciparum population exhibited several clusters of identical infections, nei-

ther species demonstrated evidence of epidemic transmission dynamics, indicating moderately

stable transmission at the time of the study. The identical infections enabled assessment of

shared origins of infection, revealing a small proportion (8.8% (6/68)) of isolates that were

shared amongst household members, likely reflecting transmission events taking place in the

vicinity of the household. However, the large majority of shared infections (70.5%) were over 1

Km apart and more likely to reflect a reservoir outside of the household. Together, the patterns

of parasite relatedness, diversity and LD suggest that broad ranging interventions are still

needed to further interrupt transmission of P. falciparum and P. vivax before more targeted

strategies can be implemented in the area. Furthermore, although the widespread deployment

of ACT has been effective in reducing the P. falciparum population, radical treatment targeting

the dormant liver stages will be required to make a similar impact on P. vivax transmission.

The most notable revelation in the Timika P. falciparum population was the distinct sub-

structure at this small spatial scale. The marked differentiation, elevated IAS on pooling the

K1 and K2 subpopulations, lack of evidence of epidemic transmission, together with the

moderate genetic diversity within each subpopulation was indicative of admixture. However,

it is unclear how sympatric P. falciparum subpopulations are genetically isolated with no sign

of geographical, temporal or demographic boundaries. We contemplated three possibilities:

1) the importation of these cases, 2) the rise of a new, fitter population, and 3) an unidenti-

fied biological barrier.

Comparison of the Papuan P. falciparum genotypes with data from Bangka, Kalimantan,

Sumba and West Timor revealed that the K1 isolates were more closely related to non-Papuan

than Papuan isolates, and K2 was strikingly different from the rest of Indonesia. In accordance

with geographic distance, the K1 sub-population demonstrated the greatest genetic similarity

to the two most proximal islands, Sumba and West Timor, where P. falciparum remains hypo-

mesoendemic. Owing to the mining industry and the National Transmigration Programme,

there has been a great deal of human movement in Timika. Thus, it is feasible that the K1

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 9 / 13

subpopulation is the product of infections acquired outside Timika. Imported infections

undermine local intervention efforts and present the threat of drug resistance introduction.

Our results emphasise the need to develop geographical markers to help to differentiate

between imported and local malaria cases. A recent study presented a barcode of organellar

genome markers to determine the geographic origin of P. falciparum isolates [34], although

this doesn’t have the resolution required to differentiate local subpopulations.

The second possibility for the unusual P. falciparum structure in Timika is the rise and

spread of a fitter subpopulation in response to recent selective pressure such as the change in

policy from chloroquine to ACT treatment in 2006. Parasites carrying mutations conferring

chloroquine resistance may lose their survival advantage after drug pressure is removed, as has

been demonstrated by the decrease in K76T pfcrt frequency in several African countries [35].

Assuming that the pre-ACT population was more comparable to the other Indonesian islands,

the K1 subpopulation might reflect a vestigial population, whilst K2 reflects a newly emerging

population. Although the recent policy change to ACT as first line-therapy was recommended

for both P. falciparum and P. vivax, this treatment regimen may have had a greater impact on

P. falciparum, since ACT alone has no activity against the dormant liver stages of P. vivax.

However, more studies are needed to confirm this.

A further possible explanation for the sympatric co-existence of the K1 and K2 subpopula-

tions is an unidentified biological barrier such as mutually exclusive vector preferences or

differential erythrocyte binding specificities. Further investigation of the local Anopheline pop-

ulations and their vectorial capacity for K1 and K2 is needed to address the first hypothesis.

Although we did not find any evidence of association between K1 or K2 with Lowland, High-

land or non-Papuan ethnicity, it is possible that a common erythrocyte polymorphism(s)

defining parasite binding and invasions preference and that this transcends the different ethnic

groups, and differentiates K1 and K2. Genomic analysis of these isolates might provide further

insights.

Although we conducted a thorough characterisation of the subpopulations, we cannot con-

clusively determine the factors underlying the marked P. falciparum substructure in Timika.

However, similar substructure has been observed in Cambodia, where it was postulated to sup-

port the emergence of drug resistance, highlighting a need for intense surveillance strategies

[36]. It is important to highlight that the distinct substructure would not have been identified

without genetic information. Furthermore, all samples from K1 were asymptomatic and mostly

submicroscopic, and, therefore, would likely be missed in routine passive surveillance studies.

Conclusions

Our study demonstrates considerable genetic heterogeneity between co-endemic species and

distinct substructure within species that can be observed at small spatial scales, and may be

reliant on passive as well as active case detection. These findings highlight the need for comple-

mentary new surveillance strategies to identify local transmission patterns that cannot be

detected with traditional malariometric methods.

Supporting information

S1 Fig. Geographical distribution of the households at which malaria cases were detected

in the cross-sectional household survey. The plot was generated using ArcGIS software on

GPS coordinate data from the individuals identified with malaria parasitaemia. Each dot pres-

ents an individual case, with colour-coding by species according to PCR data.

(TIFF)

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 10 / 13

S2 Fig. Geographical distribution of identical MLGs in P. falciparum. This plot was gener-

ated using ArcGIS software on the GPS coordinates for the 22 identical MLGs in the P. falcipa-rum population. Different MLGs are distinguished with different colours and shapes. Circles,

pentagons and diamonds indicate MLGs found in two, three, and more than three individuals

respectively.

(TIFF)

S3 Fig. Geographical distribution of the households at which P. falciparum K1 and K2 iso-

lates were detected in the cross-sectional household survey. The plot was generated using

ArcGIS software on GPS coordinate data. Each dot presents an individual P. falciparum case,

with colour-coding by subpopulation as defined by STRUCTURE.

(TIFF)

S4 Fig. Location of the study sites. This map is a modified version of Fig 1 presented by

Noviyanti et al., with the addition here of a site label for Timika, Papua Indonesia [10]. The

original map was generated by the Malaria Atlas Project, University of Oxford. The colour

scales reflect the model-based geostatistical point estimates of the annual mean P. falciparumparasite rate in the 2–10 year age group (PfPR2–10) within the stable spatial limits of transmis-

sion in 2010. The approximate locations of the study sites described here are indicated with

black stars.

(TIF)

S5 Fig. Principal coordinate analysis (PCoA) between P. vivax isolates from Timika rela-

tive to Kalimantan, Bangka, Sumba and West Timor. This plot illustrates the limited differ-

entiation between the Timika P. vivax population and the other four Indonesian islands.

(TIFF)

S1 Table. Summary of the diversity and genotyping success rate for each of the P. falcipa-rum and P. vivax markers.

(DOCX)

Acknowledgments

We would like to thank the patients who contributed their samples to the study, and the health

workers and field teams who assisted with the sample collections. We would also like to thank

Dr Iqbal Elyazar (Malaria Atlas Project) who provided details for the geospatial maps.

Author Contributions

Conceptualization: ZP RNP JM SA.

Data curation: ZP HT.

Formal analysis: ZP HT SA.

Funding acquisition: RNP.

Investigation: ZP IH LT FHB EK RASU YKT FC JRP.

Project administration: RN RNP JM SA.

Resources: FHB EK JRP.

Supervision: RN RNP JM SA.

Writing – original draft: ZP JM SA.

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 11 / 13

Writing – review & editing: RN RNP.

References1. World Malaria Report 2015. 2015; [280]

2. Elyazar IR, Hay SI, Baird JK. Malaria distribution, prevalence, drug resistance and control in Indonesia.

Advances in parasitology. 2011; 74:41–175. https://doi.org/10.1016/B978-0-12-385897-9.00002-1

PMID: 21295677

3. Pava Z, Burdam FH, Handayuni I, Trianty L, Utami RA, Tirta YK, et al. Submicroscopic and Asymptom-

atic Plasmodium Parasitaemia Associated with Significant Risk of Anaemia in Papua, Indonesia. PloS

one. 2016; 11(10):e0165340. https://doi.org/10.1371/journal.pone.0165340 PMID: 27788243

4. Ratcliff A, Siswantoro H, Kenangalem E, Wuwung M, Brockman A, Edstein MD, et al. Therapeutic

response of multidrug-resistant Plasmodium falciparum and P. vivax to chloroquine and sulfadoxine-

pyrimethamine in southern Papua, Indonesia. Trans R Soc Trop Med Hyg. 2007; 101(4):351–9. https://

doi.org/10.1016/j.trstmh.2006.06.008 PMID: 17028048

5. Sumawinata IW, Bernadeta, Leksana B, Sutamihardja A, Purnomo, Subianto B, et al. Very high risk of

therapeutic failure with chloroquine for uncomplicated Plasmodium falciparum and P. vivax malaria in

Indonesian Papua. The American journal of tropical medicine and hygiene. 2003; 68(4):416–20. PMID:

12875290

6. Barry AE, Waltmann A, Koepfli C, Barnadas C, Mueller I. Uncovering the transmission dynamics of

Plasmodium vivax using population genetics. Pathog Glob Health. 2015; 109(3):142–52. https://doi.org/

10.1179/2047773215Y.0000000012 PMID: 25891915

7. Koepfli C, Barry A, Javati S, Timinao L, Nate E, Mueller I, et al. How molecular epidemiology studies

can support the National Malaria Control Program in Papua New Guinea. P N G Med J. 2014; 57(1–

4):75–85. PMID: 26930891

8. Ferreira MU, Rodrigues PT. Tracking malaria parasites in the eradication era. Trends Parasitol. 2014;

30(10):465–6. https://doi.org/10.1016/j.pt.2014.08.003 PMID: 25154542

9. Auburn S, Barry AE. Dissecting malaria biology and epidemiology using population genetics and geno-

mics. International journal for parasitology. 2016.

10. Noviyanti R, Coutrier F, Utami RA, Trimarsanto H, Tirta YK, Trianty L, et al. Contrasting Transmission

Dynamics of Co-endemic Plasmodium vivax and P. falciparum: Implications for Malaria Control and

Elimination. PLoS neglected tropical diseases. 2015; 9(5):e0003739. https://doi.org/10.1371/journal.

pntd.0003739 PMID: 25951184

11. WHO. Eliminating malaria. Geneva: WHO 2016:24.

12. Singh B, Bobogare A, Cox-Singh J, Snounou G, Abdullah MS, Rahman HA. A genus- and species-spe-

cific nested polymerase chain reaction malaria detection assay for epidemiologic studies. The American

journal of tropical medicine and hygiene. 1999; 60(4):687–92. PMID: 10348249

13. Anderson TJ, Su XZ, Bockarie M, Lagog M, Day KP. Twelve microsatellite markers for characterization

of Plasmodium falciparum from finger-prick blood samples. Parasitology. 1999; 119 (Pt 2):113–25.

14. Karunaweera ND, Ferreira MU, Munasinghe A, Barnwell JW, Collins WE, King CL, et al. Extensive

microsatellite diversity in the human malaria parasite Plasmodium vivax. Gene. 2008; 410(1):105–12.

https://doi.org/10.1016/j.gene.2007.11.022 PMID: 18226474

15. Koepfli C, Mueller I, Marfurt J, Goroti M, Sie A, Oa O, et al. Evaluation of Plasmodium vivax genotyping

markers for molecular monitoring in clinical trials. The Journal of infectious diseases. 2009; 199

(7):1074–80. https://doi.org/10.1086/597303 PMID: 19275476

16. Hamedi Y, Sharifi-Sarasiabi K, Dehghan F, Safari R, To S, Handayuni I, et al. Molecular Epidemiology

of P. vivax in Iran: High Diversity and Complex Sub-Structure Using Neutral Markers, but No Evidence

of Y976F Mutation at pvmdr1. PloS one. 2016; 11(11):e0166124. https://doi.org/10.1371/journal.pone.

0166124 PMID: 27829067

17. Hedrick PW. A standardized genetic differentiation measure. Evolution. 2005; 59(8):1633–8. PMID:

16329237

18. Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics

analyses under Linux and Windows. Mol Ecol Resour. 2010; 10(3):564–7. https://doi.org/10.1111/j.

1755-0998.2010.02847.x PMID: 21565059

19. Sutton PL. A call to arms: on refining Plasmodium vivax microsatellite marker panels for comparing global

diversity. Malaria journal. 2013; 12:447. https://doi.org/10.1186/1475-2875-12-447 PMID: 24330329

20. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype

data. Genetics. 2000; 155(2):945–59. PMID: 10835412

Genetic micro-epidemiology of malaria in Papua Indonesia

PLOS ONE | https://doi.org/10.1371/journal.pone.0177445 May 12, 2017 12 / 13

21. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software

STRUCTURE: a simulation study. Mol Ecol. 2005; 14(8):2611–20. https://doi.org/10.1111/j.1365-294X.

2005.02553.x PMID: 15969739

22. NA R. Distruct: a program for the graphical display of population structure. Molecular Ecology Notes.

2004; 4:137–8.

23. Haubold B, Hudson RR. LIAN 3.0: detecting linkage disequilibrium in multilocus data. Linkage Analysis.

Bioinformatics. 2000; 16(9):847–8. PMID: 11108709

24. Bowcock AM, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd JR, Cavalli-Sforza LL. High resolution of

human evolutionary trees with polymorphic microsatellites. Nature. 1994; 368(6470):455–7. https://doi.

org/10.1038/368455a0 PMID: 7510853

25. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol

Biol Evol. 1987; 4(4):406–25. PMID: 3447015

26. Paradis E, Claude J, Strimmer K. APE: Analyses of Phylogenetics and Evolution in R language. Bioin-

formatics. 2004; 20(2):289–90. PMID: 14734327

27. Jennison C, Arnott A, Tessier N, Tavul L, Koepfli C, Felger I, et al. Plasmodium vivax populations are

more genetically diverse and less structured than sympatric Plasmodium falciparum populations. PLoS

neglected tropical diseases. 2015; 9(4):e0003634. https://doi.org/10.1371/journal.pntd.0003634 PMID:

25874894

28. Orjuela-Sanchez P, Sa JM, Brandi MC, Rodrigues PT, Bastos MS, Amaratunga C, et al. Higher micro-

satellite diversity in Plasmodium vivax than in sympatric Plasmodium falciparum populations in Pursat,

Western Cambodia. Experimental parasitology. 2013; 134(3):318–26. https://doi.org/10.1016/j.

exppara.2013.03.029 PMID: 23562882

29. Ferreira MU, Karunaweera ND, da Silva-Nunes M, da Silva NS, Wirth DF, Hartl DL. Population structure

and transmission dynamics of Plasmodium vivax in rural Amazonia. The Journal of infectious diseases.

2007; 195(8):1218–26. https://doi.org/10.1086/512685 PMID: 17357061

30. Gray KA, Dowd S, Bain L, Bobogare A, Wini L, Shanks GD, et al. Population genetics of Plasmodium

falciparum and Plasmodium vivax and asymptomatic malaria in Temotu Province, Solomon Islands.

Malaria journal. 2013; 12:429. https://doi.org/10.1186/1475-2875-12-429 PMID: 24261646

31. Pumpaibool T, Arnathau C, Durand P, Kanchanakhan N, Siripoon N, Suegorn A, et al. Genetic diversity

and population structure of Plasmodium falciparum in Thailand, a low transmission country. Malaria

journal. 2009; 8:155. https://doi.org/10.1186/1475-2875-8-155 PMID: 19602241

32. Koepfli C, Rodrigues PT, Antao T, Orjuela-Sanchez P, Van den Eede P, Gamboa D, et al. Plasmodium

vivax Diversity and Population Structure across Four Continents. PLoS neglected tropical diseases.

2015; 9(6):e0003872. https://doi.org/10.1371/journal.pntd.0003872 PMID: 26125189

33. Cheng Q, Cunningham J, Gatton ML. Systematic review of sub-microscopic P. vivax infections: preva-

lence and determining factors. PLoS neglected tropical diseases. 2015; 9(1):e3413. https://doi.org/10.

1371/journal.pntd.0003413 PMID: 25569135

34. Preston MD, Campino S, Assefa SA, Echeverry DF, Ocholla H, Amambua-Ngwa A, et al. A barcode of

organellar genome polymorphisms identifies the geographic origin of Plasmodium falciparum strains.

Nature communications. 2014; 5:4052. https://doi.org/10.1038/ncomms5052 PMID: 24923250

35. Laufer MK, Plowe CV. Withdrawing antimalarial drugs: impact on parasite resistance and implications

for malaria treatment policies. Drug Resist Updat. 2004; 7(4–5):279–88. https://doi.org/10.1016/j.drup.

2004.08.003 PMID: 15533765

36. Miotto O, Almagro-Garcia J, Manske M, Macinnis B, Campino S, Rockett KA, et al. Multiple populations

of artemisinin-resistant Plasmodium falciparum in Cambodia. Nat Genet. 2013; 45(6):648–55. https://

doi.org/10.1038/ng.2624 PMID: 23624527

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CHAPTER 3

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Supplementary Material

S1 Table. Summary of the diversity and genotyping success rate for each of the P.

falciparum and P. vivax markers.

Species Marker HE Failure, n(%)

P. vivax

MS12 0.741 28 (8.1)

pv3.27 0.879 32 (9.3)

msp1f3 0.808 36 (10.5)

MS10 0.899 21 (6.1)

MS5 0.849 16 (4.7)

MS1 0.77 23 (6.7)

MS16 0.941 33 (9.6)

MS20 0.891 53 (15.4)

P. falciparum

Poly-alpha 0.717 12 (3.3)

TA42 0.315 76 (20.6)

TA81 0.82 40 (10.8)

TA87 0.672 13 (3.5)

ARAII 0.805 12 (3.3)

PfPK2 0.722 19 (5.1)

TA60 0.724 4 (1.1)

TA1 0.679 62 (16.8)

TA109 0.176 10 (2.7)

HE: Expected heterozygosity

CHAPTER 3

102

Figure S1. Geographical distribution of the households at which malaria cases were

detected in the cross-sectional household survey. The plot was generated using ArcGIS

software on GPS coordinate data from the individuals identified with malaria parasitaemia.

Each dot presents an individual case, with colour-coding by species according to PCR data.

CHAPTER 3

103

Figure S2. Geographical distribution of identical MLGs in P. falciparum. This plot was

generated using ArcGIS software on the GPS coordinates for the 22 identical MLGs in the P.

falciparum population. Different MLGs are distinguished with different colours and shapes.

Circles, pentagons and diamonds indicate MLGs found in two, three, and more than three

individuals respectively.

CHAPTER 3

104

Figure S3. Geographical distribution of the households at which P. falciparum K1 and K2

isolates were detected in the cross-sectional household survey. The plot was generated

using ArcGIS software on GPS coordinate data. Each dot presents an individual P.

falciparum case, with colour-coding by subpopulation as defined by STRUCTURE.

CHAPTER 3

105

Figure S4. Location of the study sites. This map is a modified version of Figure 1 presented by Noviyanti et al., with the addition here of a site label

for Timika, Papua Indonesia (Noviyanti et al. 2015). The original map was generated by the Malaria Atlas Project, University of Oxford. The colour

scales reflect the model-based geostatistical point estimates of the annual mean P. falciparum parasite rate in the 2–10 year age group (PfPR2–10)

within the stable spatial limits of transmission in 2010. The approximate locations of the study sites described here are indicated with black stars.

CHAPTER 3

106

Figure S5. Principal Coordinate Analysis (PCoA) between P. vivax isolates from Timika

relative to Kalimantan, Bangka, Sumba and West Timor. This plot illustrates the limited

differentiation between the Timika P. vivax population and the other four Indonesian

islands.

107

CHAPTER 4 – Passively versus Actively Detected Malaria: Similar

Genetic Diversity but Different Complexity of Infection

Am. J. Trop. Med. Hyg., 97(6), 2017, pp. 1788–1796doi:10.4269/ajtmh.17-0364Copyright © 2017 by The American Society of Tropical Medicine and Hygiene

Passively versus Actively Detected Malaria: Similar Genetic Diversity butDifferent Complexity of Infection

Zuleima Pava,1 Irene Handayuni,1 Leily Trianty,2 Retno A. S. Utami,2 Yusrifar K. Tirta,2 Agatha M. Puspitasari,2

Faustina Burdam,3,4,5 Enny Kenangalem,3,4 Grennady Wirjanata,1 Steven Kho,1 Hidayat Trimarsanto,2 Nicholas Anstey,1

Jeanne Rini Poespoprodjo,3,4,5 Rintis Noviyanti,2 Ric N. Price,1,6 Jutta Marfurt,1† and Sarah Auburn1*†1Global andTropicalHealthDivision,MenziesSchool ofHealthResearch,CharlesDarwinUniversity,Darwin,NorthernTerritory, Australia; 2Eijkman

Institute for Molecular Biology, Jakarta, Indonesia; 3Mimika District Health Authority, Timika, Papua, Indonesia; 4Timika Malaria ResearchProgramme, Papuan Health and Community Development Foundation, Timika, Papua, Indonesia; 5Maternal and Child Health and

Reproductive Health, Department of Public Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia; 6Centre for TropicalMedicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom

Abstract. The surveillance of malaria is generally undertaken on the assumption that samples passively collected athealth facilities are comparable to or representative of the broader Plasmodium reservoir circulating in the community.Further characterization and comparability of the hidden asymptomatic parasite reservoir are needed to inform on thepotential impact of sampling bias. This study explores the impact of sampling strategy on molecular surveillance bycomparing the genetic make-up of Plasmodium falciparum and Plasmodium vivax isolates collected by passive versusactive case detection. Sympatric isolates of P. falciparum andP. vivaxwere collected from a large community survey andongoing clinical surveillance studies undertaken in the hypomesoendemic setting of Mimika District (Papua, Indonesia).Plasmodium falciparum isolates were genotyped at ninemicrosatellite loci andP. vivax at eight loci. Measures of diversityand differentiation were used to compare different patient and parasitological sample groups. The results demonstratedthat passively detected cases (symptomatic) had comparable population diversity to those circulating in the community(asymptomatic) in both species. In addition, asymptomatic patent infections were as diverse as subpatent infections.However, a significant difference in multiplicity of infection (MOI) and percentage of polyclonal infections was observedbetweenactively andpassively detectedP. vivax cases (meanMOI: 1.7± 0.7 versus1.4 ± 1.4, respectively;P=0.001). Thestudy findings infer that, in hypomesoendemic settings, passive sampling is appropriate for molecular parasite surveil-lance strategies using thepredominant clone in anygiven infection; however, the findings suggest cautionwhenanalyzingcomplexity of infection. Further evaluation is required in other endemic settings.

BACKGROUND

Calls for the global elimination of malaria have renewedpolitical interest in public health interventions, reigniting con-trol activities and placing elimination back on the agenda formany endemic countries.1 With the wide-scale deployment ofkey intervention strategies such as early diagnosis, prompttreatment, and intense vector control, 33 countries havereached the malaria elimination stage between 2000 and2015.2 However, as the incidence of malaria falls in endemicsettings, increasingly sensitive diagnostic tools are reveal-ing large reservoirs of low-density malaria infections high-lighting a far greater ‘depth’ of malaria burden.3–6 Thesesubpatent infections are not routinely detectable by conven-tional microscopy and can persist for several months withoutclinical manifestations. Both of the predominant species,P. falciparum and P. vivax, are able to cause submicroscopicinfections, although the relative proportion of subpatent casestends to be higher in P. vivax than P. falciparum.2,7

Subpatent infections can be detected in a wide range ofmalaria-endemic settings, with the proportion tending to risein low transmission areas.7–9 As countries approach malariaelimination, it is crucial to identify every infection to stop on-going transmission and prevent resurgence. In this context,intense surveillance is required to monitor clinical and para-sitological changes that can inform the most effective in-tervention strategies.

The majority of such surveillance efforts have been un-dertaken at health facilities, recruiting symptomatic individu-als with patent parasitaemia seeking treatment (“passive casedetection”). As malaria elimination efforts progress, more in-depth knowledge of the parasite population in asymptomaticcommunity cases is needed, as this reservoir contributes agreater proportion of the total parasite burden. An importantquestion is whether the genetic make-up of actively detectedparasites is adequately represented by those detected pas-sively, or whether the different sampling strategies can resultin a significant bias which can affect the interpretation of agiven study. The surveillance of antimalarial drug resistancecould be particularly vulnerable to sampling bias, becausephenotypic and molecular studies are generally applied toclinical isolates with little focus given to the asymptomaticpopulation. In the case of ex vivo drug susceptibility testing,stringent criteria are applied which can result in even moreconstrained sampling.10,11 Furthermore, in pre-eliminationsettings, the low number of malaria cases in parasite pop-ulation genetic studies often requires pooling of samples frompatients with different disease presentations, collected us-ing different sampling strategies and methodologies. Thesestudies are being used increasingly to inform local trends inparasite transmission as reflected by measures of within-hostand population diversity.12,13 A recent population geneticstudy ofP. falciparum in a lowendemic area of Zambia inferredthat actively and passively detected infections formed distinctsubpopulations with differing levels of diversity, but the studywas partly constrained by the difference in years of collectionbetween the actively and passively detected cases.14 Anotherstudy conducted in the higher endemic setting of Mimika

*Addresscorrespondence toSarahAuburn,MenziesSchool ofHealthResearch, PO Box 41096, Casuarina, Darwin, NT 0811, Australia.E-mail: sarah.auburn@menzies.edu.au†These authors contributed equally to the work.

1788

District, Papua, Indonesia, using samples from both clini-cal and community cases revealed a subpopulation ofP. falciparum isolates only observed in community cases.15

However, the latter study did not directly investigate the levelof genetic differentiation or differences in population diversityand infection complexity between the actively and passivelydetected P. falciparum isolates. Furthermore, to date, nostudies have directly compared the geneticmake-up betweenactively and passively detected P. vivax infections, whichcontribute a large burden of malaria infections in the Asia-Pacific region.This study used sympatric P. falciparum and P. vivax iso-

lates obtained from a community survey and ongoing clinicalsurveillance studies in Mimika District, Papua, Indonesia, tocompare the genetic make-up of samples collected by pas-sive versus active case detection, and between patent andsubpatent groups.

MATERIALS AND METHODS

Study site and sampling. Passive case detection. Thestudy was performed in Mimika District, Papua, Indonesia.Nearly 90% of the population of approximately 182,000 peo-ple live in lowland areas where unstable malaria transmissionoccurs. Although all four species of human-only infectingPlasmodium parasites can be found in the area, P. falciparumand P. vivax are the most common species causing 41% and49% of malaria monospecies infections, respectively. Plas-modium malariae accounts for approximately 4% of malariacases and less than 1% of cases are caused by P. ovale.Mixed-species malaria infections can be found in up to 6% ofcases.16 Rumah Sakit Mitra Masyarakat (RSMM) is the mainhospital in the district. RSMM is located in the capital city,Timika, and serves approximately 150,000 people, assessingup to 300 patients/day, 6 days/week. Patients with un-complicated malaria due to any Plasmodium species aretreated with dihydroartemisinin-piperaquine as per the na-tional first-line protocol.17

Samples at the RSMM were collected within the frame-work of two studies. Between March 2011 and August 2014,patients presenting with an uncomplicated P. vivax or P.falciparummonoinfection, a peripheral parasitaemia between1,000 μL−1 and 80,000 μL−1, as determined by microscopicexamination, andmore than 70% of ring stage parasites wereinvited to participate in an ongoing ex vivo antimalarial sus-ceptibility surveillance study (ex vivo susceptibility study,ESS). Between March 2015 and February 2016, patientspresenting with uncomplicated P. falciparum or P. vivaxmalaria with peripheral parasitaemia greater than 1,000 μL−1

and 250 μL−1, respectively, but with no restriction by parasitestage, were invited to participate in an ongoing treatment ef-ficacy study (TES). After obtaining written consent, 5 mL ofvenous blood was collected for all patients by venipunctureand filtered using cellulose columns to deplete the host whiteblood cells.18 The postfiltration packed infected red bloodcells were used for DNA extraction.Active case detection. Active case detection was un-

dertaken within the framework of a cross-sectional surveyconducted in Timika between April and July 2013. Details onthe sampling methods of the survey can be found else-where.16 Briefly, 800 houses were selected by cluster ran-domized sampling. First, the four largest subdistricts were

chosen purposively. Consecutively, the number of clustersper district was calculated according to their respective rela-tive population. Finally, 20 houses were chosen randomlywithin each cluster, following World Health Organization rec-ommendations. Individuals living in the same house and re-siding in the study area for at least 6 months were included inthe study. Symptomatic patients were defined as any indi-vidual with a current fever episode (i.e., axillary temperature >37.5�C) or in the last 24 hours. Venous blood from one adultvolunteer and 200 mL of capillary blood in aMicrotainer™ fromthe remainingmembers of eachhouseholdwere collected andused for blood film examination, hemoglobin measurement,and molecular analysis.DNA extraction and species confirmation. Plasmodium

species was assessed initially by microscopy using Giemsa-stained thick and thinblood films.Peripheral parasitaemiawasdetermined from the number of parasites per 200 white bloodcells, assuming a white cell count of 7,300 cells/mL. Asexualstage composition was determined by classifying 100 para-sites into ring, trophozoite, or schizont stage on examinationof a thick smear.For actively detected cases, genomic DNA (gDNA) was

extracted from 50 mL packed red blood cell (RBC) pellets usingthe QIAamp 96 DNA Blood Kit (Qiagen, Chadstone, Victoria,Australia). For passively detected cases, 2 mL of packed RBCswere extracted using the QIAamp DNA Midi Kit (Qiagen, Chad-stone, Victoria, Australia).Plasmodium species confirmationwasundertaken in duplicatewith 2 mL gDNA template using a nestedPCR protocol as described elsewhere.19 Plasmodium falcipa-rum, P. vivax, P. malariae, and P. ovale small-subunit rRNA DNAclones (MRA-177, MRA-178, MRA-179, and MRA-180; ATCC,Manassas, VA) were used as positive controls.Microsatellite typing. Plasmodium falciparum genotyping

was conducted using nine previously described short tandemrepeat (STR) markers (ARAII, PfPK2, poly-alpha, TA1, TA42,TA60, TA81, TA87, and TA109), following the protocol de-scribed by Anderson et al.20 For P. vivax genotyping, ninepreviously described STR markers selected as a consensuspanel by the Asia Pacific Malaria Elimination Network VivaxWorking Group (Pv3.27, MS16, MS1, MS5, MS8, MS10,MS12, MS20, and msp1F3) were used following previouslydescribed protocols.21,22 The fluorescently labeled PCRproducts were separated by capillary electrophoresis on anABI 3100 Genetic Analyser with GeneScan LIZ-600 sizestandard (Applied Biosystems, Mulgrave, Victoria, Australia).The resulting electrophoretograms were analyzed usingVivaxGEN version 1.0 and verified manually.23 To reduce po-tential artifacts from background noise, an arbitrary fluores-cent intensity threshold of 50 relative fluorescence units wasused. Only samples with information in at least 50%of the lociwere considered as successfully genotyped.Dataanalysis.Populationdiversityandgeneticdifferentiation.

Diversity was estimated bymeans of allelic richness (Rs).Rswascalculated by quantifying the number of alleles and normalizingthe value to the sample size, using the hierfstat package in R.24

The expected heterozygosity (HE), which is defined as the prob-ability of finding a different allele at a specific locus when ran-domly selecting a pair of samples from the samepopulation, wasmeasured as an additional index of population diversity. Thepairwise FST metric was used to determine the genetic distancebetween populations and interpreted according to the classifi-cationofBallouxandLugon-Moulin25 (i.e., low if theFSTvaluewas

PASSIVELY VERSUS ACTIVELY DETECTED MALARIA 1789

less than 0.05; moderate if between 0.05 and 0.15; high if be-tween 0.15 and 0.25; and very high if greater than 0.25). Arlequinsoftware (version 3.5) was used to calculate HE, FST, and thestandardized measure (F’ST), which adjusts for high marker di-versity. Significance values for the FSTwere derived using 10,000permutations of the data in Arlequin.Linkage disequilibrium (LD). To assess changes in trans-

mission over time and to further characterize the parasitesconstituting the different subgroups, multilocus LD wasmeasured using the standardized index of association (IA

S).First, multilocus genotypes (MLGs) were reconstructed fromthepredominant allele at each locus in isolateswith nomissingdata. Then, using theMLGs, IA

Swasmeasured using theweb-based LIAN 3.5 software, assessing the significance ofthe estimates using 10,000 permutations of the data.26 Theanalysis was also performed using low-complexity infections(maximumof onemultiallelic locus) andwith each uniqueMLGrepresented just once.Polyclonality and multiplicity of infection. Malaria parasites

are haploid during the asexual stage; hence, the presence ofmore than one allele at a given locus in an individual sample isindicative of multiple parasite clones. In contrast to singlenucleotide polymorphism-based markers, which would onlyreach up to four variants, the abundance of alleles at STRmarkers greatly facilitates the detection of polyclonal infec-tions. However, the quality and quantity of parasite DNA mayimpact the sensitivity to detect minor alleles in polyclonal in-fections. In theory,minor clones aremore likely to be identifiedin samples with larger quantities of parasite DNA. For thisreason, Anderson and colleagues introduced the applicationof aminimum relative peak intensity threshold for callingminoralleles to enhance the comparability of multiplicity of infection(MOI) and polyclonality estimates between parasite pop-ulations with differing DNA quality or quantities.20 In view ofthe different sample qualities and quantities evaluated in thecurrent study, minor alleles were only called if their height wasat least 33% of the major allele’s height: a threshold that hasbeen applied in multiple previous population genetic studiesof malaria.27 An infection was defined as polyclonal if morethan one allele was observed at any locus. TheMOI for a givensample was defined as the maximum number of alleles at anylocus. The number of polyclonal loci per sample was alsoestimated for closer inspection of the complexity of individualinfections in the effort to identify any subtle differences be-tween groups.Statistical tests. R and SPSS software were used for sta-

tistical analysis with a significance level at P < 0.05.28 Sub-group analysis was performed according to 1) patientrecruitment method (active or passive) and 2) infection pa-tency (i.e., patent or subpatent) using the χ2 test and Fisher’sExact Test. Differences in MOI and HE between subgroupsand species were assessed using the Mann–Whitney U orKruskal–Wallis test.Ethics approval. Ethical approval for this study has been

obtained from the Eijkman Institute Research Ethics Com-mission, Eijkman Institute for Molecular Biology, Jakarta,Indonesia (EIREC-47, EIREC-67, and EIREC-75), and theHuman Research Ethics Committee of the Northern TerritoryDepartment of Health & Families and Menzies School ofHealth Research, Darwin, Australia (HREC 2010-1396).

RESULTS

Marker properties. A summary of the diversity and geno-typing success rate for each of the P. falciparum and P. vivaxmarkers in all genotyped samples is presented in Supple-mental Table 1. Moderate to high diversity was found for allP. falciparummarkers except for TA42 and TA109, particularlyin the TES and ESS samples (Supplemental Table 1). Bycontrast, highdiversitywas found inallP. vivaxmarkers acrossall sample sets. However, the MS8 assay presented artifactsthat affected the reliability of the allele calling and this locuswas, therefore, excluded from further analysis. Genotype in-formation for 50% of the marker set (five loci for P. falciparumand four loci for P. vivax) was obtained from 84.5% (414/492)P. falciparum samples and from 70.7% (390/552) of P. vivaxsamples. Most of the failed samples were asymptomaticP. vivax or P. falciparum infections (97%; 157/162 and 99%;77/78, respectively).Sampling groups. Passively detected cases. All passively

detected cases presented with microscopically confirmedparasitaemia. The comparability of the isolates collected fromthe clinical and ex vivo studies were assessed before pooling.No difference was observed between the median para-sitaemia of the TES and ESS samples either in P. falciparum(N = 45, medianTES 12,240 parasites μL−1; and N = 94,medianESS 37,143 parasites μL−1; P = 0.145) or P. vivax(N = 46, medianTES 11,041 parasites μL−1; and N = 104,medianESS 16,224 parasites μL−1; P = 0.306). Furthermore,given that the TES and ESS samples were collected acrossmultiple years, an assessment of temporal differences inMOI,polyclonality, LD,HE, andRswasundertakenwith grouping byyear of collection. Over the 6 years of sample collection, theproportion of polyclonal infections for P. falciparum variedfrom 0% to 31%, (P = 0.085) and from 23% to 67% (P = 0.104)for P. vivax, whilst the mean MOI ranged from 1.0 to 1.3 forP. falciparum, and from 1.2 to 2.0 for P. vivax (P = 0.085 andP = 0.104, respectively; Supplemental Table 2). Populationdiversity (HE) values ranged from 0.448 to 0.608 forP. falciparum (P = 0.648) and from 0.840 to 0.881 for P. vivax(P = 0.932). Furthermore, there was no difference between Rsfor P. falciparum (RS = 2.571–3.328; P = 0.333) or P. vivax(RS = 4.927–5.481; P = 0.429). LD demonstrated modestfluctuations between the study years, with a slight increaseobserved in the P. falciparum cases in 2015 (SupplementalTable 3). Nonetheless, because there were no significant dif-ferences in diversity or complexity of infection between thestudy years, all TES and ESS samples were pooled into asingle group defined as the passively detected patent samplegroup (PP) for subsequent analyses.Actively detected cases.Only 18 of the isolates collected by

active case detection were symptomatic (N = 5 P. vivax andN = 13 P. falciparum). The small sample size did not allowcomparisons with other subgroups and, therefore, these wereexcluded from further analyses. Because venous bloodsamples were only taken from adults, MOI, polyclonality, Rs,and HE were compared between adults (³ 15 years) and chil-dren (< 15 years), but no significant differenceswere observedin any of these measures (Supplemental Table 4). Likewise,there were no significant differences in MOI, polyclonality, Rs,and HE between isolates from capillary or venous samples(Supplemental Table 5). Samples were, therefore, pooledinto the following two subgroups: actively detected patent

1790 PAVA AND OTHERS

infections (AP) and actively detected subpatent infections(AS). In summary, AP comprises asymptomatic cases with aslide positive for P. falciparum or P. vivax monoinfection,whereas AS comprises asymptomatic cases with a slidenegative for malaria, but PCR positive for either P. falciparumor P. vivax monoinfection.The demographic details of all subgroups are presented in

Table 1. Similar characteristics were observed across thegroups in all features other than parasitaemia. The overallmedian parasitaemia was similar between the two species(Mann–Whitney U Test, P = 0.482), but significantly differentbetween active and passively detected cases for both species(P < 0.0001).Stage composition andwithin-host diversity. Isolates for

ex vivo susceptibility testing (ESS) were only selected if thering stage composition was ³ 70% and thus, the potentialconfounding of parasite staging was assessed. After exclud-ing the ESS and AS samples, P. falciparum infections almostall comprised predominantly ring stages in both actively de-tected patent (AP; 89.4%, 84/94) and passively detectedcases (PP, 97.8%, 44/45; P = 0.104). By contrast, the per-centage of synchronous ring stage P. vivax infections was11.1% (9/81) in AP compared with 41.3% (19/46) in PP sam-ples (P > 0.001; Supplemental Table 6).Among the AP cases, P. falciparum infections showed

similar complexity between synchronous (MOIS = 1.23; SD ±0.4) and asynchronous infections (MOIA = 1.30; SD ± 0.5;P = 0.604), with polyclonal infections present in 23% (19/84)and30%(3/10) of the cases, respectively (P=0.694). Similarly,P. vivax infections showed comparable complexity of infec-tion between synchronous and asynchronous infections:MOIS = 1.78 (SD ± 1.1) versus MOIA = 1.75 (SD ± 0.8;P = 0.820), as well as proportion of polyclonal infections: 44%(4/9) and 54% (39/72), respectively (P = 0.728). Likewise,comparison between synchronous and asynchronous pas-sively detected P. vivax cases (ESS and TES) revealed nosignificant difference in complexity of infection (MOIS = 1.39(±0.6) versus MOIA = 1.52 (±0.8); P = 0.463), or percentage ofpolyclonal infections (34% (42/123) versus 41% (11/27),P = 0.516; Table 2). Because there was only one asynchro-nous P. falciparum infection among the passively detectedcases, no further comparisons could be performed. However,

when the three datasets were combined, the results remainedsimilar (Table 2).Parasite density and within-host diversity. Assessment

of MOI and parasitaemia in patent infections revealed a weak,but significantly negative correlation between MOI and para-sitaemia for P. vivax (rho = −0.400, P = 0.032), but not forP. falciparum (rho = −0.095, P = 0.146). However, the corre-lation was not apparent after excluding the ESS samples forneither P. vivax (rho = −0.117, P = 0.184), nor P. falciparum(rho = −0.133, P = 0.112).Population diversity, population differentiation, and

LD analysis. There was no significant difference in theP. falciparum Rs between AP and AS (P = 0.635), between APand PP (P = 0.170), and between PP and AS (P = 0.095;Table 3). Similarly, P. vivax Rs was comparable between APand AS (P = 0.640), between AP and PP (P = 0.809), and be-tween PP and AS (P = 0.799; Table 3). HE values were alsocomparable across the subgroups in both species (Table 3).FST values varied from 0.007 to 0.047 and −0.002 to 0.003 forP. falciparum and P. vivax, respectively. Likewise, no differ-entiation between subgroups was observed for P. falciparumor P. vivax infections (Table 4).Therewasno evidenceof significant LD in anyof theP. vivax

subgroups, with IAS scores ranging from 0 to 0.008 (Table 5).

By contrast, significant LD was observed in each of theP. falciparum subgroups, with higher IA

S scores, ranging from0.05 to 0.15 (Table 5). The highest levels of LD were observedin the actively detected P. falciparum subgroups. Furtheranalysis using unique MLGs did not reveal evidence of clonalexpansion in any of the subgroups in either species (Table 5).Within-host diversity: complexity of infection

and polyclonality. As summarized in Table 3, the mean MOIand the proportion of polyclonal infections was similar be-tween AP and AS infections in P. falciparum (MOIAP = 1.2(±0.42), 23%; andMOISP = 1.2 (±0.39), 16%;P= 0.199 andP=0.189, respectively). Similar MOI and polyclonality rates werealso observed between AP and AS infections in P. vivax(MOIAP = 1.7 [±0.84], 52%; andMOIAS = 1.6 [±0.69], 50%; P =0.407 and P = 0.721, respectively).Although actively detected P. falciparum cases showed a

higher mean MOI and a higher proportion of polyclonal in-fections compared with the passively detected infections, thedifference was only statistically significant between the

TABLE 1Demographic data of all cases

Subgroups

Age,* N (%)

Males N (%) Parasitaemia† median (range) Total< 5 5–15 > 15

P. falciparum13

Passively detected‡ 0 19 (13.6) 121 (86.4) 68 (48.6) 14,837 (3,280–361,728) 140Actively detected patent§ 9 (8.6) 31 (29.5) 65 (61.9) 49 (46.7) 600 (27–132,256) 105Actively detected subpatentk 9 (4.1) 57 (26.3) 151 (69.6) 81 (37.3) n/a 217

All cases 18 (4.4)2 107 (23.1)5 337 (72.9)6 204 (42.9) 7,595 (27–361,728) 4,6213P. vivax5

Passively detected‡ 0 28 (18.2) 126 (81.8) 75 (48.4) 15,072 (1,640–120,576) 155Actively detected patent§ 20 (23.3) 25 (29.1) 41 (47.7) 40 (46.5) 385 (38–19,200) 86Actively detected subpatentk 17 (7.4) 72 (31.4) 140 (61.1) 90 (39.3) n/a 229

All cases 37 (8.0)1 125 (26.4) 311 (65.6)4 207 (43.6) 7,884 (38–120,576) 4,755Superscript numbers indicate excluded symptomatic community survey samples.* Age in years.†Parasites/mL.‡Symptomatic patent infections.§ Asymptomatic patent infections.kAsymptomatic subpatent infections.

PASSIVELY VERSUS ACTIVELY DETECTED MALARIA 1791

proportion of polyclonal infections in the AP (23%) comparedwith thePPcases (12%;P=0.035; Table 3). By contrast, whencomparing P. vivax subgroups, the actively detected casesshowed consistently higher mean MOI (MOIAP = 1.7, [±0.84],MOIAS = 1.6, [±0.69]) compared with the passively detectedinfections (MOIPP = 1.4, [SD ± 0.63], P = 0.003), and a higherproportion of polyclonal infections (51% and 35%, re-spectively, P = 0.003; Table 3).The number of multiallelic loci (MLOCI) in the polyclonal in-

fections in each of the P. vivax and P. falciparum subgroups isillustrated in Figure 1. The actively detected polyclonal P. vivaxinfections presented a higher average MLOCI than those de-tected passively (MLOCIA = 2.8, [±1.7], MLOCIP = 2.1, [±1.5];P = 0.010). The actively detected P. falciparum infections pre-sented a lower average MLOCI than passively detected infec-tions but the results were not statistically significant (MLOCIA =1.6, [±0.9], MLOCIP = 2.0, [±1.1]; P = 0.140).

DISCUSSION

This study presents a comprehensive comparative geneticanalysis of actively versus passively detected cases of P.falciparum and P. vivax. Knowledge regarding potential sam-pling bias is vital for the effective design and interpretation ofsurveillance studies of malaria, particularly passive samplingstrategies. The current study highlights the challenges that are

associatedwith the interpretation ofmolecular data generatedfromdifferent sampling sourcesand thekey issues to considerfor sampling strategies.In a previous study conducted in Mimika district, genetic

substructure was observed in the P. falciparum population,with a small subpopulation of infections found in actively de-tected cases only.15 On further investigation of the geneticdifferences between Plasmodium populations collected byactive versus passive sampling strategies conducted in thesame population, the current study revealed that there was noevidence of any significant differences in genetic diversitybetween the passively and actively detected subgroups ineither P. falciparum or P. vivax. Furthermore, low differentia-tion between the subgroups in both P. falciparum and P. vivaxconfirmed that thedifferent subgroups appear to comprise thesame general pool of parasite strains. The highest levels ofgenetic differentiation were observed in comparisons of ac-tively versus passively detected cases in P. falciparum (FST =0.023–0.05; F’ST = 0.05–0.12), likely reflecting the aforemen-tioned small subpopulation of asymptomatic infections andpossibly also inflated by modest temporal changes in LD. In-deed, LD analysis revealed higher allelic association amongthe actively detectedP. falciparum cases in comparison to thepassively detected cases. Nonetheless, the FST and F’ST val-ues observed between the P. falciparum subgroups wereall markedly lower than those reported by Noviyanti et al.29

TABLE 2Within-host diversity and ring stage synchronicity in Plasmodium falciparum and Plasmodium vivax sample sets

Sample sets Synchronous* N (%) MOI Mean (SD)† P‡ Polyclonality N (%) P§

P. falciparumActively detected casesk1 Yes 84 (89) 1.23 (0.42) 0.604 19 (23) 0.694

No 10 (11) 1.30 (0.48) 3 (30)Passively detected cases{ Yes 138 (99) 1.12 (0.33) – 17 (12) –

No 1 (1) – –

P. vivaxActively detected casesk6 Yes 9 (11) 1.78 (1.09) 0.820 4 (44) 0.728#

No 72 (89) 1.75 (0.82) 39 (54)Passively detected cases{5 Yes 123 (82) 1.39 (0.60) 0.463 42 (34) 0.516

No 27 (18) 1.52 (0.75) 11 (41)MOI = multiplicity of infection.* Ring stage >70%.†Standard deviation.‡Mann–Whitney U test.§ χ2.kCommunity survey.{Treatment efficacy study and ex vivo surveillance study.# Fisher’s exact test.** Superscript indicates number of cases with missing data.

TABLE 3Within host and genetic diversity analysis of Plasmodium falciparum and Plasmodium vivax subgroups

Subgroups N MOI Mean* (±SD) Polyclonal infections N (%) RS† Mean (±SD)

P. falciparumPassively detected§ 139 1.1 (0.329) 17 (12) 5.771 (2.81)Actively detected patentk 100 1.2 (0.423) 23 (23) 6.476 (2.06)Actively detected subpatent 164 1.2 (0.393) 27 (16) 6.392 (1.95)All actively detected 264 1.2 (0.405) 50 (19) 6.731 (2.08)

P. vivaxPassively detected 150 1.4 (0.626) 53 (35) 17.790 (7.20)Actively detected patent 82 1.7 (0.843) 43 (52) 14.645 (7.72)Actively detected subpatent 155 1.6 (0.686) 78 (50) 14.654 (6.75)All actively detected 237 1.7 (0.745) 121 (51) 16.540 (8.80)MOI = multiplicity of infection.* Standard deviation.†Allelic richness.‡Symptomatic patent infections§Asymptomatic patent infections.kAsymptomatic subpatent infections.

1792 PAVA AND OTHERS

between different Indonesian islands (FST = 0.08–0.41;F’ST = 0.23–0.78).The equivalence in population diversity and low differenti-

ation between the ESS and the other sample subgroupssuggests that despite the application of strict sampleselection,10,11 the results of drug susceptibility testing arelikely to be representative of the parasites circulating in thebroader population. Therefore, if parasites with resistance-conferring mutations were arising in the community, theywould be likely to bedetected in the ex vivo surveillance study.The results of the study have important implications for our

understanding of the molecular epidemiology of the parasitesbeneath the surface of the “malaria-iceberg.”This is especiallyrelevant in the context of mass drug administration cam-paigns, as one of the greatest concerns of this practice is theselection of resistant parasites circulating in the community atvery low frequencies.30 Our results suggest that passive sur-veillance studies are a reliable “index” of the parasite strainscirculating in the broader population. However, direct com-parison of known resistance genotypes in patent versussubpatent infections will be required to confirm thesefindings.31

Our study also suggests that parasites derived from pas-sively detected symptomatic infections had similar genotypesto actively detected asymptomatic infections. These findingssuggest that the development of clinical symptoms may bemore attributable to host factors such as immunity and RBCpolymorphisms, than to intrinsic parasite factors resulting ina higher multiplication rate. Similar results were reportedby Ferreira et al.32 who explored the genetic background ofP. falciparum parasites in cases of uncomplicated versus

cerebral malaria, finding no evidence of closer relatedness(i.e., no evidence of strain specificity) among the cerebralcases. In a genetic study of P. falciparum and P. vivax inColombia, Pacheco et al.33 also found no evidence of differ-ences in the circulating genotypes between severe and un-complicated cases in either species. However, further studiesneed to be performed to support this hypothesis.These observations are particularly relevant when consid-

ering the sampling strategy for population genetic studies ofP. falciparum and P. vivax. The similar estimates of diversityand low differentiation in comparisons of different bloodwithdrawal methods (capillary versus venous samples), dis-ease presentation (symptomatic/passively detected versusasymptomatic/actively detected), and concentration of DNA(patent versus subpatent) in both species, indicate that di-versity estimates are not affected by any of these conditions.Therefore, population diversity and other population geneticmeasures derived frompredominant allele calls can be reliablyestimated on pooled samples and effectively compared be-tween different sample types.Population genetic studies routinely use estimates of com-

plexity of infection as indicators of transmission intensity.34 Incontrast to the population-level estimates of diversity, whichare based on frequencies of the major alleles in a given in-fection, assessmentsof thedeeper complexitywithin infectionsrevealed several differences between sampling groups. Ac-tively detected cases exhibited a higher MOI than passivelydetected cases of both species, albeit only reaching statisticalsignificance inP. vivax, but not inP. falciparum. This finding hasimportant implications for sampling strategies in populationgenetic studies of P. vivax investigating within-host diversity.

TABLE 4Pairwise differentiation between Plasmodium falciparum and Plasmodium vivax subgroups

P. falciparum Passively detected* Actively detected patent† Actively detected subpatent‡

Passively detected* * 0.0532 0.1191Actively detected patent† 0.02287** * 0.021Actively detected subpatent‡ 0.04713*** 0.00707NS *P. vivaxPassively detected* * 0.0131 0.0205Actively detected patent† 0.00194NS * −0.0100Actively detected subpatent‡ 0.00318NS −0.00154NS *FST (P value) in lower left triangle. F’ST in upper right triangle. NS = not significant; P > 0.05 = *; P > 0.001 = **; P > 0.0001 = ***.* Symptomatic patent infections.†Asymptomatic patent infections.‡Asymptomatic subpatent infections.

TABLE 5Linkagedisequilibriumbetween actively detectedpatent infections (AP), actively detected subpatent infections (AS), and passively detected patentinfections (PP)

Subgroup All infections N IAS

Low complexity

IAS Unique MLGs N IA

SN

P. vivaxPassively detected* 150 0.0062NS 123 0.0043NS 119 0.0038NS

Actively detected patent† 82 0.008NS 49 −0.0001NS 73 0.008NS

Actively detected subpatent‡ 155 −0.0067NS 107 −0.01NS 67 −0.0067NS

P. falciparumPassively detected* 139 0.0542*** 129 0.0543*** 95 0.02***Actively detected patent† 100 0.1354*** 158 0.1325*** 62 0.12***Actively detected subpatent‡ 164 0.1462*** 85 0.146*** 71 0.13***Only samples with no missing data were included in the analyses. *** P < 0.001; NS = not significant.* Symptomatic patent infections.†Asymptomatic patent infections.‡Asymptomatic subpatent infections.

PASSIVELY VERSUS ACTIVELY DETECTED MALARIA 1793

The contrast between the species could reflect interspecies dif-ferences in the persistence of subpatent infections resulting fromnew infections or relapses. In a recent longitudinal survey of in-dividuals with asymptomatic malaria parasitaemia in a low en-demic setting inCambodiawhereP. vivax predominates, Tripuraet al.35 showed that 35% of individuals with P. vivax infectionremainedparasitaemic forup to11months,whereasonly13%ofindividualswithP. falciparum infection remainedparasitaemic forup to 4 months. The persistence of asymptomatic parasitaemiamight enhance the opportunity for multiple inoculations ofP. vivax by different mosquitoes before the onset of clinicalsymptoms or parasite clearance.Several previous studies have highlighted the challenges of

characterizing multiple-clone infections using STR typing.27,36

These challengesmainly relate to the presence of stutter peaksor nonspecific PCR products that can interfere with the dis-crimination of artifacts from trueminor alleles.Moreover, higherDNA concentrations can increase the detection of minoralleles.20,36,37 To counter potential differences in samplequalityor quantity in population genetic studies of Plasmodium, mini-mum threshold values of 25% or 33% for calling additionalalleles relative to the major allele are commonly applied.20,27,36

The negative correlation between parasitaemia and MOI in thecurrent study suggests that applying a relative threshold suchas 33% is not as effective in P. vivax samples as it was inP. falciparum samples when pooling samples from the com-munity with those attending health facilities. Consequently,these results suggest caution when inferring transmission in-tensity from MOI and percentage of polyclonal infections frompooled samples.The results of our study differ from those reported by Searle

et al.14 who found that actively detected P. falciparum infec-tions collected in a low endemic setting in Zambia were sig-nificantly divergent from passively detected cases, not only in

terms of complexity of infection, but also in their geneticbackground. Key differences between the two studies thatmay explain the observed discrepancy are the greater tem-poral heterogeneity in sampling of active versus passivelydetected cases combined with greater bottlenecking in theP. falciparum population in the Zambian study. The Searleet al.14 study shows evidence of increasingly unstableP. falciparum transmission over time, including increasedinbreeding, consistent with rapidly declining transmission.These temporal genetic changes could account for the dif-ferences found between the beginning of the sampling period(when most of the actively detected cases were enrolled) andthe end of the sampling period (when most of the passivelydetected cases were enrolled) as observed in other temporalgenetic studies of P. falciparum.38,39 By contrast, the currentstudy comprises samples collected in a period during whichtherewas nomarked change inmalaria transmission and, withthe actively detected cases enrolled within the sampling pe-riod of the passively detected cases. Differing from the situ-ation in P. falciparum, reports on P. vivax population diversityremaining relatively resilient to changes in the intensity oftransmission suggest that,12,34 except in areas with extremebottlenecking such as Malaysia,40 passively detected casesshould remain representative of the broader P. vivaxpopulation.A limitation of this study is the large proportion of adult

malesmissed fromsampling during the cross-sectional study,which constitutes a bias per se that needs to be considered.The study also presents a potential bias reflecting the omis-sion of very low-density infections that exhibited low geno-typing success. Furthermore, the recently describedhigh-volume, ultrasensitive PCR approach by Imwong et al.3

may have identified parasites at an even lower level ofthe “malaria-iceberg” that were not identified with the

FIGURE 1. Polyclonal loci between asymptomatic subpatent infections (AS), asymptomatic patent infections (AP), and symptomatic patentinfections (PP) in (A) Plasmodium vivax and (B) Plasmodium falciparum. This figure appears in color at www.ajtmh.org.

1794 PAVA AND OTHERS

conventional PCR approach used here. However, it is alsohighly likely that these very low-density infections would berefractory to STR genotyping.3 Lastly, given the multiple fac-tors affecting the dynamics of asymptomatic malaria and theinfluence of malaria transmission on parasite diversity andparasite differentiation, the results of the current studymay notnecessarily be extrapolated to areas with different malariaepidemiology.41,42 Indeed, available evidence inferred that theminor subpopulation of asymptomatic P. falciparum infectionsobserved in Mimika district might reflect infections importedfrom other parts of Indonesia.16 As local transmission declines,the impact of imported cases on the parasite population di-versity and structure may become more pronounced.40

In conclusion, asymptomatic malaria infections constitute amajor challenge in pre-elimination areaswhere their prevalencemay force theneed tomodifyongoingcasedetectionstrategiesfor surveillance. Many studies continue to use passively de-tected samples as an index of diversity and drug resistance inthe broader parasite population. Our results suggest that pas-sively detected samples provide effective representation of the‘strains’ circulating in the broader community including thehidden asymptomatic reservoir. However, passive samplingstrategies may not be effective for investigation of within-hostinfection complexity in the asymptomatic reservoir. These re-sults highlight critical considerations when undertaking mo-lecular surveillance of malaria.

Received May 8, 2017. Accepted for publication July 16, 2017.

Published online September 5, 2017.

Note: Supplemental tables appear at www.ajtmh.org.

Acknowledgments: We would like to thank the study participants andtheir parents or legal guardians who made themselves available forboth the surveillance study at the hospital and the community surveyin 2013.We are also grateful to Lembaga PengembanganMasyarakatAmungme Kamoro and the staff of the RSMM hospital for their hardworkandsupport for this study. Furthermore,wewould like to thankallfield and laboratory staff members of the Research Facility at thePapuan Health and Community Development Foundation (PHCDF) inTimika, Papua, Indonesia, for their efforts and support.

Financial support: The study was supported by theWellcome Trust (aSenior Fellowship in Clinical Science awarded to RNP - 200909) andFellowships to: JRP (Wellcome Trust - 099875), ZP (Instituto Colom-biano para el Desarrollo de la Ciencia y la Tecnologıa Francisco Josede Caldas, Colciencias - 512-2010) and NMA (NHMRC). The TimikaResearch Facility and Papuan Health and Community DevelopmentFoundation were supported by DFAT (Australian Department of For-eign Affairs and Trade) and the NHMRC (Program Grant 1037304).

Authors’ addresses: Zuleima Pava, Irene Handayuni, GrennadyWirjanata, Steven Kho, Nicholas Anstey, Jutta Marfurt, and SarahAuburn, Global and Tropical Health Division, Menzies School ofHealth Research, Charles Darwin University, Darwin, NorthernTerritory, Australia, E-mails: scribem13@gmail.com, fyireneh@gmail.com, gwirjanata@gmail.com, steven.kho@menzies.edu.au,nicholas.anstey@menzies.edu.au, jutta.marfurt@menzies.edu.au,and sarah.auburn@menzies.edu.au. Leily Trianty, Retno A. S.Utami, Yusrifar K. Tirta, AgathaM. Puspitasari, andRintis Noviyanti,Eijkman Institute of Molecular Biology, Jakarta, Indonesia, E-mails:leily@eijkman.go.id, tami@eijkman.go.id, r.yusrifar@gmail.com, agatha@eijkman.go.id, and rintis@eijkman.go.id. Faustina Burdam and JeanneRini Poespoprodjo, Mimika District Health Authority, Timika, Papua,Indonesia, Timika Malaria Research Programme, Papuan Health andCommunity Development Foundation, Timika, Papua, Indonesia,and Child Health, Universitas Gadjah Mada, Yogyakarta, Indonesia,E-mails: lenny_burdam@yahoo.co.id and didot2266@yahoo.com. EnnyKenangalem, Mimika District Health Authority, Timika, Papua, Indo-nesia, and Timika Malaria Research Programme, Papuan Health and

Community Development Foundation, Timika, Papua, Indonesia,E-mail: ennykenangalem@yahoo.com. Hidayat Trimarsanto, EijkmanInstitute of Molecular Biology, Bioinformatic Unit, Jakarta, Indonesia,and Badan Pengkajian da Penerapan Teknologi, Jakarta, Indonesia,E-mail: anto@eijkman.go.id. Ric N. Price, Global and Tropical HealthDivision,Menzies School of Health Research, CharlesDarwinUniversity,Darwin, Northern Territory, Australia, and Centre for Tropical Medicine,Nuffield Department of Clinical Medicine, University of Oxford, Oxford,Oxfordshire, United Kingdom, E-mail: ric.price@menzies.edu.au.

This is an open-access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricteduse, distribution, and reproduction in any medium, provided theoriginal author and source are credited.

REFERENCES

1. Feachem RG, Sabot OJ, 2007. Global malaria control in the21st century: a historic but fleeting opportunity. JAMA 297:2281–2284.

2. WHO, 2016. Eliminating Malaria. Geneva, Switzerland: WHO, 24.3. ImwongM, Hanchana S, Malleret B, Renia L, Day NP, Dondorp A,

Nosten F, Snounou G, White NJ, 2014. High-throughput ul-trasensitive molecular techniques for quantifying low-densitymalaria parasitemias. J Clin Microbiol 52: 3303–3309.

4. Phommasone K et al., 2016. Asymptomatic Plasmodium infec-tions in 18 villages of southernSavannakhet Province, LaoPDR(Laos).Malar J 15: 296.

5. Waltmann A et al., 2015. High rates of asymptomatic, sub-microscopic Plasmodium vivax infection and disappearingPlasmodium falciparummalaria in anareaof low transmission inSolomon Islands. PLoS Negl Trop Dis 9: e0003758.

6. Britton S, Cheng Q, McCarthy JS, 2016. Novel molecular di-agnostic tools for malaria elimination: a review of options fromthe point of view of high-throughput and applicability in re-source limited settings.Malar J 15: 88.

7. Cheng Q, Cunningham J, Gatton ML, 2015. Systematic review ofsub-microscopic P. vivax infections: prevalence and de-termining factors. PLoS Negl Trop Dis 9: e3413.

8. Bousema T, Okell L, Felger I, Drakeley C, 2014. Asymptomaticmalaria infections: detectability, transmissibility and publichealth relevance. Nat Rev Microbiol 12: 833–840.

9. WuL, vandenHoogenLL,SlaterH,WalkerPG,GhaniAC,DrakeleyCJ, Okell LC, 2015. Comparison of diagnostics for the detectionof asymptomatic Plasmodium falciparum infections to informcontrol and elimination strategies. Nature 528: S86–S93.

10. Russell B et al., 2008. Determinants of in vitro drug susceptibil-ity testing of Plasmodium vivax. Antimicrob Agents Chemother52: 1040–1045.

11. KerlinDH,BoyceK,Marfurt J, SimpsonJA,KenangalemE,ChengQ, Price RN, Gatton ML, 2012. An analytical method forassessing stage-specific drug activity in Plasmodium vivaxmalaria: implications for ex vivo drug susceptibility testing.PLoS Negl Trop Dis 6: e1772.

12. Barry AE, Waltmann A, Koepfli C, Barnadas C, Mueller I, 2015.Uncovering the transmission dynamics of Plasmodium vivaxusing population genetics. Pathog Glob Health 109: 142–152.

13. Arnot D, 1998. Unstable malaria in Sudan: the influence of the dryseason. Clonemultiplicity of Plasmodium falciparum infectionsin individuals exposed to variable levels of disease trans-mission. Trans R Soc Trop Med Hyg 92: 580–585.

14. Searle KM et al.; Southern Africa International Centers of Excel-lence for Malaria Research, 2017. Distinct parasite populationsinfect individuals identified through passive and active casedetection in a region of declining malaria transmission insouthern Zambia.Malar J 16: 154.

15. Pava Z et al., 2017. Genetic micro-epidemiology of malaria inPapua Indonesia: extensive P. vivax diversity and a distinctsubpopulation of asymptomatic P. falciparum infections. PLoSOne 12: e0177445.

16. Pava Z et al., 2016. Submicroscopic and asymptomatic Plas-modium parasitaemia associated with significant risk of anae-mia in Papua, Indonesia. PLoS One 11: e0165340.

17. Elyazar IR, Hay SI, Baird JK, 2011. Malaria distribution, preva-lence, drug resistance and control in Indonesia. Adv Parasitol74: 41–175.

PASSIVELY VERSUS ACTIVELY DETECTED MALARIA 1795

18. Auburn S et al., 2013. Effective preparation of Plasmodium vivaxfield isolates for high-throughput whole genome sequencing.PLoS One 8: e53160.

19. Singh B, Bobogare A, Cox-Singh J, Snounou G, Abdullah MS,Rahman HA, 1999. A genus- and species-specific nested po-lymerase chain reaction malaria detection assay for epidemi-ologic studies. Am J Trop Med Hyg 60: 687–692.

20. Anderson TJ, Su XZ, BockarieM, LagogM, DayKP, 1999. Twelvemicrosatellite markers for characterization of Plasmodium fal-ciparum from finger-prick blood samples. Parasitology 119:113–125.

21. Karunaweera ND, Ferreira MU, Munasinghe A, Barnwell JW,Collins WE, King CL, Kawamoto F, Hartl DL, Wirth DF, 2008.Extensivemicrosatellite diversity in the humanmalaria parasitePlasmodium vivax. Gene 410: 105–112.

22. Koepfli C, Mueller I, Marfurt J, Goroti M, Sie A, Oa O, Genton B,Beck HP, Felger I, 2009. Evaluation of Plasmodium vivax gen-otyping markers for molecular monitoring in clinical trials.J Infect Dis 199: 1074–1080.

23. TrimarsantoHet al., 2017. VivaxGEN:anopenaccessplatform forcomparative analysis of short tandem repeat genotyping datain Plasmodium vivax populations. PLoS Negl Trop Dis 11:e0005465.

24. Goudet J, 2005.HIERFSTAT, a package forR to compute and testhierarchical F-statistics. Mol Ecol Notes 5: 184–186.

25. Balloux F, Lugon-Moulin N, 2002. The estimation of populationdifferentiation with microsatellite markers. Mol Ecol 11:155–165.

26. Haubold B, Hudson RR, 2000. LIAN 3.0: detecting linkage dis-equilibrium in multilocus data. Linkage analysis.Bioinformatics16: 847–848.

27. Havryliuk T, Orjuela-Sanchez P, Ferreira MU, 2008. Plasmodiumvivax: microsatellite analysis of multiple-clone infections. ExpParasitol 120: 330–336.

28. Team RC, 2013. R: A Language and Environment for StatisticalComputing. Vienna, Austria: R Foundation for StatisticalComputing.

29. Noviyanti R et al., 2015. Contrasting transmission dynamics ofco-endemicPlasmodium vivax andP. falciparum: implicationsfor malaria control and elimination. PLoS Negl Trop Dis 9:e0003739.

30. Poirot E, Skarbinski J, Sinclair D, Kachur SP, Slutsker L, Hwang J,2013. Mass drug administration for malaria. Cochrane Data-base Syst Rev CD008846.

31. Marfurt J, Smith TA, Hastings IM, Muller I, Sie A, Oa O, Baisor M,Reeder JC, Beck HP, Genton B, 2010. Plasmodium falciparumresistance to anti-malarial drugs in Papua New Guinea: evalu-ation of a community-based approach for the molecular mon-itoring of resistance.Malar J 9: 8.

32. Ferreira MU, Nair S, Hyunh TV, Kawamoto F, Anderson TJ, 2002.Microsatellite characterization of Plasmodium falciparum fromcerebral and uncomplicated malaria patients in southern Viet-nam. J Clin Microbiol 40: 1854–1857.

33. Pacheco MA, Lopez-Perez M, Vallejo AF, Herrera S, Arevalo-HerreraM,EscalanteAA, 2016.Multiplicity of infectionanddiseaseseverityin Plasmodium vivax. PLoS Negl Trop Dis 10: e0004355.

34. Auburn S, Barry AE, 2017. Dissecting malaria biology and epi-demiology using population genetics and genomics. Int JParasitol 47: 77–85.

35. Tripura R et al., 2016. Persistent Plasmodium falciparum andPlasmodium vivax infections in a western Cambodian pop-ulation: implications for prevention, treatment and eliminationstrategies.Malar J 15: 181.

36. Havryliuk T, Ferreira MU, 2009. A closer look at multiple-clonePlasmodium vivax infections: detection methods, prevalenceand consequences.Mem Inst Oswaldo Cruz 104: 67–73.

37. VafaM,Troye-BlombergM,AnchangJ,GarciaA,Migot-NabiasF,2008. Multiplicity of Plasmodium falciparum infection in asymp-tomatic children in Senegal: relation to transmission, age anderythrocyte variants.Malar J 7: 17.

38. Escalante AA et al., 2015. Malaria molecular epidemiology: les-sons from the International Centers of Excellence for MalariaResearch Network. Am J Trop Med Hyg 93: 79–86.

39. Nkhoma SC, Nair S, Al-Saai S, Ashley E, McGready R, Phyo AP,Nosten F, Anderson TJ, 2013. Population genetic correlates ofdeclining transmission inahumanpathogen.MolEcol22:273–285.

40. Abdullah NR et al., 2013. Plasmodium vivax population structureand transmission dynamics in Sabah Malaysia. PLoS One 8:e82553.

41. Galatas B, Bassat Q, Mayor A, 2016. Malaria parasites in theasymptomatic: looking for the hay in the haystack. TrendsParasitol 32: 296–308.

42. Gatei W et al., 2014. Genetic diversity of Plasmodium falciparumparasite bymicrosatellite markers after scale-up of insecticide-treated bed nets in western Kenya.Malar J 13 (Suppl 1): 495.

1796 PAVA AND OTHERS

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CHAPTER 5 - Longitudinal characterization of P. vivax and P. falciparum genetic diversity and structure in Papua, Indonesia between 2004 and 2016

- This chapter will be submitted for publication after further revisions.

Zuleima Pava1, Rintis Noviyanti2, Agatha Mia Puspitasari2, Irene Handayuni1, Hidayat

Trimarsanto2,3, Leily Trianty2, Faustina H Burdam4,5,6 Enny Kenangalem4,5 Retno Ayu Setya

Utami2, Yusrifar Kharisma Tirta2, Nicholas M Anstey1, Jeanne R Poespoprodjo4,5,6, Ric N

Price1,7, Jutta Marfurt1, Sarah Auburn1*

1 Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811, Darwin, Australia

2 Eijkman Institute for Molecular Biology, Jl. Diponegoro 69, 10430 Jakarta, Indonesia

3 Agency for Assessment and Application of Technology, Jl. MH Thamrin 8, Jakarta 10340, Indonesia

4 Mimika District Health Authority, Timika, Papua, Indonesia.

5 Timika Malaria Research Programme, Papuan Health and Community Development Foundation, Timika, Papua, Indonesia

6 Maternal and Child Health and Reproductive Health, Department of Public Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia

7 Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, United Kingdom

*Corresponding author: Sarah Auburn, Menzies School of Health Research, PO Box 41096,

Casuarina, Darwin, NT 0811, Australia; Email: sarah.auburn@menzies.edu.au; Phone: +61

(0)8 8946 8503, Fax: +61 (0)8 8927 5187.

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5.1 Abstract

Background The province of Papua has the highest burden of malaria in Indonesia, with

both drug resistant P. vivax and P. falciparum prevalent. The National Malaria Control

Programme has demonstrated dedication in reducing transmission in the effort to achieve

malaria elimination by 2030, with prevalence declining gradually over the past decade. One

of the most critical changes during this period was the implementation of a universal policy

of dihydroartemisinin-piperaquine in March 2006 as a result of high-grade chloroquine

resistance against the local P. vivax and P. falciparum populations. The current study

characterizes longitudinal changes in the genetic diversity and population structure of co-

endemic P. vivax and P. falciparum populations in Mimika, Papua, with a particular focus on

the periods before and after ACT implementation.

Methodology/Principal Findings The study was conducted in Mimika District, in southern

Papua Province. Microsatellite typing was undertaken on randomly selected parasite

isolates (231 P. falciparum and 225 P. vivax) collected between 2004 and 2006 (pre-ACT)

and between 2011 and 2016 (post-ACT), from symptomatic patients with uncomplicated

malaria. Compared to pre-ACT samples, there was a decrease in the proportion of

polyclonal infections post-ACT for P. falciparum (18% (16/91) to 9% (13/139); p=0.104) and

P. vivax (57% (43/75) to 33% (49/150); p=0.001). P. falciparum strains persisted for longer

than P. vivax (up to 8 years versus 1 year), and demonstrated increased inbreeding and

prevalence of identical infections post-ACT (24% (21/89) versus 46% (60/130), p=0.001).

Conclusions There was evidence of a reduction in both P. falciparum and P. vivax

transmission from the period 2004-6 to 2011-16. The change on P. falciparum was greater

as demonstrated by the degree of population bottlenecking relative to P. vivax. A number

of factors may have contributed to the decline in local malaria transmission, with the

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implementation of ACT likely playing a major role. Anti-relapse therapy is likely to be a key

component to achieve further impact in the P. vivax population.

5.2 Introduction

Papua, the most easterly province of Indonesia, presents the greatest challenge for the

country’s aspirations to eliminate malaria. The Province has the highest malaria prevalence

in the country, with an annual parasite incidence (API) greater than 10%. The parasite

prevalence ranges from 10-50%, in contrast to the national averages of less than 1%

(Hanandita & Tampubolon 2016). Papua Province also harbours high levels of antimalarial

drug resistance in both P. vivax and P. falciparum, and this contributes significantly to the

severity and mortality of disease locally (Tjitra et al. 2008). Surveys conducted between

2004 and 2006 demonstrated treatment failure to chloroquine (CQ) monotherapy at day 28

of 65% for vivax malaria and to CQ plus sulphadoxine-pyrimethamine (SP) at day 28 of 48%

for P. falciparum (Ratcliff, Siswantoro, Kenangalem, Wuwung, et al. 2007). Microscopy-

based API estimation during this period was 876 per 1,000 per year for all malaria, with 58%

of cases due to P. falciparum and 37% due to P. vivax (Ratcliff, Siswantoro, Kenangalem,

Wuwung, et al. 2007). As a consequence of the declining efficacy of CQ in the region, in

2006, the first-line malaria treatment policy was changed to the artemisinin-based

combination therapy (ACT) dihydroartemisinin-piperaquine (DHP) to treat all species of

malaria (Ratcliff, Siswantoro, Kenangalem, Wuwung, et al. 2007). Since then, official reports

have shown a decline in malaria cases by almost 50% and that the decline has been

significantly greater for P. falciparum than for P. vivax (unpublished data). However,

obtaining accurate estimates of malaria incidence is challenging given the limitations of

standard case reporting (Karyana et al. 2008). Furthermore, malariometric measures may

not be able to capture important genetic changes in the parasite population resulting from

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changing selective pressures (Daniels, R et al. 2013; Daniels, RF, Schaffner, et al. 2015;

Miotto et al. 2013; Ocholla et al. 2014).

Parasite genetic studies can provide information on changes in population structure, gene

flow and parasite diversity, thus complementing traditional transmission indicators (Auburn

& Barry 2016; Barry et al. 2015; Daniels, RF, Rice, et al. 2015; Ferreira & de Oliveira 2015;

Kwiatkowski 2015). However, owing in part to sampling challenges, few molecular studies

have assessed longitudinal changes in parasite population diversity and structure. In

Senegal and along the Thai-Myanmar border, decreasing complexity of P. falciparum

infections has been correlated with decreasing malaria incidence (Daniels, R et al. 2013;

Nkhoma et al. 2013). Even fewer longitudinal studies have been performed in P. vivax

(Abdullah et al. 2013; Batista et al. 2015; Kim et al. 2011), although cross-sectional surveys

have correlated genetic diversity and P. vivax prevalence in South Korea (Iwagami et al.

2013). Whilst providing useful insights, longitudinal comparisons of co-endemic P.

falciparum and P. vivax population genetics have not been undertaken over periods greater

than four years (Bruce et al. 2000). Such longitudinal evaluation of co-endemic parasite

populations can provide further insights on the way interventions may differentially affect

P. vivax and P. falciparum (Auburn & Barry 2016).

The aim of the current study was to characterize longitudinal changes in the genetic

diversity and population structure of co-endemic P. vivax and P. falciparum populations in

Papua, Indonesia, with a focus on the periods before and after the implementation of a

universal policy of ACT for all species of malaria. The study capitalized on a unique sample

collection obtained through passive surveillance between 2004 and 2006, and between

2011 and 2016 in Mimika District, Papua, Indonesia.

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5.3 Materials and Methods

5.3.1 Study site and sampling

The study was conducted in Mimika District, located in the south of Papua Province,

Indonesia (Supplementary Figure 1). Mimika is one of the 28 districts constituting Papua

Province, covering a coastal and mountainous area of 21,522 km2 (Hanandita &

Tampubolon 2016). Mimika hosts one of the largest gold mining operations in the world,

which greatly enhances economic migration, increasing the local population by up to

approximately 16% per year. This has contributed to the highly diverse ethnic background

of the population inhabiting the area, comprising highland Papuans, lowland Papuans and

non-Papuans. According to the 2010 census, the overall population size was estimated to

be 182,000 (Statistics 2010). The main hospital, Rumah Sakit Mitra Masyarakat (RSMM)

services the whole District. RSMM is located in Timika (the administrative capital of Mimika

District), and is the only hospital available for the lowland population. The hospital attends

to approximately 150,000 people, assessing approximately 300 patients per day, 6 days per

week (Karyana et al. 2008).

Malaria transmission is perennial in Mimika, but almost exclusive to the lowlands. The

majority of malaria infections are caused by P. falciparum and P. vivax, but P. malariae and

P. ovale are also endemic. In 2013, malaria prevalence by microscopy was estimated to be

12.2% (5.7% due to P. vivax and 5.2% due to P. falciparum) (Pava et al. 2016). For Pre-ACT

assessment, the study used a subset of samples from symptomatic patients with

uncomplicated malaria. These patients were recruited in the framework of clinical and ex

vivo surveillance studies, carried out in the area between 2003 and 2005 (Supplementary

Figure 2) (Hasugian et al. 2009; Ratcliff, Siswantoro, Kenangalem, Maristela, et al. 2007;

Ratcliff, Siswantoro, Kenangalem, Wuwung, et al. 2007; Russell et al. 2008). For Post-ACT

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assessment, the study used a similar subset of samples collected in the framework of ex

vivo drug susceptibility surveillance study from 2011-2014 and a therapeutic efficacy

studies from 2015-2016. The comparability between samples meeting different inclusion

criteria in terms of the genetic background was assessed in Chapter 4.

Peripheral parasitaemia and species identity were determined by light microscopy

examination of Giemsa-stained blood smears. Genomic DNA (gDNA) was extracted from

either 2 mL of venous blood using the QIAamp DNA Midi Kit (Qiagen), or 100 µL iRBC pellet

using the QIAamp DNA Mini Kit. Species confirmation was performed using a nested PCR

protocol described elsewhere (Singh et al. 1999).

5.3.2 Microsatellite typing

Nine short tandem repeat (STR) markers (ARAII, PfPK2, poly-alpha, TA1, TA42, TA60, TA81,

TA87 and TA109) described by Anderson et al were used to genotype P. falciparum isolates

(Anderson et al. 1999). For P. vivax, a panel comprising nine STR markers (MS1, MS5, MS10,

MS12, MS20, MS16, msp1F3, PV3.27) described by Koepfli et al. and Karunaweera et al.

were used (Karunaweera et al. 2008; Koepfli et al. 2009). The primers and PCR conditions

for the assays are described elsewhere (Hamedi et al. 2016). The fluorescently-labelled PCR

products were sized on an ABI 3100 Genetic Analyser with GeneScan LIZ-600 size standard

(Applied Biosystems). The resulting electrophoretograms were analysed using GeneMapper

version 4.0 software (Applied Biosystems, Life Technologies, Australia) and verified

manually. An arbitrary fluorescent intensity threshold of 100 relative fluorescence units

(RFU) and minimal 33% peak intensity of minor relative to predominant peaks was used to

reduce background noise and artefacts. Only samples with information in at least 50% of

the loci were considered successfully genotyped.

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5.3.3 Data Analysis

SPSS software was used for statistical analysis. Differences in MOI, percentage of polyclonal

infections, allelic richness (Rs), and expected heterozygosity (HE) between subgroups and

species were assessed using the Mann-Whitney U or Kruskal-Wallis test, and chi-square test

for trends and differences in proportion.

For P. vivax, analysis of population diversity and differentiation was performed using i) the

full marker set, and ii) excluding 3 markers with intrinsically high diversity, which have been

shown to confound the detection of subtle differences in diversity or differentiation (Sutton

2013). All markers were used in all P. falciparum analyses. The presence of more than one

allele at a given locus in an individual sample was considered indicative of multiple parasite

clones or polyclonal infection. The multiplicity of infection (MOI) was defined as the

maximum number of alleles at any locus for a given sample. The number of polyclonal loci

per sample was also estimated for closer inspection of the complexity of individual

infections in the effort to identify any subtle changes in transmission patterns over time. A

relative threshold of 33% to score minor peaks was used to improve the comparability of

MOI and polyclonality estimates between parasite populations with differing DNA quality or

quantities (Anderson et al. 1999).

Temporal analysis of the genetic relatedness was conducted by assessment of the

proportion of shared alleles, frequency and duration of identical multi-locus genotypes

(iMLG), and the proportion of individuals harbouring iMLGs. Isolates without missing data

were used to build MLGs from the predominant allele at each locus. The proportion of

alleles shared between MLGs (ps) was calculated using 1-ps as a measure of genetic

relatedness (R package hierfstat) (Jombart 2015), and illustrated with neighbour-joining

trees generated using the ape package in R (Paradis, Claude & Strimmer 2004). The

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frequency and temporal duration of iMLGs was estimated using the R packages adegenet

and RClone (Diane Bailleul 2016).

Multi-locus linkage disequilibrium (LD) was measured using the standardised index of

association (IAS), which “compares the observed variance in the numbers of alleles shared

between parasites with that expected when parasites share no alleles at different loci”

(Sisya et al. 2015). The web-based LIAN 3.5 software was used to calculate the estimates

(Haubold & Hudson 2000). Briefly, multi-locus LD was compared between years and

between groups in search of evidence of increasing LD. Ten thousand permutations of the

data was used to assess the significance of the estimates. LD analysis was performed on 1)

all MLGs, 2) using low complexity infections (maximum of 1 multi-allelic locus) only, and 3)

with each unique MLG represented just once.

Population genetic diversity was estimated using the allelic richness (Rs), a measure of the

number of alleles at a given locus with normalisation (rarefaction) for sample size. Rs was

calculated using the hierfstat package in R (Jombart 2015). Measures of the expected

heterozygosity (HE) were also provided for comparison with previous studies. Arlequin

software (version 3.5) was used to calculate the pairwise FST metric and the standardized

measure (F’ST), to determine the genetic distance across years (Excoffier & Lischer 2010).

The F’ST measure adjusts for high marker diversity enabling greater comparability between

different studies. The results were interpreted according to the classification of Balloux et

al. as follows (Balloux & Goudet 2002); FST < 0.05 = little/no differentiation, 0.05 ≥ FST < 0.15

= moderate differentiation; 0.15 ≥ FST < 0.25 = high differentiation; and FST > 0.25 = very

high differentiation (Balloux & Lugon-Moulin 2002). Population structure was assessed

using STRUCTURE software version 2.3.3 (Pritchard, Stephens & Donnelly 2000). The

simulation was run using 20 replicates, with 100,000 burn-in and 100,000 post burn-in

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iterations for each estimate of K (number of sub-populations), ranging from 1-10. The

model parameters included admixture with correlated allele frequencies. The delta K

method was used to derive the most probable K, implemented with STRUCTURE

HARVESTER (Earl 2012; Evanno, Regnaut & Goudet 2005). Distruct software version 1.1 was

used to display the results from STRUCTURE as bar plots (NA 2004).

5.3.4 Ethics

Ethical approval for this study has been obtained from the Eijkman Institute Research Ethics

Commission, Eijkman Institute for Molecular Biology, Jakarta, Indonesia (EIREC-47, EIREC-

67, and EIREC-75), the EC from which ethics approval for the pre-ACT studies was obtained

(NIHRD: KS.01.01.6.591 and MSHR: 02/55, NIHRD: KS.02.01.2.3.4579 and MSHR: 07/06,

NIHRD: KS.02.01.2.1.4042 and MSHR: 03/64, NIHRD: KS.02.01.2.1.1615 and MSHR: 05/16

and NIHRD: LB.03.02/KE/4099/2007 and MSHR: 07/14) and the Human Research Ethics

Committee of the Northern Territory (NT) Department of Health & Families and Menzies

School of Health Research, Darwin, Australia (HREC 2010-1396).

5.4 Results

In total, 10% (168/1,660) of parasite isolates collected during pre-ACT (91 P. falciparum and

77 P. vivax) and 44% (295/665) during the post-ACT period (140 P. falciparum and 155 P.

vivax) were processed in the current study. The parasitaemia and gender composition of

the subset of samples were comparable with all the samples enrolled during the studies.

However, to ensure comparability across the years, the sample set was mostly composed of

adults Table 1.

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Table 1. Demographic comparison of samples available for the study and those collected across that period.

P. falciparum (N=1,346)

Pre-ACT Post-ACT

Allabc Availabledef Allg Availableh

Age n (%) Children <15 years 364 (37) 2 (2) 62 (18) 19 (14)

Adults ≥ 15 years 619 (67) 84 (98) 290 (82) 121 (86)

Males n (%) 585 (59) 49 (57) 168 (48) 67 (48)

Parasitaemia Parasites/µL GM (95%CI)

4,428 (3,962-4,949)

13,740 (10,929-17,273)

19,094 (17,233-21,157)

16,563 (14,189-19,336)

Total n (%) 994 91 (9) 352 140 (40)

P. vivax (N=979)

Pre-ACT Post-ACT

Allij Availablekm Alllo Availablelo

Age n (%) Children <15 years 278 (43) 14 (19) 67 (21) 28 (18)

Adults ≥ 15 years 372 (57) 59 (81) 245 (79) 126 (82)

Males n (%) 346 (53) 36 (49) 143 (46) 75 (49)

Parasitaemia Parasites/µL GM (95%CI)

2,248 (1976-2557)

4,449 (3152-6279)

13,903 (12495-15470)

15,917 (13718-18470)

Total n (%) 666 77 (12) 313 155 (50)

GM: Geometric mean; 95%CI: 95% Confidence interval; # Superscript indicate number of missing data; a 11 individuals missing data on age; b 9 individuals missing data on gender; c 44 individuals missing data on parasitaemia; d 4 individuals missing data on age; e 4 individuals missing data on gender; f 1 individual missing data on parasitaemia; g 7 individual missing data on parasitaemia; h 3 individual missing data on parasitaemia; i 16 individuals missing data on age and gender; j 27 individuals missing data on parasitaemia; k 5 individuals missing data on age and gender; m 4 individuals missing data on parasitaemia; l 1 individual missing data on age and gender; o 2 individuals missing data on parasitaemia

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Genotyping was successful in 99% (n=230) of P. falciparum isolates and in 94% (n=225) of P.

vivax isolates. Aside from the P. falciparum collections from years 2011, 2013 and 2016,

and the P. vivax collections from years 2004, 2005, 2014 and 2016, a minimum of 25

samples were successfully genotyped in each year. At this sample size, it is expected that

allele frequencies have stabilized (Hale, Burg & Steeves 2012). Thus, study years with less

than 25 successfully genotyped samples were only included in the further population

genetic analyses of the pooled pre- and post-ACT populations. All markers exhibited a

minimum 5% minor allele frequency. The P. vivax MS8 assay presented artefacts that

affected the reliability of the allele calling and, therefore, this locus was excluded from

further analysis. The remaining markers exhibited >80% genotyping success in both species

except for the P. falciparum Poly-alpha marker, which was only genotyped successfully in

41% (37/91) of the pre-ACT samples and was, therefore, excluded from further analysis

(Supplementary Table 1).

5.4.1 Comparable genetic diversity amongst the Pre-ACT and Post-ACT years in P.

falciparum and P. vivax

There was little genetic differentiation observed for both P. falciparum and P. vivax in the

pre-ACT years (FST range: 0.011-0.015 and 0.007-0.04, respectively) and the post-ACT years

(FST range: 0.006-0.07 and -0.008-0.02, respectively); Table 2. Therefore, the samples were

pooled into two groups: pre-ACT (P. vivax=70, P. falciparum=90) and post-ACT (P.

vivax=139, P. falciparum=150).

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Table 2. Annual population differentiation.

Species Year 2004* 2005* 2006 2011 2012 2013 2014* 2015 2016*

(N=20) (N=19) (N=31) (N=35) (N=30) (N=25) (N=14) (N=37) (N=9)

P. vivax

2006 0.01543NS -0.01058NS - 0.2564 0.0763 0.0748 0.0066 -0.063 -0.0183

2011 0.03413* 0.03782* 0.04086** - 0.1198 0.109 0.0805 0.1416 0.1299

2012 0.0061NS 0.02861* 0.01119NS 0.02087NS - -0.0532 -0.0356 -0.0315 0.0005

2013 0.00858NS 0.00834NS 0.01084NS 0.01888NS -0.00848NS - -0.1412 -0.028 -0.0775

2015 0.02034* 0.00388NS -0.00848NS 0.0226* -0.00464NS -0.00408NS -0.00387NS - -0.0373

Species Year 2004 2005 2006 2011* 2012 2013* 2014 2015 2016*

(N=35) (N=25) (N=31) (N=11) (N=36) (N=13) (N=34) (N=37) (N=8)

P. falciparum

2004 - 0.1443 0.1066 0.2428 0.2026 0.1595 0.3318 0.3327 0.3893

2005 0.05351* - 0.0575 0.2039 0.1967 0.1845 0.3254 0.4251 0.469

2006 0.04214* 0.02479NS - 0.2586 0.18 0.1624 0.3252 0.3795 0.5023

2012 0.08485** 0.08799* 0.08362* 0.00085NS - -0.0376 0.0123 0.1412 0.4041

2013 0.05846NS 0.07342NS 0.0686* -0.01429NS -0.01654NS - -0.0333 0.0579 0.3044

2014 0.14466*** 0.15202*** 0.15742*** -0.00507NS 0.00595NS -0.01527NS - 0.112 0.3625

2015 0.15483*** 0.21162*** 0.1947*** 0.11183* 0.07172** 0.02827NS 0.05877* - 0.1985

2016 0.16688*** 0.2218** 0.24947*** 0.23964*** 0.20037*** 0.13997* 0.18767*** 0.10868* -

FST (p-value) in lower left triangle; NS = Not significant; p<0.05 =*; p<0.001 = **; p<0.0001 = ***. F’ST in upper right triangle.

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5.4.2 Comparable demographics between the pre-ACT and post-ACT patient

groups in both species

Demographic data were available for 98% (225/229) individuals presenting with P.

falciparum and 99% (218/220) with P. vivax infection (Table 3) successfully genotyped. The

majority of individuals were adults (aged ≥15 years) in both the pre-ACT (88%; 141/160)

and post-ACT years (84%; 242/289) and this was apparent for both P. falciparum and P.

vivax. Likewise, approximately half of the patients were males; 52% (83/160) pre-ACT and

47% (137/289) post-ACT.

There was no difference in the geometric mean parasitaemia of P. falciparum infections

between the pre-ACT (15,056 [95%CI: 12,35 –18,349] parasites/µL) and post-ACT (17,095

[95%CI: 14,655–19,941] parasites/µL) populations (p=0.190; Table 3). However, for P. vivax,

the geometric mean parasitaemia rose from 4,865 [95%CI: 3,489–6,783] parasites/µL pre-

ACT to 16,216 [95%CI: 13,84 – 8,785] parasites/µL post-ACT; p<0.001). However, there was

no association between MOI and parasite density for either species (P. vivax: rho = -0.064, p

= 0.351; P. falciparum: rho = -0.027, p = 0.693).

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Table 3. Demographic data per year.

Year

Age n (%) Males

n (%)

Parasitaemia (parasites/µL) Total

Genotyped

n (%) Children <15

years Adults >= 15

years Geometric mean (95%CI)

P. falciparum4

2004 2 (6) 32 (94) 24 (71) 18,183 (12,186-27,132) 35 34 (97)

2005 0 (0) 22 (100) 11 (50) 8,858 (5,596-14,022) 25 25 (100)

2006 0 (0) 30 (100) 14 (47) 14,193 (10,4767-19,229) 31 31 (100)

Pre 2 (2) 84 (98) 49 (57) 13,740 (10,929-17,273) 91 90 (99)

2011 3 (27) 8 (73) 6 (54) 34,760 (15,282-79,059) 11 11 (100)

2012 0 (0) 36 (100) 15 (42) 14,312 (11,050-18,537) 36 36 (100)

2013 1 (8) 12 (92) 6 (46) 22,375 (15,246-32,838) 13 13 (100)

2014 5 (15) 29 (85) 11 (32) 22,763 (15,601-33,212) 34 34 (100)

2015 7 (19) 30 (81) 24 (65) 12,184 (10,094-14,706) 38 37 (97)

2016 2 (25) 6 (75) 5 (62) 10,835 (7,780-15,089) 8 8 (100)

Post 18 (13) 121 (87) 67 (48) 16,797 (14,407-19,583) 140 139 (99)

P. vivax2

2004 11 (55) 9 (45) 12 (60) 1,408 (593-3,344) 25 20 (80)

2005 1 (6) 17 (94) 7 (39) 8,135 (5,456-12,129) 20 19 (95)

2006 1 (3) 31 (97) 15 (47) 7,718 (6,158-9,673) 32 31 (97)

Pre 13 (19) 57 (81) 34 (49) 4,865 (3,489-6,783) 77 70 (91)

2011 7 (20) 28 (80) 21 (60) 15,860 (11,919-21,104) 35 35 (100)

2012 5 (17) 25 (83) 12 (40) 25,366 (18,047-35,654) 32 30 (94)

2013 3 (12) 22 (88) 10 (40) 21,050 (14,238-31,121) 26 25 (96)

2014 1 (8) 12 (92) 7 (54) 16,556 (9,297-29,482) 14 14 (100)

2015 11 (30) 26 (70) 19 (51) 11,015 (8,483-14,302) 39 37 (95)

2016 1 (11) 8 (89) 1 (11) 8,391 (5,228-13,467) 9 9 (100)

Post 28 (19) 121 (81) 70 (47) 16,126 (13,843-18,785) 155 150 (97)

# Superscript indicate number of missing data

5.4.3 Significant decrease in complexity of infection in P. vivax after ACT

implementation

There was a significant decrease in the proportion of polyclonal P. vivax infections from

57% (40/70) pre-ACT to 33% (49/150) post-ACT (Odds Ratio (OR) = 0.36 [95%CI: 0.20-0.65],

p=0.001; Table 4a). A similar trend was observed in P. falciparum (18% (16/91) and 9%

(13/139), respectively), although this was of borderline significance (OR = 0.48 [0.22-1.06],

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p=0.052; Table 4b). The corresponding mean (SD) MOI decreased significantly between pre-

and post-ACT years in P. vivax (from 1.73 (0.8) to 1.4 (0.6); p<0.001), but not in P.

falciparum (1.18 (0.4) to 1.10 (0.3); p=0.067). Likewise, there was a significant decrease in

the number of polyclonal loci before and after ACT implementation in P. vivax, but not in P.

falciparum (Figure1).

Table 4a. Within-host and population diversity in P. vivax isolates.

P. vivax N MOI

Mean (SD) MLOCI

Median (Max) Polyclonal

N (%) HE

8

Mean (SD) Rs8

Mean (SD)

2004* 20 1.8 (0.8) 2 (6) 11 (55) 0.831 (0.1) 4.85 (1.0)

2005* 19 1.8 (0.7) 4 (6) 12 (63) 0.849 (0.1) 5.01 (0.9)

2006 31 1.7 (0.7) 3 (6) 17 (55) 0.867 (0.1) 5.37 (0.9)

Pre-ACT 70 1.7 (0.8) 3 (6) 40 (57) 0.854 (0.1) 10.0 (3.9)

2011 34 1.2 (0.5) 2 (6) 7 (21) 0.844 (0.1) 5.08 (1.2)

2012 30 1.5 (0.8) 1 (5) 11 (37) 0.847 (0.1) 5.29 (0.9)

2013 25 1.2 (0.4) 3 (4) 6 (24) 0.833 (0.1) 4.93 (0.9)

2014* 13 1.6 (0.6) 2 (5) 7 (54) 0.860 (0.1) 5.33 (1.2)

2015 37 1.3 (0.5) 2 (4) 12 (32) 0.872 (0.04) 5.50 (0.7)

2016* 9 1.8 (0.7) 1 (1) 6 (67) 0.877 (0.03) 5.24 (0.9)

Post-ACT 148 1.4 (0.6) 2 (6) 49 (33) 0.854 (0.1) 12.1 (3.1)

Table 4b. Within-host and population diversity in P. falciparum isolates.

P. falciparum N MOI

Mean (SD) MLOCI

Median(Max) Polyclonal

N (%) HE

Mean (SD) Rs

Mean (SD)

2004 35 1.1 (0.3) 1 (1) 3 (9) 0.577 (0.293) 3.19 (1.2)

2005 25 1.2 (0.4) 1 (2) 5 (20) 0.565 (0.236) 3.102 (0.9)

2006 31 1.3 (0.4) 1 (3) 8 (26) 0.57 (0.234) 3.078 (0.9)

Pre-ACT 91 1.2 (0.38) 1 (2) 16 (18) 0.571 (0.25) 6.1 (1.6)

2011 11* 1.1 (0.3) 1 (1) 1 (9) 0.498 (0.318) 2.552 (1.2)

2012 36 1.1 (0.2) 1 (1) 2 (6) 0.519 (0.327) 2.791 (1.2)

2013 13* 1.2 (0.4) 3 (3) 2 (15) 0.593 (0.293) 3.157 (1.1)

2014 34 1.1 (0.4) 1 (3) 5 (15) 0.502 (0.302) 2.653 (1.1)

2015 37 1.1 (0.3) 1 (3) 3 (8) 0.462 (0.27) 2.628 (1.1)

2016 8* 1 (0) 0 0 (0) 0.411 (0.31) 2.625 (1.5)

Post-ACT 139 1.1 (0.29) 1 (2) 13 (9) 0.520 (0.30) 5.1 (2.0)

MOI: Multiplicity of Infection; MLOCI: Number of polyclonal loci; Rs: Allelic richness; HE: Expected heterozygosity; Year*: Caution advised in interpretation owing to small sample size (n<25). Superscript # refers to the number of markers used for the analysis.

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Figure1. Polyclonal loci before and after change in treatment in A) P. vivax and B) P. falciparum. Proportion of polyclonal infections at 1, 2, 3, 4, 5

and 6 markers

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5.4.4 No difference in population genetic diversity before and after ACT

implementation in P. falciparum and P. vivax

The mean (SD) allelic richness (Rs) in the P. falciparum population was 6.1 (1.6) pre-ACT and

5.1 (2.0) post-ACT; p=0.876 Table 4a. Conversely, Rs was 10.0 (3.9) pre-ACT and 12.1 (3.1)

post-ACT in P. vivax; p=0.210; Table 4b). Similar trends were observed for the expected

heterozygosity in both species (Table 4a and 4b). Restricting the analysis to the five

balanced P. vivax markers did not alter these results (Supplementary Table 2).

5.4.5 Evidence of increasing self-fertilization in the P. falciparum population after

ACT implementation

Neighbour-joining analysis illustrated increased clusters of identical or highly related P.

falciparum infections after ACT implementation (Figure 2a), but this was not apparent for P.

vivax (Figure 2b). No identical MLGs (iMLGs) could be found in 157 (71%) P. vivax isolates

with complete data. In contrast, of the 219 (92%) P. falciparum isolates with complete data,

27 8-locus iMLGs were observed in 81 infections, of which three iMLGs persisted for up to 8

years (n=11; Figure 3a). The proportion of individuals carrying P. falciparum iMLGs

increased from 24% (21/89) pre-ACT to 46% (60/130) post-ACT (p=0.001; Figure 3b).

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Figure 2. Neighbour-joining tree for P. falciparum and P. vivax. a) P. vivax and b) P. falciparum.

P. falciparum P. vivax

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Figure 3. a) Persistence of multilocus genotypes (MLGs) in P. falciparum population (days) b) Distribution of persistence times in the whole P. falciparum dataset analysed (219 samples in 165 MLGs).

The LD in the P. vivax population was low both before and after ACT implementation (IAS

PRE

= 0.0097, p<0.05; IAS

POST = 0.0073, p<0.05; Table 5a). The LD in the P. falciparum population

was higher relative to P. vivax, with the index of association increasing 1.5-fold after ACT

implementation (IAS

PRE = 0.0285, p<0.01; IAS

POST = 0.0418, p<0.01). After restricting the

analysis to low complexity infections, similar trends were observed for both species (Table

5a and 5b). After restricting the analysis to unique MLGs, similar LD trends were observed

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in P. vivax, but there was a 4.5-fold decrease in the post-ACT P. falciparum population,

indicative of a moderate degree of epidemic transmission (Table 5b).

Table 5a. Linkage disequilibrium P. vivax.

Subgroup P. vivax

All infections N

IAS

Low complexity N

IAS

Unique haplotypes N

IAS

2004 16 0.017* 12 0.0219 NS 16 0.017NS

2005 19 0.0072NS 8 -0.0025NS 19 0.0072NS

2006 25 0.0197* 14 0.0202NS 25 0.0197NS

Pre-ACT 65 0.0097* 38 0.0112NS 64 0.0097NS

2011 17 0.0556* 13 0.0425* 17 0.0556**

2012 17 0.0243NS 15 0.041* 17 0.0243NS

2013 14 -0.0453NS 11 -0.0452NS 14 -0.0453NS

2014 11 0.0033NS 9 -0.0323NS 11 0.0033NS

2015 30 0.0096NS 24 0.0191NS 29 -0.0075NS

2016 8 0.3113** 8 0.3113** 7 -0.0036NS

Post-ACT 97 0.0073** 80 0.0035 95 0.0038

Table 5b. Linkage disequilibrium P. falciparum.

Subgroup P. falciparum

All infections N

IAS

Low complexity N

IAS

Unique haplotypes N

IAS

2004 34 0.0724** 34 0.0724** 29 0.0539**

2005 25 0.0228 NS 23 0.0366* 25 0.0228NS

2006 30 0.0059 NS 28 0.0084 NS 30 0.0059NS

Pre-ACT 89 0.0285** 85 0.0319** 78 0.0181*

2011 11 -0.0343NS 11 -0.0343NS 11 -0.0343NS

2012 34 0.0958** 34 0.0958** 27 0.0324*

2013 13 0.02NS 11 0.015NS 13 0.02NS

2014 30 0.0109NS 28 0.0086NS 26 -0.0017NS

2015 34 0.1338NS 33 0.1215** 24 0.0388*

2016 8 0.275** 8 0.275** 5 0.0406NS

Post-ACT 130 0.0418** 125 0.0414** 90 0.0094*

Only samples with no missing data were included in the analyses. * p<0.05; ** p< 0.01; NS: not significant.

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5.4.6 Evidence of increasing genetic differentiation over time in P. falciparum

There was little genetic differentiation in P. falciparum between pre- and post-ACT isolates

(FST=0.051; p<0.001), and almost no differentiation in P. vivax (FST=0.009; p=0.001; Table 2).

The highest levels of differentiation in the P. falciparum population were apparent between

the earliest and latest study years, specifically between 2015 relative to 2004, 2005 and

2006 (FST range: 0.101-0.114; p<0.001 and F’ST range: 0.207-229; Table 2). However, there

was no temporal trend in the genetic differentiation of P. vivax, even after excluding the

high diversity markers (Supplementary Table 2).

Analysis using STRUCTURE software, revealed 4 distinct P. falciparum subpopulations

(Supplementary Figure 4a), which were present across the years (Figure 4), with one

subpopulation increasing in frequency over time to become the predominant cluster by

2015. There was no evidence of sub-structure in P. vivax (Supplementary Figure 4b).

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Figure 4. STRUCTURE barplot of P. falciparum over the years.

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5.5 Discussion

This study presents the first longitudinal (2004-2016) investigation of the genetic make-up

of co-endemic P. falciparum and P. vivax populations in Indonesia, where a universal

antimalarial policy of ACT has been implemented for over a decade. The study incorporates

an analysis of P. falciparum and P. vivax isolates collected over more than ten years and

highlights a decrease in the proportion of polyclonal infections in both P. vivax and P.

falciparum, consistent with declining transmission. In P. falciparum, but not P. vivax

isolates, there was also evidence of increased inbreeding and an increase in the number of

identical strains.

Although a number of factors may have contributed to the genetic changes observed in the

P. vivax and P. falciparum populations in Papua, Indonesia, one of the greatest changes to

take place in the duration of the study was the implementation of an effective antimalarial

treatment policy in 2006. Although we considered the contribution of vector control, there

is unpublished evidence that vector indices have not changed significantly over the years in

the study area. In addition, given the bionomics of Anopheles mosquitoes (mostly exophilic

behaviour) it is acknowledged that bed-nets have little impact in this population. In

contrast, as demonstrated in genomic studies of P. vivax and P. falciparum (including a

recent study of Papua, Indonesian isolates), antimalarial drugs appear to have among the

strongest selective pressures on the parasite genome (Hemming-Schroeder & Lo 2017;

Hupalo et al. 2016; Nair et al. 2003; Pearson et al. 2016). The recent demonstration of

marked population structure reflecting the emergence of artemisinin-resistant P.

falciparum founder populations in Cambodia presents a striking example of the impact of

antimalarial drugs on the transmission and associated genetic diversity and structure of

parasite populations (Miotto et al. 2013).

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In Papua, Indonesia, therapeutic efficacy surveys conducted in 2004-6, prior to policy

change, revealed treatment efficacy of less than 60% for both P. falciparum and P. vivax

following the recommended standard treatment regimens (Ratcliff, Siswantoro,

Kenangalem, Wuwung, et al. 2007), but this rose to greater than 90% following treatment

with dihydroartemisinin-piperaquine (Ratcliff, Siswantoro, Kenangalem, Maristela, et al.

2007). Artemisinins clear parasitaemias faster than less-potent drugs such as chloroquine,

and have demonstrated potency against the transmissible gametocyte stages. In regions

where they remain effective, ACTs therefore have a greater impact in reducing parasite

biomass and reducing transmission than other conventional drugs. Intense vector control,

annual bednet distribution (implemented since 2004), and the change to ACT as first-line

treatment for all species of malaria in 2006 were instigated to reduce the clinical burden

and transmission intensity in the region. Epidemiological surveillance data collected from

local health facilities and cross-sectional studies in Mimika District suggest that these

control measures were successful, with the incidence of malaria falling from 876 per 1,000

in 2004-2005 (Karyana et al. 2008) to 539 per 1,000 in 2008-2010 (unpublished data).

Larger reductions have been observed in the number of P. falciparum cases, which fell from

584 per 1,000 pre-ACT to 257 per 1,000 post-ACT, compared to P. vivax cases, which fell

from 378 to 241 per 1,000 over the same period. The results of the molecular analysis

presented here highlight genetic changes in both species that reflect the declining

transmission. In keeping with the observed differential impact on the incidence of clinical

disease, the impact on the population genetic structure of P. vivax and P. falciparum also

differed, likely reflecting underlying differences in the biology and local epidemiology of

these species and the susceptibility of the parasites to the intensive malaria control

activities.

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Multiple clone infections can arise from serial infected mosquito bites before the onset of

clinical symptoms (super-infection), or a single mosquito bite carrying multiple clones. The

risk of the latter is likely to be greater in high transmission settings. Several studies have

shown a positive correlation between number of clones and/or proportion of polyclonal

infections and transmission intensity (Anderson et al. 2000; Nkhoma et al. 2013).

Consequently, reduction in the complexity of infection has been proposed as an early

marker of decreasing transmission within a parasite population (Nkhoma et al. 2013).

Whilst ACTs have efficacy against both the asexual and sexual stages of P. falciparum, they

have no effect on the dormant liver stages of P. vivax (Cui & Su 2009). Therefore, we

hypothesised that the policy change to ACT would reduce transmission and superinfection

in P. falciparum to a much greater extent than in P. vivax. Our study revealed a 2-fold

reduction in the proportion of polyclonal P. falciparum infections and a 1.7-fold reduction

in polyclonal P. vivax infections. The difference in the magnitude of reduction of polyclonal

infections between the species most likely reflects the comparably lower pre-ACT baseline

rate of polyclonal infections in P. falciparum versus P. vivax. The higher complexity and

genetic diversity of P. vivax in comparison with P. falciparum infections has been reported

by several studies (Noviyanti et al. 2015). These findings have important implications for

the utility of the prevalence of polyclonal infections to gauge transmission intensity. Most

longitudinal studies have been undertaken in areas of low endemicity where baseline

prevalence of polyclonal infections may be low, and have observed no significant change in

the complexity of infection observed (Abdullah et al. 2013; Batista et al. 2015; Ferreira et al.

2007; Iwagami et al. 2012). However, comparability between studies is confounded by

different sample sizes (i.e., power of the study), the applied intervention, and the length of

the period evaluated.

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The increasing proportion of individuals harbouring identical multi-locus genotypes (iMLGs)

was a strong predictor of declining transmission in P. falciparum studies in Africa, South-

America and Asia (Echeverry et al. 2013; Nkhoma et al. 2013; Sisya et al. 2015). In the

current analysis, there was a moderate proportion of iMLGs in the P. falciparum population

(27 iMLGs accounting for 81 infections), some persisting for as long as 8 years. The

distribution in longevity of the iMLGs was similar to that observed in previous studies

(Echeverry et al. 2013), where a short duration (up to 100 days) was most frequently

observed, but a gap in sampling between 2007 and 2011 may have had modest impact on

the distribution. We were unable to assess the longevity of iMLGs for either species before

and after ACT implementation due to the shorter collection period in the pre-ACT group.

Nonetheless, the proportion of individuals carrying identical P. falciparum strains doubled

after ACT implementation (from 24% to 46%), suggesting increasing self-fertilization events

in the population (Sisya et al. 2015). Other factors may also have contributed to the

maintenance of iMLGs over time, including changes in parasite mutation, migration rates,

population size and acquired specific immunity in the host (Ferreira et al. 2007). In contrast

to P. falciparum, no identical P. vivax strains were identified across the years, suggesting

that inbreeding remained infrequent in this species.

Since cross-fertilisation is only possible between mixed-clone infections, LD is expected to

correlate negatively with the proportion of polyclonal infections and thus, theoretically,

should increase as the transmission intensity decreases. Although overall LD levels

remained moderate, a 1.5-fold increase in IAS estimates was observed in the P. falciparum

population after ACT implementation. In conjunction with the increasing prevalence of

iMLGs and long persistence of strains, this suggests that recombination is declining in the P.

falciparum population. Assessment of the impact of iMLGs on LD also suggests that the

small increment in IAS estimates was due mainly to the increasing frequency of iMLGs and

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increasingly unstable transmission. In contrast, LD in the P. vivax population remained low

throughout the study period with no evidence of unstable transmission.

The population-level genetic diversity has been proposed to correlate positively with

endemicity in P. falciparum populations (Anderson et al. 2000). This is supported by an

investigation of P. falciparum diversity in four Indonesian islands using the same

microsatellite markers as applied in the current analysis (Noviyanti et al. 2015). The current

study presents a similar analysis conducted over time rather than geographical location. We

observed a modest reduction in P. falciparum population diversity after ACT

implementation, although this did not reach statistical significance. A finding which is in

keeping with Nkhoma and colleagues who also observed that population diversity was

limited as a measure of endemicity in a longitudinal survey on the Thai-Myanmar border

(Nkhoma et al. 2013).

In contrast to P. falciparum, the relationship between population diversity and endemicity

has been less apparent in P. vivax (Auburn & Barry 2016). Several studies have

demonstrated high levels of P. vivax population diversity in low endemic settings and

account for this by the high rates of relapsing infections and importation of cases

(Gunawardena et al. 2014; Liu et al. 2014; Wangchuk et al. 2016). We also observed no

significant difference in P. vivax population genetic diversity post-ACT.

In Malaysian Borneo, P. falciparum parasite populations became more structured and

fragmented as transmission declined (Anthony et al. 2005). Whilst P. falciparum sub-

structure has been observed in several endemic settings, P. vivax sub-structure has only

been observed in very low endemic, pre-elimination settings (Abdullah et al. 2013; Hamedi

et al. 2016). In accordance, we observed evidence of sub-structure in P. falciparum, but not

in P. vivax. Moreover, although a small genetic differentiation was found between pre-

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versus post- ACT P. falciparum populations, the increasing frequency of individuals carrying

identical clones and inbreeding are suggestive of early signs of fragmentation.

A key challenge in undertaking a comprehensive temporal analysis is gathering a large and

consistent sample size over a prolonged period (Hale, Burg & Steeves 2012). Our samples

were selected from large clinical trial and malariometric surveillance studies, with sufficient

sample size for 6 and 5 of the 9 years evaluated for P. falciparum and P. vivax, respectively.

This allowed robust trend and subgroup analyses. However, several limitations need to be

considered. The sample period included years when vector interventions were being

carried out, thus, it should be acknowledged that additional control measures other than

ACT implementation may have contributed to the changes in diversity and genetic

structure. Additionally, the number of isolates selected for this study was limited to

availability of samples and feasibility of large scale molecular analysis. This could have

potentially affected the estimates of complexity of infection. However, despite potential

selection bias, the demographics of the sample subset evaluated were comparable across

years and to the total patients enrolled at the RSMM hospital (Supplementary Table 2).

Moreover, the higher parasitaemia found in the P. vivax post-ACT versus the pre-ACT

sample set is probably explained by the higher proportion of samples enrolled from ex vivo

surveillance studies, which required a minimum parasitaemia of 1,000 parasites/µL

(Supplementary Figure 2). Nevertheless, previous studies have proved suitable to perform

population genetic analysis from sample sets enrolled from ex vivo surveillance studies

(Chapter 3).

Although the sample size was adequate for the analyses applied here, it did not permit

accurate analysis of effective population size, and larger sample sizes may have allowed

more subtle changes in population genetic diversity to be detected. Furthermore,

increasing the number of P. falciparum loci assessed might have provided a more robust

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measure of polyclonality. Finally, considering the findings of our previous studies (i.e.,

Chapters 3 and 4), demonstrating differences in polyclonality and hidden parasite sub-

populations in community samples, it would be informative to conduct similar longitudinal

analyses with active, as well as passively detected cases.

In conclusion, enhanced interventions, including the implementation of ACT and, to a lesser

extent, enhanced vector control strategies in Mimika District has had a significant impact

on the local P. falciparum and P. vivax clinical burden and transmission dynamics. The

significant reduction in the complexity of infections in the P. vivax population, but negligible

impact on the parasite’s population structure reflects for the first time the impact of these

interventions on transmission dynamics of this species. Further impact on the P. vivax

population may require additional efforts targeting the dormant liver stage reservoir. The

greater changes observed in the genetic structure and relatedness of the P. falciparum

population in the period after ACT implementation likely reflect the comparatively more

advanced progress towards elimination relative to the co-endemic P. vivax population. The

study highlights the utility, as well as the limitations, of molecular surveillance and the key

molecular cues that can be incorporated into the surveillance of co-endemic Plasmodium

species.

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5.6 Supplementary Material

Table S1. Marker diversity and genotyping success rate in P. falciparum and P. vivax.

Species Markers Pre-ACT

HE Success

N Post-ACT

HE Success

N

P. vivax

MS12 0.794 74 0.779 150

pv3.27 0.818 74 0.89 142

msp1f3 0.840 75 0.805 140

MS10 0.889 77 0.898 148

MS5 0.863 79 0.848 148

MS1 0.767 75 0.784 139

MS16 0.926 74 0.931 114

MS20 0.930 76 0.895 144

Total 0.854 70 0.854 150

P. falciparum

Poly-alpha 0.806 37 0.613 139

TA42 0.329 91 0.085 137

TA81 0.752 91 0.822 139

TA87 0.703 90 0.643 135

ARAII 0.742 91 0.774 139

PfPK2 0.721 89 0.608 138

TA60 0.629 91 0.652 139

TA1 0.645 91 0.562 136

TA109 0.044 91 0.014 139

Total 0.597 91 0.530 139

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Table S2. P. vivax FST and F’ST per year with more stable markers.

2004 2005 2006 2011 2012 2013 2014 2015 2016

2004 - 0.1587 0.1291 0.0714 0.0042 0.134 0.1457 0.1199 0.0396

2005 0.02853* - -0.0822 0.0371 0.2135 0.0145 -0.0171 0.0405 -0.0625

2006 0.02025 -0.01167 - 0.0602 0.0734 -0.0289 -0.0618 -0.0691 -0.0515

2011 0.01355 0.00647 0.00924 - 0.0184 0.0127 -0.0737 -0.0181 0.1019

2012 0.00072 0.03353* 0.00997 0.00309 - -0.0004 -0.006 -0.0549 0.0191

2013 0.02526 0.0025 -0.00435 0.00231 -0.00006 - -0.0313 -0.0897 -0.0601

2014 0.02513 -0.00266 -0.00826 -0.01235 -0.00089 -0.00517 - -0.0707 0.0964

2015 0.01928 0.00592 -0.00874 -0.00284 -0.0077 -0.01387 -0.00977 - -0.0291

2016 0.00659 -0.00928 -0.00643 0.01644 0.00269 -0.00953 0.01338 -0.00378 -

FST (p value) in lower left triangle; * p<0.05. F’ST in upper right triangle.

Table S3. P. vivax population diversity across the years with more stable markers.

P. vivax Rs

Mean (SD) HE

Mean (SD)

2004 4.845 (1.04) 0.825 (0.087)

2005 5.007 (0.88) 0.832 (0.08)

2006 5.373 (0.88) 0.872 (0.055)

2011 5.075 (1.2) 0.818 (0.079)

2012 5.285 (0.9) 0.846 (0.065)

2013 4.927 (0.9) 0.823 (0.075)

2014 5.336 (1.23) 0.853 (0.103)

2015 5.496 (0.69) 0.868 (0.037)

2016 5.236 (0.88) 0.854 (0.03)

P. vivax [Rs: p = 0.429], [HE: p = 0.952]

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5.7 Supplementary material

Supplementary Figure 1. Papua Indonesia Provinces. The area coloured in red denotes Mimika District.

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Supplementary Figure 2. Study profile.

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Supplementary Figure 3. Delta K method for a) P. vivax and b) P. falciparum.

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5.8 References

Abdullah, NR, Barber, BE, William, T, Norahmad, NA, Satsu, UR, Muniandy, PK, Ismail, Z, Grigg, MJ, Jelip, J, Piera, K, von Seidlein, L, Yeo, TW, Anstey, NM, Price, RN & Auburn, S 2013, 'Plasmodium vivax population structure and transmission dynamics in Sabah Malaysia', PLoS One, vol. 8, no. 12, p. e82553.

Anderson, TJ, Haubold, B, Williams, JT, Estrada-Franco, JG, Richardson, L, Mollinedo, R, Bockarie, M, Mokili, J, Mharakurwa, S, French, N, Whitworth, J, Velez, ID, Brockman, AH, Nosten, F, Ferreira, MU & Day, KP 2000, 'Microsatellite markers reveal a spectrum of population structures in the malaria parasite Plasmodium falciparum', Mol Biol Evol, vol. 17, no. 10, pp. 1467-1482.

Anderson, TJ, Su, XZ, Bockarie, M, Lagog, M & Day, KP 1999, 'Twelve microsatellite markers for characterization of Plasmodium falciparum from finger-prick blood samples', Parasitology, vol. 119 ( Pt 2), pp. 113-125.

Anthony, TG, Conway, DJ, Cox-Singh, J, Matusop, A, Ratnam, S, Shamsul, S & Singh, B 2005, 'Fragmented population structure of plasmodium falciparum in a region of declining endemicity', J Infect Dis, vol. 191, no. 9, pp. 1558-1564.

Auburn, S & Barry, AE 2016, 'Dissecting malaria biology and epidemiology using population genetics and genomics', Int J Parasitol, vol. 47, no. 2-3, pp. 77-85.

Balloux, F & Goudet, J 2002, 'Statistical properties of population differentiation estimators under stepwise mutation in a finite island model', Mol Ecol, vol. 11, no. 4, pp. 771-783.

Balloux, F & Lugon-Moulin, N 2002, 'The estimation of population differentiation with microsatellite markers', Mol Ecol, vol. 11, no. 2, pp. 155-165.

Barry, AE, Waltmann, A, Koepfli, C, Barnadas, C & Mueller, I 2015, 'Uncovering the transmission dynamics of Plasmodium vivax using population genetics', Pathog Glob Health, vol. 109, no. 3, pp. 142-152.

Batista, CL, Barbosa, S, Da Silva Bastos, M, Viana, SA & Ferreira, MU 2015, 'Genetic diversity of Plasmodium vivax over time and space: a community-based study in rural Amazonia', Parasitology, vol. 142, no. 2, pp. 374-384.

Bruce, MC, Galinski, MR, Barnwell, JW, Donnelly, CA, Walmsley, M, Alpers, MP, Walliker, D & Day, KP 2000, 'Genetic diversity and dynamics of plasmodium falciparum and P. vivax populations in multiply infected children with asymptomatic malaria infections in Papua New Guinea', Parasitology, vol. 121 ( Pt 3), pp. 257-272.

CHAPTER 5

154

Cui, L & Su, XZ 2009, 'Discovery, mechanisms of action and combination therapy of artemisinin', Expert Rev Anti Infect Ther, vol. 7, no. 8, pp. 999-1013.

Daniels, R, Chang, HH, Sene, PD, Park, DC, Neafsey, DE, Schaffner, SF, Hamilton, EJ, Lukens, AK, Van Tyne, D, Mboup, S, Sabeti, PC, Ndiaye, D, Wirth, DF, Hartl, DL & Volkman, SK 2013, 'Genetic surveillance detects both clonal and epidemic transmission of malaria following enhanced intervention in Senegal', PLoS One, vol. 8, no. 4, p. e60780.

Daniels, RF, Rice, BL, Daniels, NM, Volkman, SK & Hartl, DL 2015, 'The utility of genomic data for Plasmodium vivax population surveillance', Pathog Glob Health, vol. 109, no. 3, pp. 153-161.

Daniels, RF, Schaffner, SF, Wenger, EA, Proctor, JL, Chang, HH, Wong, W, Baro, N, Ndiaye, D, Fall, FB, Ndiop, M, Ba, M, Milner, DA, Jr., Taylor, TE, Neafsey, DE, Volkman, SK, Eckhoff, PA, Hartl, DL & Wirth, DF 2015, 'Modeling malaria genomics reveals transmission decline and rebound in Senegal', Proc Natl Acad Sci U S A, vol. 112, no. 22, pp. 7067-7072.

Diane Bailleul, SS, Sophie Arnaud-Haond 2016, 'RClone: a package to identify MultiLocus Clonal Lineages and handle clonal data sets in r.', Methods in Ecology and Evolution, vol. 7, no. 8, pp. 966-970.

Earl, DAv, B.M. 2012, 'STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method', Conservation Genetics Resources, vol. 4, no. 2, pp. 359-361.

Echeverry, DF, Nair, S, Osorio, L, Menon, S, Murillo, C & Anderson, TJ 2013, 'Long term persistence of clonal malaria parasite Plasmodium falciparum lineages in the Colombian Pacific region', BMC Genet, vol. 14, p. 2.

Evanno, G, Regnaut, S & Goudet, J 2005, 'Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study', Mol Ecol, vol. 14, no. 8, pp. 2611-2620.

Excoffier, L & Lischer, HE 2010, 'Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows', Mol Ecol Resour, vol. 10, no. 3, pp. 564-567.

Ferreira, MU & de Oliveira, TC 2015, 'Challenges for Plasmodium vivax malaria elimination in the genomics era', Pathog Glob Health, vol. 109, no. 3, pp. 89-90.

Ferreira, MU, Karunaweera, ND, da Silva-Nunes, M, da Silva, NS, Wirth, DF & Hartl, DL 2007, 'Population structure and transmission dynamics of Plasmodium vivax in rural Amazonia', J Infect Dis, vol. 195, no. 8, pp. 1218-1226.

CHAPTER 5

155

Gunawardena, S, Ferreira, MU, Kapilananda, GM, Wirth, DF & Karunaweera, ND 2014, 'The Sri Lankan paradox: high genetic diversity in Plasmodium vivax populations despite decreasing levels of malaria transmission', Parasitology, vol. 141, no. 7, pp. 880-890.

Hale, ML, Burg, TM & Steeves, TE 2012, 'Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies', PLoS One, vol. 7, no. 9, p. e45170.

Hamedi, Y, Sharifi-Sarasiabi, K, Dehghan, F, Safari, R, To, S, Handayuni, I, Trimarsanto, H, Price, RN & Auburn, S 2016, 'Molecular Epidemiology of P. vivax in Iran: High Diversity and Complex Sub-Structure Using Neutral Markers, but No Evidence of Y976F Mutation at pvmdr1', PLoS One, vol. 11, no. 11, p. e0166124.

Hanandita, W & Tampubolon, G 2016, 'Geography and social distribution of malaria in Indonesian Papua: a cross-sectional study', Int J Health Geogr, vol. 15, p. 13.

Hasugian, AR, Tjitra, E, Ratcliff, A, Siswantoro, H, Kenangalem, E, Wuwung, RM, Purba, HL, Piera, KA, Chalfien, F, Marfurt, J, Penttinen, PM, Russell, B, Anstey, NM & Price, RN 2009, 'In vivo and in vitro efficacy of amodiaquine monotherapy for treatment of infection by chloroquine-resistant Plasmodium vivax', Antimicrob Agents Chemother, vol. 53, no. 3, pp. 1094-1099.

Haubold, B & Hudson, RR 2000, 'LIAN 3.0: detecting linkage disequilibrium in multilocus data. Linkage Analysis', Bioinformatics, vol. 16, no. 9, pp. 847-848.

Hemming-Schroeder, E & Lo, E 2017, 'Evidence of a hard selective sweep for artemisinin resistant Plasmodium falciparum', Lancet Infect Dis, vol. 17, no. 5, pp. 462-463. Hupalo, DN, Luo, Z, Melnikov, A, Sutton, PL, Rogov, P, Escalante, A, Vallejo, AF, Herrera, S, Arevalo-Herrera, M, Fan, Q, Wang, Y, Cui, L, Lucas, CM, Durand, S, Sanchez, JF, Baldeviano, GC, Lescano, AG, Laman, M, Barnadas, C, Barry, A, Mueller, I, Kazura, JW, Eapen, A, Kanagaraj, D, Valecha, N, Ferreira, MU, Roobsoong, W, Nguitragool, W, Sattabonkot, J, Gamboa, D, Kosek, M, Vinetz, JM, Gonzalez-Ceron, L, Birren, BW, Neafsey, DE & Carlton, JM 2016, 'Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax', Nat Genet, vol. 48, no. 8, pp. 953-958. Iwagami, M, Fukumoto, M, Hwang, SY, Kim, SH, Kho, WG & Kano, S 2012, 'Population structure and transmission dynamics of Plasmodium vivax in the Republic of Korea based on microsatellite DNA analysis', PLoS Negl Trop Dis, vol. 6, no. 4, p. e1592.

Iwagami, M, Hwang, SY, Kim, SH, Park, SJ, Lee, GY, Matsumoto-Takahashi, EL, Kho, WG & Kano, S 2013, 'Microsatellite DNA analysis revealed a drastic genetic change of Plasmodium vivax population in the Republic of Korea during 2002 and 2003', PLoS Negl Trop Dis, vol. 7, no. 10, p. e2522.

CHAPTER 5

156

Jombart, JGaT 2015, hierfstat: Estimation and Tests of Hierarchical F-Statistics, 0.04-22 edn., R package, <https://CRAN.R-project.org/package=hierfstat>.

Karunaweera, ND, Ferreira, MU, Munasinghe, A, Barnwell, JW, Collins, WE, King, CL, Kawamoto, F, Hartl, DL & Wirth, DF 2008, 'Extensive microsatellite diversity in the human malaria parasite Plasmodium vivax', Gene, vol. 410, no. 1, pp. 105-112.

Karyana, M, Burdarm, L, Yeung, S, Kenangalem, E, Wariker, N, Maristela, R, Umana, KG, Vemuri, R, Okoseray, MJ, Penttinen, PM, Ebsworth, P, Sugiarto, P, Anstey, NM, Tjitra, E & Price, RN 2008, 'Malaria morbidity in Papua Indonesia, an area with multidrug resistant Plasmodium vivax and Plasmodium falciparum', Malar J, vol. 7, p. 148.

Kim, JY, Suh, EJ, Yu, HS, Jung, HS, Park, IH, Choi, YK, Choi, KM, Cho, SH & Lee, WJ 2011, 'Longitudinal and Cross-Sectional Genetic Diversity in the Korean Peninsula Based on the P vivax Merozoite Surface Protein Gene', Osong Public Health Res Perspect, vol. 2, no. 3, pp. 158-163.

Koepfli, C, Mueller, I, Marfurt, J, Goroti, M, Sie, A, Oa, O, Genton, B, Beck, HP & Felger, I 2009, 'Evaluation of Plasmodium vivax genotyping markers for molecular monitoring in clinical trials', J Infect Dis, vol. 199, no. 7, pp. 1074-1080.

Kwiatkowski, D 2015, 'Malaria genomics: tracking a diverse and evolving parasite population', Int Health, vol. 7, no. 2, pp. 82-84.

Liu, Y, Auburn, S, Cao, J, Trimarsanto, H, Zhou, H, Gray, KA, Clark, TG, Price, RN, Cheng, Q, Huang, R & Gao, Q 2014, 'Genetic diversity and population structure of Plasmodium vivax in Central China', Malar J, vol. 13, p. 262.

Miotto, O, Almagro-Garcia, J, Manske, M, Macinnis, B, Campino, S, Rockett, KA, Amaratunga, C, Lim, P, Suon, S, Sreng, S, Anderson, JM, Duong, S, Nguon, C, Chuor, CM, Saunders, D, Se, Y, Lon, C, Fukuda, MM, Amenga-Etego, L, Hodgson, AV, Asoala, V, Imwong, M, Takala-Harrison, S, Nosten, F, Su, XZ, Ringwald, P, Ariey, F, Dolecek, C, Hien, TT, Boni, MF, Thai, CQ, Amambua-Ngwa, A, Conway, DJ, Djimde, AA, Doumbo, OK, Zongo, I, Ouedraogo, JB, Alcock, D, Drury, E, Auburn, S, Koch, O, Sanders, M, Hubbart, C, Maslen, G, Ruano-Rubio, V, Jyothi, D, Miles, A, O'Brien, J, Gamble, C, Oyola, SO, Rayner, JC, Newbold, CI, Berriman, M, Spencer, CC, McVean, G, Day, NP, White, NJ, Bethell, D, Dondorp, AM, Plowe, CV, Fairhurst, RM & Kwiatkowski, DP 2013, 'Multiple populations of artemisinin-resistant Plasmodium falciparum in Cambodia', Nat Genet, vol. 45, no. 6, pp. 648-655.

NA, R 2004, 'Distruct: a program for the graphical display of population structure', Molecular Ecology Notes, vol. 4, pp. 137–138.

Nair, S, Williams, JT, Brockman, A, Paiphun, L, Mayxay, M, Newton, PN, Guthmann, JP, Smithuis, FM, Hien, TT, White, NJ, Nosten, F & Anderson, TJ 2003, 'A selective sweep driven

CHAPTER 5

157

by pyrimethamine treatment in southeast asian malaria parasites', Mol Biol Evol, vol. 20, no. 9, pp. 1526-1536.

Nkhoma, SC, Nair, S, Al-Saai, S, Ashley, E, McGready, R, Phyo, AP, Nosten, F & Anderson, TJ 2013, 'Population genetic correlates of declining transmission in a human pathogen', Mol Ecol, vol. 22, no. 2, pp. 273-285.

Noviyanti, R, Coutrier, F, Utami, RA, Trimarsanto, H, Tirta, YK, Trianty, L, Kusuma, A, Sutanto, I, Kosasih, A, Kusriastuti, R, Hawley, WA, Laihad, F, Lobo, N, Marfurt, J, Clark, TG, Price, RN & Auburn, S 2015, 'Contrasting Transmission Dynamics of Co-endemic Plasmodium vivax and P. falciparum: Implications for Malaria Control and Elimination', PLoS Negl Trop Dis, vol. 9, no. 5, p. e0003739.

Ocholla, H, Preston, MD, Mipando, M, Jensen, AT, Campino, S, MacInnis, B, Alcock, D, Terlouw, A, Zongo, I, Oudraogo, JB, Djimde, AA, Assefa, S, Doumbo, OK, Borrmann, S, Nzila, A, Marsh, K, Fairhurst, RM, Nosten, F, Anderson, TJ, Kwiatkowski, DP, Craig, A, Clark, TG & Montgomery, J 2014, 'Whole-genome scans provide evidence of adaptive evolution in Malawian Plasmodium falciparum isolates', J Infect Dis, vol. 210, no. 12, pp. 1991-2000.

Paradis, E, Claude, J & Strimmer, K 2004, 'APE: Analyses of Phylogenetics and Evolution in R language', Bioinformatics, vol. 20, no. 2, pp. 289-290.

Pava, Z, Burdam, FH, Handayuni, I, Trianty, L, Utami, RA, Tirta, YK, Kenangalem, E, Lampah, D, Kusuma, A, Wirjanata, G, Kho, S, Simpson, JA, Auburn, S, Douglas, NM, Noviyanti, R, Anstey, NM, Poespoprodjo, JR, Marfurt, J & Price, RN 2016, 'Submicroscopic and Asymptomatic Plasmodium Parasitaemia Associated with Significant Risk of Anaemia in Papua, Indonesia', PLoS One, vol. 11, no. 10, p. e0165340.

Pearson, RD, Amato, R, Auburn, S, Miotto, O, Almagro-Garcia, J, Amaratunga, C, Suon, S, Mao, S, Noviyanti, R, Trimarsanto, H, Marfurt, J, Anstey, NM, William, T, Boni, MF, Dolecek, C, Tran, HT, White, NJ, Michon, P, Siba, P, Tavul, L, Harrison, G, Barry, A, Mueller, I, Ferreira, MU, Karunaweera, N, Randrianarivelojosia, M, Gao, Q, Hubbart, C, Hart, L, Jeffery, B, Drury, E, Mead, D, Kekre, M, Campino, S, Manske, M, Cornelius, VJ, MacInnis, B, Rockett, KA, Miles, A, Rayner, JC, Fairhurst, RM, Nosten, F, Price, RN & Kwiatkowski, DP 2016, 'Genomic analysis of local variation and recent evolution in Plasmodium vivax', Nat Genet, vol. 48, no. 8, pp. 959-964.

Pritchard, JK, Stephens, M & Donnelly, P 2000, 'Inference of population structure using multilocus genotype data', Genetics, vol. 155, no. 2, pp. 945-959.

Ratcliff, A, Siswantoro, H, Kenangalem, E, Maristela, R, Wuwung, RM, Laihad, F, Ebsworth, EP, Anstey, NM, Tjitra, E & Price, RN 2007, 'Two fixed-dose artemisinin combinations for drug-resistant falciparum and vivax malaria in Papua, Indonesia: an open-label randomised comparison', Lancet, vol. 369, no. 9563, pp. 757-765.

CHAPTER 5

158

Ratcliff, A, Siswantoro, H, Kenangalem, E, Wuwung, M, Brockman, A, Edstein, MD, Laihad, F, Ebsworth, EP, Anstey, NM, Tjitra, E & Price, RN 2007, 'Therapeutic response of multidrug-resistant Plasmodium falciparum and P. vivax to chloroquine and sulfadoxine-pyrimethamine in southern Papua, Indonesia', Trans R Soc Trop Med Hyg, vol. 101, no. 4, pp. 351-359.

Russell, B, Chalfein, F, Prasetyorini, B, Kenangalem, E, Piera, K, Suwanarusk, R, Brockman, A, Prayoga, P, Sugiarto, P, Cheng, Q, Tjitra, E, Anstey, NM & Price, RN 2008, 'Determinants of in vitro drug susceptibility testing of Plasmodium vivax', Antimicrob Agents Chemother, vol. 52, no. 3, pp. 1040-1045.

Singh, B, Bobogare, A, Cox-Singh, J, Snounou, G, Abdullah, MS & Rahman, HA 1999, 'A genus- and species-specific nested polymerase chain reaction malaria detection assay for epidemiologic studies', Am J Trop Med Hyg, vol. 60, no. 4, pp. 687-692.

Sisya, TJ, Kamn'gona, RM, Vareta, JA, Fulakeza, JM, Mukaka, MF, Seydel, KB, Laufer, MK, Taylor, TE & Nkhoma, SC 2015, 'Subtle changes in Plasmodium falciparum infection complexity following enhanced intervention in Malawi', Acta Trop, vol. 142, pp. 108-114.

Statistics, CBo 2010, Population census, February 22nd of 2017, <http://sp2010.bps.go.id/index.php/site/index>.

Sutton, PL 2013, 'A call to arms: on refining Plasmodium vivax microsatellite marker panels for comparing global diversity', Malar J, vol. 12, p. 447.

Tjitra, E, Anstey, NM, Sugiarto, P, Warikar, N, Kenangalem, E, Karyana, M, Lampah, DA & Price, RN 2008, 'Multidrug-resistant Plasmodium vivax associated with severe and fatal malaria: a prospective study in Papua, Indonesia', PLoS Med, vol. 5, no. 6, p. e128.

Wangchuk, S, Drukpa, T, Penjor, K, Peldon, T, Dorjey, Y, Dorji, K, Chhetri, V, Trimarsanto, H, To, S, Murphy, A, von Seidlein, L, Price, RN, Thriemer, K & Auburn, S 2016, 'Where chloroquine still works: the genetic make-up and susceptibility of Plasmodium vivax to chloroquine plus primaquine in Bhutan', Malar J, vol. 15, no. 1, p. 277.

CHAPTER 6 – Expression of Plasmodium vivax crt-o is related

to parasite stage but not ex vivo chloroquine susceptibility

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CHAPTER 7 – General Discussion and Outlook

7.1 Introduction

Malaria monitoring and surveillance systems have been a key component in the great

progress made towards interrupting local mosquito-borne malaria transmission (WHO

2016). Surveillance systems are particularly important in the pre-elimination stages, when

information is needed to direct resources to populations and locations with the greatest

burden of clinical disease (Bridges, Winters & Hamer 2012), but as the malaria burden

decreases, the local malaria epidemiology becomes more complex (Cotter et al. 2013); the

prevalence of asymptomatic infection rises, with a shift to a predominance of P. vivax and

greater contribution of imported cases (Cotter et al. 2013). Although most infections are

asymptomatic, the risk of epidemic outbreaks rises in vulnerable populations with reduced

immunity (Cotter et al. 2013). Identifying populations and endemic settings at greatest risk

of infection (i.e., “hot pops” and “hot spots”) can be extremely challenging (Cotter et al.

2013; Ferreira & Castro 2016). These issues highlight the dangers of relying solely on

traditional epidemiological tools. Thus, there is a growing awareness that malaria

elimination will require traditional surveillance approaches to be aligned with the

continuous research and development of new tools and strategies (Tanner et al. 2015).

Molecular tools have the potential to inform on several key gaps in the traditional

surveillance systems, particularly with regard to the burden of infection, transmission

patterns, and rise and spread of antimalarial resistance. The overarching aim of my thesis

was to provide evidence of the benefits of molecular approaches that can enhance

traditional malaria surveillance.

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7.2 Characterization of Asymptomatic and Subpatent/Submicroscopic

Malaria

Asymptomatic and subpatent/submicroscopic malaria presents one of the greatest

challenges to malaria elimination. Asymptomatic patent infections have been widely

reported in high transmission settings in the past (Bottius et al. 1996), but the development

of improved molecular diagnostic tools have uncovered an even larger reservoir of

asymptomatic submicroscopic infections across all endemic settings. As transmission

declines, these subclinical infections contribute an increasing proportion of the total

parasite burden (Cheng, Cunningham & Gatton 2015; Okell et al. 2009). However, the

degree to which I these infections contribute to malaria associated morbidity and ongoing

transmission remain unclear.

7.2.1 How deep do we need to look in the “malaria iceberg” to reduce transmission?

Our understanding of the relevance of asymptomatic infections in the context of malaria

elimination is incomplete. It is likely that asymptomatic infections maintain low-level

transmission and seed new infections when environmental conditions become favorable

for the vector again (Roper et al. 1996). Several studies support the contribution of

asymptomatic microscopic infections to malaria transmission, but the evidence of the role

of asymptomatic submicroscopic infections is inconclusive (Bousema et al. 2012; Churcher

et al. 2013; Okell et al. 2012; Vallejo et al. 2016). Despite the great progress in developing

very sensitive molecular tools to quantify low-density gametocytemia (Bousema & Drakeley

2011), better tools are needed to dissect other key components of host-vector interaction

and transmission, such as male/female gametocyte ratios, and gametocyte maturity

markers for each parasite species. Nevertheless, mathematical models suggest that the

greater prevalence of asymptomatic relative to symptomatic infections, rather than their

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efficiency to infect mosquitoes, is a major determinant of their contribution to malaria

transmission (Okell et al. 2012).

7.2.2 What is the clinical impact of subpatent/submicroscopic malaria?

The pathology associated with asymptomatic patent infection in children and pregnant

women has been described. Moreover, in the same area asymptomatic parasitaemia in

pregnant women has been shown to increase the risk of maternal anaemia and the delivery

of low birth weight babies. However, few studies have addressed the clinical relevance of

submicroscopic infections (Chen et al. 2016). In Chapter 2, the results of a cross-section al

survey demonstrated that asymptomatic infections were associated with a greater risk of

anaemia not only in children and pregnant women, but participants of all ages and that the

risk of anaemia was apparent even at submicroscopic parasitaemia. Although it is generally

believed that targeting submicroscopic infections benefits the community more than the

individual, Chapter 2 provides evidence of the benefits of treating low level infections at an

individual level. The implications of these findings are greater justification for public health

interventions and policies to treat all parasite reservoirs, with benefits for both the

individual and the community.

The degree of hemolysis is correlated positively with the parasite density, as well as

duration of asymptomatic infections (Chen et al. 2016; Ghosh & Ghosh 2007). Hence

longitudinal studies will be needed to characterize further the long-term risks of anemia

and other morbidities related to submicroscopic infection.

The degree of transmissibility, risk factors for chronic carriage, and clinical impact of

submicroscopic infections varies in different endemic settings (Chen et al. 2016; Lin,

Saunders & Meshnick 2014). This heterogeneity will need to be explored further in different

endemic settings so that cost-effective strategies to target the critical reservoirs can be

optimized for specific environments (Lin, Saunders & Meshnick 2014).

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7.3 Molecular Epidemiology and Population Genetics

7.3.1 Characterizing malaria transmission patterns

Molecular approaches are increasingly being applied to inform on parasite transmission

dynamics, complementing the information gathered through traditional surveillance

methods. Population genetic studies can provide insights on transmission intensity,

stability, foci, gene flow, and epidemic outbreaks, improving the characterization of local

transmission dynamics (Auburn & Barry 2016). For P. falciparum infections, there is a

positive correlation between genetic diversity and the intensity of transmission which has

been observed in multiple endemic settings (Auburn & Barry 2016). However, this

association has rarely been observed in P. vivax infections, and even at several low endemic

areas there is moderate to high genetic diversity (Barry et al. 2015). The few endemic

locations where moderately low P. vivax genetic diversity has been reported include pre-

elimination settings such as Malaysia and South Korea, where autochthonous infections

appear to outnumber imported infections (Abdullah et al. 2013; Choi et al. 2011; Delgado-

Ratto et al. 2016; Iwagami et al. 2013). These findings suggests that, relative to P.

falciparum, longer periods of diligent transmission control will be required to impact on the

genetic diversity (i.e., transmission intensity) in P. vivax populations, and that these trends

may be obscured by imported infections .

Chapters 3 and 5 provide examples on the contribution of microsatellite typing to

traditional malaria surveillance in P. falciparum and P. vivax populations in a low-moderate

endemic setting. In Chapter 3, the transmission intensity (indirectly measured by genetic

diversity and polyclonality) is contrasted between co-endemic P. falciparum and P. vivax

populations in Mimika District, Papua Province, Indonesia. The variation between the

diversity of P. falciparum and P. vivax is likely explained by the biological differences that

enhance P. vivax transmission, including the reservoir of dormant hypnozoites in the liver,

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early development of sexual stages, and adaptation to vectors with a wider temperature

tolerance. Several studies have reported similar findings, underscoring the special

difficulties in efforts to reduce P. vivax transmission (Ferreira & de Oliveira 2015; Popovici &

Menard 2015). Moreover, recent studies have shown the need for longer term sustained

malaria control to achieve similar impact in P. vivax population. Chapter 3 provides a new

point of reference for southern Papua, presenting a baseline for future molecular

surveillance in the region.

Another finding from Chapter 3 that will re-enforce local intervention strategies was the

demonstration of a small degree of sharing of identical P. falciparum infections amongst

household members in Mimika District. This suggests that modest levels of P. falciparum

transmission are taking place within the vicinity of the household, with implications for the

utility of household level intervention strategies such as indoor residual spraying. This

particular finding emphasizes the need for improving vector control strategies such as

environmental sanitation in this area. Moreover, it brings more evidence on the utility of

the combination of genetic and spatial tools to characterize and design local strategies.

Chapter 3 also demonstrated the discovery by microsatellite-based analysis of a hidden

subpopulation of asymptomatic P. falciparum infections in the area. Further comparison of

this subpopulation with available data from four other Indonesian islands suggested these

infections could reflect imported and/or recently introduced malaria cases. Intriguingly,

these infections were clustered around the airport, however, travel history details were not

available for the patients, and thus the likelihood of ‘local importation’ could not be

evaluated further. Whilst regional geographic markers are available for P. falciparum and P.

vivax (Baniecki et al. 2015; Preston et al. 2014; Rodrigues et al. 2014), higher resolution

markers are needed to accurately define the origin of these cases. Similar geographic

surveillance tools are critical in low endemic areas where a large proportion of malaria

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cases may be imported. Identifying the burden of imported infections and, where possible,

their geographic origin, can inform National Malaria Control Programs (NMCPs) on effective

strategies for case detection. In the specific case of Indonesia, where marked heterogeneity

is observed in malaria epidemiology, molecular tools for tracking the influx of cases

imported across islands will support the formulation of more efficient malaria control

strategies on a nation-wide level.

7.3.2 Sampling strategies

Owing to the logistic and economic feasibility of passive sampling, many molecular

surveillance approaches, including antimalarial efficacy and population genetic studies,

sample patients presenting at health care centers. However, the demonstration of a distinct

sub-population of P. falciparum infections comprising asymptomatic cases only (Chapter 3),

raises important questions regarding the representativeness of passively detected strains

relative to those circulating in the community. The rise and spread of clinically important

strains such as drug resistant strains has potential to occur unnoticed if surveillance

systems rely exclusively on traditional passive surveillance for early warning (Falq et al.

2016). In Chapter 4, the genetic diversity of P. falciparum and P. vivax parasites causing

asymptomatic patent and asymptomatic submicroscopic infections were compared directly

to those from patients enrolled at health facilities (i.e., symptomatic patent cases) in a low-

moderate endemic setting. Comparable genetic diversity of these subgroups provided

evidence supporting the efficacy of passive surveillance estimates as indicators of the

parasite population diversity in this endemic setting. However, significant differences in the

complexity of infection between symptomatic and asymptomatic P. vivax cases emphasized

the need for caution when comparing the level of polyclonality between studies using

different sampling techniques or, indeed, using pooled samples (i.e., samples from

symptomatic and asymptomatic cases). Further investigation of the comparability between

symptomatic and asymptomatic cases is warranted in different endemic settings since the

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proportions, distributions, and transmission patterns of these subgroups appear to vary

across endemicities, with potential impact on the underlying parasite diversity and

structure.

7.3.3 Longitudinal surveys to monitor the efficacy of interventions

Molecular methods can also be used to gauge the efficacy of ongoing control strategies to

reduce malaria transmission and to assess their impact on the genetic structure of the

parasite population. Traditional malaria surveillance assesses the efficacy of interventions

by measuring the number of suspected and laboratory-confirmed cases. However, even in

areas with a relatively strong surveillance system, subtle changes in malaria transmission

patterns can be missed. The situation is considerably worse in areas with poor case

reporting and/or wide variation in treatment seeking behavior, and high prevalence of low-

density infections. Conventional measures of parasite prevalence or incidence may miss

potentially important changes in the parasite population such as the early emergence of

clonal expansions.

Population genetic studies have the potential to fill several gaps in the information

provided by traditional surveillance, and this may be especially powerful in longitudinal

frameworks where changes in infection complexity, and population diversity and structure

can be tracked over time. Population genetic features including decreasing proportions of

polyclonal infections, increased genetic relatedness, increased inbreeding rates, and

increasing fragmentation/structure have been reported in P. falciparum populations after a

severe reduction in malaria prevalence (Chenet et al. 2015; Nkhoma et al. 2013; Sisya et al.

2015). In contrast, only a few studies have monitored the effect of control measures on P.

vivax population diversity and structure over time, and none have investigated the relative

impacts on co-endemic P. vivax and P. falciparum populations (Batista et al. 2015; Ferreira

et al. 2007; Iwagami et al. 2013).

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Chapter 5 describes a longitudinal genetic survey assessing the transmission-related

population genetic parameters after ACT implementation in Southern Papua where P.

falciparum and P. vivax are co-endemic and where chloroquine (CQ) resistance is

widespread in both species (Ratcliff et al. 2007; Russell et al. 2008; Suwanarusk et al. 2007).

In the P. falciparum population, we observed genetic changes pre and post policy change,

including a decreased proportion of polyclonal infections and an increased frequency of

identical infections, and evidence of increased inbreeding, reflective of declining

transmission and high efficacy of ACTs against P. falciparum. Our results on the temporal

trends in P. vivax present the first longitudinal genetic assessment of ACT implementation

on P. vivax population diversity and structure. The observed reduction of polyclonal

infections and declining complexity of P. vivax infections in Mimika concurs with previous

studies suggesting that a reduction in complexity of infection is the earliest sign of declining

malaria transmission (Nkhoma et al. 2013). Although the observation provides

corroborating evidence of the improved efficacy of ACTs in an area with a high level of CQ

resistant P. vivax infections (Douglas et al. 2010), the reduction in transmission of P. vivax

was lower than that of P. falciparum and highlights the lack of efficacy of ACT against the

hypnozoite reservoir (Petersen, Eastman & Lanzer 2011). This study complements the

information gathered through traditional surveillance systems, by providing detailed

characteristics of transmission patterns in the area and should be incorporated in the

current surveillance system.

7.3.4 Surveillance of chloroquine efficacy in P. vivax

To date, few countries have presented very high rates of treatment failure to CQ in P. vivax

infections (Price et al. 2014). Unsurprisingly, CQ therefore remains the most common first-

line treatment for P. vivax infection in most of the world. However, given the drawbacks to

characterize P. vivax treatment failure, it is likely that the true distribution of P. vivax CQ

resistance remains unknown. Current approaches have allowed the identification and

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characterization of clinical and ex vivo CQ resistant (CQR) P. vivax (Grigg et al. 2016; Price et

al. 2014; Suwanarusk et al. 2007). Whilst ex vivo susceptibility testing offers a useful tool to

detect early warnings of changes in P. vivax susceptibility to antimalarials such as CQ, the

logistical constraints of these assays, including the requirement for highly synchronous and

patent infections, do not allow high throughput in community level studies. Therapeutic

efficacy studies (TES) provide an alternative approach to assess antimalarial susceptibility

profiles, but are constrained by challenges in the differentiation of recrudescence from

relapse and re-infection events. In this context, molecular tools would greatly strengthen

efforts to characterize and monitor P. vivax drug susceptibility profiles. The search for

molecular markers of CQ resistance is a key priority towards improving the surveillance of

drug resistant P. vivax and optimizing its radical cure. Chapter 6 contributes to the current

investigation of molecular markers of CQ resistance in P. vivax and potential resistance

mechanisms.

Investigations into the role of orthologous genes associated with antimalarial resistance in

P. falciparum have proven to be useful to identify markers for sulphadoxine and

pyrimethamine resistance in P. vivax (Peterson, Walliker & Wellems 1988; Triglia et al.

1997). The orthologue of pfcrt, the CQ resistance transporter gene which plays a pivotal

role in P. falciparum CQ resistance, in P. vivax (pvcrt-o) has, therefore, been the subject of

several studies of P. vivax CQ resistance (Fidock et al. 2000). However, no conclusive

evidence has been found relating point mutations in pvcrt-o and treatment outcome (Lu et

al. 2011; Rungsihirunrat et al. 2015; Suwanarusk et al. 2007). Additional functional studies,

suggest a role of the pvcrt-o gene in CQ transportation and accumulation (Baro, Pooput &

Roepe 2011; Sa et al. 2006) and expression levels of pvcrt-o have been proposed to

correlate with clinical CQ resistance and severe vivax malaria in Brazil (Fernandez-Becerra

et al. 2009; Melo et al. 2014).

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Chapter 6, therefore, explored the association between pvcrt-o expression and ex-vivo

susceptibility to CQ in P. vivax isolates sourced from Mimika District. The findings

demonstrate that pvcrt-o expression levels were associated with the parasite stage, rather

than ex vivo susceptibility to CQ. These findings concur with evidence acquired from

genomic approaches including signatures of selection (Hupalo et al. 2016; Pearson et al.

2016) which found no evidence of selection on pvcrt-o in parasite populations from

Indonesia, Thailand and Cambodia. It seems likely that a multi-gene mechanism is

responsible for treatment failure to CQ in P. vivax. Taken together, these studies and the

observations presented in Chapter 6 imply that the mechanism of CQ resistance in P. vivax

and underlying molecular determinant(s) is different from those in P. falciparum.

Further investigations using ‘candidate-free’ approaches are therefore needed. Genome-

wide association studies (GWAS) represent a candidate-free strategy to identify new P.

vivax CQ resistance-related candidates, although accurate resistance phenotypes are

critical to this approach. In this regard, genomic approaches that do not rely on a

phenotype, but on signals of natural selection in response to pressures such as antimalarial

treatment, provide an alternative strategy. Recent genomic studies of P. vivax using these

phenotype-free approaches have provided several new candidates for CQ resistance in P.

vivax (Hupalo et al. 2016; Pearson et al. 2016). However, validation of these findings using

samples with reliable phenotypes is critical.

Whilst ACTs may provide an effective replacement for CQ in regions with declining efficacy

against P. vivax, a major drawback towards sustainable malaria control and elimination

strategies is the lack of safe and effective treatment targeting latent hypnozoites. Failure to

detect and effectively treat hypnozoite carriage in the population will result in ongoing

transmission, associated morbidity due to constant relapses and, constant risk of re-

introduction to malaria-free regions. Thus, the development of strategies addressing the

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hypnozoite reservoir is needed to achieve and maintain malaria elimination (Beeson et al.

2015). Meanwhile, improved surveillance systems and tools can contribute to alleviate the

problem by enabling strategies to identify and target high-risk groups (Beeson et al. 2015).

7.4 Limitations of the Study

7.4.1 Political and logistical challenges

Indonesian law instigated in 2007 prevents processing and analysis of samples outside the

country if appropriate infrastructure and technologies are locally available and feasible.

This was a major hurdle and a challenge for our team. As a consequence, most of the

experiments for the studies presented in my thesis were carried out in-country and

timeframes were dictated by approval and duration of respective working VISA permits.

Moreover the selection of molecular tools used in this study was based on existing

infrastructure in the Jakarta laboratories.

However, this approach to sample processing allowed building and strengthening local

Indonesian capacity including training staff in high-throughput molecular techniques. Most

experiments pertaining to the molecular analyses of samples such as, submicroscopic

detection and speciation and MS genotyping, were restricted to the availability of reagents

and feasibility of technologies in-country. Hence the studies presented demonstrate a proof

of concept of molecular tools for surveillance being implemented under local capacity with

the support from international collaborators.

7.4.2 Technical limitations

Molecular epidemiology studies rely on the ability to genotype low density parasitaemias,

but in some cases, where Plasmodium DNA could be detected by the diagnostic PCR, the

samples could not be reliably genotyped at a suitable number of markers. This was

especially evident in the P. vivax sample set, likely reflecting overall lower density of

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infection in this species relative to P. falciparum. Thus, some of the results presented may

reflect a subtle bias towards samples with higher content and better DNA quality.

Additionally, the quality and quantity of parasite DNA is likely to impact the sensitivity to

detect minor alleles in polyclonal infections. In theory, minor clones are more likely to be

identified in samples with larger quantities of parasite DNA. However, as outlined in

Chapter 4, although using a relative threshold contributes to standardize these differences;

active cases seem to be more inherently complex than passive cases. Thus, caution needs

to be exercised when analyzing within-host diversity from pooled samples applying

different case detection approaches.

Furthermore, it is important to highlight that not all longitudinal studies have observed

noticeable population genetic changes after interventions (Sisya et al. 2015). This could

either be the result of the implementation of ineffective malaria control strategies, or

limitations in the study design and laboratory methodologies. The ability to detect genetic

changes over time is vulnerable to the sample size, the duration of the study, and the tools

used. Thus, standardized surveillance protocols including sample collection and molecular

methods must be developed suitable for different endemic environments in order to

improve the effectiveness and comparability of this approach.

7.5 Outlook and Conclusive Remarks

There is a huge potential for the application of molecular tools to complement traditional

malariometric surveillance. Further research will identify new genetic candidates and

technical developments to optimize existing molecular assays, but this must go hand in

hand with operational research to incorporate new techniques into routine surveillance

systems. Critical operational issues that need to be addressed include the establishment of

robust frameworks for appropriate sample collection, high-throughput sample processing,

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data analysis, data interpretation, and the feedback of useful information to policy makers

and malaria control programs.

International partnerships can greatly contribute to this aim, through funding mechanisms

that support the implementation and maintenance of molecular surveillance. Moreover, a

joint initiative can achieve development and establishment of standardized protocols and

data sharing platforms, improving investment of resources.

The performance of all molecular tools for diagnosis and genotyping depends heavily on

the quality and quantity of the samples collected, hence further research into sensitive,

specific, and cost-effective platforms will be crucial to the success of this initiative. Further

research into appropriate markers to answer a suite of different epidemiological questions

pertaining to parasite detection, resistance, and transmission within and across borders

would be needed.

In conclusion, molecular tools provide vital information to complement malaria surveillance

systems, from improving the estimates of malaria burden to the potential to strengthen the

evaluation and impact of malaria control interventions. As progress is made in developing

these tools, greater efforts will be needed to integrate these into a comprehensive suite of

information for routine surveillance.

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7.6 References

Abdullah, NR, Barber, BE, William, T, Norahmad, NA, Satsu, UR, Muniandy, PK, Ismail, Z, Grigg, MJ, Jelip, J, Piera, K, von Seidlein, L, Yeo, TW, Anstey, NM, Price, RN & Auburn, S 2013, 'Plasmodium vivax population structure and transmission dynamics in Sabah Malaysia', PLoS One, vol. 8, no. 12, p. e82553.

Auburn, S & Barry, AE 2016, 'Dissecting malaria biology and epidemiology using population genetics and genomics', Int J Parasitol.

Baniecki, ML, Faust, AL, Schaffner, SF, Park, DJ, Galinsky, K, Daniels, RF, Hamilton, E, Ferreira, MU, Karunaweera, ND, Serre, D, Zimmerman, PA, Sa, JM, Wellems, TE, Musset, L, Legrand, E, Melnikov, A, Neafsey, DE, Volkman, SK, Wirth, DF & Sabeti, PC 2015, 'Development of a single nucleotide polymorphism barcode to genotype Plasmodium vivax infections', PLoS Negl Trop Dis, vol. 9, no. 3, p. e0003539.

Baro, NK, Pooput, C & Roepe, PD 2011, 'Analysis of chloroquine resistance transporter (CRT) isoforms and orthologues in S. cerevisiae yeast', Biochemistry, vol. 50, no. 31, pp. 6701-6710.

Barry, AE, Waltmann, A, Koepfli, C, Barnadas, C & Mueller, I 2015, 'Uncovering the transmission dynamics of Plasmodium vivax using population genetics', Pathog Glob Health, vol. 109, no. 3, pp. 142-152.

Batista, CL, Barbosa, S, Da Silva Bastos, M, Viana, SA & Ferreira, MU 2015, 'Genetic diversity of Plasmodium vivax over time and space: a community-based study in rural Amazonia', Parasitology, vol. 142, no. 2, pp. 374-384.

Bottius, E, Guanzirolli, A, Trape, JF, Rogier, C, Konate, L & Druilhe, P 1996, 'Malaria: even more chronic in nature than previously thought; evidence for subpatent parasitaemia detectable by the polymerase chain reaction', Trans R Soc Trop Med Hyg, vol. 90, no. 1, pp. 15-19.

Bousema, T, Dinglasan, RR, Morlais, I, Gouagna, LC, van Warmerdam, T, Awono-Ambene, PH, Bonnet, S, Diallo, M, Coulibaly, M, Tchuinkam, T, Mulder, B, Targett, G, Drakeley, C, Sutherland, C, Robert, V, Doumbo, O, Toure, Y, Graves, PM, Roeffen, W, Sauerwein, R, Birkett, A, Locke, E, Morin, M, Wu, Y & Churcher, TS 2012, 'Mosquito feeding assays to determine the infectiousness of naturally infected Plasmodium falciparum gametocyte carriers', PLoS One, vol. 7, no. 8, p. e42821.

CHAPTER 7

184

Bousema, T & Drakeley, C 2011, 'Epidemiology and infectivity of Plasmodium falciparum and Plasmodium vivax gametocytes in relation to malaria control and elimination', Clin Microbiol Rev, vol. 24, no. 2, pp. 377-410.

Bridges, DJ, Winters, AM & Hamer, DH 2012, 'Malaria elimination: surveillance and response', Pathog Glob Health, vol. 106, no. 4, pp. 224-231.

Chen, I, Clarke, SE, Gosling, R, Hamainza, B, Killeen, G, Magill, A, O'Meara, W, Price, RN & Riley, EM 2016, '"Asymptomatic" Malaria: A Chronic and Debilitating Infection That Should Be Treated', PLoS Med, vol. 13, no. 1, p. e1001942.

Chenet, SM, Taylor, JE, Blair, S, Zuluaga, L & Escalante, AA 2015, 'Longitudinal analysis of Plasmodium falciparum genetic variation in Turbo, Colombia: implications for malaria control and elimination', Malar J, vol. 14, no. 1, p. 363.

Cheng, Q, Cunningham, J & Gatton, ML 2015, 'Systematic review of sub-microscopic P. vivax infections: prevalence and determining factors', PLoS Negl Trop Dis, vol. 9, no. 1, p. e3413.

Choi, KM, Choi, YK, Kang, YA, Seo, SY, Lee, HW, Cho, SH, Lee, WJ, Rhie, HG, Lee, HS & Kim, JY 2011, 'Study of the genetic discrimination between imported and autochthonous cases of malaria in South Korea', J Travel Med, vol. 18, no. 1, pp. 63-66.

Churcher, TS, Bousema, T, Walker, M, Drakeley, C, Schneider, P, Ouedraogo, AL & Basanez, MG 2013, 'Predicting mosquito infection from Plasmodium falciparum gametocyte density and estimating the reservoir of infection', Elife, vol. 2, p. e00626.

Cotter, C, Sturrock, HJ, Hsiang, MS, Liu, J, Phillips, AA, Hwang, J, Gueye, CS, Fullman, N, Gosling, RD & Feachem, RG 2013, 'The changing epidemiology of malaria elimination: new strategies for new challenges', Lancet, vol. 382, no. 9895, pp. 900-911.

Delgado-Ratto, C, Gamboa, D, Soto-Calle, VE, Van den Eede, P, Torres, E, Sanchez-Martinez, L, Contreras-Mancilla, J, Rosanas-Urgell, A, Rodriguez Ferrucci, H, Llanos-Cuentas, A, Erhart, A, Van Geertruyden, JP & D'Alessandro, U 2016, 'Population Genetics of Plasmodium vivax in the Peruvian Amazon', PLoS Negl Trop Dis, vol. 10, no. 1, p. e0004376.

Douglas, NM, Anstey, NM, Angus, BJ, Nosten, F & Price, RN 2010, 'Artemisinin combination therapy for vivax malaria', Lancet Infect Dis, vol. 10, no. 6, pp. 405-416.

Falq, G, Van Den Bergh, R, De Smet, M, Etienne, W, Nguon, C, Rekol, H, Imwong, M, Dondorp, A & Kindermans, JM 2016, 'Assessing the asymptomatic reservoir and

CHAPTER 7

185

dihydroartemisinin-piperaquine effectiveness in a low transmission setting threatened by artemisinin resistant Plasmodium falciparum', Malar J, vol. 15, no. 1, p. 446.

Fernandez-Becerra, C, Pinazo, MJ, Gonzalez, A, Alonso, PL, del Portillo, HA & Gascon, J 2009, 'Increased expression levels of the pvcrt-o and pvmdr1 genes in a patient with severe Plasmodium vivax malaria', Malar J, vol. 8, p. 55.

Ferreira, MU & Castro, MC 2016, 'Challenges for malaria elimination in Brazil', Malar J, vol. 15, no. 1, p. 284.

Ferreira, MU & de Oliveira, TC 2015, 'Challenges for Plasmodium vivax malaria elimination in the genomics era', Pathog Glob Health, vol. 109, no. 3, pp. 89-90.

Ferreira, MU, Karunaweera, ND, da Silva-Nunes, M, da Silva, NS, Wirth, DF & Hartl, DL 2007, 'Population structure and transmission dynamics of Plasmodium vivax in rural Amazonia', J Infect Dis, vol. 195, no. 8, pp. 1218-1226.

Fidock, DA, Nomura, T, Talley, AK, Cooper, RA, Dzekunov, SM, Ferdig, MT, Ursos, LM, Sidhu, AB, Naude, B, Deitsch, KW, Su, XZ, Wootton, JC, Roepe, PD & Wellems, TE 2000, 'Mutations in the P. falciparum digestive vacuole transmembrane protein PfCRT and evidence for their role in chloroquine resistance', Mol Cell, vol. 6, no. 4, pp. 861-871.

Ghosh, K & Ghosh, K 2007, 'Pathogenesis of anemia in malaria: a concise review', Parasitol Res, vol. 101, no. 6, pp. 1463-1469.

Grigg, MJ, William, T, Menon, J, Barber, BE, Wilkes, CS, Rajahram, GS, Edstein, MD, Auburn, S, Price, RN, Yeo, TW & Anstey, NM 2016, 'Efficacy of Artesunate-mefloquine for Chloroquine-resistant Plasmodium vivax Malaria in Malaysia: An Open-label, Randomized, Controlled Trial', Clin Infect Dis, vol. 62, no. 11, pp. 1403-1411.

Hupalo, DN, Luo, Z, Melnikov, A, Sutton, PL, Rogov, P, Escalante, A, Vallejo, AF, Herrera, S, Arevalo-Herrera, M, Fan, Q, Wang, Y, Cui, L, Lucas, CM, Durand, S, Sanchez, JF, Baldeviano, GC, Lescano, AG, Laman, M, Barnadas, C, Barry, A, Mueller, I, Kazura, JW, Eapen, A, Kanagaraj, D, Valecha, N, Ferreira, MU, Roobsoong, W, Nguitragool, W, Sattabonkot, J, Gamboa, D, Kosek, M, Vinetz, JM, Gonzalez-Ceron, L, Birren, BW, Neafsey, DE & Carlton, JM 2016, 'Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax', Nat Genet, vol. 48, no. 8, pp. 953-958.

Iwagami, M, Hwang, SY, Kim, SH, Park, SJ, Lee, GY, Matsumoto-Takahashi, EL, Kho, WG & Kano, S 2013, 'Microsatellite DNA analysis revealed a drastic genetic change of Plasmodium

CHAPTER 7

186

vivax population in the Republic of Korea during 2002 and 2003', PLoS Negl Trop Dis, vol. 7, no. 10, p. e2522.

Lin, JT, Saunders, DL & Meshnick, SR 2014, 'The role of submicroscopic parasitemia in malaria transmission: what is the evidence?', Trends Parasitol, vol. 30, no. 4, pp. 183-190.

Lu, F, Lim, CS, Nam, DH, Kim, K, Lin, K, Kim, TS, Lee, HW, Chen, JH, Wang, Y, Sattabongkot, J & Han, ET 2011, 'Genetic polymorphism in pvmdr1 and pvcrt-o genes in relation to in vitro drug susceptibility of Plasmodium vivax isolates from malaria-endemic countries', Acta Trop, vol. 117, no. 2, pp. 69-75.

Melo, GC, Monteiro, WM, Siqueira, AM, Silva, SR, Magalhaes, BM, Alencar, AC, Kuehn, A, del Portillo, HA, Fernandez-Becerra, C & Lacerda, MV 2014, 'Expression levels of pvcrt-o and pvmdr-1 are associated with chloroquine resistance and severe Plasmodium vivax malaria in patients of the Brazilian Amazon', PLoS One, vol. 9, no. 8, p. e105922.

Nkhoma, SC, Nair, S, Al-Saai, S, Ashley, E, McGready, R, Phyo, AP, Nosten, F & Anderson, TJ 2013, 'Population genetic correlates of declining transmission in a human pathogen', Mol Ecol, vol. 22, no. 2, pp. 273-285.

Okell, LC, Bousema, T, Griffin, JT, Ouedraogo, AL, Ghani, AC & Drakeley, CJ 2012, 'Factors determining the occurrence of submicroscopic malaria infections and their relevance for control', Nat Commun, vol. 3, p. 1237.

Okell, LC, Ghani, AC, Lyons, E & Drakeley, CJ 2009, 'Submicroscopic infection in Plasmodium falciparum-endemic populations: a systematic review and meta-analysis', J Infect Dis, vol. 200, no. 10, pp. 1509-1517.

Pearson, RD, Amato, R, Auburn, S, Miotto, O, Almagro-Garcia, J, Amaratunga, C, Suon, S, Mao, S, Noviyanti, R, Trimarsanto, H, Marfurt, J, Anstey, NM, William, T, Boni, MF, Dolecek, C, Tran, HT, White, NJ, Michon, P, Siba, P, Tavul, L, Harrison, G, Barry, A, Mueller, I, Ferreira, MU, Karunaweera, N, Randrianarivelojosia, M, Gao, Q, Hubbart, C, Hart, L, Jeffery, B, Drury, E, Mead, D, Kekre, M, Campino, S, Manske, M, Cornelius, VJ, MacInnis, B, Rockett, KA, Miles, A, Rayner, JC, Fairhurst, RM, Nosten, F, Price, RN & Kwiatkowski, DP 2016, 'Genomic analysis of local variation and recent evolution in Plasmodium vivax', Nat Genet, vol. 48, no. 8, pp. 959-964.

Petersen, I, Eastman, R & Lanzer, M 2011, 'Drug‐resistant malaria: Molecular mechanisms and implications for public health', FEBS letters, vol. 585, no. 11, pp. 1551-1562 %@ 1873-3468.

CHAPTER 7

187

Peterson, DS, Walliker, D & Wellems, TE 1988, 'Evidence that a point mutation in dihydrofolate reductase-thymidylate synthase confers resistance to pyrimethamine in falciparum malaria', Proc Natl Acad Sci U S A, vol. 85, no. 23, pp. 9114-9118.

Popovici, J & Menard, D 2015, 'Challenges in Antimalarial Drug Treatment for Vivax Malaria Control', Trends Mol Med, vol. 21, no. 12, pp. 776-788.

Preston, MD, Campino, S, Assefa, SA, Echeverry, DF, Ocholla, H, Amambua-Ngwa, A, Stewart, LB, Conway, DJ, Borrmann, S, Michon, P, Zongo, I, Ouedraogo, JB, Djimde, AA, Doumbo, OK, Nosten, F, Pain, A, Bousema, T, Drakeley, CJ, Fairhurst, RM, Sutherland, CJ, Roper, C & Clark, TG 2014, 'A barcode of organellar genome polymorphisms identifies the geographic origin of Plasmodium falciparum strains', Nat Commun, vol. 5, p. 4052.

Price, RN, von Seidlein, L, Valecha, N, Nosten, F, Baird, JK & White, NJ 2014, 'Global extent of chloroquine-resistant Plasmodium vivax: a systematic review and meta-analysis', Lancet Infect Dis, vol. 14, no. 10, pp. 982-991.

Ratcliff, A, Siswantoro, H, Kenangalem, E, Wuwung, M, Brockman, A, Edstein, MD, Laihad, F, Ebsworth, EP, Anstey, NM, Tjitra, E & Price, RN 2007, 'Therapeutic response of multidrug-resistant Plasmodium falciparum and P. vivax to chloroquine and sulfadoxine-pyrimethamine in southern Papua, Indonesia', Trans R Soc Trop Med Hyg, vol. 101, no. 4, pp. 351-359.

Rodrigues, PT, Alves, JM, Santamaria, AM, Calzada, JE, Xayavong, M, Parise, M, da Silva, AJ & Ferreira, MU 2014, 'Using mitochondrial genome sequences to track the origin of imported Plasmodium vivax infections diagnosed in the United States', Am J Trop Med Hyg, vol. 90, no. 6, pp. 1102-1108.

Roper, C, Elhassan, IM, Hviid, L, Giha, H, Richardson, W, Babiker, H, Satti, GM, Theander, TG & Arnot, DE 1996, 'Detection of very low level Plasmodium falciparum infections using the nested polymerase chain reaction and a reassessment of the epidemiology of unstable malaria in Sudan', Am J Trop Med Hyg, vol. 54, no. 4, pp. 325-331.

Rungsihirunrat, K, Muhamad, P, Chaijaroenkul, W, Kuesap, J & Na-Bangchang, K 2015, 'Plasmodium vivax drug resistance genes; Pvmdr1 and Pvcrt-o polymorphisms in relation to chloroquine sensitivity from a malaria endemic area of Thailand', Korean J Parasitol, vol. 53, no. 1, pp. 43-49.

Russell, B, Chalfein, F, Prasetyorini, B, Kenangalem, E, Piera, K, Suwanarusk, R, Brockman, A, Prayoga, P, Sugiarto, P, Cheng, Q, Tjitra, E, Anstey, NM & Price, RN 2008, 'Determinants of in vitro drug susceptibility testing of Plasmodium vivax', Antimicrob Agents Chemother, vol. 52, no. 3, pp. 1040-1045.

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Sa, JM, Yamamoto, MM, Fernandez-Becerra, C, de Azevedo, MF, Papakrivos, J, Naude, B, Wellems, TE & Del Portillo, HA 2006, 'Expression and function of pvcrt-o, a Plasmodium vivax ortholog of pfcrt, in Plasmodium falciparum and Dictyostelium discoideum', Mol Biochem Parasitol, vol. 150, no. 2, pp. 219-228.

Sisya, TJ, Kamn'gona, RM, Vareta, JA, Fulakeza, JM, Mukaka, MF, Seydel, KB, Laufer, MK, Taylor, TE & Nkhoma, SC 2015, 'Subtle changes in Plasmodium falciparum infection complexity following enhanced intervention in Malawi', Acta Trop, vol. 142, pp. 108-114.

Suwanarusk, R, Russell, B, Chavchich, M, Chalfein, F, Kenangalem, E, Kosaisavee, V, Prasetyorini, B, Piera, KA, Barends, M, Brockman, A, Lek-Uthai, U, Anstey, NM, Tjitra, E, Nosten, F, Cheng, Q & Price, RN 2007, 'Chloroquine resistant Plasmodium vivax: in vitro characterisation and association with molecular polymorphisms', PLoS One, vol. 2, no. 10, p. e1089.

Tanner, M, Greenwood, B, Whitty, CJ, Ansah, EK, Price, RN, Dondorp, AM, von Seidlein, L, Baird, JK, Beeson, JG, Fowkes, FJ, Hemingway, J, Marsh, K & Osier, F 2015, 'Malaria eradication and elimination: views on how to translate a vision into reality', BMC Med, vol. 13, p. 167.

Triglia, T, Menting, JG, Wilson, C & Cowman, AF 1997, 'Mutations in dihydropteroate synthase are responsible for sulfone and sulfonamide resistance in Plasmodium falciparum', Proc Natl Acad Sci U S A, vol. 94, no. 25, pp. 13944-13949.

Vallejo, AF, Garcia, J, Amado-Garavito, AB, Arevalo-Herrera, M & Herrera, S 2016, 'Plasmodium vivax gametocyte infectivity in sub-microscopic infections', Malar J, vol. 15, p. 48.

WHO 2016, 'Eliminating malaria', Geneva:WHO, p. 24.