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Research Paper
Prediction of the spread of highly pathogenic avianinfluenza using a multifactor network: Part1 e Development and application of computationalfluid dynamics simulations of airborne dispersion
Il-Hwan Seo a,d, In-Bok Lee a,*, Oun-Kyung Moon b, Nam-Su Jung c,Hyung-Jin Lee a, Se-Woon Hong a, Kyeong-Seok Kwon a, Jessie P. Bitog a
aDepartment of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences,
College of Agriculture and Life Sciences, Seoul National University, 599, Gwanakno, Gwanakgu,
Seoul 151-921, Republic of KoreabAnimal and Plant Quarantine Agency, Veterinary Epidemiology Division, 175 Anyang-ro, Manan-gu, Anyang-si,
Gyeonggi-do 480-757, Republic of KoreacDepartment of Rural Construction Engineering, College of Industrial Sciences, Kongju National University,
56 Gongjudaehak-ro, Gongju-si, Chungcheongnam-do 314-701, Republic of KoreadCenter for Green Eco Engineering, Institute of Green Bio Science and Technology, Seoul National University, 1200,
Shin-ri, Daehwa-myeon, Pyeongchang-gun, Gangwon-do 232-916, Republic of Korea
a r t i c l e i n f o
Article history:
Received 15 July 2013
Received in revised form
14 January 2014
Accepted 27 February 2014
Published online 28 March 2014
Keywords:
Aerosol
Dispersion modelling
GIS
Livestock disease
Wind frequency
Highly pathogenic avian influenza (HPAI) virus can be spread rapidly, resulting in high
mortality and severe economic damage to the poultry industry. A prediction of HPAI
dispersion is challenging considering various spread factors, such as indirect transmission
by airborne spread as well as direct transmission through contact by humans, vehicles,
wild animals, and migratory birds. Because of the complexity of the spread of HPAI, it is
difficult to provide prompt treatments against epidemics. Moreover, there is little infor-
mation on the airborne spread of the HPAI virus because of the limitations of field ex-
periments for determining the mechanism of the spread of the disease due to the difficulty
of making accurate measurements in the presence of unstable and uncontrollable weather
conditions. In this study, CFD (computational fluid dynamics) was used to estimate the
dispersion of the virus attached to aerosols produced by livestock using a GIS (geographical
information system) to model a three-dimensional specific topography that includes the
farm location, road network, and related facilities. The CFD simulation was conducted to
predict the dispersion of virus from source farms according to various wind conditions.
The weather conditions during the period of interest were analysed using CFD simulations
to complete a frequency matrix form. The results were used as background data, to be used
to take preventive measures against HPAI occurrences and spread based on the multifactor
network process introduced in Part II.
ª 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ82 2 880 4586; fax: þ82 2 873 2087.E-mail address: [email protected] (I.-B. Lee).
Available online at www.sciencedirect.com
ScienceDirect
journal homepage: www.elsevier .com/locate/ issn/15375110
b i o s y s t em s e n g i n e e r i n g 1 2 1 ( 2 0 1 4 ) 1 6 0e1 7 6
http://dx.doi.org/10.1016/j.biosystemseng.2014.02.0131537-5110/ª 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.
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1. Introduction
Highly pathogenic avian influenza (HPAI) is a highly conta-
gious virus that affects mortality and egg production in the
poultry industry. There is a high risk of HPAI outbreaks in
Korea because of frequent human and supply exchanges with
neighbouring South-East-Asian countries where HPAI has
often occurred. Furthermore, the habitats of migratory birds
in the infected regions of Korea are located near the poultry
industry. There have been fourmajor HPAI outbreaks in Korea
since 2000. They have resulted in much economic damage,
including the costs of disease control, compensation for
farmers, and decline in the related industries. During the 2008
HPAI epidemic, 8.46 million birds within 950 poultry farms
were culled, with an economic loss of at least 575 million US$,
including direct costs (compensation for farmers) and indirect
damages (the poultry industry, including feed, circulation,
and processed food) from April to May (Woo, Lee, Hwang, Lee,
& Kim, 2008). It was recently reported that long-term exposure
and close contact with HPAI-infected poultry has produced a
mutation in the virus strain that can be transmitted among
humans (Hayden & Croisier, 2005; Koopmans et al., 2004;
Ungchusak et al., 2005; Yang, Halloran, Sugimoto, & Longini,
2007). Dozens of victims of avian influenza were reported in
China and Taiwan in 2013, and airborne dispersion was
strongly presumed to be the main mechanism for the spread
of the avian influenza virus.
HPAI can be spread very quickly through livestock farms
and cannot be controlled only by vaccine protection because
of genetic diversity andmutation of the virus. Therefore, early
preventive measures against epidemics are the most impor-
tantmethod of reducing the damage of an outbreak.Migratory
birds are major candidates for the long-distance dispersal of
zoonotic pathogens (Gaidet et al., 2010), and HPAI viruses have
been frequently detected in wild birds (Kim et al., 2012). When
HPAI initially occurs, it can be spread farm-to-farm for short
distances by means of direct and indirect transmissions.
Direct transmission of HPAI involves mechanical movements
of viruses between farms through humans, vehicles, and wild
and domestic animals, while indirect transmission involves
the airborne transmission of HPAI virus. Airborne trans-
mission has been verified by field experiments (Spekreijse,
Bouma, Koch, & Stegeman, 2011; Tsukamoto et al., 2007).
During an HPAI outbreak, general disease control in Korea
has been conducted by preventing direct transmission using
the slaughter of animals, access control, and disinfection for
0.5 km, 3 km, and 10 km from the infected farm. However, the
criteria for these disinfection ranges are unclear, and this
Nomenclature
C13, C23 constants with values of 1.44 and 1.92
C33 constant for the ratio of the horizontal and
perpendicular velocity components to the
gradient direction
Cm empirical constant of the turbulence model
(approximately 0.09)
Fwind weighting factor for the frequency of wind
conditions during the research period (%)
Gk turbulent kinetic energy generated by mean
velocity gradients (kg m�1 s�2)
Gb turbulent kinetic energy generated by buoyancy
(kg m�1 s�2)
k turbulent kinetic energy (m2 s�2)
MCFD pathogen-laden aerosol concentration from
infected farms and affecting nearby farms
arranged in a 39 by 39 matrix form during the
research period (mg m�3)
Mwind distribution of aerosol concentrations generated
from the infected farm at a steady-state condition
without a change in the wind speed and direction
(mg m�3)
MRoad distribution of aerosol concentrations generated
from the road through livestock-related vehicles
(mg m�3)
Pt possibility of airborne HPAI spread on the time
period t(%)
Pt�1 HPAI infection possibility in the poultry house on
the (t � 1)th day (%)
t time (s)
u velocity at a height of z m (m s�1)
u* fiction velocity (m s�1)
YM fluctuating dilation of the compressible
turbulence in the overall dissipation rate
(kg m�1 s�2)
z0 height of the surface roughness (m)
d boundary layer depth (m).
3 turbulent dissipation rate (m2 s�3)
k von Karman constant
m viscosity (kg m�1 s�1)
r density (kg m�3)
qe characteristic angle (60� for triangular and 90� fortetragonal angles)
qmax, qmin maximum and minimum side angle of each
mesh (�)
Abbreviations
APQA Animal and Plant Quarantine Agency in Korea
CFD computational fluid dynamics
FMD foot-and-mouth disease
GIS geographical information system
HPAI highly pathogenic avian influenza
PCR polymerase chain reaction
PM10 particulatematter less than 10 mm in aerodynamic
diameter
TCID50 tissue culture median infective dose for influenza
infection
TIN triangular irregular network
TSP total suspended particle
UDF user-defined function
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strategy has sometimes been unsuccessful in preventing the
spread of disease. It is quite likely that a considerable amount
of HPAI virus could be spread by means of a variety of
mechanisms to neighbouring farms, including vehicle move-
ment, frequent farm-related human contact, wild animals
(including migratory birds), and the airborne transmission of
the HPAI virus (Jewell, Kypraios, Christley, & Roberts, 2009).
To trace the infection route for preventive measures, a
multifactor HPAI network is required to explain both the
direct and indirect disease transmissions during HPAI out-
breaks. Using an expected spread route of HPAI, estimated by
themultifactor HPAI network, counter plans can be suggested
to address disease control and prevention of HPAI occur-
rences. There are three challenging tasks for designing the
multifactor HPAI network: 1) a decision to determine the
factors to be included in the model in various ways, through
direct and indirect transmission obtained by epidemiological
investigation and field measurements; 2) background data for
the airborne spread of HPAI, which is not available in field
experiments due to the difficulty in making accurate mea-
surements and the risk of disease spread; and 3) the estima-
tion of weight factors between each transmission route in the
network.
To solve these difficulties, an HPAI spread model that
contains stochastic concepts and engineering approacheswas
developed by a specific epidemiological investigation during
HPAI outbreaks based. Study of a network model could
explain a long-distance HPAI spread by person and vehicle
movements, which are not limited by the distances between
the farms. Airflow patterns formed from a specific geograph-
ical configuration and the local weather conditions could
explain the airborne spread of HPAI on a regional basis.
Despite of its importance as a public health problem, the
airborne spread of HPAI was little known until recently (Fiegel,
Clarke, & Edwards, 2006), although the possibility of airborne
disease transmission was suggested by many research groups
from the 1930s to the 1980s based on contested epidemiolog-
ical support (Weber & Stilianakis, 2008). Pathogens are emitted
in aerosol form during the coughing of infected animals and
are also dispersed from the excrement on the floor surfaces.
Aerosol containing virus can be suspended in the air for long
periods and can infect other animals via the respiratory tract.
Airborne disease transmission is possible because a single
aerosol particle in the 1e10 mm in size range has a calculated
103e107 virons, which satisfies 0.67 TCID50 (tissue culture
median infective dose) for influenza infection (Weber &
Stilianakis, 2008). Fine aerosols that are generated by cough-
ing from infected animals and by excrements infected by
avian influenza can directly penetrate into the lower respira-
tory tract, resulting in disease infection and spread (DFG,
2008). Therefore, the influenza virus can be spread in the air
by adhesion to aerosols (Cox et al., 2005; Hayden & Palese,
2002; Treanor, 2004; Wright & Webster, 2001).
There are a few research studies that have attempted to
directly prove the mechanism of airborne transmission
quantitatively using field experiments by detecting avian
influenza viruses (Chen et al., 2009; Tsukamoto et al., 2007).
However, these field experiments have relied on laboratory
experiments in highly quarantined situations or using epide-
miological investigations. Although field experiments on virus
spread are highly important, it is very difficult to achieve the
goal of studying the real phenomena because of the many
difficulties in working with viruses, such as 1) the lack of
ability of experimental techniques to capture and test
airborne viruses that are attached to aerosols floating through
an invisible airflow pattern, 2) an extremely low probability to
detect viruses from aerosol samples that have a low concen-
tration in the air, and 3) uncontrollable and unpredictable
weather conditions involving many variables.
Stochastic models have been used to predict and analyse
the airborne spread of disease. Stochastic models solve the
probability functions that represent the complex character-
istics of viruses, including the infection rates and spread
routes. A stochastic agent-based discrete-time simulation
model was suggested by Germann, Kadau, Longini, and
Macken (2006) to statistically calculate the average number
of secondary infections produced by a single unit using the
concept of the basic reproductive number. Kim, Hwang,
Zhang, Sen, and Ramanathan (2008) analysed a trend in
HPAI spread in Korea by evaluating the mutual relations in a
multi-agent model, including the quarantine range, incuba-
tion period, and infection probability. However, these sto-
chastic models estimated the disease spread based on
subjective assumptions of the main constants, including
diffusion coefficients and contact frequencies, which are
important for solving the probability functions. The value of
each constant related with direct and indirect transmission of
an avian influenza (AI) virus has not been clearly established.
Recently, numerical simulation models have been shown
to predict and to estimate disease transmission routes
(Krumkamp et al., 2009; Mayer, Reiczigel, & Rubel, 2008;
Mikkelsen et al., 2003; Sørensen, Jensen, Mikkelsen, Mackay,
& Donaldson, 2001). A Lagrangian particle dispersion model
was used to calculate the distribution of a pathogen-attached
aerosol concentration by tracking the particle trajectories
from an infected source farm (Mayer et al., 2008). An atmo-
spheric dispersion model was also developed to evaluate the
airborne spread of foot-and-mouth disease (FMD) virus, which
can be dispersed by direct contact between animals, livestock
products, and mechanical transmission through vehicles of
infection. The results based on the numerical models showed
that topographical information and weather conditions were
major factors that influence the airborne spread of virus
through an airflow pattern. This finding is very important in
Korea because most of the livestock farms in the country are
located in mountainous and hilly areas with complex topog-
raphy and this could be used to develop strategies that could
avoid virus dispersion to neighbouring villages.
Meso-scale meteorological models have been effectively
used to calculate the pollutant dispersion using satellite im-
ages and a synoptic meteorology; however, the resolution of
themethod is appropriate for large areas. Gaussian dispersion
models have been used to predict pollutant dispersion in the
meteorological models based on steady conditions but they
are not sufficient when considering complex airflow patterns
and topography. The accuracy of a Gaussian-based model is
largely dependent on the width of the plume dispersion,
which results in a large error in the pollutant distribution in
complex terrains. It is also difficult to apply regional and
temporal variations in a Gaussian model due to the
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assumption of having a horizontally uniformly distributed
concentration. However, computational fluid dynamics (CFD)
has a significant advantage when solving an airflow pattern
using the modelling of specific meteorological and topo-
graphical information. This is important in Korea, where
livestock farms are typically located around mountainous or
hilly regions. CFD modelling using complex terrain has been
studied recently to predict aerosol dispersion from reclaimed
land (Seo et al., 2010) and odour dispersion from livestock
farms (Hong et al., 2011a, 2011b). Therefore, it was thought
likely that CFD could be used to solve the airflow patterns
required to predict airborne HPAI spread between poultry
farms and livestock-related facilities.
In this study, there are two parts: Part I focuses on devel-
oping a CFD model to estimate the airborne spread of HPAI
virus as an indirect transmission. The airborne spread of HPAI
was investigated according to changes in wind speed, wind
direction, and the characteristics of infected farms. In Part II a
multifactor HPAI network was developed using indirect
airborne transmission via and direct transmission through
mutual connections between farms obtained using epidemi-
ological investigations and field surveys. The resulting model
combined the routes of disease spread via different di-
mensions of connectivity between farms and the diffusivity
from farms, and as a consequence the uncertainty in the HPAI
spread model can be minimised.
The objective this part of our work was to develop and
apply a CFD simulationmodel for predicting the airborneHPAI
spread based on the specific geographical and meteorological
information. A large number of theoretical and technical
methods were used to solve three-dimensional specific
airflow patterns, such as three-dimensional complex
topography from geographical information system (GIS) data,
atmospheric boundary layers, and vertical profiles for turbu-
lent and wind speeds. The airborne HPAI spread was simu-
lated by injecting pathogen-laden aerosols from various
sources, including infected farms and the roads to nearby
livestock farms. The CFD-computed results for the pathogen-
laden aerosol concentrations were used to construct a
weighting matrix database according to a combination of
various wind directions and wind speeds. The CFDmodel that
predicted the airborne HPAI spread was applied to the 2008
HPAI outbreak in Korea by means of frequency analysis under
wind conditions. The constructed database was also con-
verted to raw data for use in predicting the airborne spread
HPAI in a multifactor network.
2. Materials and methods
2.1. Research site
A research site for analysing the airborne HPAI spread while
considering climate and topography was located in Kimje-si,
Jeollabuk-do, Korea, where an HPAI outbreak occurred in
2008. Poultry farms for broiler, layer chicken, breeding
chicken, Korean native chickens, and ducks were located in
this region, including several mixed-feed factories and
slaughter houses. Thus, substantial movement of livestock-
related vehicles, humans, and livestock products is observed
in this area. Farms for the study were selected based on the
specific epidemiological investigation conducted by the Ani-
mal and Plant Quarantine Agency (APQA) in Korea during the
2008 HPAI outbreak at the research site. A total number of 39
Fig. 1 e Satellite image for the research site in Yongji-myeon, Kimje-si, Jeollabuk-do, Korea (35�5002300N, 126�5805400E) andpoultry farms, including 8 where outbreaks occurred (O), 5 positive (P), and 26 farms suspicious (S) for HPAI; the occurred
and positive farms are represented by large circles.
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farms was selected, including 8 farms in which had clinical
signs of HPAI, 5 diagnosed with a positive reaction to HPAI
pathogens from samples, and 26 suspected farms, whichwere
located close to the HPAI-infected farms (Fig. 1).
Figure 2 shows the locations of the research farms, which
are categorised by time-periodic occurrences and notification
of HPAI. The first HPAI outbreak in 2008 was on two poultry
farms located in the south-eastern area in April 1e5 (1st
outbreak) by means of clinical signs and the diagnosis of the
HPAI pathogen using the polymerase chain reaction (PCR)
method. Then, second and third outbreaks occurred during
April 9e12 and 16e20, respectively, and were notified by
means of a positive reaction for an HPAI pathogen and a
suspicion of HPAI (Lee et al., 2008). The direction of spread
followed the westerly wind direction that occurred during the
outbreak. Therefore, it was necessary to find the relationship
between the airborne spread of HPAI and the prevailing
meteorological conditions using a specific airflow pattern at
the research site by means of CFD simulation.
The prevailing wind is a westerly direction there is a
stronger westerly wind occurs. Data from the last 20 years, is
shown in Fig. 3 with a series of wind roses formed from three-
hourly data measured by the weather station in Gun-San. The
daily trend shows an easterly prevailing wind from midnight
to 9 A.M.; thewind is shifted from easterly to westerly during 9
A.M.e12 P.M. when the ground temperature is increased by
solar radiation. From noon to 9 P.M., a westerly wind prevails
and wind speeds are increased with less clam periods. From 9
A.M. to 9 P.M., is the time to perform livestock-related tasks,
such as feeding and management, feed supply, livestock
shipment, cleaning, disinfection, and excrement treatment,
which results in frequent livestock-related movements of
humans and vehicles. Because the ventilation rate usually
increases during the day, there is a high risk that airborne
HPAI can spread from an infected farm to neighbouring farms
in a westerly wind direction through increased wind speeds.
Then, the wind shifts from westerly to easterly during the
period 9 P.M. to midnight after the ground has cooled down.
2.2. Simulation tools and theoretical backgrounds
CFD simulation can be divided into three sub-processes 1) pre-
processing, to construct a computational domain, mesh
design, and boundaries; 2) main-processing, to calculate the
mass, momentum, and energy equivalent equations within
each mesh; and 3) post-processing to analyse the computed
results. A geographic information system (GIS) was used to
realise specific airflow using CFD based on three-dimensional
complex topography. This process is important for finding the
characteristics of the airflow that affect the accuracy of
dispersion modelling in the CFD simulation because a nu-
merical map from a GIS includes not only topographical in-
formation in a region but also land use, including
mountainous, hilly, urban, rural, and agricultural areas.
ARCGIS (version 9.2, ESRI Inc., Redlands, CA, USA) was used to
control the numerical map; then, various programs, including
AUTOCAD (version 2006, Autodesk, San Rafael, CA, USA) and
RHINOCEROS (version 3.0, McNeel, Seattle, WA, USA), were
used to design a three-dimensional surface for CFD simulation
divided by different areas according to the land use situations.
GAMBIT (version 2.3, ANSYS Inc., Canonsburg, PA, USA)
was used to make a computational domain, a mesh design for
the analysis of the internal fluid dynamics, and boundary
conditions for each surface. A modeller’s experience, tech-
nique, and know-how are very important in making the mesh
design, considering that the density and stability of the mesh
affects the accuracy of the CFD simulation in a sensitive way.
An overall computational domain was created by a three-
dimensional volume based on the specific rendering result
of the numerical map. The size of the computational domain
was determined by the height or width of the obstacles, which
researchers have studied using wind tunnel experiments
(Bournet & Boulard, 2010; Franke et al., 2004; Hefny & Ooka,
2009; Tominaga et al., 2008). In Fig. 4 an integrated sugges-
tion is presented of the computational domain from an illus-
tration of the longest recommended distances against the
height and width of the obstacles among the references. The
computational domain used in this study was designed for a
large area and it considered the height of the farm area and
the mountainous areas following the appropriate guidelines.
The surface mesh on the complex topography was gener-
ated to estimate the aerosol spread based on the previous
related research. Because truncation errors can occur with a
sudden change in mesh size between nearby cells, a value of
1.1 was used for any reduction and expansion ratio between
the cells. It should be noted that Franke et al. (2004) suggested
a 1:3 ratio. The stability of the mesh design was evaluated by
the amount of skewness in the meshes from an equiangular
skew, as shown in Eq. (1). When skewness was <0.85 in
hexahedron meshes and 0.9 in tetrahedron meshes, the re-
sults can be expected to be reliable (Fluent Manual, 2008).
Qs ¼ max
�qmax � qe
180� qe;qe � qmin
qe
�(1)
where, qmax is the maximum side angle of each mesh, qmin is
Fig. 2 e Tendency of HPAI spread between farms during
the 2008 outbreak in Kimje-si, Korea.
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the minimum side angle of each mesh, and qe is the charac-
teristic angle of the regular polygons, with a value of 60� in the
triangular and 90� in the tetragonal meshes.
The mesh design, including the number, shape, and den-
sity of the mesh, can be empirically changed according to the
purpose and importance of the simulation with respect to
how the phenomenon should be realised or simplified. When
densemeshes and a large number ofmeshes are used in a CFD
simulation, a more reliable result can be obtained, but the
computational time can also be increased, resulting in
resource limitations of the computing system. The guideline
for mesh design that was validated by Seo et al. (2010) was
implemented. TGRID (version 5.0, ANSYS Inc., Canonsburg,
PA, USA) was used to effectively create a volumetric mesh
based on the mesh design for a complex ground surface.
The wind environment, including wind speed, direction,
and turbulent intensity, is the most important factor for
estimating the airborne spread of pathogen-laden aerosols. A
standard ke3 turbulence model was used in this research
while considering the physical characteristics, accuracy re-
quirements, and computation time. The standard ke3 turbu-
lence model suggested by Launder and Spalding (1974) is
known as an economical and reasonablemodel in engineering
fields. It has been used in CFD simulations for estimating the
regional spread of pollutants and aerosols (Li & Guo, 2008;
Milliez & Carissimo, 2007; Sabatino, Buccolieri, Pulvirenti, &
Britter, 2007; Seo et al., 2010). The standard ke3 model solves
the turbulent kinetic energy (k) and turbulent dissipation rate
(3) based on the semi-empirical constants from field experi-
ments, as presented in Eqs. (2) and (3). To arrive at reliable
wind conditions compared to the real situation, vertical pro-
files for wind and turbulence conditionswere designed using a
user-defined functions (UDFs). The turbulent kinetic energy
and dissipation rate were calculated by means of the friction
velocity and surface roughness, as shown in Eqs. (4)e(6).
rvkvt
¼ v
vxi
��mþ mt
sk
�vkvxi
�þ Gk þ Gb � r3� YM (2)
rv3
vt¼ v
vxi
��mþ mt
s3
�v3
vxi
�þ C13
3
kðGk þ C33GbÞ � C23r
32
k(3)
UðzÞ ¼ u�klog
�zz0
�(4)
k ¼ u2�ffiffiffiffiffiffiCm
p �1� z
d
�(5)
3 ¼ u3�
kz
�1� z
d
�(6)
where r is the density (kg m�3), k is the turbulent kinetic en-
ergy (m2 s�2), t is the time, m is the viscosity (kg m�1 s�1), Gk is
the turbulent kinetic energy generated by mean velocity gra-
dients (kg m�1 s�2), Gb is the turbulent kinetic energy gener-
ated by buoyancy (kg m�1 s�2), 3 is the turbulent dissipation
Fig. 3 e Wind conditions by 3-h intervals averaged over the last 20 years in Gunsan-si: upper labels refer to the 24 h clock.
Fig. 4 e Proposed design suggestions for the computational
domain based on the height (H) and width (W) of obstacles
integrated by the longest distances (from studies by
Bournet & Boulard, 2010; Franke et al., 2004; Hefny & Ooka,
2009; Tominaga et al., 2008).
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rate (m2 s�3), YM is the fluctuating dilation of the compressible
turbulence in the overall dissipation rate (kg m�1 s�2), C13 is a
constant that equals 1.44, C23 is a constant that equals 1.92, C33
is a constant for the ratio of the horizontal and perpendicular
velocity components to the gradient direction, u is the velocity
at a height of z m (m s�1), u* is the fiction velocity (m s�1), k is
the von Karman constant, z0 is the height of the surface
roughness (m), Cm is the empirical constant of the turbulence
model (approximately 0.09), and d is the boundary layer depth
(m).
2.3. Method
Figure 5 illustrates themethod to estimate the airborne spread
of HPAI. The epidemiological data from the 2008 outbreak on
vehicle and human movement for the multifactor network
were used. The airborne spread of HPAI was simulated by
means of a CFD model that included pathogen-laden aerosol
dispersion grafted to a wind frequency analysis. The purpose
of the multifactor network is to analyse the HPAI spread
tendency during the outbreak based on various scenarios, and
help suggest effective preventive measures against an HPAI
pandemic by the development of a forecasting system and
analysis of a hub farm that significantly affects the spread of
HPAI spread by its close connectivity with nearby farms.
The CFD computational domain was 5.6 km in diameter in
Kimje-si, Jeollabuk-do, Korea, including 39 poultry farms on
which the 2008 HPAI outbreak occurred. A numerical map,
including the research area, was used to make a three-
dimensional surface that considered the complex topog-
raphy. A contour line was extracted from the numerical map
using AUTOCAD; then, a triangular irregular network (TIN)
was created using SKETCHUP (version 8, Trimble, Sunnyvale,
CA, USA). The created TIN was imported to RHINOCEROS to
make a three-dimensional surface using the drape option and
dividing it into subparts according to topographical
classifications, such as lakes, roads, farms, and mountainous
areas. The divided surfaces were imported in GAMBIT to
prepare a design for the surface mesh.
Specific mesh design methods were used to enhance the
mesh quality and to reduce the total number of meshes, to
reduce the computational time. Dense meshes were designed
in the important area for fluid dynamics, such as hilly or
mountainous areas and source areas, which can significantly
affect the modelling results. The surface mesh was used to
make a volumetric mesh that included the overall computa-
tional domain using TGRID, which is a useful simulation tool
for designing mesh for complicated configurations and verti-
cal surface extrusions. Considering the guidelines for the
recommended heights to account for the obstacles, the
computational domain was 800 m in height, which is 10 times
higher than the obstacles, such as the livestock houses and
mountains. The designed mesh in the computational domain
was moved to FLUENT to simulate the airborne HPAI spread
from an infected livestock farm to nearby farms according to a
combination three wind speeds and eight wind directions.
The total number of cases of CFD models was 960, which
included 24 wind environments and source areas, with 39
farms and a main road.
Reliable values for the aerosol size, aerosol density, wind
conditions, turbulence characteristics, porosity of wooden
area, surface roughness height, and other factors were
entered into the CFD model by means of theoretical and
empirical considerations. The aerosols generated from the
livestock house are likely to be mixtures of feed particles,
feathers, skin fragments, and bran particles from the litter
surface. It is very challenging to simultaneously model parti-
cle mixtures; thus, only the feed particles were considered to
mainly form the livestock aerosols based on the field mea-
surements conducted by Seo et al. (2011). The concentrations
of aerosols generated by poultry farms are altered by many
variables, such as the farm size, farm type, poultry type,
Fig. 5 e Flowchart of the research procedure and sources of data for predicting HPAI spread using a multifactor network.
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poultry age, season, ventilation method, floor type, and
operating conditions. Therefore, a representative aerosol
concentration for a general poultry house was investigated
and based on previous research and empirical results from
field monitoring this was used in the CFD model. Takai et al.
(1998) measured inhalable dust in poultry houses which
resulted in an average value of 3600 mg m�3 in aerosol con-
centration. Choi et al. (2006) have provided concentrations of
total suspended particle (TSP) fora typical Korean windowless
broiler house; 3670 mg m�3, on average, ranging from 465 to
6873 mg m�3 with poultry age. The size of the largest aerosols
emitted through a ventilation fan from a livestock house were
PM10 (particulate matter less than 10 mm in aerodynamic
diameter), which is the general size and shape of feed parti-
cles investigated by scanning electron microscope (SEM), JSM
5410LV, JEOL, Tokyo, Japan (Seo et al., 2011). Therefore, the
aerosol concentration of 3600 mg m�3 was used in the CFD
model with a value of 1388 kg m�3 as aerosol density,
considering the feed density measured by a gas pycnometer
(IQI, InstruQuest Inc., Coconut Creek, FL, USA). For the aerosol
concentration generated from the adjacent road, the value of
116.4 mg m�3 was used, following the work of Seo et al. (2011),
who measured the real-time aerosol concentration and its
size distribution according to distance from the road when
livestock-related vehicles, checked by video analysis, passed
by an aerosol spectrometer (1.109, Grimm Inc., Ainring,
Germany).
Deposition of aerosols emitted from the poultry farm were
considered using vertical volumes with a 0.5 m height from
the ground surface bymeans of a sink term. This removed the
incoming aerosol concentration into the volume using UDF, as
suggested by Seo et al. (2010). Surface roughness generates
turbulence near the ground reducing the windward wind
speed, which results in a change in the vertical wind profile.
The surface roughness was applied with a value of 0.0002 for
the lakes and road, 0.25 for the agricultural area, and 1.0 for
the forest (Wieringa, Davenport, Grimmond, & Oke, 2001). The
airborne HPAI spread was estimated using CFD based on the
weather data during the 2008 HPAI outbreak. Three wind
speeds (3, 5, 7 m s�1) and eight wind directions (E, W, S, N, SE,
SW, NE, NW) were simulated individually; these speeds and
directions were change continuously with the frequency of a
sinusoidal curve (by �25�), which is similar to the assumption
made with general Gaussian atmospheric dispersion models.
A linear interpolation was then used to analyse the airborne
spread of HPAI over the experimental period (Table 1).
The dispersion tendency of airborne pathogen-laden
aerosols was estimated and saved to a database of CFD-
computed results according to a combination of environ-
mental conditions. A grafting algorithm was developed to
combine a CFD-computed database of airborne HPAI spread
transformed by matrix form and weighting factors for
weather conditions bymeans of frequency analysis during the
specific research period.
The airborne HPAI spread from an infected farm to a
neighbouring farm was estimated by CFD simulation and
compared with the dispersion tendency found in the 2008
HPAI outbreak using the weather frequency data found during
the outbreak. The airborne spread of HPAI in matrix form can
be directly used as raw data for the multifactor network,
combining indirect airborne spread and direct contact spread
via human and livestock vehicles, which is discussed in Part II
of this research.
3. Results and discussion
3.1. Computational domain for CFD simulation
A numerical map, on the scale of 1:5,000, for the research re-
gion was prepared from the GIS information supported by the
National Geographic Information Institute, Korea. The nu-
merical map contained the network of roads, rivers, tribu-
taries, buildings, livestock facilities, geographical feathers,
and administrative districts information. The exact positions
of the road, river, and livestock houses for the disease
spreadingmodel were located by the building and contour line
layers compared to the satellite photograph and farm address.
A total of 39 research farms were selected for modelling for
the specific epidemiological investigation during the 2008
HPAI outbreak in Korea, which required securing detailed in-
formation on those farms. The location of the farms can be
identified by means of a comparison between a numerical
map and a satellite photograph. It is not efficient to model
each poultry house using the CFD simulation because of the
size of the overall computational domain. Each infected farm
was simplified as a circular source based on the location in-
formation, considering the size variance with the overall
computational domain.
A volume size of the source represented an emission rate
of pathogen-laden aerosols from the infected farms by means
of a fixed value for the aerosol concentration in the volume,
which could be spread through a computed airflow field using
CFD. The volume size of each farm was proportionally
adjusted based on the investigated data of the poultry
numbers from a field survey, as shown in Table 2. The poultry
numbers of the 39 farms showed 25,100 birds on average,
Table 1 e Input and boundary conditions for CFDanalysis.
Pre-processing Designed domain 5.6 km Diameter
800 m Height from the
ground surface
Mesh types Hexagedron/prism
Number of
meshes
6,784,362
Main module Wind condition Wind velocity: 3, 5, 7 m s�1
Wind direction: N, NE, E,
SE, S, SW, W, NW
Number of farms 39 (8 outbreak, 5 positive,
26 prevention
Turbulence model Standard ke3 model
Surface roughness Road, lake: 0.0002 m
Agricultural surface: 0.25 m
Forest: 1.0 m (porous media)
Aerosol
concentration
Broiler house: 3600 mg m�3
Adjacent road: 116.4 mg m�3
UDF modules Source term for deposition
Wind profile at the
boundary surfaces
Wind frequency
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ranging from a minimum of 4000 to a maximum of 151,000
birds investigated by APQA. The farm labelled S-13 in the GIS
information, which included 25,000 birds, was initially chosen
as a standard farm in size, and the number of chicken houses
in S-13 was investigated and compared with the field survey.
The circle that contains all of the chicken houses in S-13 was
used as the standard for the volume size in the farm source for
calculating the volume ratio of the remaining farm sources in
terms of the poultry number ranging from 0.16 to 6.04,
compared to the standard volume size in S-13. By entering the
exact volume ratio in RHINOCEROS during partitioning of the
computational domain, the aerosol concentrations emitted
from the infected farms were considered to be fixed values
that change by the poultry number of the farms.
A triangular irregular network (TIN) was created to make a
three-dimensional surface from the contour lines in a nu-
merical map using SCETCHUP, as shown in Fig. 6. TIN was
essential for CFD modelling of airflow fields because it could
change two-dimensional line information from the contour
lines into three-dimensional surface information for a mesh
design. During the TIN process, parts of the flat areas, such as
the agricultural field and lake, were aberrantly distorted due
to large triangles, while some nodes were connected with far
away nodes inside one large contour line without a change in
elevation. To solve the distortion problem on the ground
surface, artificial lines were created within the large contours
by changing their size proportionally at the same elevation to
make more dense triangles. The three-dimensional surface
was divided into subparts following the overlapped lines for
topographical classification using RHINOCEROS.
Wooded areas, including forest and mountain, can signif-
icantly affect the airflow field and should be considered in the
CFD simulation. A leafy tree or a dense forest can change the
surface airflow field by increasing the surface roughness and
can be used as a wind break for aerosol dispersion. To
consider the effect of the wind break, the wooden area was
also divided into subparts on the computation domain and
was assumed to be a porous media. An aerodynamic porosity
of 91.76% and a drag coefficient (Cd) of 0.55 were used as
characteristic values for the porous media based on the wind
tunnel test for wind breaks conducted by Bitog et al. (2011). A
source term in the momentum equation was applied to the
wooden area using a viscous resistance coefficient and an
inertial resistance coefficient. The viscous resistance coeffi-
cient calculated a decrease in the pressure by the fluid velocity
with the assumption of laminar flow inside the porous media
using Darcy’s law. The inertial resistance coefficient repre-
sents the decrease in the pressure by the length following the
fluid flow direction.
Thewooded areaswere not classified in the numericalmap
except for a mountainous region and was distributed
sporadically. Therefore, a wooded area was divided from the
computational domain by comparing it to the relevant satel-
lite photograph, as shown in Fig. 7. The dividing lines were
considered to be obtuse angles with longer than 10 m of a
Table 2 e Volume ratio of each farm according to the number of poultry being raised.
Farm label Poultry numbers Volume ratio Farm label Poultry numbers Volume ratio
O-1 151,000 6.04 S-8 10,000 0.4
O-2 61,000 2.44 S-9 4000 0.16
O-3 11,400 0.456 S-10 20,000 0.8
O-4 12,700 0.508 S-11 14,000 0.56
O-5 30,900 1.236 S-12 16,000 0.64
O-6 40,000 1.6 S-13 25,000 1 (Standard)
O-7 20,000 0.8 S-14 32,000 1.28
O-8 16,000 0.64 S-15 14,900 0.596
P-1 30,000 1.2 S-16 6000 0.24
P-2 36,000 1.44 S-17 17,000 0.68
P-3 13,000 0.52 S-18 13,000 0.52
P-4 60,000 2.4 S-19 18,000 0.72
P-5 15,100 0.604 S-20 27,000 1.08
S-1 4000 0.16 S-21 16,400 0.656
S-2 13,500 0.54 S-22 18,800 0.752
S-3 42,000 1.68 S-23 9000 0.36
S-4 40,000 1.6 S-24 11,000 0.44
S-5 18,000 0.72 S-25 15,000 0.6
S-6 16,000 0.64 S-26 21,000 0.84
S-7 41,000 1.64
Fig. 6 e Three-dimensional ground surface of the
triangular irregular network (TIN) overlapped by the
numerical map with roads, lakes, and contour lines.
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segment length in the lines for the mesh design. For simpli-
fication of the CFD model, it was assumed that all of the trees
had a value of 5m in height, and differences in the species and
planting densities were ignored. The road information in the
numerical map was represented by a line. To make a surface
source, the road lines were copied under the assumption of a
10 m width and were simplified into gentle curves to divide
the computational domain as shown in Fig. 8.
3.2. Development of the CFD model for airborne HPAIspread
Themesh structurewas designed using GAMBIT following the
guideline used by Seo et al. (2010), which ranged between 5 m
and 30 m in mesh size. This was validated from the concen-
tration of aerosol spread found over the reclaimed land. A
dense mesh size was used for significant areas, such as farm
sources, roads, and wooded areas. A size function technique
was used to save the total amount of mesh by means of
changing the size of the mesh with a certain increase or
decrease in the ratios based on a standard point, line, or sur-
face. The density of the mesh was adjusted by a coarser mesh
while using the size function technique, which increased the
size of themesh by 10% between adjacent meshes that ranged
from 5m to 30m in the flat area; this procedure resulted in not
only saved on the total amount of mesh but also maintained
the accuracy of the hydrodynamic modelling. The overall
computational domain was designed as a cylinder shape to
control the boundary conditions at the side surfaces by
windward inlets and leeward outlets, as shown in Fig. 9. The
total number of meshes at the three-dimensional ground
surfaces was 173,958, with a maximum skewness number of
0.714. This is relevant in terms of the of mesh stability criteria
suggested by the Fluent Manual (2008).
The vertical volume of themeshwas designed using TGRID
while considering dense meshes that were near to the ground
surface and that can enhance the CFD accuracy. Based on the
mesh guideline as previously discussed, the size of the first
mesh from the ground surface should be larger than the sur-
face roughness andmore than threemeshes should be used at
a 3m height for analysis of the aerosol concentration from the
Fig. 7 e Satellite photograph showing the wooden area around the livestock farm area (left) and the simplification process
using topographical classification in the numerical map.
Fig. 8 e Simplified road surfaces divided from the
computational domain using RHIOCEROS.
Fig. 9 e The three-dimensional volumetric computational
domain, considering 16 side surfaces for changes in the
boundary conditions according to 8 different wind
directions.
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infected livestock building, while considering the location of
the ventilation fan and the entrance. The size of the firstmesh
used in the CFD model was 0.25 m but this size was gradually
increased in the vertical direction by representing a dense
mesh near the ground surface and a vertical coarser mesh. A
volume at a 0.5 m height from the ground surface was
assigned as a sink volume for aerosol deposition near the
ground activated by UDF. A volume from the ground to a 5 m
height was used for farm sources by a fixed value of aerosol
concentration and a wooden area by porosity. The total
amount of volumetric mesh was 6,784,362 using 35 vertical
meshes at an 860 m height size increment using a geometric
growth method, as shown in Fig. 10.
3.3. Aerosol dispersion modelling for construction ofdatabase matrices
Figure 11 shows the example of CFD-computed results for
aerosol dispersion from different source farms with a 3 m s�1
wind speed and an NW wind direction. The leeward direction
aerosol concentration distribution was changed by the wind
direction and the topographic characteristics. The strength of
the aerosol concentration was increased by the size of the
source farm; aerosol concentrations generated from the O-2
and O-6 farms were dispersed further and were stronger than
from the O-8 farm due to their differences in size. A total of
960 cases with a combination of 40 sources, 3 wind speeds,
and 8 wind directions were computed independently by the
CFD simulation.
CFD-computed pathogen-laden aerosol concentrations
were tabulated from sources of 39 farms and roads in a hori-
zontal axis to neighbouring farms in a vertical axis by amatrix
form according to eight wind speeds and three wind di-
rections, as illustrated in Fig. 12, to provide a sample among 24
matrices of the wind conditions based on the 960 CFD-
computed results. The horizontal axis shows source farms
that are assumed to be HPAI-infected farms, and the vertical
axis shows the effect of airborne HPAI spreading to the
neighbouring farms (it is standardised by a range of 0e1).
There is little information on the decision criteria required for
aerosol concentrations carrying airborne diseases through
dispersion; the value of 20 mg m�3 was assumed to be an in-
fectious exposure concentration based on the previous com-
parison between CFD prediction using random concentrations
ranging from 10 to 100 mg m�3 and spread tendency from the
epidemiological investigation report of the 2008 outbreak. The
infection probability valuemeans the possibility that the farm
can be infected by HPAI through airborne spread. The colour
in Fig. 12 was divided into four steps, including a weak caution
of less than 0.1 (white), a light alert with the range 0.1e0.3
(light grey), a strong alert with the range 0.3e1.0 (dark grey),
and an extreme danger ofmore than 1.0 (black). The range can
be easily changed by a researcher’s consideration after more
specific research on airborne virus transmission.
The distributions of aerosol concentrations generated from
each farm were simulated by the farm size, as defined by the
poultry numbers and airflow patterns related with complex
topography and wind conditions. It was assumed that a spe-
cific concentration of accumulated aerosol from the sources
represents the possibility of HPAI disease spread, which was
converted into a percentage with the range 0e1. The effect of
aerosol spread from the vehicle movement on the road was
calculated by the proximity between the road and farm and
resulted in a value of less than 0.1 because the emissions rate
from the road was only 3.2% compared with the infected
farms. An HPAI spread possibility shows a high value in the
farm located in the windward direction and a low value in the
leeward direction from sources, according to the wind speeds
and directions. However, wind is characterised by succes-
sional changes in wind speed and direction. Thus, it is not
sufficient to predict the airborneHPAI spread bymeans of only
one CFD-computed result under stable wind conditions. The
effects of wind frequency should be analysed and connected
to a CFD simulation based on the meteorological data.
3.4. Wind frequency analysis
Airborne HPAI spreading from an infected farm to neigh-
bouring farms was predicted from the CFD-computed back-
ground data with 960 combinations among 39 poultry houses
during the 2008 HPAI outbreak from April 1e20 in Kimje-si.
During the period, the meteorological data showed an
average temperature of 12.2 �C, which ranged from a mini-
mum of 3.6 �C to amaximum of 24.2 �C, and a wind velocity of
2.1 m s�1 (an hourly average), which ranged from 0 to
7.8 m s�1. It was assumed that there are no airborne spread of
HPAI during calm and rainy periods. Finally, 403 meteorolog-
ical data were chosen after a deduction of 77 h due to calm
(54 h) and precipitation (23 h) periods among the 480 h of the
whole period. Table 3 represents the wind frequency
Fig. 10 e Mesh design of the computational domain that is 5.6 km in diameter and 0.86 km in height.
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distribution of the wind conditions using data from the
Meteorological Administration Agency, Korea, which was
recorded as 1-h averaged values for the 480 h covering the
research period. Among various meteorological data, time,
wind speed, wind direction, and precipitation were utilised in
the frequency analysis by a 16 by 8matrix. Themeteorological
matrix was transformed to the 8 by 3 matrix with the same
combination used for CFD simulation by multiplying a
weighting factor; e.g., the frequency of the NNEwind direction
was divided in half, and each half frequency was added to N
and NE, as shown in Table 4.
For application of the transformed wind frequency table
the 960 CFD cases were arranged into 24matrices according to
the combinations of three wind speeds and eight wind di-
rections. These matrices were then unified into the final ma-
trix and represented the possibility of HPAI spread between
farms using Eq. (8). The HPAI spread prediction can be calcu-
lated using Eq. (9), which adds the possibility of HPAI spread
multiplied by the daily weighting factors and the road through
the vehicle movements. The possibility of airborne HPAI
spread was accumulated until it exceeded the standard con-
centration assumed by HPAI infection, showing a value of 1.
This value was maintained until the end of the research
period.
MCFD ¼X
Mwind � Fwind (8)
Pt ¼ fðMCFD � Pt�1Þ þ ðMRoadÞg � t (9)
In Eqs. (8) and (9), MCFD is a pathogen-laden aerosol con-
centration from infected farms and affecting nearby farms
arranged in a 39 by 39 matrix form during the research period
(mg m�3), Mwind is the distribution of aerosol concentrations
Fig. 11 e Distribution of HPAI-laden aerosol concentrations from source farms (O-2, O-6, O-8) with a wind speed of 3 m sL1
and a wind direction from the NW.
Fig. 12 e CFD-computed aerosol concentration distribution for the wind direction N and the wind speed 3 m sL1; different
colours represent the disease spread by weak (light grey), moderate (dark grey), and strong (black) possibilities based on
standard accumulated aerosol concentrations.
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generated from the infected farm at a steady-state condition
without a change in the wind speed and direction (mg m�3),
Fwind is the weighting factor for the frequency of wind con-
ditions during the research period (%), Pt is the possibility of
airborne HPAI spread on the time period t(%), Pt�1 is an HPAI
infection possibility in the poultry house on the (t � 1)th day
(%), MRoad is the distribution of aerosol concentrations
generated from the road through livestock-related vehicles,
and t is the time period.
3.5. Development of algorithm for predicting airbornespread of HPAI
The occurrence of HPAI and its propagation pathway during
the 2008 HPAI outbreak was analysed by a specific epidemio-
logical investigation. In general, the clinical signs of HPAI,
such as changes in mortality rate, cough, decreased egg pro-
duction, and decrease in feed consumption, can be recognised
by farmers 5 days after the first occurrence of HPAI. However,
it is difficult to recognise an HPAI outbreak from the clinical
signs that occur in only a small number of poultry. At the first
stage of an HPAI outbreak, the disease was spread to a large
number of poultry without recognising the clinical signs due
to the incubation period, and it was transferred to nearby
farms through airborne transmission and direct contact
transmission by livestock-related human and vehicle move-
ments. After notification of the HPAI outbreak, the recognition
period of the other farms was drastically decreased by early
reports of the clinical signs, by means of concern for the
possibility of HPAI inflow from infected farms. An estimated
infection period (EI period) was assumed at 4 days before
confirmation of the HPAI disease by a PCR test, and a possible
inflow period (PI period) was assumed by 8 days before the EI
period following the comment from APQA. An evaluation
guideline was compared according to the standard data and
wind frequency analysis method to compare the HPAI prop-
agation pathway between the CFD-computed prediction and
epidemiological investigation. Three different standard dates
were considered from the epidemiological investigation by an
HPAI confirmation date, a first day of the EI period and a first
day of the PI period. The wind frequency analysis was con-
ducted by three different divisions of the overall research
period: the whole period, four divided periods following the
spread trend, and daily analysis. The algorithm for the pre-
diction of airborne HPAI spread followed the steps described
below (illustrated by Fig. 13):
1) Construction of a matrix database computed by CFD
simulation according to 3 wind speeds, 8 wind directions,
and 40 sources, including 39 farms and a road;
2) Frequency analysis of the wind conditions by the wind
speed, direction, and precipitation from meteorological
data during the research period;
3) Converting wind conditions into a 3 by 8 table for the wind
weighting factor using the conversion algorithm;
4) Combining the CFD-computed matrix database and wind
weighting factor table for the prediction of the airborne
HPAI spread; and
5) Calculation of the matrix for the accumulated probability
of HPAI occurrences on farms using MATLAB.
3.6. Validation of the prediction model for airbornespread of HPAI
Previous research related to the airborne spread of HPAI has
concerned Gaussian-based simulations, which suggests
approximate distributions of aerosol or gas concentrations.
Most of these studies could not find a specific criterion for the
possibility of infection through airborne transmission and
showed the dispersion of the aerosol or gas distribution from
one infected farm under a steady-state condition. However,
the airborne HPAI spread occurred by a pathogen-laden
aerosol generated from one infected farm at the first stage
but involved multiple infected farms in later stages. In this
study, it was assumed that the farms were infected by HPAI
and emitted pathogens as infected farms when the accumu-
lated aerosol concentration exceeded the criterion concen-
tration. With the passage of time, the number of infected
farms was increased, which resulted in a geometrical
Table 3 e Frequency distribution of the wind conditions during the research period measured by the MeteorologicalAdministration Agency, Korea.
Wind speed (m s�1) N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW
1 7 3 2 4 1 4 13 4 3 6 2 2 6 3 6 10
2 13 3 5 6 8 7 10 22 6 2 0 2 5 10 6 15
3 2 1 0 5 9 4 6 10 1 4 1 2 7 7 13 20
4 2 0 1 6 5 3 1 1 1 2 1 0 2 12 11 11
5 1 0 0 1 4 2 0 0 0 0 1 1 0 3 9 11
6 0 0 0 0 0 0 0 0 0 0 0 1 1 1 5 10
7 0 0 0 0 0 0 0 0 0 1 0 0 0 2 1 2
8 0 0 0 0 0 0 0 0 1 0 0 1 3 0 0 1
Table 4 e Transformation of the wind conditions for combining the CFD-computed results with the wind frequency.
Wind speed (m s�1) N NE E SE S SW W NW
3 10.00% 3.75% 6.88% 11.35% 7.08% 2.50% 6.46% 11.98%
5 2.40% 0.10% 1.15% 0.21% 0.00% 0.42% 0.83% 5.52%
7 1.35% 0.00% 0.00% 0.00% 0.31% 0.31% 1.35% 2.92%
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increment of HPAI spread to neighbouring farms. However,
there is very little research on how much pathogen-laden
aerosol can affect HPAI infection by means of the respiration
of animals based on field experiments. There is a large un-
certainty in the HPAI infection depending on the farms, even
though the poultry are exposed to high concentrations of
aerosols, because of variations in the ventilation system, farm
operation method, hygienic condition, containing ratio of
pathogens in aerosols, and physical conditions of poultry.
When the averaged aerosol concentrations for the infection
criteria were assumed to be 10, 20, 30, and 50 mg m�3, the
standard deviations between CFD-computed timeline of
airborne HPAI spread and the recorded HPAI propagation
timeline during the outbreak were shown to be 0.764, 0.581,
0.966, and 1.206 based the epidemiological investigation.
Therefore, an accumulated concentration of 20 mg m�3 was
chosen as a criterion for airborne HPAI infection.
The computed results for airborne HPAI spread were
compared with the propagation results of the investigation of
the 2008 HPAI outbreak using the nine combinations with
three standard dates (confirmation, estimated infection, and
possible inflow periods) and three wind frequency divisions.
Table 5 shows the errors between the results of the epidemi-
ological investigation and the CFD-predicted values for
airborne HPAI spread through the farms. When the whole
period was used for weather frequency analysis, the error
compared to the epidemiological investigation was 4.52 days
on average, while the error was 5.29 days based on the daily
weather frequency analysis, which resulted in an 18.9%
increased error. The higher possibility of airborne HPAI spread
could be predicted by a specific windward direction when the
aerosol concentration was accumulated daily at the specific
wind direction or wind speed. The CFD prediction result was
overestimated because of the accumulation of the daily
airborne HPAI spread during the overall research period. The
CFD prediction using four divided periods for the weather
frequency analysis showed the most similar result with an
error of 4.45 days. Finally, the prediction result in terms of the
PI period and the four divided weather frequency data was
shown to be the closest prediction to the epidemiological re-
sults among the nine combinations based on the average er-
rors and standard deviations.
Figure 14 shows the propagation of HPAI disease by means
of airborne transmission during four divided periods during
the 2008 HPAI outbreak in Kimje-si. Therewere two farms that
were initially infected that were located in the south-west
segment of the computational domain. The propagation
pathway was in a north-east direction following the principle
wind direction during the research period; there were north-
erly, north-westerly, and westerly winds with a wind fre-
quency of 55.1%. During the first stage, when just two farms
were infected, the airborne HPAI spread was slow. The prop-
agation rate increased after additional farms were infected
because the emission sources of the pathogen-laden aerosols
were increased. Therefore, appears important to conduct
preventive measures against HPAI disease in the infected
farms at an earlier stage as possible. Following prediction of
airborne HPAI spread using the developedmodel, to minimise
the damage from the HPAI outbreak possible infected farms
Fig. 13 e Schematic diagram of the algorithm for predicting airborne spread of HPAI, combining frequency analysis wind
conditions and CFD-computed results.
Table 5 e Comparison of the CFD-computed airborneHPAI spread prediction with the epidemiological resultsfrom the HPAI outbreak in Kimje-si in 2008, based on thedifference in the standard day, including HPAIconfirmation, estimated infection (EI), and possibleinflow (PI) days.
Standards PI day EI day Confirmation day
Whole period 4.43 (3.09) 4.57 (3.01) 4.57 (2.94)
Four divided
periods
4.12 (2.51) 4.74 (3.19) 4.48 (2.82)
Daily analysis 4.42 (3.22) 5.52 (4.07) 5.93 (4.29)
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should be controlled by slaughtering, cutting off entrances,
isolation, and disinfection.
A specific comparison of the propagation pathways be-
tween the epidemiological investigation and the CFD-
computed prediction was analysed to check the geograph-
ical locations of farms with overestimated and under-
estimated infections in terms of the HPAI confirmation days.
The underestimated locations, as shown in the light blue
circle in Fig. 14, stand for the distance of farms (O-4, O-5, O-8,
and S-02) from the initial infected farms (O-1 and P-5) and the
farm (O-7) far away from the northwest principal wind. These
underestimated locations had less of an effect on the
pathogen-laden aerosol from the initial and additionally
infected farms. As a result, the HPAI infections occurred more
rapidly than the predicted airborne HPAI spread. This
outcome means that the HPAI infection was caused by other
factors, such as direct contact transmission, which is stronger
than airborne transmission. This result provides an illustra-
tion of the important limitations of the one-factor prediction
of HPAI spread and also provides an opportunity to make a
multifactor network for HPAI spread, including both indirect
airborne and direct contact transmissions of HPAI disease
between farms. The overestimated locations, as shown in the
dark red circles in Fig. 15, were located along the principle
wind direction and resulted in a significant effect by the
pathogen-laden aerosol from the infected farms. The over-
estimated locations were close to the road, affecting the
nearby farms from aerosols released by livestock vehicle
movements. The assumption of continuous aerosol emission
from a road could affect the overestimation compared to the
actual conditions by the intermittent movement of livestock
vehicles. Therefore, the weighting factors for the strength of
the road emissions must be controlled by the researchers
according to the field conditions.
4. Conclusions
The CFDmodel for airborne spread of HPAI was developed not
only using specific topography and obstacles, including live-
stock farms, vehicle movement, wooden area, and topo-
graphical classifications, but also connecting weather
Fig. 14 e CFD-predicted airborne propagation of HPAI disease from two initial infected farms during the 2008 HPAI outbreak
in Korea during Apr. 1st to Apr. 20th, 2008.
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frequency data. By the prediction of a pathogen-laden aerosol
concentration generated from infected farms, the airborne
spread of HPAI was calculated by means of prediction algo-
rithm grafting CFD simulation and meteorological informa-
tion. This algorithm was used not only to compare the CFD-
computed airborne spread of HPAI with the specific case
during HPAI outbreak but also to make background database
for the multifactor network predicting HPAI spread discussed
on Part II. The CFD-computed simulation can provide valuable
data for predicting the possible infected farms from airborne
HPAI spread, which results in early control of HPAI propaga-
tion by effective preventive measures and the isolation of
infected farms.
The CFD-predicted data can provide data for epidemio-
logically important farms with a high possibility of HPAI
infection resulting in a decrease in the risk of transmission of
the disease. Moreover, the CFD-predicted data can be used as
a database for airborne spread of HPAI combined with the
forecasting information to estimate airborne HPAI spread
from an infected farm to neighbouring farms. Because the
CFD-computed results were used to predict and forecast the
airborne spread of HPAI, the CFD calculation process can be
omitted from the forecasting. This omission provides for rapid
predictions that could be very effective in selecting preventive
measures against HPAI outbreaks.
The accuracy of the CFD prediction should be enhanced by
using more reliable data for how much pathogen-laden
aerosol is required to infect animals with HPAI via respira-
tion. Nevertheless, the CFD prediction of indirect airborne
transmission provides important data, not only for building
the multifactor network of HPAI spread, but also for demon-
strating how engineering CFD simulations can be usefully
combined with epidemiological investigations while taking a
scientific approach.
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