Original article

Anthropogenic factors and the risk of highlypathogenic avian influenza H5N1:

prospects from a spatial-based model

Mathilde PAUL1,2*, Saraya TAVORNPANICH3, David ABRIAL1, Patrick GASQUI

1,Myriam CHARRAS-GARRIDO

1, Weerapong THANAPONGTHARM3, Xiangming XIAO

4,Marius GILBERT

5,6, Francois ROGER2, Christian DUCROT

1

1 INRA, UR 346, F-63122 Saint-Genes-Champanelle, France2 Unite AGIRs, CIRAD, France

3 Department of Livestock Development, Bangkok, Thailand4 Department of Botany and Microbiology, Center for Spatial Analysis, University of Oklahoma,

Norman, OK 73019, USA5 Biological Control and spatial Ecology, Universite Libre de Bruxelles, Belgium

6 Fonds National de la Recherche Scientifique, Brussels, Belgium

(Received 23 July 2009; accepted 11 December 2009)

Abstract – Beginning in 2003, highly pathogenic avian influenza (HPAI) H5N1 virus spread acrossSoutheast Asia, causing unprecedented epidemics. Thailand was massively infected in 2004 and 2005 andcontinues today to experience sporadic outbreaks. While research findings suggest that the spread of HPAIH5N1 is influenced primarily by trade patterns, identifying the anthropogenic risk factors involved remainsa challenge. In this study, we investigated which anthropogenic factors played a role in the risk of HPAI inThailand using outbreak data from the ‘‘second wave’’ of the epidemic (3 July 2004 to 5 May 2005) in thecountry. We first performed a spatial analysis of the relative risk of HPAI H5N1 at the subdistrict level basedon a hierarchical Bayesian model. We observed a strong spatial heterogeneity of the relative risk. We thentested a set of potential risk factors in a multivariable linear model. The results confirmed the role of free-grazing ducks and rice-cropping intensity but showed a weak association with fighting cock density. Theresults also revealed a set of anthropogenic factors significantly linked with the risk of HPAI. High risk wasassociated strongly with densely populated areas, short distances to a highway junction, and short distancesto large cities. These findings highlight a new explanatory pattern for the risk of HPAI and indicate that, inaddition to agro-environmental factors, anthropogenic factors play an important role in the spread of H5N1.To limit the spread of future outbreaks, efforts to control the movement of poultry products must besustained.

avian influenza / epidemiology / poultry farming / spatial analysis / Thailand

1. INTRODUCTION

After emerging in southern China in the mid-1990s, the highly pathogenic avian influenza

(HPAI) H5N1 virus spread across east andSoutheast Asia, causing unprecedented epidem-ics in 2003–2004 [13]. As of 24 September2009, the virus has caused 442 human cases,

* Corresponding author: mpaul@clermont.inra.fr

Vet. Res. (2010) 41:28DOI: 10.1051/vetres/2009076

� INRA, EDP Sciences, 2010

www.vetres.org

This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial License(http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted use, distribution, and reproduction in anynoncommercial medium, provided the original work is properly cited.

Article published by EDP Sciences

with 262 deaths worldwide1. Controlling thespread of H5N1 disease in poultry may contrib-ute to the reduction of risk for humans [27] bypreventing the emergence of a viral form withefficient human-to-human transmission capableof triggering a global pandemic [13]. Determin-ing the factors involved in the spread of H5N1in poultry and producing risk maps are criticalto disease control as they would enable controlmeasures to be targeted and surveillance in‘‘high-risk’’ areas to be strengthened. The HPAIH5N1 virus is now well established in the poul-try population in Asia, where the virus has beenable to maintain itself and spread as well as peri-odically re-emerge. The main pathways thathave been identified for the spread of H5N1are the migration and trade of wild birds andthe transport of poultry and poultry products[12]. However, the respective roles of thesepathways at the global or national scale are stillunclear [12, 18]. The persistence of HPAI H5N1virus in Southeast Asia has been linked to a spe-cific agro-ecosystem [9] that associates free-grazing ducks with rice cultivation. A separatestudy found that the risk of HPAI outbreakswas reduced in areas with agricultural activitiesother than rice farming [10]. Free-grazing ducksform a reservoir of HPAI H5N1 in Asia [29] andmay contribute to the spread of the virus whenthey are moved among rice fields which alsoconstitute a habitat for wild waterfowl [8]. Inaddition to free-grazing ducks, it is likely thatbackyard poultry raised in low biosecurity sys-tems and fighting cocks are involved in the dif-fusion of the virus [8]; however, their roles arestill unclear. Research to date suggests that thespread of HPAI H5N1 is influenced primarilyby human activities related to poultry produc-tion and poultry trading [18], however, little isknown about the underlying processes involved.Live poultry markets probably play a role in themaintenance of the virus in Asia [26, 28] and themovement of poultry within trade chains may

have facilitated the spread of the HPAI H5N1virus.

In Southeast Asia, Thailand was affected byHPAI H5N1 early, with the first official reportof poultry and human outbreaks on 23 January2004. By the end of January 2004, 32 provincesthroughout the north and several in the southexperienced outbreaks in many types of poultry.The disease caused 17humancases fromJanuary2004 to June 20052. The epidemic peaked dur-ing a ‘‘second wave’’ with 1 717 outbreaks inpoultry. Beginning in early 2004, Thai authori-ties implemented a control strategy based on theprohibition of vaccination and the use of pre-emptive culling. Approximately 60 millionpoultry were culled during the first wave, withstamping-out measures applied inside a 5-kmradius around an outbreak. From July 2004,culling was restricted to suspected farms orvillages; 3 million poultry were destroyed dur-ing the second wave of outbreaks. The move-ment of poultry and poultry products was alsorestricted around infected areas and fightingcocks and free-grazing ducks were targeted bycontrol measures. Apart from these controlstrategies, the Thai authorities strengthenedthe surveillance of HPAI. In addition to the pas-sive surveillance system and routine laboratorysurveillance, the Government implemented anintensive survey known as the ‘‘X-ray cam-paign’’ in October 2004 with 990 000 volun-teers conducting door-to-door surveys [23] tocheck poultry in every house nationwide. Frommid-2005, the number of outbreaks in poultrydecreased substantially but the occurrence ofpoultry outbreaks in two provinces of Thailandin late 2008 indicates that the threat of HPAI inThailand remains present.

The fact that surveillance was strengthenedin response to the large HPAI epidemic makesThailand a prime place to analyse which factorsplay an important role in the spread of the dis-ease. A set of environmental risk factors wasidentified in Thailand [8], but aside from the

1 WHO, Cumulative number of confirmed humancases of avian influenza A/(H5N1) reported toWHO [on line] (2009) http://www.who.int/csr/disease/avian_influenza/country/cases_table_2009_09_24/en/index.html [consulted 4 November 2009].

2 WHO, Cumulative number of confirmed humancases of avian influenza A/(H5N1) reported toWHO [on line] (2009) http://www.who.int/csr/disease/avian_influenza/country/cases_table_2005_06_08/en/index.html [consulted 4 November 2009].

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findings of Tiensin et al. [24], little has beenlearned about anthropogenic risk factors in thecountry. Some aspects of the role of humanactivities in HPAI risk were recently reportedfrom Vietnam [19]. The discovery of HPAIH5N1 in Thai poultry markets in 2006 and2007 [2] suggests that the HPAI virus has con-tinued to spread among poultry through tradeactivities despite the presence of control mea-sures. Apart from the duck-rice agro-ecosystemwhich has been shown to be a source of infec-tion, the role of humans in the spread of HPAIH5N1 has not yet been fully investigated. Therisk of HPAI varies spatially according to theanthropogenic characteristics of the differentgeographical areas of interest, each character-ized by a variety of human activities such aspoultry farming practices, trade activities andmarket rules, land use and agro-ecosystems,and veterinary services structure and control.The objective of the present work was to study,through a spatial approach, the risk factors ofHPAI H5N1 linked to human activities. It com-plements previous work by identifying thehigh-risk areas of HPAI H5N1 in Thailandand by determining which anthropogenicfactors are associated with an increased risk.

2. MATERIALS AND METHODS

2.1. Data

The subdistrict (an administrative unit covering anaverage area of 69.7 km2, called ‘‘Tambon’’ in Thaı)was used as the spatial scale of interest for the study.Out of the 7 408 subdistricts in Thailand, weexcluded from the analysis those located on islandssince we assumed that they played a minor role indisease distribution. This resulted in a geo-databaseof 7 366 subdistricts which we considered as thestatistical units for the analysis.

Epidemiological data relevant to HPAI H5N1 out-breaks in poultry were provided by the Avian Influ-enza Control Center, Department of LivestockDevelopment (DLD, Bangkok, Thailand), a unit incharge of surveillance and monitoring of avian influ-enza (AI) in poultry. Since January 2004, DLD hasbeen recording information on all poultry outbreaksconfirmed by a diagnostic test. Tests were carriedout by diagnostic laboratories on sick or dead poultry

or cloacal samples using reverse-transcriptase poly-merase chain reaction and virus isolation [22]. Werestricted the study to outbreaks in chickens andducks which occurred during the second wave ofHPAI because at that point both the passive andactive components of the surveillance system werefully implemented to detect the disease. The dataincluded records of 1 717 laboratory-confirmed cases(flocks of affected poultry) dating from 3 July 2004to 5 May 2005. The background susceptible popula-tion was calculated for each subdistrict based on thepoultry census data that DLD collected during theX-ray survey of February 2005. This was used tomodel the relative risk. We computed several anthro-pogenic factors to investigate the role of humans indisease spread since poultry product trading activitiesoperate at different geographic scales. To take intoaccount these different commercial activities, we builtanthropogenic indicators based on the road networkand human population settlements. Using the 2004human population census database of the ThailandDepartment of Provincial Administration3, we calcu-lated the human population density in subdistricts tostudy its association with the relative risk of HPAI,previous papers having found differing results regard-ing the effect of human population density on HPAI[8, 24]. In addition, we computed the distance fromthe subdistrict to major cities (defined as having apopulation of 100 000 or more). We believed thatmajor cities may have played a role in disease spreaddue to the intensity of poultry trade in the areas sur-rounding them. Information on the road network (pri-mary and secondary roads, highways) was obtainedfrom the Ministry of Transport, Bangkok, Thailand.This information made it possible to compute theroad density per subdistrict (grouping primary andsecondary roads) which was taken as an indicatorof the intensity of the local trade of poultry productswithin a subdistrict. We suspected that highwaysplayed a role not only in the long-distance spreadof the virus through the dispersal of infected materi-als, but also in the short-distance spread to subdis-tricts located in their vicinity. Therefore, weintroduced the distance of a subdistrict to the closesthighway as an explanatory variable. Finally, we com-puted the distance to the closest highway junction,which was assumed to function as a ‘‘disseminationnode’’ for the HPAI virus. We assumed that if thevirus was transported mainly through the roadnetwork, the subdistricts located close to a highway

3 Department of Provincial Administration, Statis-tics on Human population [on line] http://www.dopa.go.th/xstat/popyear.html [consulted 4 November 2009](in Thai).

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junction were more likely to come in contact withthe virus than those located further away. To takeinto account environmental risk factors, we usedtopographic data (altitude, hydrology) whichwere obtained from the 1996 digital database ofthe Thailand Environment Research Institute4

(Bangkok). Research has shown that rice-croppingintensity is a relevant risk factor in Thailand and otherAsian countries [9]. Therefore, we included maps ofrice-cropping intensity based on MODIS sensorimages processed from satellite-based mapping algo-rithms developed by Xiao et al. [30]. Using data fromthe DLD poultry census, we also estimated for eachsubdistrict the density of animals and householdsraising poultry for different types of poultry: nativechickens, fighting cocks, broiler and layer chickens,free-grazing ducks, and broiler and layer ducks.

All data were integrated into a geographical data-base and geoprocessing was carried out with the Spa-tial Analyst extension of ArcGIS software v.9.1(ESRI Inc.) and HawthsTools software, v.3.27(Hawth’s Analysis Tools, 2002–2006).

2.2. Modelling the relative risk

Disease modelling and mapping was performedfor the whole of Thailand at the subdistrict level.We ran two parallel models (one for chickens, onefor ducks) since we assumed that the respective spa-tial patterns for chickens and ducks were different.We aimed to produce disease maps based on ‘‘rela-tive risk’’, which was taken to be a ratio of the riskof HPAI in a given subdistrict to the average risknationwide. The latter was estimated from the overallnumber of cases and the poultry farm population inThailand. Due to the widely varying number of poul-try farms in each subdistrict, and because of spatialdependency between the subdistricts [17], we appliedthe hierarchical Bayesian approach described byBesag et al. [3] to the HPAI H5N1 data. This methodmade it possible to compute area-specific relative riskestimates [17] while considering spatial interactionsthrough a spatial smoothing based on a Gaussianauto-regressive model [3]. We used a first order spa-tial interaction neighbourhood based on the contigu-ity between the spatial units. The original method inBesag et al. [3] uses a Poisson distribution to modelthe occurrence of cases, which is appropriate for rare,non-contagious diseases such as cancers in humans

[15] or bovine spongiform encephalopathy in cattle[1]. However, due to the contagiousness of HPAIwithin each spatial unit, applying this method toHPAI would result in overdispersion compared tothe Poisson distribution. We handled this problemby modelling the locally observed number of casesusing a negative binomial distribution [14], anapproach that has been used to model influenzaby Fraser et al. [7]. Monte Carlo Markov Chain(MCMC) simulations were used to estimate theparameters of the model, including the estimate ofrelative risk for each spatial unit [16]. The estimationwas performed using LinBugs [21], with 1 millioniterations, each producing a random simulation ofthe relative risk for all of the statistical units (e.g. sub-districts). Geweke and Heidelberger–Welch testswere used to assess the convergence of the models[5]. Considering a long safety burn-in period, param-eters were estimated from a subset of 3 000 of therandom simulations (with a systematic step of 1 over3 to overcome auto-correlation problems). From thissubset of 3 000 simulations, we computed credibleintervals containing 95% of the values of relativerisk. We tested the link between the relative risk val-ues in subdistricts on chickens and ducks using aSpearman rank order correlation test. The relative riskwas mapped for chickens and ducks using ArcGISsoftware v.9.1 (ESRI Inc.). Maps made it possibleto identify groups of subdistricts with either a signif-icantly high or a significantly low risk of HPAI-infected flocks compared to the rest of the country.

2.3. Study of spatial risk factors

We aimed to identify the factors associated withthe spatial risk of HPAI. To do so, we constructed alinear model with fixed effects. We used the logarithmof the relative risk estimated through the Bayesianapproach (which modelled the exponential of relativerisk values through a Gaussian distribution in eachstatistical unit, as mentioned by Besag et al. [3]) asthe dependent variable. Separate models of HPAIoutbreaks were constructed for the chicken andduck populations. Each of the two models contained18 variables which included environmental, poultryfarming, and anthropogenic factors. Multicollinearitywas investigated by checking the standard errors ofregression coefficients and the variance inflationfactors (VIF) [6]. The density of native chickensand the density of farms with native chickens werefound to be positively correlated; consequently, onlythe former was introduced into the analysis. Multicol-linearity was finally assumed not to cause any seri-ous problem in the model (VIF values < 5.1) [6].

4 Thailand Environment Institute, Thailand on adisk: Digital Database for Use with PC ArcInfoand/or ArcView (CD-ROM), Thailand Environ-ment Research Institute, Bangkok, 1996.

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Since we expected some non-linear relationships, con-tinuous variables were transformed into categoricaldata before they were entered into the model. Fourcategories were chosen for each variable with theexception of free-grazing ducks (density of animalsand density of farms) whose distribution allowed only3 categories. We selected the thresholds that simulta-neously fitted the non-linear relationships and had asufficient number of statistical units per category.Finally, we added the level of relative risk in the duckpopulation as a covariate in the chicken model andvice versa for the duck model. For each species, thisthereby made it possible to adjust the analysis toaccount for the level of disease occurrence in the otherspecies. The multivariate analysis was carried outusing a stepwise backward elimination process; thesignificance of each variable in the full model wasassessed in turn, with the least significant variabledeleted and the process repeated until all of theremaining variables were significant at p value 0.05(Fisher test) [6]. From the effect estimates computedin the final linear model, we deduced values of riskratios (RR) and their confidence intervals (95%) forthe different variables. For each variable, the referencecategory was defined as the category expected to be atthe lowest level of risk based on our hypothesis andfindings of previous studies in Thailand [8, 24].Although spatial dependence already had been takeninto account through the spatial contiguity in theBayesian approach, we looked for any remainingspatial autocorrelation in the linear models. Thesemi-variograms of model residuals showed that auto-correlation may have played a role only in a veryshort-distance range (2 250 m). Computing the dis-tances between each subdistrict to the next nearestsubdistrict, we found that only a small portion ofsubdistricts (< 5%) actually had a chance of beinginfluenced by their neighbours within that range. Thusthe likelihood that spatial autocorrelation affected theresults was assumed to be low. The statistical analysiswas performed using R software v.2.9.25.

3. RESULTS

3.1. Mapping the relative risk

Out of the 1 717 HPAI H5N1 outbreaksreported in Thailand between 3 July 2004 and5 May 2005, 1 158 outbreaks were reportedin the chicken population (5 451 643 farms or

households with chickens in Thailand) and495 in the duck population (728 750 farms orhouseholds with ducks). Figure 1 shows thegeographical distribution of HPAI outbreaksin chicken and duck flocks during this timeperiod. Figure 2 presents the spatially smoothedrelative risk maps for chickens and ducksand shows that the two maps resemble eachother fairly closely (Spearman rho = 0.91,p < 1e�16). The maps visually confirm thepresence of a ‘‘hot spot’’ of HPAI risk in thecentral plain of Thailand where the relative riskwas significantly higher than the national aver-age (relative risk > 10 for both chickens andducks). For ducks, however, the high-risk areatended to extend further across the western partof the central plain of Thailand. In contrast, theextreme south of Thailand appeared to be ahigh-risk area for chickens, with values of rela-tive risk significantly > 10. On the contrary,some areas were especially low-risk for bothchickens and ducks despite the occurrence ofoutbreaks (relative risk significantly < 0.5), asin northeastern Thailand and in the middle partof the peninsula. Northern Thailand had lowvalues of relative risk (significantly < 0.5) onlyfor chickens.

3.2. Spatial risk factors

We focussed on the RR values and high-lighted the variables with high or very lowRR. The estimated effects of the environmental,poultry farming and anthropogenic risk factorsare displayed in Tables Ia, Ib and Ic. The levelof relative risk of HPAI for ducks was the mainrisk factor associated with the relative risk forchickens, and vice versa. Apart from this, themean number of rice crops per year was themost relevant risk factor for the relative riskof HPAI for both chickens and ducks. A lowaverage altitude in a subdistrict (� 50 m) wasalso found to be a risk factor of HPAI forchickens and ducks, while medium altitudewas associated to RR below 1. A high densityof free-grazing ducks appeared to be one ofthe main risk factors for HPAI. For chickensand ducks, the HPAI risk was connected moreclosely to animal density than to the densityof farms or households with poultry. Areas with

5 The R Foundation for Statistical Computing,2009.

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a high density of broiler and layer ducks wereassociated with a strong increase in the relativerisk for both chickens and ducks (RR > 4 forsubdistricts with more than 80 ducks perkm2). A high density of broiler and layer chick-ens (> 500 chickens per km2) was found to beassociated with a high risk of HPAI (RR > 3)only in the duck model. Our results demon-strated a relationship between a high densityof native chickens and a low risk of HPAIH5N1 for both chickens and ducks. To a lesserextent, a high density of fighting cocks was

found to be associated with an increase in theHPAI risk for both species. The role of severalanthropogenic factors related to proximity tomain transportation axes and major cities(Fig. 3) still remained strong after adjustingfor the effects of the other variables. We foundthat a high HPAI risk was strongly associatedwith highly-populated areas, short distances tothe highway junction (< 20 km), and a highdensity of roads in a subdistrict. Moreover,the HPAI risk decreased when the distanceradius to major cities (with a human population

Figure 1. Number of outbreaks per subdistrict in chicken and duck flocks in Thailand from 3 July 2004 to 5May 2005. (A color version of this figure is available at www.vetres.org.)

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of over 100 000) increased. To a lesser extent, avery short distance to the closest highway(< 5 km) was also associated significantly witha higher HPAI risk for chickens and ducks.

4. DISCUSSION

We used Bayesian spatial analysis to charac-terise HPAI risk areas in Thailand during thesecond wave of the HPAI H5N1 epidemicand to explore the link between anthropogenicfactors and the relative risk of HPAI. We

focussed on risk factors that contributed to thespread of HPAI H5N1. This analysis showsthat, when adjusted for the effects of environ-mental and poultry variables, several anthropo-genic factors were significantly associated withan increased risk of HPAI in both chicken andduck populations.

First, we generated maps of the relative riskof HPAI H5N1 for chicken and duck flocks,and showed that the spatial pattern for chickensand ducks was similar. This indicated thatchickens and ducks either infected each otheror shared the same spatial source of infection.

Figure 2. Relative risk of HPAI H5N1 for chicken and duck flocks in Thailand from 3 July 2004 to 5 May2005. Relative risk was computed from hierarchical Bayesian modelling at the subdistrict level. (A colorversion of this figure is available at www.vetres.org.)

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Table I. Results of the multivariate analysis for factors associated with risk of HPAI H5N1 in chicken andduck flocks from 3 July 2004 to 5 May 2005 in Thailand (p � 0.05).

Variable Categories Number ofsubdistricts

Chicken flocks Duck flocks

Risk ratio 95% CI Risk ratio 95% CI

(a) Environmental factorsRelative risk for ducks

� 0.5 2 769 1.000.51–1.5 825 0.76 0.63–0.911.51–10 1 310 1.86 1.55–2.22� 10.01 2 462 13.11 10.92–15.73

Relative risk for chickens� 0.5 3 175 1.00

0.51–1.5 633 1.10 0.86–1.421.51–10 1 824 6.13 4.83–7.78� 10.01 1 734 33.86 26.33–43.53

Average altitude (m)� 400.01 632 1.00 1.00150.01–400 2 605 0.38 0.32–0.45 0.27 0.21–0.3450.01–150 1 701 0.53 0.45–0.63 0.24 0.19–0.30� 50 2 428 5.73 4.77–6.90 10.16 7.89–13.09

Mean number of rice crops per year� 0.2 1 167 1.00 1.000.21–1 2 405 0.77 0.65–0.92 0.88 0.70–1.101.01–1.5 3 308 1.47 1.24–1.76 1.12 0.88–1.42� 1.51 486 11.21 9.15–13.73 17 12.89–22.42

Distance to the closest river (km)� 10.01 1 328 1.00 1.005.01–10 1 509 0.68 0.57–0.82 0.59 0.47–0.752.01–5 1 966 0.83 0.69–0.99 0.74 0.58–0.94� 2 2 563 2.05 1.71–2.46 2.88 2.25–3.69

(b) Poultry farming factorsDensity of native chickens (no poultry/km2)

� 50 1 441 1.00 1.0050.01–100 1 324 1.12 0.94–1.33 1.57 1.24–2.00100.01–300 3 319 0.73 0.61–0.87 0.52 0.41–0.66� 300.01 1 282 0.52 0.43–0.63 0.46 0.36–0.60

Density of fighting cocks (no poultry/km2)� 0.1 4 191 1.00 1.000.11–1 1 682 0.59 0.50–0.71 0.53 0.42–0.671.01–3 832 0.75 0.62–0.91 0.62 0.48–0.80� 3.01 661 1.52 1.24–1.87 2.3 1.75–3.03

Density of houses with fighting cocks (no house/km2)� 0.01 3 152 1.00 1.000.01–0.1 2 163 0.67 0.56–0.79 0.69 0.55–0.870.11–0.4 1 439 0.86 0.71–1.04 0.73 0.57–0.94� 0.41 612 1.26 1.01–1.54 1.59 1.21–2.10

Density of free-grazing ducks (no poultry/km2)� 0.1 5 953 1.00 1.000.11–10 779 0.68 0.57–0.83 0.37 0.29–0.48� 10.01 634 10.13 8.29–12.37 18.26 13.88–24.04

Continued on next page

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Table I. Continued.

Variable Categories Number ofsubdistricts

Chicken flocks Duck flocks

Risk ratio 95% CI Risk ratio 95% CI

Density of farms with free-grazing ducks (no farm/km2)� 0.001 4 539 1.00 n.s. n.s.

0.0011–0.05 1 367 0.65 0.54–0.78 n.s. n.s.� 0.051 1 460 1.61 1.33–1.96 n.s. n.s.

Density of broiler and layer chickens (no poultry/km2)� 1 2 997 n.s. n.s. 1.00

1.01–50 2 475 n.s. n.s. 0.78 0.61–0.9850.01–500 1 076 n.s. n.s. 0.68 0.53–0.88� 500.01 818 n.s. n.s. 3.04 2.34–3.95

Density of farms with broiler and layer chickens (no farm/km2)� 0.01 932 1.00 1.00

0.011–0.05 1 092 0.78 0.65–0.93 0.75 0.59–0.940.051–1 4 794 1.01 0.85–1.21 1.03 0.81–1.31� 1.01 548 0.61 0.50–0.75 0.66 0.50–0.87

Density of broiler and layer ducks (no poultry/km2)� 5 3 374 1.00 1.00

5.01–20 2 195 0.96 0.80–1.15 0.62 0.49–0.7920.01–80 1 222 0.97 0.81–1.17 0.51 0.39–0.65� 80.01 575 4.6 3.76–5.63 6.85 5.19–9.04

Density of farms with broiler and layer ducks (no farm/km2)� 0.5 3 228 1.00 1.000.51–1 1 471 1.41 1.18–1.69 1.32 1.03–1.691.01–2 1 423 1.22 1.02–1.47 0.88 0.69–1.13� 2.01 1 244 0.7 0.58–0.84 0.28 0.22–0.36

(c) Anthropogenic factorsRoad density

� 0.001 1 897 1.00 1.000.0011–0.002 1 849 0.73 0.61–0.87 0.68 0.54–0.860.0021–0.005 2 538 0.69 0.58–0.82 0.46 0.36–0.58� 0.0051 1 082 4 3.32–4.82 6.82 5.29–8.80

Density of human population (km2)� 100 2 206 1.00 1.00

100.01–300 3 744 0.86 0.72–1.02 0.57 0.45–0.72300.01–600 758 2.61 2.15–3.18 4.18 3.19–5.46� 600.01 658 5.56 4.62–6.68 11.61 9.04–14.90

Distance to the closest highway (km)� 50.01 1 021 1.00 1.0020.01–50 2 109 0.76 0.64–0.91 0.52 0.41–0.665.01–20 2 494 1.19 0.99–1.43 1.28 1.01–1.64� 5 1 742 1.96 1.63–2.35 2.84 2.21–3.63

Distance to the closest highway junction (km)� 100.01 1 964 1.00 1.0050.01–100 2 569 0.78 0.66–0.94 0.78 0.61–0.9920.01–50 1 863 2.09 1.75–2.51 2.81 2.19–3.60� 20 970 6.15 5.13–7.38 9.46 7.38–12.13

Distance to the closest major city (km)� 80.01 3 427 1.00 1.0040.01–80 2 176 1.15 0.96–1.37 1.02 0.80–1.3020.01–40 965 1.78 1.48–2.15 2.06 1.60–2.66� 20 798 4.14 3.44–4.98 7.12 5.53–9.15

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The results of the multivariate analysis suggestthat both explanations may be valid. When, forexample, we considered chickens, we foundthat the relative risk in ducks and several otherrisk factors were significant when adjusted foreach other in the final model. Furthermore,the model for chickens and the model for ducksindicated a common set of risk factors.

Second, our results were consistent with pre-vious studies on ecological risk factors of HPAIin Thailand [8, 9]. However, adding on to pre-vious work, our analysis made it possible toidentify classes of values associated with higherrisk, which provides greater detail regarding the

possible role of ecological risk factors. A highHPAI risk was associated with a high densityof free-grazing ducks (> 10 ducks per km2),more than one rice crop per year, and a shortdistance to a river (� 2 km). Altitude may beconsidered as an indicator of other unmeasuredenvironmental variables related to HPAIrisk. Subdistricts with a low average altitude(� 50 m) were associated with a high risk ofHPAI. The mixture of wetlands, ponds, irriga-tion networks and agriculture in these areascombined with intensive land use [9], may haveconstituted a favourable environment for theHPAI H5N1 virus. In contrast, subdistricts with

Figure 3. Spatial distribution of the main anthropogenic indicators associated with the relative risk forHPAI H5N1 in chicken and duck flocks: population density, location of major cities and highways. (A colorversion of this figure is available at www.vetres.org.)

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a medium altitude (50.01–400 m) have higherslopes and a land cover dominated by forestsand permanent vegetation [9]. Medium averagealtitude in subdistricts associated with low RRwas found to constitute a kind of protective fac-tor regarding HPAI risk. We also provide newinsight into the role of factors related to poultryfarming in the spread of HPAI. Like Tiensinet al. [24], we found no indication that nativechickens represent an increased HPAI riskdespite the fact that these chickens are raised inlow biosecurity systems and were affected mas-sively by the disease (920 out of the 1 158chicken flocks infected during the second wave).Conversely, a high density of native chickens(100–300 and > 300 native chickens/km2)was associated with RR significantly below 1.In Thailand, wet markets have always been rare[2] and native chickens mainly are raised forfamily consumption using little input andinvolving little trading activity. This may haveresulted in a protective effect against HPAIin subdistricts with a high density of nativechickens. These subdistricts probably were lessexposed to the virus because they were notconnected to trading chains which potentiallyspread the disease. In addition, the pre-emptiveculling, which focussed in the beginning onnative chickens around an outbreak, may havecontributed to containing the spread withinthese subdistricts. Fighting cocks are believedto have worsened the HPAI situation in otherAsian countries [29] and in Thailand [22].The association we found between highdensities of fighting cocks and HPAI risk wassignificant but weak, as did Gilbert et al. [8].In Thailand, fighting cocks were also targetedwhen control measures were implemented in2004, with a prohibition on cockfighting, com-pulsory registration, and disease monitoring.Given their high monetary and cultural value,roosters receive very special attention from theirowners, who may have changed their practicesearly to protect their poultry from the disease.Together, these two elements may have resultedin a decreased effect of fighting cock abundanceon the HPAI risk.

Third, we identified a new set of signifi-cant risk factors that help to refine the current

understanding of the HPAI H5N1 epidemic inThailand. Previous studies have suggested thatbroiler and layer ducks do not constitute a riskfactor for HPAI risk in Thailand [8, 9]. In con-trast, we found a significantly increased risk ofHPAI, both in chicken and duck flocks, in areaswith a high density of broiler and layer ducks.During the period studied, only a quarter ofthe total number of broiler and layer ducks wereraised in closed facilities with high biosecuritysystems [20]. Since it has been proven that farmduck breeds can shed the H5N1 virus with min-imal clinical signs [11], our results suggest thatfarm ducks may also have played the role ofsilent carriers during the second wave of theepidemic, contributing to the spread of the dis-ease. In addition, an increased risk in duckflocks was shown for subdistricts with a highdensity of broiler and layer chickens (> 500chicken/km2). In Thailand, broiler and layerchicken production range from large-scaleindustrial farms to small, family-run operations[24]. The latter refer to small or medium-scalebusinesses with links to several middlemen orcompanies for the transformation and transpor-tation of both farm inputs and outputs (feed,wastes, poultry products. . .) [23]. During thesecond wave of the epidemic, it is possible thatbiosecurity rules were not applied fullythroughout these complex poultry productionchains, thus resulting in the spread of H5N1in subdistricts with a high density of broilerand layer chickens.

Furthermore, we identified several statisti-cally significant relationships between indica-tors of human activity and the relative risk forHPAI in chicken and duck populations. Humanpopulation density was the only anthropogenicrisk factor thus far identified in previousresearch in Thailand. On the contrary to Tiensinet al. [24], but in accordance with Gilbert et al.[8, 9], our study found a progressive increase ofHPAI risk with an increase of human popula-tion density for both chickens and ducks. Inaddition, we showed that areas located withina short distance radius around major cities andhighway junctions constitute ‘‘hot spots’’ forHPAI risk. Cities are characterised by highlyintense poultry trade activities involving live

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poultry markets, food markets, slaughterhouses,and poultry plants. This intensity may haveresulted in increased possibilities of virus intro-duction and spread in surrounding areas. If theHPAI virus was transported through the roadnetworks, the subdistricts located a short dis-tance from the highway junction were morelikely to be in contact with the virus than thosesituated further away. Highway junctions thusmay have functioned as ‘‘dissemination nodes’’for the HPAI H5N1 virus. A significant associ-ation was identified between a high HPAI riskand proximity to the closest highway. Thismay underline the role of the movement ofpoultry and poultry products during the secondwave of the epidemic, not only in the long-dis-tance spread of the HPAI virus, but also in theshort distance dissemination in the areas sur-rounding the highways. Free-grazing ducks thatare moved along the central plain for hundredsof kilometers and are known to be reservoirs ofHPAI may have contributed to the spread of thedisease [8, 20]. Other types of poultry also weremoved from farms to slaughterhouses, markets,or fighting arenas, while farm inputs and poul-try products such as eggs, meat, litter, and poul-try manure were transported along collectioncircuits. The movement of live poultry, people,and infected material may have resulted in thespread of the virus between houses throughdirect or indirect transmission. Subdistricts witha high road density were associated with anincreased risk of HPAI H5N1. Local poultryproduct and by-product business activitiesinvolve frequent contacts which revolve aroundroad networks. Once the HPAI virus was intro-duced into a subdistrict, a dense road networkmay have facilitated its local spread.

Thus, while the spatial pattern of relative riskis known to be largely associated with an abun-dance of free-grazing ducks and rice-croppingintensity, we found that several indicators ofhuman activities were also associated withHPAI risk in Thailand during the second waveof the epidemic. This suggests that in additionto the ‘‘rice paddy-free grazing duck system’’,major cities, the highway network, and localroad networks may also have played key rolesin the spread of HPAI in Thailand. Throughthe transportation of potentially infected live

poultry or contaminated poultry products, high-ways may have contributed to both the long dis-tance and the local spread of the HPAI H5N1virus. Local road networks were possiblyinvolved in short-distance spread. In addition,major cities and highway junctions may havefunctioned as ‘‘dissemination nodes’’ for theHPAI H5N1 virus through the intense trafficof poultry products and poultry trading activi-ties in their surrounding areas. Moreover, ourresults suggest that activities related to layerand broiler ducks may have played a more sig-nificant role than previously thought, as well asto a lesser extent – layer and broiler chickens.

To tackle the outbreaks, Thai authoritiesimplemented different control measures whichevolved over time, but the initial plan aimed tocontrol poultry product movement countrywide.Beginning in July 2004, in addition to pre-emp-tive culling, the movement of poultry and avianproducts was restricted within a 5-km radius ofan infected flock; these restrictions wereextended during the second wave. The wholecountry was also zoned into 5 areas, and poultrymovements were strictly controlled through 32check points between zones [4]. This helped tocontain the disease from spreading countrywide.In addition to this set of control measures, andbecause free-grazing ducks were suspected ofbeing H5N1 HPAI reservoirs, the Thai Govern-ment encouraged duck producers to change theirpractices from a free-grazing to a housed system.However, farmers were not able to change theirpractices in a short period of time [23]. In 2005,ducks were still allowed to graze in paddy fields,but the DLD prohibited long-distance move-ments.The free-ranging practice became illegalin March 2006, obliging farmers to house everyduck flock [23]. However, by this time theepidemic was already under control: the numberof outbreaks had dropped from 1 717 during thesecond wave to 75 during the third wave (1 July2005 to 9 November 2005). Thus, while thehousing of all free-grazing ducks took timeto achieve, restrictions on the long-distancemovements of free-grazing ducks had alreadycontributed largely to limiting HPAI spread inThailand.

The H5N1 virus may now be well estab-lished in different Southeast Asian countries.

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Despite the implementation of control mea-sures, it is probable that these countries willcontinue to face new outbreaks in poultry.The conditions under which the virus maintainsitself in the environment are not well known. Itis difficult to prevent virus re-emergence in pos-sible local persistence spots, or the periodicreintroduction of the virus [25]. Controllingthe disease within the poultry population is acritical issue for both the public health and agri-cultural economic systems. The restructuring ofpoultry production from open-housed to closedsystems has started in Thailand but the processwill take time and considerable cooperativeeffort. Therefore, to limit the number and sizeof future outbreaks in the poultry population,the focus of efforts should be on controllingthe movement of both live poultry and avianproducts.

Acknowledgements. We thank the Department ofLivestock Development (DLD), Bangkok, Thailandfor its support in this research. Data acquisition andintegration was performed with funding from theInternational Emerging Infections Program (IEIP) inThailand through a DLD-IEIP collaborative project.We also thank the French Research Agency (ANR)project EcoFlu and the PHC program which providedus additional support. Drs. Xiao and Gilbert weresupported by the grant from the US National Institutesof Health (R01-007869-03).

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