Spatio-temporal epidemiology of highly pathogenic avian influenza (subtype H5N1) in poultry in eastern India

Spatial and Spatio-temporal Epidemiology 11 (2014) 45–57

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Original Research

Spatio-temporal epidemiology of highly pathogenic avian influenza (subtype H5N1) in poultry in eastern India Madhur S. Dhingra a,b,⇑, Ravi Dissanayake c, Ajender Bhagat Negi a, Mohinder Oberoi c, David Castellan d, Michael Thrusfield e, Catherine Linard f,g, Marius Gilbert f,g a Emergency Centre for Transboundary Animal Diseases – India, Food and Agriculture Organization of the United Nations, Animal Quarantine & Certification Service Station Kapashera, New Delhi 110037, India b Division of Pathway Medicine, School of Biomedical Sciences, College of Medicine and Veterinary Medicine, Chancellor’s Building, 49 Little France Crescent, Edinburgh EH16 4SB, United Kingdom c Emergency Centre for Transboundary Animal Diseases (ECTAD)/Regional Support Unit for SAARC Countries, Food and Agriculture Organization of the United Nations, KSK Building, Block B, Third Floor, Pulchowk, Kathmandu, Nepal d Emergency Center for Transboundary Animal Diseases (ECTAD), FAO Regional Office for Asia and the Pacific (FAO-RAP), 39 Phra Atit Road, Bangkok 10200, Thailand e Veterinary Clinical Sciences, Royal (Dick) School of Veterinary Studies, College of Medicine and Veterinary Medicine, University of Edinburgh, Easter Bush Veterinary Centre Roslin, Midlothian EH25 9RG, United Kingdom f Biological Control and Spatial Ecology, CP160/12 Université Libre de Bruxelles, Avenue FD Roosevelt 50, B-1050 Brussels, Belgium g Fonds National de la Recherche Scientifique (F.R.S.-FNRS), rue d’Egmont 5, B-1000 Brussels, Belgium

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Article history: Received 3 December 2013 Revised 2 June 2014 Accepted 28 June 2014 Available online 5 July 2014 Keywords: Eastern India HPAI H5N1 in poultry Spatial and temporal clusters Risk factors Predictive risk modeling Disease surveillance

a b s t r a c t In India, majority outbreaks of highly pathogenic avian influenza (HPAI) H5N1 have occurred in eastern states of West Bengal, Assam and Tripura. This study aimed to identify disease clusters and risk factors of HPAI H5N1 in these states, for targeted surveillance and disease control. A spatial scan statistic identified two significant disease clusters in West Bengal and Assam, occurring during January and November–December 2008, respectively. Key risk factors were identified at sub-district level using bootstrapped logistic regression and boosted regression trees model. With both methods, HPAI H5N1 outbreaks in backyard poultry were associated with accessibility in terms of time taken to access a city with >50,000 persons, human population density and duck density (P < 0.005). In addition, areas at lower elevation were also identified as high risk by BRT model. It is recommended that risk-based surveillance should be implemented in high duck density areas and all live-bird markets in high-throughput locations. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Highly pathogenic avian influenza (HPAI) virus, H5N1 subtype has been a animal health concern since its spread across several continents from China in 2003 (Li et al., ⇑ Corresponding author at: Food and Agriculture Organization of the United Nations, 55 Lodhi Estate, New Delhi 110003, India. Tel.: +91 65 98390284; fax: +91 65 66411076. E-mail address: [email protected] (M.S. Dhingra). http://dx.doi.org/10.1016/j.sste.2014.06.003 1877-5845/Ó 2014 Elsevier Ltd. All rights reserved.

2004). Up to 2012, 63 countries across Asia, Europe and Africa have reported the virus (OIE, 2013) in poultry and wild birds. It has resulted in 650 cases of human infections with 386 fatalities up to January 2014 (WHO, 2014). Currently, HPAI H5N1 is considered endemic in China, Bangladesh, parts of eastern India, Indonesia, Viet Nam and Egypt; other countries in Asia, including the Lao People’s Democratic Republic, Cambodia, Myanmar and Nepal, have experienced sporadic outbreaks on a regular basis (FAO, 2013).

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India remained free from HPAI H5N1 until February 2006. In the subsequent years, up to December 2012, 95 outbreaks of HPAI H5N1 in domestic poultry were reported to the World Organization for Animal Health (OIE), (OIE, 2013). Barring the outbreaks in 2006, and the latest outbreak from Bangalore, in Karnataka, all other outbreaks were reported from the eastern and north-eastern states of India (Fig. 1). Within the eastern states, West Bengal, Assam and Tripura have shared the highest burden of outbreaks. These states have a large poultry population with West Bengal having approximately 86 million, Assam with 29 million and Tripura with 3.7 million domestic birds (DADF, 2012a,b). From 2008 to 2012, a total of 55 outbreaks from West Bengal, 18 outbreaks from Assam and 8 outbreaks from Tripura had been notified. The outbreaks occurred as epidemic waves during the period of 2008– 2009, and thereafter, as sporadic occurrences (Fig. 1). These eastern states also share borders with Nepal, Bangladesh, Bhutan and Myanmar (Fig. 1) and it is increasingly being recognized that HPAI is now a regional crossborder problem with porous borders and illegal movement of poultry and poultry products contributing to the threat of potential endemic circulation within the region. HPAI H5N1 remains an economically important disease in India. Along the eastern part of the country, poultry represent a common and affordable source of protein, a ready source of income and a sustainable means of livelihood for millions of backyard and rural village communities. Nearly half (49–54%) of households in the state of West Bengal,

44% in Tripura and 67% in Assam were practicing backyard poultry production during a survey conducted in 2001– 2003 (NSSO, 2006). Following confirmation of infection, control and containment for HPAI is carried out according to guidelines laid down in the Action Plan (revised in 2012a) for Preparedness, Control and Containment of Avian Influenza drafted by Department of Animal Husbandry, Dairying and Fisheries (DADF), Ministry of Agriculture (DADF, 2012b). According to the Action Plan, control of infection is by ‘‘stamping out’’ of in-contact and neighbouring premises within a designated culling zone. There is no official vaccination policy for control of the disease in India. However, wide-area culling of birds in a 3–5 km radius of declared infected premises following an outbreak has had a significant impact on farmers’ livelihoods and food security. Up to September 2012, a total of 6.9 million poultry have been culled as a part of the disease control response measures, and approximately USD 4 million had been paid in compensation (DADF, 2013). Disease surveillance and control are inextricably linked and whilst it is widely recognized that early detection is key, there are several challenges associated with achieving it in this part of India. First, the poultry production system in these states is dominated by backyard smallholder type farms, with poultry allowed to forage freely, not being restricted to formally established flocks. Uncontrolled movements of live birds, people, traders and middlemen between farms and markets to meet demand lead to many

Fig. 1. Map of study area showing different phases of HPAI (H5N1) outbreaks and location of primary and secondary disease clusters. Phase-I (January 2008), phase-II (March–May 2008) and phase-III (November to May 2009) of outbreaks occurred as epidemic waves. The remaining outbreaks occurred as sporadic outbreaks. The primary HPAI H5N1 cluster is located in West Bengal and occurred during January 2008. The cluster spans Birbhum district and surrounding areas. The secondary cluster is located in Assam and occurred during November–December 2008. The cluster spans Kamrup district and surrounding areas.

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opportunities for disease transmission over short and longdistances. Passive surveillance for HPAI often results in under reporting, especially in areas where the culling of birds has been carried out as a control measure. There is a belief among policy makers that providing compensation for culling will make up for losses and will overcome the barrier for reporting the disease transparently. However, as Hinrichs et al. (2008) calculated, based upon a survey of poultry production economics in West Bengal (as cited in Otte, 2008); losses incurred through dead birds, ban on restocking and restrictions in poultry movement have an economic impact that largely exceeds the monetary compensation that is provided. Therefore, there is little incentive for farmers to report infection, which compromises the efficiency of passive surveillance. Another factor contributing to under reporting is the endemicity of Newcastle disease (ND) in these areas. It is reported that the sero-prevalence of ND may be as high as 83% in India (Geetha et al., 2008). Outbreaks of ND are common, especially during winter months and it is usual to suspect ND when unusual mortality occurs, because the clinical signs are hardly distinguishable from those of HPAI H5N1. This results in unnecessary delay in reporting and confirmation of HPAI. Thus, a better approach combining the benefit of passive and active surveillance would need to be implemented in order to achieve early detection, reasonable compensation, swift stamping out and prevention of further spread. All the outbreaks of HPAI in India have been caused by H5N1 subtype (DADF, 2013). Within H5N1 the virus, clade 2.2 was responsible for all outbreaks until 2010 (Pattnaik et al., 2006; Chakrabarti et al., 2009). From 2011 onwards, all outbreaks have been attributed to HPAI H5N1 clade 2.3.2.1 (Nagarajan et al., 2012). There are several risk factors associated with HPAI outbreaks. Initial introduction of HPAI to a country is usually associated with long distance transmission from infected areas through migratory birds (Si et al., 2009; Newman et al., 2012). Following introduction, as seen in several earlier studies, disease persistence and spread is closely associated with poultry population densities, human population density, land-use patterns, and movement of poultry and humans through road networks (Gilbert and Pfeiffer, 2012). Chicken density (Pfeiffer et al., 2007; Gilbert et al., 2008; Ward et al., 2008; Martin et al., 2011) and duck density (Gilbert et al., 2006) have been shown to have an association with HPAI H5N1 occurrence. Anthropogenic variables such as human population density have been correlated with HPAI H5N1 risk in several studies in countries with diverse ecologies, such as Thailand (Gilbert et al., 2008; Tiensin et al., 2009; Van Boeckel et al., 2012), Bangladesh (Loth et al., 2010); Vietnam (Pfeiffer et al., 2007); China (Martin et al., 2011) and Romania (Ward et al., 2008). This is most likely due to humans serving as drivers of poultry trading and marketing practices that lead to the introduction and spread of infection, and it may also reflect the reporting bias. Road networks have also been associated with risk of HPAI H5N1 outbreaks as they help in transport of infected poultry to markets with

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improved road connectivity (Paul et al., 2009; Yupiana et al., 2010). Land-cover, such as river networks, presence of inland water bodies have also been associated with HPAI risk in several studies (Biswas et al., 2009; Ward et al., 2008; Martin et al., 2011). Intensification of poultry production may also contribute to increase in HPAI risk (Van Boeckel et al., 2012). Water presence may be considered a direct risk by supporting transmission through contaminated water because virus can remain infective for several days at ambient water temperature (Nazir et al., 2010). Spatial and temporal clusters of HPAI have also been identified and mapped in various countries; Thailand (Tiensin et al., 2009); Vietnam (Pfeiffer et al., 2007; Henning et al., 2009; Minh et al., 2009), Bangladesh (Ahmed et al., 2010; Loth et al., 2010) and Nigeria (Ekong et al., 2012). Other studies focused on quantifying transmissibility of HPAI H5N1 following outbreaks at within flock level (Tiensin et al., 2007) and global level (Zhang et al., 2012) have also been conducted for planning improved control policies. Similar studies focused on epidemiology of HPAI H5N1 in India have been few. On a larger global scale, Stevens et al. (2013) used multicriteria decision analysis (MCDA) to predict areas suitable for HPAI H5N1 occurrence in Asia. Adhikari et al. (2009) used an ecological niche-modeling tool to predict the areas of potential HPAI presence in the Indian subcontinent. Gilbert et al. (2011) quantified and mapped HPAI H5N1 risks in India, Bangladesh and Myanmar and observed that the population of domestic ducks was the main risk factor for HPAI H5N1 persistence and subsequent spread to domestic poultry in south Asia. Recently, Pandit et al. (2013) investigated the spatio-temporal clusters and transmissivity of HPAI H5N1 outbreaks in West Bengal. They also investigated the role of wild bird staging areas as risk factors in the epidemiology of HPAI outbreaks in poultry in West Bengal. A comprehensive examination of other risk factors that may be contributing to disease epidemiology needs to be undertaken in order to inform surveillance and prevent recurrence of outbreaks in the states that have borne the highest burden of disease. The aim of this study was to detect spatial and temporal clusters of disease, identify associated risk factors, and subsequently model the risk of occurrence of HPAI in three eastern states of India, namely West Bengal, Assam and Tripura.

2. Materials and methods 2.1. Study area The study area included the Indian states of West Bengal, Assam and Tripura (Fig. 1). At the time of the study, there were 50 districts and 545 sub districts in the area. The sub-districts are called as ‘‘blocks’’ in West Bengal and Tripura, and as ‘‘circle’’ in Assam. The mean area of a sub-district in the study area is 344 km2. It is partly delineated by the border with Nepal in the north and Bhutan in the east, and surrounds Bangladesh.

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2.2. Outbreak data HPAI H5N1 outbreak data were collected from the period of January 2008 to May 2012. Outbreak data were based on outbreak notifications provided by the Department of Animal Husbandry, Dairying and Fisheries, to World Organization for Animal Health (OIE, 2013). A case definition of an HPAI H5N1 outbreak in India includes (a) unusual high mortality in domestic poultry, (b) preliminary confirmation of HPAI by Reverse Transcriptase Polymerase Chain Reaction (RT-PCR), (c) final confirmation by virus isolation of a HPAI virus by the High Security Animal Disease Laboratory (HSADL), Bhopal. A case series of outbreaks was compiled with data on: the start date of the outbreak, the geocodes of the outbreak locations, the affected sub-district, district and state, the number and type of birds affected, number dead and number culled. During 2008–2009, an outbreak notification to OIE sometimes contained outbreak data of several blocks/ villages combined into a single outbreak notification. Such outbreak notifications, which reported multiple outbreaks within them, were separated into individual case reports, with each report containing only one outbreak location. Fourteen out of 101 outbreak locations had inaccurate coordinates in the OIE reports and their coordinates were corrected to match the location name, making sure that they fell within the correct sub-district. These geographic coordinates were used as the positive HPAI H5N1 case data set in risk factor analysis. 2.3. Risk factor data Data on risk factors was collected at the sub-district level, as sub-districts were the smallest unit at which key risk factor data such as poultry and human census data were available. Data on backyard poultry like domestic ducks, broiler chicken, layer chicken, and indigenous (desi) chickens were obtained from the 18th Livestock Census conducted in 2007 (DADF, 2012a). No data on commercial poultry like number and type of farms, number of birds etc. was available for the study area either from the census or the states. Human population data at sub-district level were obtained from the census carried out in 2011 (http://censusindia.gov.in). Spatial data of administrative boundaries at the state, district and sub-district level was also sourced (www.rmsi.com). Raster data on accessibility to cities of more than 50,000 people (Nelson, 2008) in terms of time taken to travel, cropping intensity (Biradar and Xiao, 2011) and elevation (LDAAC, 2004) were also compiled. Sub-district polygon data were converted into raster ASCII (American Standard Code for Information Interchange) Geographical Information System (GIS) format with a 1 km resolution to exploit the higher resolution of other covariates, and to allow predictions to be made at a lower level than sub-district. All predictor variables are mapped as Supplementary information. We checked the pairwise correlation of the different predictors and the only correlation coefficient >0.5, was between layer chicken density and desi chicken density, with a correlation coefficient of 0.93. We choose to exclude the density of layer chickens from the set of predictors, and

the final set of seven predictors included duck density (dudn), desi chicken density (dsdn), broiler density (brdn), human density (hpdn), accessibility (access), elevation (dem) and cropping intensity (ncrop). 2.4. Spatial and temporal cluster analysis A spatial scan statistic was used to carry out a spatial and space–time cluster analysis using SatScan, version 9.1.1 (SatScan, 2011). The case-control Bernoulli method (Kulldorff, 1997) of cluster detection was used to observe spatial and temporal clustering over sub-districts. HPAI H5N1 outbreak data covering the period of January 2008 to May 2012 were aggregated at sub-district level. A subdistrict with one or more HPAI H5N1 event was denoted as a ‘‘positive case’’ whereas a sub-district without HPAI H5N1 events was a ‘‘negative case/control’’. There were 101 positive case and 444 control sub-districts. For the purpose of the cluster analysis, centroids of sub-districts were used as locational information. The centroids were calculated in ArcGIS-ArcMap version 10.0 (ESRI, 2011). All the control sub-district were used as controls for the analysis as specified for the Bernoulli model (Kulldorff, 2010). A purely spatial and a retrospective space–time cluster analysis were carried out using a maximum cluster size of 30% of the population at risk. Circular and cylindrical scanning windows were used for the purely spatial and the space–time analysis, respectively (Kulldorff, 2010). Likelihood ratios and P-values were calculated for each scanning window, by comparing the risk within and outside the scanning window to observe if the risk of disease is significantly higher within the window. This is done by generating random datasets and comparing the maximum likelihood of the cluster in the real dataset with the most likely clusters in the random datasets (Kulldorff, 1999). A cluster was identified when the null hypothesis of complete spatial randomness was rejected. The P-value for the cluster was calculated by the scan statistic using the default P-value setting to ensure sufficient power for the dataset. In case the maximum likelihood ratio of the identified cluster was within the top 5% of the other clusters, then the identified cluster was considered to be significant at the P = 0.05 level (Kulldorff and Nagarwalla, 1995; Kulldorff, 1997). 2.5. Risk factor analysis All analyses were carried out using the open source software R (R Core Team, 2014). The HPAI H5N1 case data with 101 presence locations was used as the positive case data set, and 10,000 pseudo absence locations were generated in the study area, all within sub-districts with a poultry and human population density >0. In the absence of reliable absence locations, this is required by most occurrence distribution modeling techniques, that make an explicit (logistic regression, boosted regression trees), or implicit (Maxent) assumption on the distribution of negatives, and it is a standard procedure commonly employed in previous studies (Martin et al., 2011). The negatives falling within a diameter of 3 km of positive cases were

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excluded as this distance was the designated culling zone for HPAI H5N1 in India, and it was therefore assumed that no positives could fall within this radius. Duplicate negative point coordinates falling within the same 1 km pixel were also removed in order to prevent having multiple points with identical predictors. The positive and negative datasets were then merged together to create positive and pseudo-absence for the analysis. We used two statistical methods for identifying risk factors. First, logistic regression was used to find the best fitting model to describe the relationship between the outcome variable (HPAI H5N1 presence and pseudoabsence) and the list of predictor variables. As often the case with disease distribution data, the model residuals were spatially auto correlated. In order to control for spatial autocorrelation, we estimated the range of the spatial autocorrelation in model residuals. A local spatial average with a radius corresponding to this range was estimated, added as covariate, and the significance of the predictor variables was re-estimated, following the method recently proposed by Crase et al. (2012). A final model including all covariates and Crase residual spatial term was built, and the Log likelihood ratio test, Chi-square and P-values were estimated for all predictor variables. The logistic regression models were bootstrapped 50 times, selecting each time a different set of 500 pseudo-negatives, to calculate average model coefficients, log likelihood ratios, chi-square and P-values. Finally the mean area under the curve (AUC) was calculated to validate the logistic regression model. Second, Boosted regression trees (BRT) method, developed by Trevor et al. (2001) was used to model the probability of HPAI outbreaks as a function of the predictor variables. BRT allows for boosting of regression trees. It follows an iterative procedure whereby, a regression tree is fitted first onto a dependent variable, followed by estimating residuals and then fitting a new tree to estimated residuals, updating predictions. The procedure is iterated until no gain in predictive power becomes measured. The predictions of BRT were measured on an estimated scale of 0–1, when the response variable was binary. These predictions were multiplied by the learning rate (which estimates the contribution of each tree to the model) and added up to produce a fitted value of the final model (Elith et al., 2008). Here too, the analysis was bootstrapped 50 times using different sets of 500 pseudo-negative points. The approach proposed by Crase et al. (2012) was also applied to the BRT modeling, i.e., creating a first model, estimating the spatial local average of residuals, and re-running the model with this term added as predictor variables. The relative contribution of all predictorvariables was hence estimated with this term included. 3. Results 3.1. Spatial and temporal pattern of disease From January 2008 to May 2012, 101 HPAI outbreaks occurred in the three states. There were a total of 101 outbreaks within the 545 sub-districts. Within the total 101 outbreaks, 63 sub-districts were affected only once (West

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Bengal-45, Assam-14 and Tripura-4). Thirteen sub-districts were affected twice, (Assam-2, West Bengal-11). Mayureshwar-1 in West Bengal was affected thrice in January 2008. Mohanpur sub-district of Tripura reported four outbreaks of HPAI-one each in April 2008 and March 2011 and two outbreaks in early 2012. Khargram subdistrict in Murshidabad district in West Bengal reported five outbreaks with one occurring in January 2008 and the remaining four in January 2010. Visual observation of the epidemic curve suggests that during 2008–2009, the outbreaks occurred in three phases, with no reported outbreaks in between (Fig. 2). Phase-I of outbreaks occurred during January 2008, when 52 affected sub-districts were in the central and southern parts of West Bengal (Figs. 1 and 2). The number of outbreaks reached a peak during mid-January 2008, and by end of January, the epidemic spanned 13 districts of West Bengal. Total poultry losses from these outbreaks, including those from culling, surpassed 4 million birds (DADF, 2013). A second, albeit smaller peak was observed in the epidemic curve from March to May 2008, which was referred to as phase-II. By this time, the epidemic had nearly spread throughout the entire state of West Bengal, with 15 out of 18 districts affected. The outbreaks in West Bengal had spread northwards and even further to the distant state of Tripura (Fig. 1). A total of 10 sub-districts were affected from West Bengal and Tripura during this phase (Fig. 2). A total of 0.45 million poultry birds were culled during this phase (DADF, 2013). During phase-III of the outbreaks from November 2008 to May 2009, 27 sub-districts were affected from West Bengal and Assam (Fig. 2). These outbreaks started from the central part of Assam in November 2008 and then spread to distant parts of Assam and northern part of West Bengal (Fig. 1). A total of 0.71 million poultry were culled during this phase of outbreaks (DADF, 2013). From 2010 onwards, outbreaks were reported from 12 sub-districts. In January 2010, five sub-districts were affected from West Bengal leading to the culling of 0.71 million poultry (DADF, 2013). In 2011, two sub-districts from Tripura and one each from West Bengal and Assam were affected. In early 2012, three sub-districts from Tripura reported HPAI outbreaks (Fig. 1). The production system in these three states is primarily of the backyard smallholder type. Hence, the majority of outbreaks were reported from the backyard sector. Only eight outbreaks out of total 101 were in commercial farms, remaining (92%) were affecting backyard sector. West Bengal reported 97% outbreaks in backyard poultry, whereas Assam reported 94%. In contrast, in Tripura, only 37% outbreaks were in backyard poultry, remaining were all reported from Government poultry farms. The primary species affected in the backyard sector were chickens. The reports to OIE simply listed the species as ‘‘birds’’. The outbreaks from commercial sector during January and March 2008 in West Bengal were reported from small marginal broiler farms rearing poultry in a backyard production system. In December 2008, a small farm supplying day-old-chicks was affected in Khanapara area of Guwahati in Assam. In Tripura, in early 2011, a government owned, state duck breeding farm in West

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Fig. 2. Temporal pattern & epidemic curve of the number of sub-districts with HPAI (H5N1) outbreaks by month and year. Phase-I, January 2008 (52 outbreaks); Phase-II, March–May 2008 (10 outbreaks), Phase-III, November 2008 to May 2009 (27 outbreaks); Sporadic outbreaks from January 2010 to May 2012 (12 outbreaks).

Tripura was affected by HPAI, H5N1, (OIE, 2013). This was followed by an outbreak in another government run poultry farm in Gandhinagar, Tripura. In 2012, three outbreaks of HPAI were reported from Tripura, all from government farms. Surprisingly, no outbreaks were reported from the villages surrounding these government poultry farms.

3.2. Spatial and temporal clusters The purely spatial scan statistic and the retrospective space–time analysis identified the same primary cluster of HPAI H5N1. The cluster was located in the central part of West Bengal spanning Birbhum and Murshidabad districts of west Bengal that had 30% of the total outbreaks reported. The cluster had a radius of 79 km (Fig. 1) a log likelihood ratio (LLR) of 22.8 and a P-value of <0.0005 (Table 1). During this time period 52 sub-districts were affected with 46% of the total 52 outbreaks located in Birbhum and Murshidabad districts. A secondary cluster (Fig. 1) was also detected and was located in Assam with a log likelihood ratio of 13.5 (Table 1) and a P-value of 0.009. The time period was from 1st November to 30th December 2008. 18 sub-districts were affected during this time. The center of the cluster was located within Kamrup district, which had 44% of the reported outbreaks. The radius of the cluster was 80 km spanning the adjoining districts of Barpeta, Bongaigaon and Baska.

3.3. Risk factor modeling Logistic regression identified duck density, accessibility and human population density as the variables most strongly associating with HPAI H5N1 presence (Table 2). In the BRT model, the variables most often selected by the BRT by decreasing order of importance were the accessibility to a city with more than 50,000 people, the elevation, the duck density, and the human population density. Three variables had a relative contribution lower than 7%; broiler density, cropping intensity and desi chicken density (Table 2). A more detailed description of the relationships between these variables and the fitted values can be observed in Fig. 3. Locations that have the lowest travel time to a city of population 50,000 (access) had the highest fitted values, and there was no further reduction in risk when travel time was higher than 200 min. Most points fell in an elevation range between 0 and 300 m, and the highest predicted risk was for the lowest elevations. For duck density, there was a relatively low predicted risk for duck density lower than 300 duck/km2, and then the predicted risk increased but remained stable for values higher than 400 ducks/km2. The predicted risk increased gradually with human population density, from 500 people/km2 up to a density of 1000 people/km2, above which there was no further change. The three other variables (desi chicken density (dsdn), broiler density (brdn) and cropping intensity (ncrop) had a much lower effect on the fitted function.

Table 1 Location and key characteristics of primary and secondary clusters of HPAI (H5N1) from Jan 2008 to May 2012. Cluster

Latitude

Longitude

Radius (km)

Log likelihood

P-value

Time period

Primary cluster Secondary cluster

23.9529 26.3240

87.7979 91.0451

79.0 80.73

22.8 13.5

<0.0005 0.009

Jan 2008 Nov to Dec 2008

The primary cluster was centered in Birbhum district in West Bengal during the time period of January 2008. The secondary cluster was centered in Kamrup district of Assam during November–December 2008.

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Table 2 Results of the General Linear Model (GLM); Log Likelihood Ratio (LLR) test and boosted regression trees (BRT). For the GLM, the average AUC value was 0.751, for the BRT, the average AUC was 0.946. Results of GLM Variable Intercept dudn dsdn brdn hpdn Access dem ncrop AR1

LLR test Estimate 1.16933 0.00325 0.00187 0.00024 0.00112 0.00334 0.00090 0.55978 4.66525

Standard error 0.33429 0.00122 0.00161 0.00043 0.00034 0.00120 0.00067 0.38246 0.92214

Z score 3.49800 2.66000 1.16300 0.56700 3.30400 2.79200 1.33000 1.46400 5.05900

BRT

P-value

Chi-square

P-value

0.00047 0.00781 0.24468 0.57067 0.00095 0.00524 0.18049 0.14330 0.00000

7.49534 1.62548 0.34352 10.3654 9.30290 1.55110 2.29470

0.0062 0.2023 0.5578 0.0013 0.0023 0.2130 0.1298

Relative contribution ***

** ***

10.03 5.03 6.88 9.96 22.26 12.00 5.03 28.77

**

Significant. Highly Significant. The abbreviated variables are: duck density/km2 (dudn), desi chicken density/km2 (dsdn), broiler density/km2 (brdn), human density/km2 (hpdn), accessibility to a city with more than 50,000 people (access), elevation (dem) and cropping intensity (ncrop). ***

Fig. 3. Partial dependence plots for the 7 variables included in the BRT model (the most influential being ranked first): accessibility to a city of >50,000 people (access), elevation (dem), duck density (dudn), human population density (hpdn), broiler density (brdn), cropping intensity (ncrop), and desi chicken density (dsdn). Y axes are on the logit scale and are centered to have zero mean over the data distribution. Dashes at inside top of plots show distribution of observations across the variable, in deciles.

The accuracy parameters of the predictions produced by the logistic regression are good with the mean AUC of the model estimated as 0.75 (Table 2). The accuracy of the BRT model appears far better than the logistic regression model with an average AUC (on the fitted values) of 0.946. The final predictive risk maps using both logistic regression and BRT are shown in Fig. 4. In both models, the central part of West Bengal and the central area of Assam surrounding the city of Guwahati in Kamrup district are predicted as high-risk areas for HPAI H5N1 presence (Fig. 4). The primary and secondary HPAI

H5N1 clusters have also been detected in these areas respectively. In the both models, the district of Cooch Behar connecting West Bengal to Assam is also predicted as a high-risk area (Fig. 4). The eastern arm of Assam, where the Brahmaputra river is wide with several tributaries and branch-offs, is also highlighted as high-risk in the logistic regression model, but is not as significant in the BRT. This may be on account of the higher duck density in this area. The northwestern area of Tripura, where the capital city of Agartala is located, is also predicted as high risk in both the models (Fig. 4).

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Fig. 4. Predictive risk maps for HPAI H5N1, produced by the logistic regression (A) and boosted regression trees (BRT) model (B). The high risk areas are depicted from clockwise direction as: K-Kolkata; B-Birbhum; M-Murshidabad; J-Jalpaiguri; C-CoochBehar; G-Guwahati; D-Dibrugarh; and A-Agartala.

4. Discussion The HPAI outbreaks reported during the study period occurred as three epidemic waves that can be visualized in Fig. 2. It may be possible that some of these outbreaks may have been ongoing for a while and the peaks in outbreak detection may simply correspond to improved post-outbreak surveillance. However, during initial phase of outbreaks, the priority was culling and there was less emphasis laid on post outbreak surveillance and post outbreak communication to curb risky practices by poultry farmers and traders, that may have contributed to localized and long distance disease spread. The study of spatio-temporal patterns has been used to study the geographic distribution and spatial and temporal

dynamics of HPAI outbreaks in several countries. In several studies, due to the non-availability of finer detail of epidemiological unit location and case data, scan statistics, using outbreak and surveillance data aggregated at various administrative levels have been used to enable detection of clusters and guide disease control interventions. These can range from using centroids of communes as locational information such as in Vietnam (Pfeiffer et al., 2007; Henning et al., 2009; Minh et al., 2009); to Local Government Area (LGA) level as in Nigeria (Ekong et al., 2012) and down to poultry household level such as done by Minh and colleagues (2009) to study the inter-household spatio-temporal pattern of HPAI H5N1 outbreaks in the Mekong River Delta. Tiensin et al. (2009) in Thailand and Loth et al. (2010) in Bangladesh used the spatial scan test

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at sub-district level using centroids as locational information. Aggregation of outbreak data at various administrative levels allows overcoming the limitation of imprecise case data and population at risk information that was the case in the present study also. As in most epidemiological quantitative analyses, the results are largely dependent on the quality of the original data, which often limit the inference one can make about reality of disease distribution. In addition to the common problem of underreporting, our data analysis is somewhat influenced by the cross-border trade of HPAI H5N1 in the region, and by the influence of HPAI H5N1 outbreaks that were reported in Bangladesh at the same period. So, to a large extent, the cluster analysis used in this study merely aimed to objectify the grouping of outbreaks into clusters in a better manner than by a simple visual interpretation, rather than assessing the precise location and significance of those clusters. It is thus more descriptive than inferential. It is however noteworthy that the identified first cluster having a radius of 79 km, was located in the district where the first outbreak was reported in the region, i.e., Birbhum, West Bengal in addition to Murshidabad district (Fig. 1). Earlier phylogenetic analyses of the isolates have confirmed that this area was the place of a new introduction of virus to the area (Chakrabarti et al., 2009). Notably, our results are in concurrence with Pandit et al. (2013), who carried out a cluster analysis of outbreaks occurring in West Bengal from 2008 to 2010, using a discrete Poisson model. They used point location of outbreaks as compared to the present study where sub-district centroids were used as locational information. The most likely cluster detected by Pandit et al. (2013) matched the location (Birbhum and Murshidabad district) and size (radius 95 km) of the most likely cluster in the present study. In nearby Bangladesh, Ahmed et al. (2010) mapped spatial clusters of HPAI at sub-district level using the Bernoulli method. They detected primary disease clusters of 67.7 km and 85.6 km radius located in the central and northwestern part of the country during the first and second epidemic waves of 2007–2008. Loth et al. (2010) also used the Bernoulli case control method of assessing clustering over sub-districts and identified HPAI clusters of 35 km, 47 km and 19 km radius in central, northwest and southeast Bangladesh. Subsequent to introduction, it is likely that this disease cluster may have acted as a source of infection for spread to other 13 districts of West Bengal. Anecdotal evidence suggests that once culling operations were initiated, backyard poultry owners tried to hide their poultry by transporting them to their relatives in areas beyond the culling zone. Other poultry owners sold their birds to traders/middlemen at throwaway prices, who in turn rapidly sold the birds off at distant markets where they fetched a better price. These practices in all likelihood contributed to the spread of infection over such a large area within a span of a week. The occurrence of smaller outbreaks seen from March to May 2008 and the subsequent spread of infection to the distant state of Tripura suggest that there was an infection reservoir located in West Bengal, within which disease was circulating undetected. By this time, there were improved surveillance systems in place. However, under-

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reporting was also rampant as there was fear of culling and under-compensation. Because of its proximity with Bangladesh, the introduction of HPAI H5N1 to Tripura from Bangladesh can hardly be dismissed, and appear, at first sight, as a likely source of introduction. However, Tripura imports a large amount of poultry to meet the local demand, for which supplies are documented to originate from the wholesale markets located in West Bengal (FARMER, 2012). So, it is hard to trace the most likely route of introduction from the pattern of spread alone, as both scenarios are plausible. There were no outbreaks reported during the period of June 2008 to October 2008. However, the secondary cluster described in Assam during November–December 2008 was unlikely to have been a new disease introduction. Indeed, phylogenetic analysis of the HPAI H5N1 isolates of 2008–2009 outbreaks indicated that all the isolates of 2008–2009 grouped together and belonged to the same sub-clade 2.2 (Chakrabarti et al., 2009). This indicates that the cluster of outbreaks in Assam was spillover from the earlier outbreaks and that the virus probably circulated undetected in poultry between these two epidemic phases. This suggests that the containment measures during the first phase of the outbreaks may have been inadequate and post outbreak surveillance during this period was not sensitive and failed to detect disease based on clinical signs in chickens and by other diagnostic surveillance methods in reservoir populations such as ducks. The secondary cluster had a radius of 80 km (Fig. 1) and spanned Kamrup district, which has the capital of Assam, Guwahati located within. Several factors may have contributed to the secondary cluster, the primary factor being the location of a large city like Guwahati driving demand of poultry products, leading to increased chances of disease transmission. This cluster is also located in the region of the Brahmaputra river basin; an almost 50 km-wide basin of networks and channels of water streams along the Brahmaputra river. There are large duck and waterfowl populations present in these river networks, which may have contributed to disease persistence in these areas through asymptomatic infection. The periods of January and November to December correspond to the cooler winter months when average temperatures are in the range of 13–16 degrees Celsius, which aid virus survival, compared with the hotter months. The major festivals of Durga Puja, Deepawali, Id and New Year, which translate into increased demand for poultry products for gifting and consumption, also fall within this period. The temporal pattern also mirrors those of other east and south-east Asian countries (Park and Glass, 2007) and Bangladesh (Ahmed et al., 2011) where the frequency of outbreaks is higher in the period of December to March. The period of 2010–2012 presents as a period of sporadic outbreaks with no outbreaks reported between June 2009 and January 2010 and then from February 2010 to February 2011. This may be due to effective control and containment following outbreak detection. It may also be possible that there was under-reporting of disease due to fear of culling and inadequate compensation. The outbreaks of January 2010 were caused by HPAI H5N1 virus belonging to clade 2.2, and the isolates clustered with the

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earlier isolates of 2008–2009 (Tosh et al., 2011) indicating that the same virus was circulating undetected and unreported. This indicates that the sensitivity of the active surveillance needs to be ascertained and optimized further. The outbreaks of 2011 in Tripura corresponded to the introduction of a new clade of HPAI H5N1, clade 2.3.2 (Nagarajan et al., 2012). Outbreaks were reported by this clade of virus in West Bengal in August 2011, in Assam in September 2011 and then in January, March and April in 2012 in Tripura. The outbreaks in Tripura in 2012 were all reported from Government poultry farms, and no disease was reported from nearby village backyard poultry, which is highly unlikely considering that these farms were supplying day-old-chicks to nearby farmers to promote poultry rearing. Many of the farm workers were also residing in nearby villages and reared poultry in their backyards. There is not enough active surveillance data from these areas to conclusively say that these were indeed sporadic occurrences of disease. The spatial cluster detection could have been made more robust by inclusion of finer detail of data on numbers of villages flocks/households affected within sub-districts, data on the population at risk within the sub-districts and more reliable surveillance data in addition to reported outbreak data. Aggregating data at county/sub-district centroids has the drawback of occasionally masking smaller clusters with high risk (Jones and Kulldorff, 2012) which may have been likely in this study. Further work could focus on adding these details to the current dataset, provided that this information could be reliably sourced and validated. It is surprising that even though an area of West Tripura is visible as high-risk area in the predictive map (Fig. 4) it is not statistically significant in the cluster analysis and this could be due to under reporting of outbreaks from this area. The two spatial clusters of disease falling within West Bengal, located around Birbhum and Murshidabad; and around Guwahati in Assam are in fact true disease clusters occurring as a result of underlying risk factors as also identified by the final predictive risk mapping (Fig. 4). We used logistic regression and BRT to identify risk factors and thereafter predict the risk of occurrence of HPAI H5N1. BRT is believed to generate better predictions as compared to linear regression methods as it accounts for interactions between the variables and allows modeling of non-monotonous relationships between the predictors and response variable (Elith et al., 2006). Based on the results of the logistic regression, HPAI H5N1 outbreaks were positively associated with duck density, accessibility and human population density (Table 2). Elevation was also found to be an important variable by the BRT model, but given its profile (Fig. 3) it is not surprising that it was not found to be significant by the logistic regression approach, which assumes a linear relationship with the logit of the response variable. Accessibility, together with human population density, reflects the highest probability of HPAI H5N1 presence. Accessibility to cities, measured in terms of travel time to cities, correlates with improved road network and market connectivity. These risk factors relate to the presence of live bird markets, which are typically located in well

connected and densely populated areas and where birds from large catchment areas are brought and concentrated. Such markets and markets networks have been shown to be able to support HPAI H5N1 spread (Fournié et al., 2013). The fitted values are highest between locations that have a travel time within 200 min (3–6 h) indicating that the risk of spread is highest to locations within that area. In these states, there are several local poultry value chains operating that collect poultry from backyard poultry farmers and supply to nearby live bird markets. The BRT model, and to a lesser extent, the logistic regression model have predicted the area of Jalpaiguri and Cooch Behar in the bottleneck between West Bengal and Assam as high risk, where large amounts of poultry and poultry products congregate into the several wholesale markets located there and then flow into the northeastern states (Fig. 4). In a poultry value chain study conducted by FARMER (2012), it was reported that 50–60% of live-bird production from Jalpaiguri and Cooch Behar area of West Bengal are transported into Assam and further northeast, which may be the likely source of infection into HPAI unaffected states. Human population density translates into increased demand of poultry products with associated trading and marketing activities. Densely populated areas around the cities of Kolkata in southern West Bengal, Guwahati (in Kamrup district) in Assam, and Agartala in Tripura are also areas of improved road connectivity with poultry being brought in from far-flung areas to meet demand leading to increased risk of spread of disease (Fig. 4). Similar associations have been described in nearby Bangladesh, where road density (Loth et al., 2010) and human population density (Loth et al., 2010; Ahmed et al., 2012) were observed as significant risk factors for HPAI H5N1 outbreaks. Areas at lower elevation are depicted at greater risk for HPAI H5N1 by the BRT model. This is consistent with the results of studies in the Indian sub-continent (Adhikari et al., 2009), Thailand (Gilbert et al., 2008) and China (Martin et al., 2011). Areas of lower elevation in the study area, which get flooded during monsoon season forming small stagnant ponds and deltas of rivers Ganges, Teesta and Brahmaputra in central West Bengal and central and northeastern Assam, create ecological niches that are favored habitats of various waterfowl species. These species interact with domestic poultry at various locations creating opportunities for virus persistence, transmission and evolution. Pandit et al. (2013) also described similar associations in West Bengal, where domestic ducks sharing habitats with wild bird congregations were a significant risk factor for HPAI occurrence. In Bangladesh also, similar river delta areas where migratory birds congregate and mingle with domestic poultry, have been identified as high-risk areas for HPAI H5N1 presence (Ahmed et al., 2012). Such areas may also be a possible source of transmission of HPAI H5N1 viruses to far flung areas falling in the path of migratory waterfowl routes as proposed by Gilbert et al. (2011). The association with duck density, although concurrent, is not as strong as was found by Gilbert et al. (2008), in south east Asia. In India, West Bengal has indeed the highest population of ducks in the country. However, the duck rearing systems in India are not as intensified and

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integrated with rice production cycles as seen in southeast Asia and China. Ducks are reared in smaller numbers around backyard ponds, using waterlogged areas for fish, snails and insect feeding and nearby post-harvest paddy fields for grain feeding of adult ducks in north-eastern India (Islam et al., 2002). These ducks and duck eggs are primarily consumed within the household and any surplus is sold in local live bird markets (Islam et al., 2002). Long distance transport of ducks such as that seen in Thailand and Vietnam is rare in India. Duck movements for grazing remain localized and the spread of infection by ducks may be limited to localized pockets rather than longer distances, which could explain their relatively lower importance in the model. More often, chickens are also reared along with ducks, and they mingle while scavenging and foraging. This provides an opportunity for sub-clinically infected ducks to transmit infection to chickens, leading to detection of outbreaks of HPAI. In neighbouring Bangladesh also, which shares a similar ecology and poultry production systems as the study area, duck density was not found associated with the risk of HPAI H5N1 outbreaks in two different studies (Loth et al., 2010; Ahmed et al., 2012). Gilbert et al. (2011) also observed that unlike the south east Asia, within South Asia, incremental increase in duck density did not translate into a proportional increase in risk of occurrence of HPAI and proposed that ducks may be acting as a reservoir of HPAI H5N1 in South Asia, rather than being implicated in spread because they found that increase in duck density beyond a certain threshold did not translate into increase in risk for HPAI H5N1. Our results corroborate these findings as we also observe that the predicted risk of HPAI increases between 300 and 400/km2 and thereafter remains stable for values higher than 400 ducks/km2. Cropping intensity was not found to be associated with HPAI risk as seen in most countries in the Asian region. This also correlates with the earlier results by Gilbert et al. (2011), where they observed similar results for south Asia. Ahmed et al. (2012) also did not report cropping intensity to be a significant risk factor in Bangladesh, where rice production cycles are similar to those in West Bengal, which produces two rice crops a year on average. This lack of association may be due to the fact that within the study area, ducks do not forage in large flocks in postharvest paddy fields as seen in south east Asia, and remain restricted to scavenging in household ponds and nearby small sized paddy fields. There is also a lack of association with both broiler and desi chicken density. Native chicken density has been used as a proxy for backyard poultry production in several studies. Within those, Gilbert et al. (2006) found a strong association in Thailand, Paul et al. (2009) found a weak association, and Tiensin et al. (2009) did not find association with density of native chickens in Thailand. In the present study, the lack of association may be explained by the fact that these states are primarily backyard poultry producing states, where the average chicken ownership per household ranges from 5 to 50 chickens (Ahuja et al., 2008). The majority of chickens in backyard are native (desi) breeds reared for egg laying, and, once spent, are consumed as meat within the household, or, in case of

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financial need, sold at nearby live-bird markets or to local traders. Hence, the risks associated with intensive poultry production are lacking in this area. It is emphasized that the above risk models are only as good as the variables that have been incorporated into the models and there may be several critical variables that may have been overlooked. This study only uses data on backyard poultry. Information related to the location, numbers, types and historical disease status of commercial poultry farms has not been included and constitutes a major gap in knowledge. Data on important aspects of poultry marketing like live bird markets and value chain data was also not available at the time of study. The epidemiological unit needs to be defined more finely with inclusion of exact location information of flocks, farms, markets, abattoirs, along with defined populations at risk, which will improve the predictive power of analysis. Data on wild bird habitats located in proximity to domestic poultry rearing areas may also be a potential risk factor, and needs to be further evaluated. HPAI data on neighbouring countries like Bangladesh, Nepal, Bhutan and Myanmar, which may influence disease risk due to illegal cross-border trade, need to be incorporated to improve the model. In fact, it would be prudent to carry out future studies in the north-eastern part of India and Bangladesh together, instead of separately, because the region shares similar agro-ecological features and poultry rearing systems.

5. Conclusion This study has highlighted the risk posed by duck rearing systems in virus persistence and the role of improved connectivity (accessibility) and human population density in transmission of HPAI H5N1 to far-flung areas. Based on these findings, it is recommended to enhance targeted surveillance in areas with high duck density for virus and antibody detection. The surveillance guidelines need to emphasize on poultry trading routes and value chains by designating high-risk poultry trading corridors. Further, all live bird markets in high throughput areas receiving poultry from diverse locations should be candidates for targeted surveillance, and sampled on a regular basis. Price monitoring to assess any unusual product flows should also be carried out on a regular basis within these markets. These surveillance activities should be scaled up during winter months. Once an outbreak is notified from a particular area, the understanding of the local poultry value chain operating within 200 min travel time is critical for setting up post-outbreak surveillance to prevent further spread of infection. The culling and compensation policies as existing need to be re-evaluated in terms of understanding stakeholder behavior and how it affects disease spread in outbreak situations. Most importantly, it is critical to promote the use of passive surveillance for which poultry rearing communities need to be engaged by increasing awareness regarding the disease so that they are encouraged to report unusual mortality. These measures are critical in ensuring that HPAI H5N1 is controlled within these states and does not risk spreading to the other states within the country.

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Conflict of interest None. Acknowledgments This study was supported by the Food and Agriculture Organization and funded by the United Nations and the United States Agency for International Development (USAID) Project OSRO/IND/802/USA and through the European Union funded Project OSRO/RAS/901/EC. The authors thank the Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture, Government of India for supporting the study. Thanks are also due to Drs. Subhash Morzaria, Nicola Wardrop, Paul White and John Weaver for the technical support and guidance provided. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.sste.2014.06.003. References Adhikari D, Chettri A, Barik SK. Modelling the ecology and distribution of highly pathogenic avian influenza (H5N1) in the Indian subcontinent. Curr Sci India 2009;97:73–8. Ahmed SSU, ErsbøLl AK, Biswas PK, Christensen JP. The space–time clustering of highly pathogenic avian influenza (HPAI) H5N1 outbreaks in Bangladesh. Epidemiol Infect 2010;138(06):843. Ahmed SSU, Ersbøll AK, Biswas PK, Christensen JP, Hannan ASMA, Toft N. Ecological determinants of highly pathogenic avian influenza (H5N1) outbreaks in Bangladesh. PLoS One 2012;7(3):e33938. Ahmed SSU, Ersbøll AK, Biswas PK, Christensen JP, Toft N. Spatio-temporal magnitude and direction of highly pathogenic avian influenza (H5N1) outbreaks in Bangladesh. PLoS One 2011;6(9):e24324. Ahuja, V, Dhawan, M, Punjabi, M, Maarse, L, 2008. Poultry based livelihoods of the rural poor: case of Kuroiler in West Bengal. South Asia pro-poor pivestock policy program. (accessed February 2013). Biradar CM, Xiao X. Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005. Int J Remote Sens 2011;32(2):367–86. Biswas PK, Christensen JP, Ahmed SSU, Das A, Rahman MH, Barua H, et al. Risk for infection with highly pathogenic avian influenza virus (H5N1) in backyard chickens, Bangladesh. Emerg Infect Dis 2009;15(12):1931–6. Chakrabarti AK, Pawar SD, Cherian SS, Koratkar SS, Jadhav SM, Pal B, et al. Characterization of the influenza a H5N1 viruses of the 2008–2009 outbreaks in India reveals a third introduction and possible endemicity. PLoS One 2009;4(11):e7846. Crase B, Liedloff AC, Wintle BA. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 2012;35(10):879–88. DADF, 2012. 18th Livestock Census, Department of Animal Husbandry, Dairying & Fisheries, M/O Agriculture. (accessed October 2012). DADF, 2012. Action plan (revised-2012). Action plan for preparedness, control and containment of avian influenza. (accessed Sept 2012). DADF, 2013. Bird flu. Department of Animal Husbandry, Dairying & Fisheries, M/O Agriculture. (accessed March 2013). Ekong PS, Ducheyne E, Carpenter TE, Owolodun OA, Oladokun AT, Lombin LH, et al. Spatio-temporal epidemiology of highly pathogenic avian influenza (H5N1) outbreaks in Nigeria, 2006–2008. Prev Vet Med 2012;103(2–3):170–7.

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