Highly pathogenic avian influenza virus (HPAIV) H5N1 continues to impact on smallholder livelihoods, to constrain development of the poultry production sector, and to cause occasional human fatalities. HPAI H5N1 outbreaks have occurred in a variety of ecological systems with economic, agricultural and environmental differences. This review aimed to identify common risk factors amongst spatial modelling studies conducted in these different agro-ecological systems, and to identify gaps in our understanding of the disease’s spatial epidemiology. Three types of variables with similar statistical association with HPAI H5N1 presence across studies and regions were identified: domestic waterfowl, several anthropogenic variables (human population density, distance to roads) and indicators of water presence. Variables on socio-economic conditions, poultry trade, wild bird distribution and movements were comparatively rarely considered. Few studies have analysed the HPAI H5N1 distribution in countries such as Egypt and Indonesia, where HPAIV H5N1 continues to circulate extensively.
Spatial epidemiology; avian influenza; H5N1
The Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress toward their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, and this has prompted the development of sophisticated cartographic techniques for mapping and modeling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition over large areas is lacking, prompting many influential studies that have rigorously accounted for health risk heterogeneities to overlook the substantial demographic variations that exist subnationally and merely apply national-level adjustments.
Here we outline the development of high resolution age- and sex-structured spatial population datasets for Africa in 2000-2015 built from over a million measurements from more than 20,000 subnational units, increasing input data detail from previous studies by over 400-fold. We analyze the large spatial variations seen within countries and across the continent for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and find that substantial differences in health and development indicators can result through using only national level statistics, compared to accounting for subnational variation.
Progress toward meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on derived metrics through accounting for spatial demographic heterogeneities that exist within nations across Africa.
Population; Demography; Mapping; Millenium development goals
TOC summary: Reassortment is most likely to occur in eastern China, central China, or the Nile Delta in Egypt.
The 1957 and 1968 influenza pandemics, each of which killed ≈1 million persons, arose through reassortment events. Influenza virus in humans and domestic animals could reassort and cause another pandemic. To identify geographic areas where agricultural production systems are conducive to reassortment, we fitted multivariate regression models to surveillance data on influenza A virus subtype H5N1 among poultry in China and Egypt and subtype H3N2 among humans. We then applied the models across Asia and Egypt to predict where subtype H3N2 from humans and subtype H5N1 from birds overlap; this overlap serves as a proxy for co-infection and in vivo reassortment. For Asia, we refined the prioritization by identifying areas that also have high swine density. Potential geographic foci of reassortment include the northern plains of India, coastal and central provinces of China, the western Korean Peninsula and southwestern Japan in Asia, and the Nile Delta in Egypt.
influenza in birds; influenza A virus H3N2 subtype; influenza A virus H5N1 subtype; reassortant viruses; viruses; zoonoses; avian influenza; influenza
Intensification of animal production can be an important factor in the emergence of infectious diseases because changes in production structure influence disease transmission patterns. In 2004 and 2005, Thailand was subject to two highly pathogenic avian influenza epidemic waves and large surveys were conducted of the poultry sector, providing detailed spatial data on various poultry types. This study analysed these data with the aim of establishing the distributions of extensive and intensive poultry farms, based on the number of birds per holder. Once poultry data were disaggregated into these two production systems, they were analysed in relation to anthropogenic factors using simultaneous autoregressive models. Intensive chicken production was clustered around the capital city of Bangkok and close to the main consumption and export centres. Intensively-raised ducks, mainly free-grazing, showed a distinct pattern with the highest densities distributed in a large area located in the floodplain of the Chao Phraya River. Accessibility to Bangkok, the percentage of irrigated areas and human population density were the most important predictors explaining the geographical distribution of intensively-raised poultry. The distribution of extensive poultry showed a higher predictability. Extensive poultry farms were distributed more homogeneously across the country and their distribution was best predicted by human population density.
Agricultural intensification; livestock mapping; disaggregation; livestock production systems; remote sensing; free-grazing ducks
Culicoides imicola KIEFFER, 1913 (Diptera: Ceratopogonidae) is the principal vector of Bluetongue disease in the Mediterranean basin, Africa and Asia. Previous studies have identified a range of eco-climatic variables associated with the distribution of C. imicola, and these relationships have been used to predict the large-scale distribution of the vector. However, these studies are not temporally-explicit and can not be used to predict the seasonality in C. imicola abundances. Between 2001 and 2006, longitudinal entomological surveillance was carried out throughout Italy, and provided a comprehensive spatio-temporal dataset of C. imicola catches in Onderstepoort-type black-light traps, in particular in Sardinia where the species is considered endemic.
We built a dynamic model that allows describing the effect of eco-climatic indicators on the monthly abundances of C. imicola in Sardinia. Model precision and accuracy were evaluated according to the influence of process and observation errors.
A first-order autoregressive cofactor, a digital elevation model and MODIS Land Surface Temperature (LST)/or temperatures acquired from weather stations explained ~77% of the variability encountered in the samplings carried out in 9 sites during 6 years. Incorporating Normalized Difference Vegetation Index (NDVI) or rainfall did not increase the model's predictive capacity. On average, dynamics simulations showed good accuracy (predicted vs. observed r corr = 0.9). Although the model did not always reproduce the absolute levels of monthly abundances peaks, it succeeded in reproducing the seasonality in population level and allowed identifying the periods of low abundances and with no apparent activity. On that basis, we mapped C. imicola monthly distribution over the entire Sardinian region.
This study demonstrated prospects for modelling data arising from Culicoides longitudinal entomological surveillance. The framework explicitly incorporates the influence of eco-climatic factors on population growth rates and accounts for observation and process errors. Upon validation, such a model could be used to predict monthly population abundances on the basis of environmental conditions, and hence can potentially reduce the amount of entomological surveillance.
Spatial ecology; Infectious disease; Remote-sensing; Dynamic model; Longitudinal entomological surveillance network; Mediterranean basin
Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.
Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China’s chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for ¼ of the sample data which was not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China’s first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives.
poultry; China; distribution modelling; population estimates; GIS; epidemiology
Domestic ducks are considered to be an important reservoir of highly pathogenic avian influenza (HPAI), as shown by a number of geospatial studies in which they have been identified as a significant risk factor associated with disease presence. Despite their importance in HPAI epidemiology, their large-scale distribution in monsoon Asia is poorly understood. In this study, we created a spatial database of domestic duck census data in Asia and used it to train statistical distribution models for domestic duck distributions at a spatial resolution of 1 km. The method was based on a modelling framework used by the Food and Agriculture Organisation to produce the Gridded Livestock of the World (GLW) database, and relies on stratified regression models between domestic duck densities and a set of agro-ecological explanatory variables. We evaluated different ways of stratifying the analysis and of combining the prediction to optimize the goodness of fit of the predictions. We found that domestic duck density could be predicted with reasonable accuracy (mean RMSE and correlation coefficient between log-transformed observed and predicted densities being 0.58 and 0.80, respectively), using a stratification based on livestock production systems. We tested the use of artificially degraded data on duck distributions in Thailand and Vietnam as training data, and compared the modelled outputs with the original high-resolution data. This showed, for these two countries at least, that these approaches could be used to accurately disaggregate provincial level (administrative level 1) statistical data to provide high resolution model distributions.
Livestock Mapping; Domestic ducks; Monsoon Asia; Regression models; Highly pathogenic avian influenza
The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.
Characterizing the interface between wild and domestic animal populations is increasingly recognized as essential in the context of emerging infectious diseases (EIDs) that are transmitted by wildlife. More specifically, the spatial and temporal distribution of contact rates between wild and domestic hosts is a key parameter for modeling EIDs transmission dynamics. We integrated satellite telemetry, remote sensing and ground-based surveys to evaluate the spatio-temporal dynamics of indirect contacts between wild and domestic birds to estimate the risk that avian pathogens such as avian influenza and Newcastle viruses will be transmitted between wildlife to poultry. We monitored comb ducks (Sarkidiornis melanotos melanotos) with satellite transmitters for seven months in an extensive Afro-tropical wetland (the Inner Niger Delta) in Mali and characterise the spatial distribution of backyard poultry in villages. We modelled the spatial distribution of wild ducks using 250-meter spatial resolution and 8-days temporal resolution remotely-sensed environmental indicators based on a Maxent niche modelling method.
Our results show a strong seasonal variation in potential contact rate between wild ducks and poultry. We found that the exposure of poultry to wild birds was greatest at the end of the dry season and the beginning of the rainy season, when comb ducks disperse from natural water bodies to irrigated areas near villages.
Our study provides at a local scale a quantitative evidence of the seasonal variability of contact rate between wild and domestic bird populations. It illustrates a GIS-based methodology for estimating epidemiological contact rates at the wildlife and livestock interface integrating high-resolution satellite telemetry and remote sensing data.
Distribution modelling; Satellite Telemetry; Contact rate; Remote sensing; MODIS; GPS; Maxent
Bark beetles (Coleoptera: Curculionidae,
Scolytinae) feed and breed in dead or severely weakened
host trees. When their population densities are high, some species aggregate on
healthy host trees so that their defences may be exhausted and the inner bark
successfully colonized, killing the tree in the process. Here we investigate
under what conditions participating with unrelated conspecifics in risky mass
attacks on living trees is an adaptive strategy, and what this can tell us about
bark beetle outbreak dynamics. We find that the outcome of individual host
selection may deviate from the ideal free distribution in a way that facilitates
the emergence of tree-killing (aggressive) behavior, and that any heritability
on traits governing aggressiveness seems likely to exist in a state of flux or
cycles consistent with variability observed in natural populations. This may
have implications for how economically and ecologically important species
respond to environmental changes in climate and landscape (forest) structure.
The population dynamics emerging from individual behavior are complex, capable
of switching between “endemic” and “epidemic” regimes
spontaneously or following changes in host availability or resistance. Model
predictions are compared to empirical observations, and we identify some factors
determining the occurrence and self-limitation of epidemics.
Highly pathogenic avian influenza (HPAI) H5N1 was first encountered in 1996 in Guangdong province (China) and started spreading throughout Asia and the western Palearctic in 2004–2006. Compared to several other countries where the HPAI H5N1 distribution has been studied in some detail, little is known about the environmental correlates of the HPAI H5N1 distribution in China. HPAI H5N1 clinical disease outbreaks, and HPAI virus (HPAIV) H5N1 isolated from active risk-based surveillance sampling of domestic poultry (referred to as HPAIV H5N1 surveillance positives in this manuscript) were modeled separately using seven risk variables: chicken, domestic waterfowl population density, proportion of land covered by rice or surface water, cropping intensity, elevation, and human population density. We used bootstrapped logistic regression and boosted regression trees (BRT) with cross-validation to identify the weight of each variable, to assess the predictive power of the models, and to map the distribution of HPAI H5N1 risk. HPAI H5N1 clinical disease outbreak occurrence in domestic poultry was mainly associated with chicken density, human population density, and elevation. In contrast, HPAIV H5N1 infection identified by risk-based surveillance was associated with domestic waterfowl density, human population density, and the proportion of land covered by surface water. Both models had a high explanatory power (mean AUC ranging from 0.864 to 0.967). The map of HPAIV H5N1 risk distribution based on active surveillance data emphasized areas south of the Yangtze River, while the distribution of reported outbreak risk extended further North, where the density of poultry and humans is higher. We quantified the statistical association between HPAI H5N1 outbreak, HPAIV distribution and post-vaccination levels of seropositivity (percentage of effective post-vaccination seroconversion in vaccinated birds) and found that provinces with either outbreaks or HPAIV H5N1 surveillance positives in 2007–2009 appeared to have had lower antibody response to vaccination. The distribution of HPAI H5N1 risk in China appears more limited geographically than previously assessed, offering prospects for better targeted surveillance and control interventions.
The geographical distribution of highly pathogenic avian influenza (HPAI) H5N1 and agro-ecological risk factors have been studied in a number of countries in Southeast Asia. However, little is know of its distribution in China where HPAI H5N1 first emerged in 1996, evolved, and spread throughout Asia and the western Palearctic in 2004–2006. This study analyzes separately the distribution, in domestic poultry, of HPAI virus (HPAIV) H5N1 isolated from active risk-based surveillance sampling and HPAI H5N1 clinical disease outbreaks. These data are analyzed in relation to the distribution of chicken and domestic waterfowl population density, proportion of land covered by rice or surface water, cropping intensity, elevation, and human population density. HPAI H5N1 viruses identified by risk-based surveillance are found to be associated with domestic waterfowl density, human population density, and the proportion of land covered by surface water. In contrast, HPAI H5N1 clinical disease outbreak occurrences were mainly associated with chicken density, human population density, and low elevation. These results show that the distribution of HPAI H5N1 risk in China appears more limited geographically than previously assessed, offering prospects for better targeted surveillance and control interventions.
Highly pathogenic avian influenza (HPAI) H5N1 virus persists in Asia, posing a threat to poultry, wild birds, and humans. Previous work in Southeast Asia demonstrated that HPAI H5N1 risk is related to domestic ducks and people. Other studies discussed the role of migratory birds in the long distance spread of HPAI H5N1. However, the interplay between local persistence and long-distance dispersal has never been studied. We expand previous geospatial risk analysis to include South and Southeast Asia, and integrate the analysis with migration data of satellite-tracked wild waterfowl along the Central Asia flyway. We find that the population of domestic duck is the main factor delineating areas at risk of HPAI H5N1 spread in domestic poultry in South Asia, and that other risk factors, such as human population and chicken density, are associated with HPAI H5N1 risk within those areas. We also find that satellite tracked birds (Ruddy Shelduck and two Bar-headed Geese) reveal a direct spatio-temporal link between the HPAI H5N1 hot-spots identified in India and Bangladesh through our risk model, and the wild bird outbreaks in May–June–July 2009 in China (Qinghai Lake), Mongolia, and Russia. This suggests that the continental-scale dynamics of HPAI H5N1 are structured as a number of persistence areas delineated by domestic ducks, connected by rare transmission through migratory waterfowl.
Electronic supplementary material
The online version of this article (doi:10.1007/s10393-010-0672-8) contains supplementary material, which is available to authorized users.
avian influenza; epidemiology; disease ecology; migration
The highly pathogenic avian influenza (HPAI) H5N1 virus has spread across Eurasia and into Africa. Its persistence in a number of countries continues to disrupt poultry production, impairs smallholder livelihoods, and raises the risk a genotype adapted to human-to-human transmission may emerge. While previous studies identified domestic duck reservoirs as a primary risk factor associated with HPAI H5N1 persistence in poultry in Southeast Asia, little is known of such factors in countries with different agro-ecological conditions, and no study has investigated the impact of such conditions on HPAI H5N1 epidemiology at the global scale. This study explores the patterns of HPAI H5N1 persistence worldwide, and for China, Indonesia, and India includes individual provinces that have reported HPAI H5N1 presence during the 2004–2008 period. Multivariate analysis of a set of 14 agricultural, environmental, climatic, and socio-economic factors demonstrates in quantitative terms that a combination of six variables discriminates the areas with human cases and persistence: agricultural population density, duck density, duck by chicken density, chicken density, the product of agricultural population density and chicken output/input ratio, and purchasing power per capita. The analysis identifies five agro-ecological clusters, or niches, representing varying degrees of disease persistence. The agro-ecological distances of all study areas to the medoid of the niche with the greatest number of human cases are used to map HPAI H5N1 risk globally. The results indicate that few countries remain where HPAI H5N1 would likely persist should it be introduced.
Electronic supplementary material
The online version of this article (doi:10.1007/s10393-010-0324-z) contains supplementary material, which is available to authorized users.
global ecology; highly pathogenic avian influenza; agro-ecology; epidemiology
Gridded population distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate population distribution data. The effectiveness of the population distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving population distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded population datasets across large areas.
Electronic supplementary material
The online version of this article (doi:10.1007/s10708-010-9364-8) contains supplementary material, which is available to authorized users.
Population mapping; Global land cover data; Census data; Dasymetric modelling; GlobCover
Beginning in 2003, highly pathogenic avian influenza (HPAI) H5N1 virus spread across Southeast Asia, causing unprecedented epidemics. Thailand was massively infected in 2004 and 2005 and continues today to experience sporadic outbreaks. While research findings suggest that the spread of HPAI H5N1 is influenced primarily by trade patterns, identifying the anthropogenic risk factors involved remains a challenge. In this study, we investigated which anthropogenic factors played a role in the risk of HPAI in Thailand using outbreak data from the “second wave” of the epidemic (3 July 2004 to 5 May 2005) in the country. We first performed a spatial analysis of the relative risk of HPAI H5N1 at the subdistrict level based on a hierarchical Bayesian model. We observed a strong spatial heterogeneity of the relative risk. We then tested 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. The results also revealed a set of anthropogenic factors significantly linked with the risk of HPAI. High risk was associated strongly with densely populated areas, short distances to a highway junction, and short distances to large cities. These findings highlight a new explanatory pattern for the risk of HPAI and indicate that, in addition 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 be sustained.
avian influenza; epidemiology; poultry farming; spatial analysis; Thailand
Outbreaks of highly pathogenic avian influenza (HPAI) H5N1 subtype have occurred in many countries across Asia, Europe, and Africa since 2003. Better understanding of the ecology and risk factors of HPAI is critical for surveillance, risk assessment, and public health policy. We introduce satellite remote sensing as one important tool, and highlight the potential of using satellite images to monitor dynamics of climate and landscapes that are related to wild bird migration and agriculture in the context of avian influenza transmission.
Avian influenza; land surface temperature; MODIS images; paddy rice
Highly pathogenic avian influenza (HPAI) caused by H5N1 viruses has become a global scale problem which first emerged in southern China and from there spread to other countries in Southeast and East Asia, where it was first confirmed in end 2003. In previous work, geospatial analyses demonstrated that free grazing ducks played critical role in the epidemiology of the disease in Thailand in the winter 2004/2005, both in terms of HPAI emergence and spread. This study explored the geographic association between free grazing duck census counts and current statistics on the spatial distribution of rice crops in Thailand, in particular the crop calendar of rice production. The analysis was carried out using both district level rice statistics and rice distribution data predicted with the aid of remote sensing, using a rice-detection algorithm. The results indicated a strong association between the number of free grazing ducks and the number of months during which second-crop rice harvest takes place, as well as with the rice crop intensity as predicted by remote sensing. These results confirmed that free grazing duck husbandry was strongly driven by agricultural land use and rice crop intensity, and that this later variable can be readily predicted using remote sensing. Analysis of rice cropping patterns may provide an indication of the location of populations of free grazing ducks in other countries with similar mixed duck and rice production systems and less detailed duck census data. Apart from free ranging ducks and rice cropping, the role of hydrology and seasonality of wetlands and water bodies in the HPAI risk analysis is also discussed in relation to the presumed dry season aggregation of wild waterfowl and aquatic poultry offering much scope for virus transmission.
Highly pathogenic avian influenza; Domestic ducks; Remote sensing; Agriculture intensification; Rice paddy production
Anatids may have spread the virus along their autumn migration routes.
During the second half of 2005, highly pathogenic avian influenza (HPAI) H5N1 virus spread rapidly from central Asia to eastern Europe. The relative roles of wild migratory birds and the poultry trade are still unclear, given that little is yet known about the range of virus hosts, precise movements of migratory birds, or routes of illegal poultry trade. We document and discuss the spread of the HPAI H5N1 virus in relation to species-specific flyways of Anatidae species (ducks, geese, and swans) and climate. We conclude that the spread of HPAI H5N1 virus from Russia and Kazakhstan to the Black Sea basin is consistent in space and time with the hypothesis that birds in the Anatidae family have seeded the virus along their autumn migration routes.
Avian influenza; Epidemiology; Disease Ecology; Migration; perspective
Free-grazing ducks in rice paddies are a critical factor in the spread and persistence of avian influenza.
Thailand has recently had 3 epidemic waves of highly pathogenic avian influenza (HPAI); virus was again detected in July 2005. Risk factors need to be identified to better understand disease ecology and assist HPAI surveillance and detection. This study analyzed the spatial distribution of HPAI outbreaks in relation to poultry, land use, and other anthropogenic variables from the start of the second epidemic wave (July 2004–May 2005). Results demonstrate a strong association between H5N1 virus in Thailand and abundance of free-grazing ducks and, to a lesser extent, native chickens, cocks, wetlands, and humans. Wetlands used for double-crop rice production, where free-grazing duck feed year round in rice paddies, appear to be a critical factor in HPAI persistence and spread. This finding could be important for other duck-producing regions in eastern and southeastern Asian countries affected by HPAI.
Avian influenza; epidemiology; spatial analysis; Thailand; animal husbandry; research
Early detection and control curtail outbreaks.
In January 2004, highly pathogenic avian influenza (HPAI) virus of the H5N1 subtype was first confirmed in poultry and humans in Thailand. Control measures, e.g., culling poultry flocks, restricting poultry movement, and improving hygiene, were implemented. Poultry populations in 1,417 villages in 60 of 76 provinces were affected in 2004. A total of 83% of infected flocks confirmed by laboratories were backyard chickens (56%) or ducks (27%). Outbreaks were concentrated in the Central, the southern part of the Northern, and Eastern Regions of Thailand, which are wetlands, water reservoirs, and dense poultry areas. More than 62 million birds were either killed by HPAI viruses or culled. H5N1 virus from poultry caused 17 human cases and 12 deaths in Thailand; a number of domestic cats, captive tigers, and leopards also died of the H5N1 virus. In 2005, the epidemic is ongoing in Thailand.
Influenza; avian influenza; epidemiology; poultry; control measures; Thailand; synopsis