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PLoS ONE. Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic
[Source: PLoS ONE, full text: (LINK). Abstract, edited.]
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Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic
Thomas P. Van Boeckel<SUP>1</SUP><SUP>,</SUP><SUP>2</SUP><SUP>*</SUP>, Weerapong Thanapongtharm<SUP>3</SUP>, Timothy Robinson<SUP>4</SUP>, Chandrashekhar M. Biradar<SUP>5</SUP>, Xiangming Xiao<SUP>5</SUP>, Marius Gilbert<SUP>1</SUP><SUP>,</SUP><SUP>2</SUP>
<SUP></SUP>
1 Biological Control and Spatial Ecology, Universit? Libre de Bruxelles, Brussels, Belgium, 2 Fonds National de la Recherche Scientifique, Brussels, Belgium, 3 Department of Livestock Development, Bangkok, Thailand, 4 Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 5 Center for Spatial Analysis, College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, Oklahoma, United States of America
Abstract
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.
Citation: Van Boeckel TP, Thanapongtharm W, Robinson T, Biradar CM, Xiao X, et al. (2012) Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic. PLOS ONE 7(11): e49528. doi:10.1371/journal.pone.0049528
Editor: Matthew Baylis, University of Liverpool, United Kingdom
Received: May 31, 2012; Accepted: October 10, 2012; Published: November 19, 2012
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This work was supported by the National Institutes of Health Fogarty International Centre through the The National Science Foundation/National Institute of Health Ecology of Infectious Diseases program (7R01TW007869-04) and the Belgian Fond National pour la Recherche Scientifique. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
* E-mail: thomas.van.boeckel@gmail.com
-Thomas P. Van Boeckel<SUP>1</SUP><SUP>,</SUP><SUP>2</SUP><SUP>*</SUP>, Weerapong Thanapongtharm<SUP>3</SUP>, Timothy Robinson<SUP>4</SUP>, Chandrashekhar M. Biradar<SUP>5</SUP>, Xiangming Xiao<SUP>5</SUP>, Marius Gilbert<SUP>1</SUP><SUP>,</SUP><SUP>2</SUP>
<SUP></SUP>
1 Biological Control and Spatial Ecology, Universit? Libre de Bruxelles, Brussels, Belgium, 2 Fonds National de la Recherche Scientifique, Brussels, Belgium, 3 Department of Livestock Development, Bangkok, Thailand, 4 Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 5 Center for Spatial Analysis, College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, Oklahoma, United States of America
Abstract
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.
Citation: Van Boeckel TP, Thanapongtharm W, Robinson T, Biradar CM, Xiao X, et al. (2012) Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic. PLOS ONE 7(11): e49528. doi:10.1371/journal.pone.0049528
Editor: Matthew Baylis, University of Liverpool, United Kingdom
Received: May 31, 2012; Accepted: October 10, 2012; Published: November 19, 2012
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This work was supported by the National Institutes of Health Fogarty International Centre through the The National Science Foundation/National Institute of Health Ecology of Infectious Diseases program (7R01TW007869-04) and the Belgian Fond National pour la Recherche Scientifique. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
* E-mail: thomas.van.boeckel@gmail.com
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