Predicting the distribution of intensive poultry farming in Thailand

Boeckel, Thomas P. Van; Thanapongtharm, Weerapong; Robinson, Timothy P.; D’Aietti, Laura; Gilbert, Marius

doi:10.1016/j.agee.2011.12.019

Cited by 34 publications

(51 citation statements)

References 35 publications

(35 reference statements)

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“…These variables were included because results of previous studies have associated them with risk for subtype H5N1 ( 11 , 13 , 14 ). For example, human population was included as a predictor of subtype H5N1 because it serves as an indirect measure of intensity of poultry trade ( 15 ). For Egypt, we used overall poultry density because density of chickens and ducks separately was not available.…”

Section: Methodsmentioning

confidence: 99%

Predicting Hotspots for Influenza Virus Reassortment

Fuller¹,

Gilbert²,

Martin³

et al. 2013

Emerg. Infect. Dis.

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Section: Methodsmentioning

confidence: 99%

Predicting Hotspots for Influenza Virus Reassortment

Fuller¹,

Gilbert²,

Martin³

et al. 2013

Emerg. Infect. Dis.

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“…These were categorized in four groups, as described in Van Boeckel et al [17]: intensively raised ducks, extensively raised ducks, intensively raised chickens and extensively raised chickens. The categorization was achieved using a data-driven approach based on the number of birds per holding.…”

Section: Methodsmentioning

confidence: 99%

Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic

et al. 2012

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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.

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“…S2) (23) and was recently validated for Thailand (24). For pigs, production systems of intermediate size (semiintensive) may represent a nonnegligible share of production, but these were not treated as an individual category for this study.…”

Section: Discussionmentioning

confidence: 99%

Global trends in antimicrobial use in food animals

Boeckel

Brower

Gilbert

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Demand for animal protein for human consumption is rising globally at an unprecedented rate. Modern animal production practices are associated with regular use of antimicrobials, potentially increasing selection pressure on bacteria to become resistant. Despite the significant potential consequences for antimicrobial resistance, there has been no quantitative measurement of global antimicrobial consumption by livestock. We address this gap by using Bayesian statistical models combining maps of livestock densities, economic projections of demand for meat products, and current estimates of antimicrobial consumption in high-income countries to map antimicrobial use in food animals for 2010 and 2030. We estimate that the global average annual consumption of antimicrobials per kilogram of animal produced was 45 mg·kg −1 , 148 mg·kg, and 172 mg·kg −1 for cattle, chicken, and pigs, respectively. Starting from this baseline, we estimate that between 2010 and 2030, the global consumption of antimicrobials will increase by 67%, from 63,151 ± 1,560 tons to 105,596 ± 3,605 tons. Up to a third of the increase in consumption in livestock between 2010 and 2030 is imputable to shifting production practices in middle-income countries where extensive farming systems will be replaced by largescale intensive farming operations that routinely use antimicrobials in subtherapeutic doses. For Brazil, Russia, India, China, and South Africa, the increase in antimicrobial consumption will be 99%, up to seven times the projected population growth in this group of countries. Better understanding of the consequences of the uninhibited growth in veterinary antimicrobial consumption is needed to assess its potential effects on animal and human health.antimicrobials | livestock | mapping | drug resistance | linear regression

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Predicting the distribution of intensive poultry farming in Thailand

Cited by 34 publications

References 35 publications

Predicting Hotspots for Influenza Virus Reassortment

Predicting Hotspots for Influenza Virus Reassortment

Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic

Global trends in antimicrobial use in food animals

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