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Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia
Marius Gilbert,
Nick Golding,
Hang Zhou,
G. R. William Wint,
Timothy P. Robinson,
Andrew J. Tatem,
Shengjie Lai,
Sheng Zhou,
Hui Jiang,
Danhuai Guo,
Zhi Huang,
Jane P. Messina,
Xiangming Xiao,
Catherine Linard,
Thomas P. Van Boeckel,
Vincent Martin,
Samir Bhatt,
Peter W. Gething,
Jeremy J. Farrar,
Simon I. Hay
et al.
Nature Communications
5,
Article number:
4116
doi:10.1038/ncomms5116
Received
19 March 2014
Accepted
13 May 2014
Published
17 June 2014
Abstract
Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.
full article
Marius Gilbert,
Nick Golding,
Hang Zhou,
G. R. William Wint,
Timothy P. Robinson,
Andrew J. Tatem,
Shengjie Lai,
Sheng Zhou,
Hui Jiang,
Danhuai Guo,
Zhi Huang,
Jane P. Messina,
Xiangming Xiao,
Catherine Linard,
Thomas P. Van Boeckel,
Vincent Martin,
Samir Bhatt,
Peter W. Gething,
Jeremy J. Farrar,
Simon I. Hay
et al.
Nature Communications
5,
Article number:
4116
doi:10.1038/ncomms5116
Received
19 March 2014
Accepted
13 May 2014
Published
17 June 2014
Abstract
Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.
full article
