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; Hongjie Yu

doi:10.1038/ncomms5116

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. HayHongjie Yu

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Résumé

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.

langue originale	Anglais
Numéro d'article	4116
journal	Nature Communications
Volume	5
Les DOIs	https://doi.org/10.1038/ncomms5116
Etat de la publication	Publié - 17 juin 2014
Modification externe	Oui

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Gilbert, M., Golding, N., Zhou, H., Wint, G. R. W., Robinson, T. P., Tatem, A. J., Lai, S., Zhou, S., Jiang, H., Guo, D., Huang, Z., Messina, J. P., Xiao, X., Linard, C., Van Boeckel, T. P., Martin, V., Bhatt, S., Gething, P. W., Farrar, J. J., ... Yu, H. (2014). Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nature Communications, 5, Article 4116. https://doi.org/10.1038/ncomms5116

@article{d430927550f34304b17ceaf2ca5bbb38,

title = "Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia",

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.",

author = "Marius Gilbert and Nick Golding and Hang Zhou and Wint, {G. R William} and Robinson, {Timothy P.} and Tatem, {Andrew J.} and Shengjie Lai and Sheng Zhou and Hui Jiang and Danhuai Guo and Zhi Huang and Messina, {Jane P.} and Xiangming Xiao and Catherine Linard and {Van Boeckel}, {Thomas P.} and Vincent Martin and Samir Bhatt and Gething, {Peter W.} and Farrar, {Jeremy J.} and Hay, {Simon I.} and Hongjie Yu",

year = "2014",

month = jun,

day = "17",

doi = "10.1038/ncomms5116",

language = "English",

volume = "5",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

}

Gilbert, M, Golding, N, Zhou, H, Wint, GRW, Robinson, TP, Tatem, AJ, Lai, S, Zhou, S, Jiang, H, Guo, D, Huang, Z, Messina, JP, Xiao, X, Linard, C, Van Boeckel, TP, Martin, V, Bhatt, S, Gething, PW, Farrar, JJ, Hay, SI & Yu, H 2014, 'Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia', Nature Communications, VOL. 5, 4116. https://doi.org/10.1038/ncomms5116

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T1 - Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

AU - Gilbert, Marius

AU - Golding, Nick

AU - Zhou, Hang

AU - Wint, G. R William

AU - Robinson, Timothy P.

AU - Tatem, Andrew J.

AU - Lai, Shengjie

AU - Zhou, Sheng

AU - Jiang, Hui

AU - Guo, Danhuai

AU - Huang, Zhi

AU - Messina, Jane P.

AU - Xiao, Xiangming

AU - Linard, Catherine

AU - Van Boeckel, Thomas P.

AU - Martin, Vincent

AU - Bhatt, Samir

AU - Gething, Peter W.

AU - Farrar, Jeremy J.

AU - Hay, Simon I.

AU - Yu, Hongjie

PY - 2014/6/17

Y1 - 2014/6/17

N2 - 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.

AB - 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.

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Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

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