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

被引：143

作者：

Gilbert, Marius ^{[1
,2
]}

Golding, Nick ^{[3
]}

Zhou, Hang ^{[4
]}

Wint, G. R. William ^{[5
]}

Robinson, Timothy P. ^{[6
]}

Tatem, Andrew J. ^{[7
,8
]}

Lai, Shengjie ^{[4
]}

Zhou, Sheng ^{[4
]}

Jiang, Hui ^{[4
]}

Guo, Danhuai ^{[9
]}

Huang, Zhi ^{[3
]}

Messina, Jane P. ^{[3
]}

Xiao, Xiangming ^{[10
,11
]}

Linard, Catherine ^{[1
,2
]}

Van Boeckel, Thomas P. ^{[1
,2
]}

Martin, Vincent ^{[12
]}

Bhatt, Samir ^{[3
]}

Gething, Peter W. ^{[3
]}

Farrar, Jeremy J. ^{[13
]}

Hay, Simon I. ^{[3
,7
]}

Yu, Hongjie ^{[4
]}

机构：

[1] Univ Libre Bruxelles, B-1050 Brussels, Belgium

[2] Fonds Natl Rech Sci, B-1000 Brussels, Belgium

[3] Univ Oxford, Dept Zool, Spatial Ecol & Epidemiol Grp, Oxford OX1 3PS, England

[4] Chinese Ctr Dis Control & Prevent, Div Infect Dis, Key Lab Surveillance & Early Warning Infect Dis, Beijing 102206, Peoples R China

[5] Univ Oxford, Dept Zool, Environm Res Grp Oxford, Oxford OX1 3PS, England

[6] ILRI, Nairobi 00100, Kenya

[7] NIH, Fogarty Int Ctr, Bethesda, MD 20892 USA

[8] Univ Southampton, Dept Geog & Environm, Southampton SO17 1BJ, Hants, England

[9] Chinese Acad Sci, Sci Data Ctr, Comp Network Informat Ctr, Beijing 100190, Peoples R China

[10] Univ Oklahoma, Ctr Spatial Anal, Dept Microbiol & Plant Biol, Norman, OK 73019 USA

[11] Fudan Univ, Inst Biodivers Sci, Shanghai 200433, Peoples R China

[12] United Nations FAO, Food & Agr Org, Dakar, Senegal

[13] Univ Oxford, Clin Res Unit, Wellcome Trust Major Overseas Unit, Ho Chi Minh City, Vietnam

来源：

NATURE COMMUNICATIONS | 2014年 / 5卷

基金：

英国医学研究理事会; 比尔及梅琳达.盖茨基金会; 英国惠康基金; 美国国家卫生研究院;

关键词：

MULTITEMPORAL MODIS IMAGES; PADDY RICE AGRICULTURE; SOUTHEAST-ASIA; VIRUS; CHINA; DUCKS; SURVEILLANCE; NETWORKS; MODELS;

D O I：

10.1038/ncomms5116

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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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