TY  - JOUR
AU  - Gilbert, M
AU  - Golding, N
AU  - Zhou, H
AU  - Wint, GRW
AU  - Robinson, TP
AU  - Tatem, AJ
AU  - Lai, S
AU  - Zhou, S
AU  - Jiang, H
AU  - Guo, D
AU  - Huang, Z
AU  - Messina, JP
AU  - Xiao, X
AU  - Linard, C
AU  - Van Boeckel, TP
AU  - Martin, V
AU  - Bhatt, S
AU  - Gething, PW
AU  - Farrar, JJ
AU  - Hay, SI
AU  - Yu, H
Y2  - 2020/12/22
Y1  - 2014/06/01
SN  - 2041-1723
UR  - http://hdl.handle.net/11343/258260
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.
LA  - English
PB  - NATURE PUBLISHING GROUP
T1  - Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia
DO  - 10.1038/ncomms5116
IS  - Nature Communications
VL  - 5
IS  - 1
L1  - /bitstream/handle/11343/258260/PMC4061699.pdf?sequence=1&isAllowed=y
ER  -