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预测亚洲各地活禽市场甲型H7N9禽流感感染风险。

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

作者信息

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

机构信息

1] Biological Control and Spatial Ecology, Université Libre de Bruxelles, av FD Roosevelt 50, B-1050 Brussels, Belgium [2] Fonds National de la Recherche Scientifique, rue d'Egmont 5, B-1000 Brussels, Belgium.

Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Tinbergen Building, South Parks Road, Oxford OX1 3PS, UK.

出版信息

Nat Commun. 2014 Jun 17;5:4116. doi: 10.1038/ncomms5116.

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.

摘要

到目前为止,甲型H7N9禽流感病毒的两波疫情已影响中国。大多数人类病例都归因于在活禽市场接触家禽,大多数阳性分离株都是在这些市场采集的。潜在再次出现的疫情的潜在地理范围尚不清楚,与之相关的因素也不清楚。利用中国8943个活禽市场位置的新汇总数据集以及环境相关因素地图,我们开发了一个统计模型,该模型能够准确预测亚洲各地H7N9在市场感染的风险。活禽市场的局部密度是市场中H7N9感染风险的最重要预测因素,这突出了它们在H7N9空间流行病学中的关键作用,其他家禽、土地覆盖和人为预测变量也同样重要。识别亚洲H7N9感染适宜性高的地区,可增强我们进行生物监测和控制的能力,有助于限制这种重要疾病的传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df15/4082633/15d80df7efad/ncomms5116-f1.jpg

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