Patuxent Wildlife Research Center, U.S. Geological Survey , Beltsville, MD , USA ; Marine Estuarine Environmental Sciences, University of Maryland , College Park, MD , USA.
Marine Estuarine Environmental Sciences, University of Maryland , College Park, MD , USA ; University of Maryland School of Medicine , Baltimore, MD , USA.
Front Public Health. 2013 Aug 30;1:28. doi: 10.3389/fpubh.2013.00028. eCollection 2013.
Emergence of avian influenza viruses with high lethality to humans, such as the currently circulating highly pathogenic A(H5N1) (emerged in 1996) and A(H7N9) cause serious concern for the global economic and public health sectors. Understanding the spatial and temporal interface between wild and domestic populations, from which these viruses emerge, is fundamental to taking action. This information, however, is rarely considered in influenza risk models, partly due to a lack of data. We aim to identify areas of high transmission risk between domestic poultry and wild waterfowl in China, the epicenter of both viruses. Two levels of models were developed: one that predicts hotspots of novel virus emergence between domestic and wild birds, and one that incorporates H5N1 risk factors, for which input data exists. Models were produced at 1 and 30 km spatial resolution, and two temporal seasons. Patterns of risk varied between seasons with higher risk in the northeast, central-east, and western regions of China during spring and summer, and in the central and southeastern regions during winter. Monte-Carlo uncertainty analyses indicated varying levels of model confidence, with lowest errors in the densely populated regions of eastern and southern China. Applications and limitations of the models are discussed within.
高致病性禽流感病毒(如目前流行的 A(H5N1)(1996 年出现)和 A(H7N9))对人类具有高致死率,这类病毒的出现引起了人们对全球经济和公共卫生部门的严重关注。了解这些病毒出现的野生和家养种群之间的时空界面对于采取行动至关重要。然而,由于数据缺乏,这些信息很少在流感风险模型中得到考虑。我们旨在确定中国这两种病毒的发源地——家禽和野生水禽之间的高传播风险区域。我们开发了两个层次的模型:一个用于预测家养和野生鸟类之间新病毒出现的热点,另一个则纳入了存在输入数据的 H5N1 风险因素。模型以 1 公里和 30 公里的空间分辨率和两个时间季节进行制作。风险模式在季节之间有所不同,春季和夏季中国东北、中东部和西部地区以及冬季中国中部和东南部地区的风险较高。蒙特卡罗不确定性分析表明,模型的置信度水平不同,中国东部和南部人口稠密地区的误差最低。本文还讨论了模型的应用和局限性。