Sun Liqian, Ward Michael P, Li Rui, Xia Congcong, Lynn Henry, Hu Yi, Xiong Chenglong, Zhang Zhijie
Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China; Department of Hospital Infection Management, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, China.
Sydney School of Veterinary Science, The University of Sydney, NSW 2570, Australia.
Prev Vet Med. 2018 Apr 1;152:32-39. doi: 10.1016/j.prevetmed.2018.02.008. Epub 2018 Feb 12.
Highly pathogenic avian influenza (HPAI) H5N1 viruses have continuously circulated throughout much of the world since 2003, resulting in huge economic losses and major public health problems. Wild birds have played an important role in the spread of H5N1 HPAI. To understand its spatial distribution, H5N1 HPAI have been studied by many disciplines from different perspectives, but only one kind of disciplinary knowledge was involved, which has provided limited progress in understanding. Combining risk information from different disciplines based on knowledge fusion can provide more accurate and detailed information. In this study, local k function, phylogenetic tree analysis, and logistic spatial autoregressive models were used to explore the global spatial pattern of H5N1 HPAI based on outbreak data in wild birds, genetic sequences, and risk factors, respectively. On this basis, Dempster-Shafer (D-S) evidence theory was further applied to study the spatial distribution of H5N1 HPAI. We found D-S evidence theory was more robust and reliable than the other three methods, providing technical and methodological support for application to the research of other diseases. The shortest distance to wild bird migration routes, roads and railways, elevation, the normalized difference vegetation index (NDVI), land use and land cover (LULC) and infant mortality rates (IMR) were significantly associated with the occurrence of H5N1 HPAI. The high-risk areas were mainly located in Northern and Central Europe, the eastern Mediterranean, and East and Southeast Asia. High-risk clusters were closely related to the social, economic and ecological environment of the region. Locations where the potential transmission risk remains high should be prioritized for control efforts.
自2003年以来，高致病性禽流感（HPAI）H5N1病毒一直在世界大部分地区持续传播，造成了巨大的经济损失和重大的公共卫生问题。野生鸟类在H5N1 HPAI的传播中发挥了重要作用。为了解其空间分布，许多学科从不同角度对H5N1 HPAI进行了研究，但仅涉及一种学科知识，在理解方面进展有限。基于知识融合结合不同学科的风险信息可以提供更准确和详细的信息。在本研究中，分别使用局部k函数、系统发育树分析和逻辑空间自回归模型，基于野生鸟类的疫情数据、基因序列和风险因素来探索H5N1 HPAI的全球空间格局。在此基础上，进一步应用Dempster-Shafer（D-S）证据理论来研究H5N1 HPAI的空间分布。我们发现D-S证据理论比其他三种方法更稳健、可靠，为应用于其他疾病的研究提供了技术和方法支持。到野生鸟类迁徙路线、公路和铁路的最短距离、海拔、归一化植被指数（NDVI）、土地利用和土地覆盖（LULC）以及婴儿死亡率（IMR）与H5N1 HPAI的发生显著相关。高风险地区主要位于北欧和中欧、东地中海以及东亚和东南亚。高风险集群与该地区的社会、经济和生态环境密切相关。对于潜在传播风险仍然较高的地点，应优先进行防控。
