Magee Daniel, Beard Rachel, Suchard Marc A, Lemey Philippe, Scotch Matthew
Department of Biomedical Informatics, Arizona State University, 13212 E. Shea Blvd., Scottsdale, 85259, AZ, USA.
Arch Virol. 2015 Jan;160(1):215-24. doi: 10.1007/s00705-014-2262-5. Epub 2014 Oct 30.
Emerging and re-emerging infectious diseases of zoonotic origin like highly pathogenic avian influenza pose a significant threat to human and animal health due to their elevated transmissibility. Identifying the drivers of such viruses is challenging, and estimation of spatial diffusion is complicated by the fact that the variability of viral spread from locations could be caused by a complex array of unknown factors. Several techniques exist to help identify these drivers, including bioinformatics, phylogeography, and spatial epidemiology, but these methods are generally evaluated separately and do not consider the complementary nature of each other. Here, we studied an approach that integrates these techniques and identifies the most important drivers of viral spread by focusing on H5N1 influenza A virus in Egypt because of its recent emergence as an epicenter for the disease. We used a Bayesian phylogeographic generalized linear model (GLM) to reconstruct spatiotemporal patterns of viral diffusion while simultaneously assessing the impact of factors contributing to transmission. We also calculated the cross-species transmission rates among hosts in order to identify the species driving transmission. The densities of both human and avian species were supported contributors, along with latitude, longitude, elevation, and several meteorological variables. Also supported was the presence of a genetic motif found near the hemagglutinin cleavage site. Various genetic, geographic, demographic, and environmental predictors each play a role in H1N1 diffusion. Further development and expansion of phylogeographic GLMs such as this will enable health agencies to identify variables that can curb virus diffusion and reduce morbidity and mortality.
诸如高致病性禽流感这类新出现和重新出现的人畜共患传染病，因其高传播性对人类和动物健康构成重大威胁。识别此类病毒的驱动因素具有挑战性，而且由于病毒从各地点传播的变异性可能由一系列复杂的未知因素导致，空间扩散的估计也很复杂。有几种技术可帮助识别这些驱动因素，包括生物信息学、系统发育地理学和空间流行病学，但这些方法通常是分别评估的，没有考虑到彼此的互补性。在此，我们研究了一种整合这些技术的方法，通过聚焦埃及的甲型H5N1流感病毒来识别病毒传播的最重要驱动因素，因为埃及最近已成为该疾病的一个中心。我们使用贝叶斯系统发育地理学广义线性模型（GLM）来重建病毒扩散的时空模式，同时评估促成传播的因素的影响。我们还计算了宿主之间的跨物种传播率，以识别推动传播的物种。人类和禽类物种的密度、纬度、经度、海拔以及几个气象变量均被证实是促成因素。血凝素裂解位点附近发现的一种基因基序的存在也得到了证实。各种遗传、地理、人口和环境预测因素在H1N1扩散中均发挥作用。像这样的系统发育地理学GLM的进一步发展和扩展将使卫生机构能够识别可抑制病毒扩散并降低发病率和死亡率的变量。
