Combining phylogeography and spatial epidemiology to uncover predictors of H5N1 influenza A virus diffusion

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• 作者：Daniel Magee (1) (2)
  Rachel Beard (1) (2)
  Marc A. Suchard (3) (5)
  Philippe Lemey (4)
  Matthew Scotch (1) (2)
  
  1. Department of Biomedical Informatics ; Arizona State University ; 13212 E. Shea Blvd. ; Scottsdale ; 85259 ; AZ ; USA
  2. Center for Environmental Security and Security and Defense Systems Initiative ; Biodesign Institute ; Arizona State University ; P.O. Box 875904 ; Tempe ; 85287-5904 ; AZ ; USA
  3. Departments of Biomathematics and Human Genetics ; David Geffen School of Medicine ; University of California ; Los Angeles ; Los Angeles ; CA ; USA
  5. Department of Biostatistics ; UCLA Fielding School of Public Health ; University of California ; Los Angeles ; Los Angeles ; CA ; USA
  4. Department of Microbiology and Immunology ; KU Leuven ; Leuven ; Belgium
  
• 刊名：Archives of Virology
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：160
• 期：1
• 页码：215-224
• 全文大小：647 KB
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• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Virology
  Medical Microbiology
  Infectious Diseases
  
• 出版者：Springer Wien
• ISSN：1432-8798

文摘

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.