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

Combining phylogeography and spatial epidemiology to uncover predictors of H5N1 influenza A virus... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Virology Springer Journals

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

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Publisher
Springer Vienna
Copyright
Copyright © 2015 by Springer-Verlag Wien
Subject
Biomedicine; Virology; Medical Microbiology; Infectious Diseases
ISSN
0304-8608
eISSN
1432-8798
D.O.I.
10.1007/s00705-014-2262-5
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Archives of VirologySpringer Journals

Published: Jan 1, 2015

References

  • A One-Step RT-PCR Array for Detection and Differentiation of Zoonotic Influenza Viruses H5N1, H9N2, and H1N1
    Chen, Y; Liu, T; Cai, L; Du, H; Li, M
  • Phylodynamics of H5N1 avian influenza virus in Indonesia
    Lam, TT-Y; Hon, C-C; Lemey, P; Pybus, OG; Shi, M; Tun, HM; Li, JUN; Jiang, J; Holmes, EC; Leung, FC-C
  • Models for estimating Bayes factors with applications to phylogeny and tests of monophyly
    Suchard, MA; Weiss, RE; Sinsheimer, JS
  • Bayes factors
    Kass, RE; Raftery, AE

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