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

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.

Fig. 1

Visual map of Egypt highlighting the governorates from which human or avian H5N1 sequences included in this study originated. 226 sequences were used in this study spanning 20 of the 27 Egyptian governorates.

Fig. 2

The 15 relevant and supported predictors are shown with inclusion probability, indicated by the blue bar, and supplemented by several BF values, indicated by vertical black lines. Also included is the posterior mean, represented by the blue dot, and 95% confidence interval of the GLM test coefficient.