September 2009 | Philippe Lemey, Andrew Rambaut, Alexei J. Drummond, Marc A. Suchard
The paper introduces a Bayesian framework for inferring, visualizing, and testing phylogeographic history, which is crucial for understanding the spatial and temporal dynamics of viruses. The authors develop a model that integrates probabilistic methods to reconstruct viral dispersal patterns while accounting for phylogenetic uncertainty. By implementing character mapping in Bayesian software that samples time-scaled phylogenies, the framework enables the reconstruction of timed viral dispersal patterns. The standard Markov model inference is extended with a stochastic search variable selection procedure to identify the most parsimonious descriptions of the diffusion process. The authors also propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about spatial dynamics. The framework is applied to Avian Influenza A H5N1 and rabies viruses in West African dog populations, demonstrating its ability to infer spatial and temporal processes from gene sequences. The results highlight the importance of continuous epidemic cycles in maintaining endemic maintenance through virus diffusion. The Bayesian phylogeographic framework is concluded to be a valuable tool in molecular epidemiology, capable of generalizing to infer biogeography from genetic data for various organisms.The paper introduces a Bayesian framework for inferring, visualizing, and testing phylogeographic history, which is crucial for understanding the spatial and temporal dynamics of viruses. The authors develop a model that integrates probabilistic methods to reconstruct viral dispersal patterns while accounting for phylogenetic uncertainty. By implementing character mapping in Bayesian software that samples time-scaled phylogenies, the framework enables the reconstruction of timed viral dispersal patterns. The standard Markov model inference is extended with a stochastic search variable selection procedure to identify the most parsimonious descriptions of the diffusion process. The authors also propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about spatial dynamics. The framework is applied to Avian Influenza A H5N1 and rabies viruses in West African dog populations, demonstrating its ability to infer spatial and temporal processes from gene sequences. The results highlight the importance of continuous epidemic cycles in maintaining endemic maintenance through virus diffusion. The Bayesian phylogeographic framework is concluded to be a valuable tool in molecular epidemiology, capable of generalizing to infer biogeography from genetic data for various organisms.