Here, we present an analysis of the H1N1pdm genetic data sampled over the initial stages in the epidemic. To infer phylodynamic spread in time and space we employ a recently developed Bayesian statistical inference framework (Lemey et al., in press). We model spatial diffusion as a continuous-time Markov chain process along time-measured genealogies. In this analysis, we consider 40 locations for which sequence data were available on 06-Aug-2009. The sampling time interval of the 242 sequences spans from 30-Mar-2009 to 12-Jul-2009. The Bayesian inference typically results in a posterior distribution of phylogenetic trees, each having an estimate of the epidemic locations at the ancestral nodes in the tree. We summarize these trees using the most representative clustering pattern and annotate these clusters with the most probable location states. We can visualize this information as tree that grows over time, seeding locations each time an ancestral node is inferred to exist at a different location. A Bayes factor test provides statistical support for epidemiological linkage throughout the evolutionary history. We demonstrate how our full probabilistic approach efficiently tracks an epidemic based on viral genetic data as it unfolds across the globe.
Swine-origin pandemic human influenza A virus (H1N1pdm) has spread rapidly around the world since its initial documentation in April 2009. Here we have updated initial estimates of the rate of molecular evolution and estimates of the time of origin of this virus in the human population using the large number of viral sequences made available as part of the public health response to this global pandemic. Currently sampled H1N1pdm sequences share a most recent common ancestor in the first 7 weeks of 2009 with the implication that the virus was transmitting cryptically for up to 3 months prior to recognition. A phylogenetic reconstruction of the data shows that the virus has been circling the globe extensively with multiple introductions into most geographical areas.