Laidback Al
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PLoS Currents: Influenza. Accepted September 2, 2009
Reconstructing the initial global spread of a human influenza pandemic
By Philippe Lemey, Marc Suchard, and Andrew Rambaut
A Bayesian spatial-temporal model for the global spread of H1N1pdm
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.
article at: http://knol.google.com/k/reconstructing-the-initial-global-spread-of-a-human-influenza-pandemic#
Animation - see: http://www.youtube.com/watch?v=aYxf8xGSavU&feature=player_embedded
Reconstructing the initial global spread of a human influenza pandemic
By Philippe Lemey, Marc Suchard, and Andrew Rambaut
A Bayesian spatial-temporal model for the global spread of H1N1pdm
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.
article at: http://knol.google.com/k/reconstructing-the-initial-global-spread-of-a-human-influenza-pandemic#
Animation - see: http://www.youtube.com/watch?v=aYxf8xGSavU&feature=player_embedded