Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor.
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- Magee, Daniel (Contributor)
- Suchard, Marc A. (Author)
- Scotch, Matthew (Contributor)
- College of Health Solutions (Contributor)
- 2017-02-07
- Digital object identifier: 10.1371/ journal.pcbi.1005389
- Identifier TypeInternational standard serial numberIdentifier Value1553-734X
- Identifier TypeInternational standard serial numberIdentifier Value1553-7358
- The article is published at http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005389, opens in a new window
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Magee, D., Suchard, M. A., & Scotch, M. (2017). Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction. PLOS Computational Biology, 13(2). doi:10.1371/journal.pcbi.1005389