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Abstract:
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Phylodynamics is an emergent field that explores the joint dynamics of disease spread and evolution. Phylodynamic models utilize traditional epidemic surveillance data in addition to pathogen genetic sequence data. When epidemic and evolutionary processes occur on similar time scales, phylodynamic models can be used to improve our understanding of disease dynamics. Specifically, with phylodynamic models the routes of disease transmission can be inferred more accurately and disease model parameter estimation can be improved. A phylodynamic extension to the individual level models of infectious disease transmission of Deardon et al. (2010) is developed. Computational methods for stochastic simulation and for Bayesian inference are described. Simulation study results are presented, highlighting the particular advantages of phylodynamic individual level models. There are opportunities to better inform infectious disease control strategies through the use of these models, when, as is increasingly common, pathogen genetic sequence data are available.
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