Phylodynamic inference for structured epidemiological models.
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Coalescent theory is routinely used to estimate past population dynamics and demographic parameters from genealogies. While early work in coalescent theory only considered simple demographic models, advances in theory have allowed for increasingly complex demographic scenarios to be considered. The success of this approach has lead to coalescent-based inference methods being applied to populations with rapidly changing population dynamics, including pathogens like RNA viruses. However, fitting epidemiological models to genealogies via coalescent models remains a challenging task, because pathogen populations often exhibit complex, nonlinear dynamics and are structured by multiple factors. Moreover, it often becomes necessary to consider stochastic variation in population dynamics when fitting such complex models to real data. Using recently developed structured coalescent models that accommodate complex population dynamics and population structure, we develop a statistical framework for fitting stochastic epidemiological models to genealogies. By combining particle filtering methods with Bayesian Markov chain Monte Carlo methods, we are able to fit a wide class of stochastic, nonlinear epidemiological models with different forms of population structure to genealogies. We demonstrate our framework using two structured epidemiological models: a model with disease progression between multiple stages of infection and a two-population model reflecting spatial structure. We apply the multi-stage model to HIV genealogies and show that the proposed method can be used to estimate the stage-specific transmission rates and prevalence of HIV. Finally, using the two-population model we explore how much information about population structure is contained in genealogies and what sample sizes are necessary to reliably infer parameters like migration rates.
Monte Carlo Method
Published Version (Please cite this version)10.1371/journal.pcbi.1003570
Publication InfoRasmussen, David A; Volz, Erik M; & Koelle, Katia (2014). Phylodynamic inference for structured epidemiological models. PLoS Comput Biol, 10(4). pp. e1003570. 10.1371/journal.pcbi.1003570. Retrieved from https://hdl.handle.net/10161/10612.
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Associate Professor in the Department of Biology
My research focuses on the ecology and evolution of infectious diseases. I use a combination of mathematical and statistical approaches to understand the processes driving the disease dynamics of pathogens. My interests include developing models to improve our understanding of how immune escape and other viral phenotypes impact the ecological dynamics of RNA viruses, and, in turn, how these ecological dynamics create selection pressures on viral pathogens. Additional interests include developing
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