Inference for nonlinear epidemiological models using genealogies and time series.
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Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.
Disease Transmission, Infectious
Monte Carlo Method
Published Version (Please cite this version)10.1371/journal.pcbi.1002136
Publication InfoRasmussen, David A; Ratmann, Oliver; & Koelle, Katia (2011). Inference for nonlinear epidemiological models using genealogies and time series. PLoS Comput Biol, 7(8). pp. e1002136. 10.1371/journal.pcbi.1002136. Retrieved from https://hdl.handle.net/10161/5952.
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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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