Mathematical models and statistical methods for understanding the ecological and evolutionary dynamics of viral infectious diseases

PI(s): Katia Koelle (Duke University)

Over my stay at NESCent, I have been working on the development of mathematical models and statistical approaches for viral infectious diseases. In addition to time series of disease incidence, we now commonly have access to viral sequences sampled over time. How can we use these genetic datasets to more fully understand a virus’s population dynamics and the drivers of these dynamics, using mechanistic population models? The focus on my research at NESCent has therefore been on developing epidemiological models with population dynamic parameters (e.g., transmission rates, recovery rates) that can be interfaced with genetic data, and on developing population genetic methods that enable this interface. Specifically, I have developed a general approach for deriving rates of coalescence for epidemiological models, which are necessary to compute the probability of a genealogy given population parameters. Together with Allen Rodrigo, I have also been working on developing models with time-varying parameters that can be interfaced with sequence data exhibiting time-varying substitution rates.