Presentation: Bayesian Modeling of Partially Observed Epidemic Count Data
Candidate: Jonathan Fintzi, Graduate Student, UW Biostatistics
Committee Members: Vladimir Minin (co-chair), Jon Wakefield (co-chair), M. Elizabeth Halloran, Jim Hughes, Neil Abernethy (GSR)
Abstract: Epidemic count data reported by public health surveillance systems reflect the incidence or prevalence of an infectious agent as it spreads through a population, and are a primary source of information for informing response strategies and predicting how the outbreak is likely to spread. Incidence and prevalence counts are also often the only source of information about historical outbreaks, or outbreaks in resource limited settings, which are of interest for researchers seeking to develop an understanding of disease transmission during “peace time,” with an eye on preparing for future outbreaks. The absence of subject-level information, and the systematic underreporting of cases, makes it difficult to disentangle whether the data arose from a severe outbreak, observed with low fidelity, or a mild outbreak where most cases were detected. The magnitude of the missing data, and the dimensionality of the state space of the latent epidemic process, present challenges for fitting stochastic epidemic models. In this dissertation, we develop computational algorithms for fitting stochastic epidemic models to partially observed incidence and prevalence data in small and large population settings. Our algorithms are not specific to a particular set of model dynamics, but rather apply to a broad class of commonly used stochastic epidemic models, including models that allow for time-inhomogeneous transmission dynamics.