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A0804
Title: Likelihood-based inference for stochastic epidemic models via data augmentation Authors:  Jason Xu - Duke University (United States) [presenting]
Abstract: Stochastic epidemic models such as the Susceptible-Infectious-Removed (SIR) model are widely used to model the spread of disease at the population level, but fitting these models to data presents significant challenges. In particular, the marginal likelihood is typically considered intractable in the presence of missing data, as practitioners resort to simulation methods or approximations. We discuss some recent contributions that enable direct inference using the likelihood of observed data, focusing on a perspective that makes use of latent variables to explore configurations of the missing data within a Bayesian framework. Motivated both by count data from large outbreaks and high-resolution contact data from mobile health studies, we show how a data-augmented MCMC approach successfully learns the interpretable epidemic parameters and scales to handle realistic data settings efficiently