Seminar | September 7 | 4 p.m. | Evans Hall, Evans 1011.
Jason Xu, Duke University
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 observational data present significant challenges. In particular, the marginal likelihood of such stochastic processes conditioned on observed endpoints a notoriously difficult task. As a result, likelihood-based inference is typically considered intractable in the presence of missing data, and practitioners often resort to simulation methods or approximations. We discuss some recent contributions that enable "exact" 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 Markov chain Monte Carlo framework. Motivated both by count data from large outbreaks and high-resolution contact data from mobile health studies, we show how our data-augmented approach successfully learns the interpretable epidemic parameters and scales to handle large realistic data settings efficiently.
CA, 6502857071
Song Mei, songmei@berkeley.edu, 650-285-7071