Likelihood-based Inference for Stochastic Epidemic Models via Data Augmentation

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 present significant challenges. In particular, the marginal likelihood is typically considered intractable in the presence of missing data. We will overview some recent contributions that enable direct inference using the marginal likelihood, as well as a perspective that makes use of latent variables to leverage the simpler complete-data likelihood in 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 interpretable epidemic parameters and scales to handle realistic data settings.

This seminar will be hybrid so you can attend in person or on zoom.

Seminars will be held weekly on Fridays 3:30 - 4:30 pm on Zoom. After the seminar, there will be a (virtual) meet-and-greet session to interact with the speaker. Please use the chat on Zoom to ask questions to the speaker. A moderator will collect questions throughout the talk and ask the speaker at appropriate times.