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Activity Number: 534 - Bayesian Inference with Complex Biomedical Systems
Type: Invited
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #314440
Title: Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model
Author(s): Tianjian Zhou*
Companies: University of Chicago

The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing pandemic affecting over 200 countries and regions. Inference about the transmission dynamics of COVID-19 can provide important insights into the speed of disease spread and the effects of mitigation policies. We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model and data of daily confirmed COVID-19 cases. In particular, we consider a probabilistic extension of the classical susceptible-infectious-recovered model, which takes into account undocumented infections and allows the epidemiological parameters to vary over time. We estimate the disease transmission rate via a Gaussian process prior, which captures nonlinear changes over time without the need of specific parametric assumptions. A parallel-tempering Markov chain Monte Carlo algorithm is used to efficiently sample from the highly correlated posterior space.

Authors who are presenting talks have a * after their name.

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