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Activity Number: 211 - Disease Prediction
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #317824
Title: BayesSMEG: Bayesian Segmentation Modeling for Epidemic Growth
Author(s): Tejasv Bedi* and Qiwei Li
Companies: University of Texas at Dallas and The University of Texas at Dallas
Keywords: COVID-19; Change-point detection; stochastic growth models; Reversible Jump MCMC

Forecasting of COVID-19 daily report data has been one of the several challenges posed to the governments and health sectors on a global scale. Several deterministic and stochastic epidemiological models, including growth and compartmental models, have been proposed in the literature. These models assume that an epidemic would last over a short duration and the observed cases/deaths would attain a single peak. However, COVID-19 has extended over a longer duration than expected. Moreover, time-varying disease transmission rates due to government interventions such as business/school closures and openings have resulted in the observed data being multi-modal. This work proposes stochastic epidemiological models under a unified Bayesian framework augmented by a change-point detection mechanism to account for multiple peaks. We use the MCMC algorithms to sample the posterior distributions of model parameters for a fixed number of change-points. Additionally, we also develop a reversible jump MCMC algorithm to estimate the number of change-points and the resulting epidemiological parameters for allowing model parameter spaces to have a variable dimensionality.

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

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