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Activity Number: 110 - Spatio-Temporal Modeling of the COVID-19 Pandemic: Statistics, Data, and the Stories They Tell
Type: Invited
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #314424
Title: Spatio-Temporal Bayesian Modeling of County-Level COVID-19 Incidence in South Carolina
Author(s): Andrew Booth Lawson* and Joanne Kim
Companies: Medical University of South Carolina and Medical University of South Carolina
Keywords: spatio-temporal; Bayesian; Covid-19; infection; SIR; Prediction

The Covid-19 pandemic has spread across the world during much of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) and state health department (cases) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Spatially-referenced Bayesian susceptible–infected-removed (SIR) models with different assumptions concerning transmission and county-neighborhood relations are examined. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Both crude daily and smoothed counts for a single time period are examined and one step prediction is provided.

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

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