Space-Time Covid-19 Bayesian SIR modeling in South Carolina

TL;DR

This paper develops a Bayesian SIR model incorporating space and time to analyze and predict Covid-19 spread in South Carolina, considering asymptomatic transmission and different time periods.

Contribution

It introduces a novel space-time Bayesian SIR modeling approach for Covid-19, accounting for asymptomatic transmission and providing short-term predictions.

Findings

Model captures Covid-19 dynamics in South Carolina.

Asymptomatic transmission significantly impacts spread.

Effective short-term predictions achieved.

Abstract

The Covid-19 pandemic has spread across the world since the beginning 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) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided.