Bayesian Space-time SIR modeling of Covid-19 in two US states during the 2020-2021 pandemic

TL;DR

This paper develops Bayesian space-time SIR models to analyze Covid-19 waves in two US states, highlighting the importance of deprivation, neighborhood effects, and mobility data in understanding transmission dynamics.

Contribution

It introduces a Bayesian SIR modeling framework incorporating deprivation, neighborhood effects, and mobility data at the county level for Covid-19 in the US.

Findings

Models with deprivation predictors improve fit.

Neighborhood effects are significant in transmission.

Google mobility data enhances model explanation.

Abstract

This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in two contrasting US states during 2020-2021. A variety of models are evaluated at the county level for goodness-of-fit and an assessment of confounding predictors is also made. It is found that models with three deprivation predictors and neighborhood effects are important. In addition the work index from Google mobility was also found to provide increased explanation of the transmission dynamic.