A Bayesian mixed-effects model to evaluate the determinants of COVID-19 vaccine uptake in the US

TL;DR

This paper employs a Bayesian mixed-effects model to analyze socio-demographic and spatial factors influencing COVID-19 vaccine uptake across the US, providing probabilistic insights with uncertainty quantification.

Contribution

It introduces a Bayesian mixed-effects modeling approach to evaluate determinants of COVID-19 vaccine acceptance, incorporating socio-demographic and spatial variables.

Findings

Identifies key socio-demographic factors affecting vaccine uptake

Quantifies spatial variation in vaccine acceptance

Provides probabilistic estimates with uncertainty quantification

Abstract

The COVID-19 pandemic has adversely affected US public health, resulting in over a hundred million cases and more than one million deaths. Vaccination is the key intervention against the COVID-19 pandemic. Multiple COVID-19 vaccines are now available for human use. However, a number of factors, including socio-demographic variables, impact the uptake of COVID-19 vaccines. In this study, we apply a Bayesian mixed-effects model to assess different socio-demographic and spatial factors that influence the acceptance of COVID-19 vaccines in the US. The fitted mixed-effects model provides the probabilistic inference about the vaccine acceptance determinants with uncertainty quantification.