A Bayesian spatio-temporal study of the association between meteorological factors and the spread of COVID-19.

ABSTRACT:

Background

The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population, transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as influenza some authors have suggested COVID-19 may become seasonal as immunity grows. Despite this, the effects of meteorological conditions on the spread of COVID-19 are poorly understood. Previous studies have produced contrasting results, due in part to limited and inconsistent study designs.

Methods

This study investigates the effects of meteorological conditions on COVID-19 infections in England using a Bayesian conditional auto-regressive spatio-temporal model. Our data consists of dail

SUBMITTER: Mullineaux JD 

PROVIDER: S-EPMC10668378 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature