Spatio-temporal spread of COVID-19 and its associations with socioeconomic, demographic and environmental factors in England: A Bayesian hierarchical spatio-temporal model

TL;DR

This study employs a Bayesian hierarchical spatio-temporal model to analyze COVID-19 spread in England, revealing key socioeconomic, demographic, and environmental factors influencing infection rates and aiding targeted public health strategies.

Contribution

The paper introduces a novel Bayesian hierarchical model that outperforms traditional methods in predicting COVID-19 spread and identifies significant local risk factors in England.

Findings

Model outperforms OLS and GWR in prediction accuracy.

COVID-19 spread is heterogeneous with fluctuating hotspots.

Socioeconomic and environmental factors are positively associated with infection rates.

Abstract

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aims to investigate the spatio-temporal spread of COVID-19 infection rate in England, and examine its associations with socioeconomic, demographic and environmental risk factors. Using weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England, we developed a Bayesian hierarchical spatio-temporal model to predict the COVID-19 infection rates and investigate the influencing factors. The analysis showed that our model outperformed the ordinary least squares (OLS) and geographically weighted regression (GWR) models in terms of prediction accuracy. The results showed that the spread of COVID-19 infection rates over space and time was heterogeneous. Hotspots of infection rate exhibited inconsistent clustered patterns over time. Among the selected risk factors, the annual household income, unemployment rate, population density, percentage of Caribbean population, percentage of adults aged 45-64 years old, and particulate matter concentrations were found to be positively associated with the COVID-19 infection rate. The findings assist policymakers in developing tailored public health interventions for COVID-19 prevention and control.