ArticleScientific reports2024
A Bayesian spatio-temporal model of COVID-19 spread in England.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Bayesian spatio-temporal modeling of COVID-19 incidence in Algerian provinces using integrated nested Laplace approximations.Scientific reports · 2025Article
- Prevalence and trend of periodontal diseases in Iran from 1990 to 2017: a national study.BMC oral health · 2025Article
- SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Associations between social determinants of health and COVID-19 outcomes by variants of concern: a Bayesian spatiotemporal analysis.BMJ public health · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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 aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005-1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024-1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129-1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009-1.0036], percentage of adults aged 45-64 years old [RR = 1.0031, 95% CI 1.0024-1.0039], and particulate matter (
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