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.

翻译：探究COVID-19传播的时空变化及其潜在决定因素，可深化对疾病传播动态的理解。本研究旨在分析英格兰地区COVID-19感染率的时空传播特征，并考察其与社会经济、人口及环境风险因素的关联。基于2020年3月7日至2022年3月26日英格兰本土中层超级输出区(MSOA)层面的周报告病例数据，我们构建了贝叶斯层次时空模型以预测COVID-19感染率并探究影响因素。分析表明，该模型在预测精度上优于普通最小二乘法(OLS)和地理加权回归(GWR)模型。结果显示，COVID-19感染率的时空传播呈现异质性特征，感染率热点区域随时间推移呈现不一致的聚集模式。在所选风险因素中，家庭年收入、失业率、人口密度、加勒比裔人口比例、45-64岁成年人比例以及颗粒物浓度与COVID-19感染率呈正相关。研究结果可为政策制定者制定针对性的COVID-19预防控制公共卫生干预措施提供依据。