Variegated spatial–temporal landscape of COVID-19 infection in England: findings from spatially filtered multilevel models

Abstract Background Although there are empirical studies examining COVID-19 infection from a spatial perspective, majority of them focused on the USA and China, and there has been a lacuna of systematic research to unpack the spatial landscape of infection in the UK and its related factors. Methods England’s spatial–temporal patterns of COVID-19 infection levels in 2020 were examined via spatial clustering analysis. Spatially filtered multilevel models (SFMLM), capturing both hierarchical and horizontal spatial interactive effects, were applied to identify how different demographic, socio-economic, built environment and spatial contextual variables were associated with varied infection levels over the two waves in 2020. Results The fragmented spatial distribution of COVID incidence in the first wave has made a rural–urban shift and resulted in a clearer north–south divide in England throughout 2020. The SFMLM results do not only identify the association between variables at different spatial scales with COVID-19 infection level but also highlight the increasing importance of spatial-dependent effect of the pandemic over time and that the locational spatial contexts also help explain variations in infection rates.