Available online 26 August 2022, 100534

Space-time clusters were observed between October 2020 and January 2021 (high-risk periods).

Lower level of education and higher concentration of immigrants in the neighbourhoods

were associated with higher incidences of COVID-19.

The geographically weighted regression model identified several locally varying

socioeconomic drivers of the COVID-19 incidences.

Early localization of clusters may help in planning for a locally adaptable protection measures to limit the spread of COVID-19.

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Space-time clusters

Spatial regression

Multiscale Geographically Weighted Regression (MGWR)

COVID-19

Clustering analysis

Data will be made available on request.

© 2022 Elsevier Ltd. All rights reserved.