Abstract
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.
【저자키워드】 COVID-19, Space-time clusters, Spatial regression, Multiscale geographically weighted regression (mgwr), Clustering analysis, 【초록키워드】 Intervention, Local, Regression model, Cluster, epidemiological, predictor, Toronto, regression models, variable, spatiotemporal, effective, identify, were used, explain, affecting, driver, 【제목키워드】 Cluster, Canada, determinant, Toronto,