ArticleSpatial and spatio-temporal epidemiology2022
Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada.
Article in Spatial and spatio-temporal epidemiology, 2022. 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
- Assessing the spatiotemporal dynamics and driving factors of human brucellosis in Northern Xinjiang, China (2015-2023).Tropical medicine and health · 2026Article
- Geographical Distribution, Spatial Directional Trends, and Spatio-Temporal Clusters of the First Rapid and Widespread Lumpy Skin Disease Outbreaks in Thailand.Transboundary and emerging diseases · 2025Article
- Spatial Autocorrelation of COVID-19 in Slovakia.Tropical medicine and infectious disease · 2023Article
- Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling.International journal of environmental research and public health · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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