ArticleScientific reports2022
Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- 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
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Oscillating spatiotemporal patterns of COVID-19 in the United States.Scientific reports · 2024Article
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
- Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia.Scientific reports · 2024Article
- COVID-19 Pandemic Risk Assessment: Systematic Review.Risk management and healthcare policy · 2024Review
- Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China.Applied geography (Sevenoaks, England) · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.
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