ArticleSpatial statistics2022
Spatial-temporal generalized additive model for modeling COVID-19 mortality risk in Toronto, Canada.
Article in Spatial statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Measuring the impact of climate and air quality on COVID-19 transmission dynamics: a tale of two cities in pandemic preparedness.BMC public health · 2026Article
- CENPM as a biomarker and therapeutic target for lymph node metastasis in thyroid carcinoma.Frontiers in genetics · 2026Article
- The effects of deprivation, age, and regional differences in COVID-19 mortality from 2020 to 2022: a retrospective analysis of public provincial data.BMC public health · 2025Article
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
- Article
- Bayesian disease mapping: Past, present, and future.Spatial statistics · 2022Article
- Effect of socioeconomic factors during the early COVID-19 pandemic: a spatial analysis.BMC public health · 2022Article
- Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling.Scientific reports · 2022Article
- Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe.Spatial statistics · 2022Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
This article presents a spatial-temporal generalized additive model for modeling geo-referenced COVID-19 mortality data in Toronto, Canada. A range of factors and spatial-temporal terms are incorporated into the model. The non-linear and interactive effects of the neighborhood-level factors, i.e., population density and average of income, are modeled as a two-dimensional spline smoother. The change of spatial pattern over time is modeled as a three-dimensional tensor product smoother. By fitting this model, the space-time effect can uncover the underlying spatial-temporal pattern that is not explained by the covariates. The performance of the modeling method based on the individual data is also compared to the modeling methods based on the aggregated data in terms of in-sample and out-of-sample predictive checking. The results suggest that the individual-level based analysis provided a better overall model fit and higher predictive accuracy for detecting epidemic peaks in this application as compared to the analysis based on the aggregated data.
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