Spatial-Temporal Modeling of COVID-19 Mortality Risk in Toronto, Canada

In this talk, a spatial-temporal model for modeling georeferenced COVID -19 mortality data in Toronto, Canada will be presented. 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 as compared to the analysis based on the aggregated data in this application.

More information about this seminar series.  This is a virtual zoom seminar.  If you would like to attend, please email the organizers for connection details.  All times are given in the Atlantic timezone.