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Abstract:
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In this talk, a spatial-temporal model for modeling georeferenced COVID -19 mortality data in Toronto, Canada will be presented. A range of individual-level and neighborhood-level factors, as well as spatial-temporal terms, are incorporated into the model. The non-linear and interactive effects of the neighborhood-level factors, i.e., population density and the average of income, are modeled as a two-dimensional spline smoother. Tensor product smoother is used for modeling the space-time interaction. By fitting this model, the spatial terms can provide insight into detecting high-risk areas not explained by the covariates. The predictive accuracy of the proposed model is evaluated based on in-sample and out-of-sample predictive checking, and the findings showed that the model has high predictive power for predicting mortality risk among positive COVID -19 cases in the studied population.
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