COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models

Since December 2019, many statistical spatial–temporal methods have been developed to track and predict the spread of COVID-19 pandemic. In this paper, we analyzed dataset which includes number biweekly infected cases registered in Ontario from March 2020 end June 2021. We made use Bayesian Spatial–temporal models Area-to-point (ATP) Area-to-area (ATA) Poisson Kriging models. With models, effects government intervention on infection risk are considered while ATP used display pandemic over space.