COVID-19 Disease Mapping Based on Poisson Kriging Model and Bayesian Spatial Statistical Model

Abstract

Since the start of the COVID-19 pandemic in December 2019, much research has been done to develop the spatial-temporal methods to track it and to predict the spread of the virus. In this thesis, a COVID-19 dataset containing the number of biweekly infected cases registered in Ontario since the start of the pandemic to the end of June 2021 is analysed using Bayesian Spatial-temporal models and Area-to-area (Area-to-point) Poisson Kriging models. With the Bayesian models, spatial-temporal effects on infected risk will be checked and ATP Poisson Kriging models will show how the virus spreads over the space and the spatial clustering feature. According to these models, a Shinyapp website https://mujingrui.shinyapps.io/covid19 is developed to present the results.