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

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  • Mayer Alvo

    (Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Jingrui Mu

    (Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

Abstract

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

Suggested Citation

  • Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1359-:d:1094000
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    References listed on IDEAS

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    1. I Gede Nyoman Mindra Jaya & Farah Kristiani & Yudhie Andriyana & Anna Chadidjah, 2024. "Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping," Mathematics, MDPI, vol. 12(3), pages 1-16, January.

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