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

Mayer Alvo () and Jingrui Mu
Additional contact information
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

Mathematics, 2023, vol. 11, issue 6, 1-13

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.

Keywords: bayesian spatial–temporal models; area-to-point and area-to-area Poisson Kriging; integrated nested laplace approximation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/6/1359/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/6/1359/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:6:p:1359-:d:1094000

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().