Comparing regional and provincial-wide COVID-19 models with physical distancing in British Columbia

Geoffrey McGregor; Jennifer Tippett; Andy T.S. Wan; Mengxiao Wang; Samuel W.K. Wong; Geoffrey McGregor; Jennifer Tippett; Andy T.S. Wan; Mengxiao Wang; Samuel W.K. Wong

doi:10.3934/math.2022376

AIMS Mathematics

2022, Volume 7, Issue 4: 6743-6778. doi: 10.3934/math.2022376

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Comparing regional and provincial-wide COVID-19 models with physical distancing in British Columbia

1.
Department of Mathematics and Statistics, University of Northern British Columbia, Prince George, Canada
2.
School of Health Sciences, University of Northern British Columbia, Prince George, Canada
3.
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada

Received: 19 April 2021 Revised: 07 November 2021 Accepted: 18 January 2022 Published: 25 January 2022
MSC : 62F15, 62P10, 92D30

We study the effects of physical distancing measures for the spread of COVID-19 in regional areas within British Columbia, using the reported cases of the five provincial Health Authorities. Building on the Bayesian epidemiological model of Anderson et al. ^[1], we propose a hierarchical regional Bayesian model with time-varying regional parameters between March to December of 2020. In the absence of COVID-19 variants and vaccinations during this period, we examine the regionalized basic reproduction number, modelled prevalence, relative reduction in contact due to physical distancing, and proportion of anticipated cases that have been tested and reported. We observe significant differences between the regional and provincial-wide models and demonstrate the hierarchical regional model can better estimate regional prevalence, especially in rural regions. These results indicate that it can be useful to apply similar regional models to other parts of Canada or other countries.
- compartmental models,
- epidemiology,
- COVID-19,
- ordinary differential equations,
- Bayesian hierarchical modelling,
- parameter estimation,
- Hamiltonian Monte Carlo
Citation: Geoffrey McGregor, Jennifer Tippett, Andy T.S. Wan, Mengxiao Wang, Samuel W.K. Wong. Comparing regional and provincial-wide COVID-19 models with physical distancing in British Columbia[J]. AIMS Mathematics, 2022, 7(4): 6743-6778. doi: 10.3934/math.2022376

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Abstract

We study the effects of physical distancing measures for the spread of COVID-19 in regional areas within British Columbia, using the reported cases of the five provincial Health Authorities. Building on the Bayesian epidemiological model of Anderson et al. ^[1], we propose a hierarchical regional Bayesian model with time-varying regional parameters between March to December of 2020. In the absence of COVID-19 variants and vaccinations during this period, we examine the regionalized basic reproduction number, modelled prevalence, relative reduction in contact due to physical distancing, and proportion of anticipated cases that have been tested and reported. We observe significant differences between the regional and provincial-wide models and demonstrate the hierarchical regional model can better estimate regional prevalence, especially in rural regions. These results indicate that it can be useful to apply similar regional models to other parts of Canada or other countries.

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