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

TL;DR

This study compares regional and provincial COVID-19 models in British Columbia, showing that regional models provide better estimates of prevalence, especially in rural areas, by accounting for local differences in physical distancing effects.

Contribution

It introduces a hierarchical regional Bayesian model for COVID-19 spread, improving regional prevalence estimates over traditional provincial-wide models.

Findings

Regional models outperform provincial models in estimating prevalence.

Significant regional differences in contact reduction due to distancing.

Hierarchical regional model better captures rural COVID-19 dynamics.

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. (2020), 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.