Investigating the implications of COVID-19 for the rural and remote population of Northern Ontario using a mathematical model

This article has been Reviewed by the following groups

Read the full article

Abstract

Background

COVID-19 has the potential to disproportionately affect the rural, remote, and Indigenous populations who typically have a worse health status and live in substandard housing, often with overcrowding. Our aim is to investigate the potential effect of COVID-19 on intensive care unit (ICU) resources and mortality in northwestern Ontario.

Methods

This study was conducted in northwestern Ontario which has a population of 230,000. A set of differential equations were used to represent a modified Susceptible-Infectious-Recovered (SIR) model with urban and rural hospital resources (i.e., ICU and hospital beds). Rural patients requiring ICU care flowed into the urban ICU. Sensitivity analyses were used to investigate the effect of poorer health status (i.e., increased hospital admission, ICU admission, and mortality) and overcrowding (i.e., increased contact rate) in the rural population as compared to the urban population. Physical distancing within the urban population was modelled as a decreased contact rate.

Results

At the highest contact rate, the peak in daily active cases, ICU bed requirements and mortality was higher and occurred earlier than lower contact rates. The urban population with a lower contact rate and baseline health status had a lower predicted prevalence of active cases and lower mortality than the rural population.

Interpretation

An increased contact rate and worse health status in the rural population will likely increase the required ICU resources and mortality as compared to the urban population. Rural populations will likely be affected disproportionately more than urban populations.

Article activity feed

  1. SciScore for 10.1101/2020.09.17.20196949: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For this study, the population in Thunder Bay will be referred to as the urban population and the remainder of the region will be referred to as the rural population.
    Thunder
    suggested: (Thunder, RRID:SCR_016556)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.