The majority of the variation in COVID-19 rates between nations is explained by median age, obesity rate, and island status

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Since the World Health Organization declared SARS-CoV-2 to be a global pandemic on March 11, 2020, nearly every nation on earth has reported infections. Incidence and prevalence of COVID-19 case rates have demonstrated extreme geospatial and temporal variability across the globe. The outbreaks in some countries are extreme and devastating, while other countries face outbreaks that are relatively minor. The causes of these differences between nations remain poorly understood, and identifying the factors that underlie this variation is critical to understand the dynamics of this disease in order to better respond to this and future pandemics.

Here, we examine four factors that we anticipated would explain much of the variation in COVID-19 rates between nations: median age, obesity rate, island status, and strength of border closure measures. Clinical evidence suggests that age and obesity increase both the likelihood of infection and transmission in individual patients, which make them plausible demographic factors. The third factor, whether or not each country is an island nation, was selected because the geographical isolation of islands is expected to influence COVID-19 transmission. The fourth factor of border closure was selected because of its anticipated interaction with island nation status.

Together, these four variables are able to explain a majority of the international variance in COVID-19 case rates. Using a dataset of 190 countries, simple modeling based on these four factors and their interactions explains more than 70% of the total variance between countries. With additional covariates, more complex modeling and higher-order interactions explains more than 80% of the variance. These novel findings offer a solution to explain the unusual global variation of COVID-19 that has remained largely elusive throughout the pandemic.

Article activity feed

  1. SciScore for 10.1101/2021.06.14.21258886: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: Thank you for sharing your data.


    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.

    Results from scite Reference Check: We found no unreliable references.


    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.