Zorro versus Covid-19: fighting the pandemic with face masks

  1. Olivier Damette

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Abstract

To confront the global Covid-19 pandemic and reduce the spread of the virus, we need to better understand if face mask use is effective to contain the outbreak and investigate the potential drivers in favor of mask adoption. It is highly questionable since there is no consensus among the general public despite official recommendations. For the first time, we conduct a panel econometric exercise to assess the dynamic impact of face mask use on both infected cases and fatalities at a global scale. We reveal a negative impact of mask wearing on fatality rates and on the Covid-19 number of infected cases. The delay of action varies from around 7 days to 28 days concerning infected cases but is more longer concerning fatalities. We also document the increasing adoption of mask use over time. We find that population density and pollution levels are significant determinants of heterogeneity regarding mask adoption across countries, while altruism, trust in government and demographics are not. Surprisingly, government effectiveness and income level (GDP) have an unexpected influence. However, strict government policies against Covid-19 have the most significant effect on mask use. Therefore, the most effective way of increasing the level of mask wearing is to enforce strict laws on the wearing of masks.

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    SciScore for 10.1101/2021.01.04.20237578: (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

    No key resources detected.

    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.

  2. Version published to 10.1101/2021.01.04.20237578v1 on medRxiv