How effective are face coverings in reducing transmission of COVID-19?

  1. Joshua Robinson
  2. Ioatzin Rios de Anda
  3. Fergus Moore
  4. Florence Gregson
  5. Jonathan Reid
  6. Lewis Husain
  7. Richard Sear
  8. C. Royall

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

In the COVID–19 pandemic, billions are wearing face masks, in both health care settings and in public. Which type of mask we should wear in what situation, is therefore important. There are three basic types: cotton, surgical, and respirators (e.g. N95 and similar). All are essentially air filters worn on the face. Here we show that the underlying physics of air filtration ensures particles with diameters ≥ 1 to 3 µm are efficiently filtered out by all three types. However, for particles in the submicrometre range the efficiency depends on the material properties of the masks. For good quality cotton and surgical masks it is in the range 30 to 60%, while it is above 95% for respirators. So air filtration is relatively well understood, however, we have almost no direct evidence on the relative role played by aerosols of differing sizes in disease transmission. Without this data, selecting the correct mask will inevitably involve some guess work. If the virus concentration is assumed independent of aerosol size, then most virus will be in aerosols & 1µm and we expect both surgical masks and multi-layered cotton masks to be effective at reducing the risk of airborne transmission in most settings.

Article activity feed

  1. Version published to 10.22541/au.170665698.86454998/v1
  2. Version published to 10.1080/02786826.2022.2042467
  3. ScreenIT

    SciScore for 10.1101/2020.12.01.20241992: (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: Thank you for sharing your code.

    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.

  4. Version published to 10.1101/2020.12.01.20241992 on medRxiv