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

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., FFP2, N95 and similar). All are essentially air filters worn on the face. Air filtration is relatively well understood, however, we have almost no direct evidence on the relative role played by aerosol particles of differing sizes in disease transmission. But if the virus concentration is assumed independent of aerosol particle size, then most virus will be in particles $\gtrsim 1\mathrm{ }$µm. We develop a model that predicts surgical masks are effective at reducing the risk of airborne transmission because the filtering material most surgical masks use is highly effective at filtering particles with diameters $\gtrsim 1\mathrm{ }$µm. However, surgical masks are significantly less effective than masks of FFP2, N95 and similar standards, mostly due to the poor fit of surgical masks. Earlier work found that $\sim 30\%$ of the air bypasses a surgical mask and is not filtered. This highlights the fact that standards for surgical masks do not specify how well the mask should fit, and so are not adequate for protection against COVID-19.

Copyright © 2022 American Association for Aerosol Research

Editor:

• Tiina Reponen

Acknowledgments

The authors wish to thank Kate Oliver for helpful discussions on textiles, Patrick Warren for guidance on LB simulations, Mahesh Bandi for making us aware of his ingenious use of a candyfloss maker, and Mike Allen, Jens Eggers, and Daan Frenkel for helpful discussions. We gratefully acknowledge Daniel Bonn, Patrick Charbonneau, K. K. Cheng, Rosie Dalzell, Tanniemola Liverpool, John Russo and Hajime Tanaka for providing valuable comments on this work. We would also like to thank the two reviewers for very useful and constructive comments on the submitted manuscript.

JFR, JPR and CPR wish to thank the Bristol Aerosol COVID-19 group for valuable discussions and feedback on this work. JFR would like to thank Kirsty Wynne for assistance in debugging the code used in the theoretical calculations.

We thank E. Chalvatzaki and M. Lazaridis for sharing their data on the probability of aerosol particle deposition in the respiratory tract.

Data availability statement

The data presented as results in this study were obtained using a computer code which implements the methods outlined in the text. This code is freely available at Robinson and Sear (2021).

Notes

1 The concentration of viable virus may be lower in oral-mode droplets: in influenza it is a factor of $10$ smaller (Milton et al. 2013), however even accounting for this a factor of 10 increase in diameter increases the expected number of virions by $\sim 100.$

2 The FFP2 standard (European Committee for Standardization 2019a) specifies the test aerosol has a (number) median diameter between 0.06 to 0.10 µm with a geometric standard deviation between 2.0 to 3.0. We therefore model the FFP2 test aerosol size distribution as a log-normal with a median of 0.08 µm and a geometric standard deviation of 2.5.

Additional information

Funding

The authors would like to thank Judith Mantell and Jean-Charles Eloi of the Wolfson Bioimaging Facility and the Chemical Imaging Facility (EPSRC Grant “Atoms to Applications”, EP/K035746/1), respectively, for the SEM images and assistance in this work.