Face Masks Against COVID-19: An Evidence Review

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The science around the use of masks by the general public to impede COVID-19 transmission is advancing rapidly. Policymakers need guidance on how masks should be used by the general population to combat the COVID-19 pandemic. Here, we synthesize the relevant literature to inform multiple areas:
1) transmission characteristics of COVID-19,
2) filtering characteristics and efficacy of masks,
3) estimated population impacts of widespread community mask use, and
4) sociological considerations for policies concerning mask-wearing.
A primary route of transmission of COVID-19 is likely via small respiratory droplets and is known to be transmissible from presymptomatic and asymptomatic individuals. Reducing disease spread requires two things: first, limit contacts of infected individuals via physical distancing and contact tracing with appropriate quarantine, and second, reduce the transmission probability per contact by wearing masks in public, among other measures.

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Author(s)

  • Jeremy Howard, Data Institute, University of San Francisco, 101 Howard St, San Francisco, CA 94105, US;
  • Austin Huang, Warren Alpert School of Medicine, Brown University, 222 Richmond St, Providence, RI 02903;

Collaborator(s)

  • Zhiyuan Li, Center for Quantitative Biology, Peking University, Haidian, Beijing 100871, China;
  • Zeynep Tufekci, University of North Carolina at Chapel Hill;
  • Zdimal Vladimir, Institute of Chemical Process Fundamentals, Czech Academy of Sciences, Rozvojová 135, CZ-165 02 Praha 6, Czech Republic;
  • Helene-Mari van der Westhuizen, Department of Primary Health Care Sciences, Woodstock Road, University of Oxford, OX2 6GG, United Kingdom; TB Proof, Cape Town, South Africa;
  • Arne von Delft, School of Public Health and Family Medicine, University of Cape Town, Anzio Road, Observatory, 7925, South Africa ; TB Proof, Cape Town, South Africa;
  • Amy Price, School of Medicine Anesthesia Informatics and Media (AIM) Lab, Stanford University, 300 Pasteur Drive, Grant S268C, Stanford, CA 94305;
  • Lex Fridman, Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139;
  • Lei-Han Tang, Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China ; Complex Systems Division, Beijing Computational Science Research Center, Haidian, Beijing 100193, China;
  • Viola Tang, Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China;
  • Gregory L. Watson, Department of Biostatistics, UCLA Fielding School of Public Health, 650 Charles E Young Drive, Los Angeles, CA 90095;
  • Christina E. Bax, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104;
  • Reshama Shaikh, Data Umbrella, 345 West 145th St, New York, NY 10031;
  • Frederik Questier, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium;
  • Danny Hernandez, OpenAI, 3180 18th St, San Francisco, CA 94110;
  • Larry F. Chu, School of Medicine Anesthesia Informatics and Media (AIM) Lab, Stanford University, 300 Pasteur Drive, Grant S268C, Stanford, CA 94305;
  • Christina M. Ramirez, Department of Biostatistics, UCLA Fielding School of Public Health, 650 Charles E Young Drive, Los Angeles, CA 90095;
  • Anne W. Rimoin, Department of Epidemiology, UCLA Fielding School of Public Health, 650 Charles E Young Drive, Los Angeles, CA 90095;

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