An evidence review of face masks against COVID-19

Abstract

The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people (“source control”) with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

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Affiliated Institutions

• University of San Francisco US
• Hong Kong Baptist University HK
• University of California, Los Angeles US
• Stanford University US
• University of Cape Town ZA
• University of Hong Kong HK
• Massachusetts Institute of Technology US
• Vrije Universiteit Brussel BE
• Brown University US
• University of Oxford GB
• University of Pennsylvania US
• American Institute of Aeronautics and Astronautics US
• Beijing Computational Science Research Center CN
• University of North Carolina at Chapel Hill US
• Czech Academy of Sciences, Institute of Chemical Process Fundamentals CZ
• Hong Kong University of Science and Technology HK
• OpenAI (United States) US
• Peking University CN

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