An evidence review of face masks against COVID-19

Jazyk angličtina Země Spojené státy americké Médium print

Typ dokumentu časopisecké články, přehledy

Perzistentní odkaz   https://www.medvik.cz/link/pmid33431650

Grantová podpora
TL1 TR001880 NCATS NIH HHS - United States

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.

Anesthesia Informatics and Media Lab School of Medicine Stanford University Stanford CA 94305

Center for Quantitative Biology Peking University Beijing 100871 China

Complex Systems Division Beijing Computational Science Research Center Beijing 100193 China

Data Institute University of San Francisco San Francisco CA 94105

Data Umbrella New York NY 10031

Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA 02139

Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles CA 90095

Department of Epidemiology Jonathan and Karin Fielding School of Public Health University of California Los Angeles CA 90095

Department of Information Systems Business Statistics and Operations Management Hong Kong University of Science and Technology Hong Kong SAR China

Department of Physics Hong Kong Baptist University Hong Kong SAR China

Department of Primary Health Care Sciences University of Oxford Oxford OX2 6GG United Kingdom

fast ai San Francisco CA 94105;

Institute of Chemical Process Fundamentals Czech Academy of Sciences CZ 165 02 Praha 6 Czech Republic

OpenAI San Francisco CA 94110

Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104

School of Information University of North Carolina at Chapel Hill Chapel Hill NC 27599

School of Public Health and Family Medicine University of Cape Town Cape Town 7925 South Africa

TB Proof Cape Town 7130 South Africa

Teacher Education Department Vrije Universiteit Brussel 1050 Brussels Belgium

Warren Alpert School of Medicine Brown University Providence RI 02903

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