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- Jeremy Howard
- fast.ai, San Francisco, CA 94105;
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- Austin Huang
- Warren Alpert School of Medicine, Brown University, Providence, RI 02903;
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- Zhiyuan Li
- Center for Quantitative Biology, Peking University, Beijing 100871, China;
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- Zeynep Tufekci
- School of Information, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599;
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- Vladimir Zdimal
- Institute of Chemical Process Fundamentals, Czech Academy of Sciences, CZ-165 02 Praha 6, Czech Republic;
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- Helene-Mari van der Westhuizen
- Department of Primary Health Care Sciences, University of Oxford, Oxford OX2 6GG, United Kingdom;
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- Arne von Delft
- TB Proof, Cape Town 7130, South Africa;
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- Amy Price
- Anesthesia Informatics and Media Lab, School of Medicine, Stanford University, Stanford, CA 94305;
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- Lex Fridman
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139;
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- Lei-Han Tang
- Department of Physics, Hong Kong Baptist University, Hong Kong SAR, China;
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- Viola Tang
- Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong SAR, China;
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- Gregory L. Watson
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA 90095;
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- Christina E. Bax
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104;
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- Reshama Shaikh
- Data Umbrella, New York, NY 10031;
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- Frederik Questier
- Teacher Education Department, Vrije Universiteit Brussel, 1050 Brussels, Belgium;
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- Danny Hernandez
- OpenAI, San Francisco, CA 94110;
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- Larry F. Chu
- Anesthesia Informatics and Media Lab, School of Medicine, Stanford University, Stanford, CA 94305;
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- Christina M. Ramirez
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA 90095;
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- Anne W. Rimoin
- Department of Epidemiology, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA 90095
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説明
<jats:p>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.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 118 (4), e2014564118-, 2021-01-11
Proceedings of the National Academy of Sciences