Association of social distancing and face mask use with risk of COVID-19

被引：0

作者：

Sohee Kwon

Amit D. Joshi

Chun-Han Lo

David A. Drew

Long H. Nguyen

Chuan-Guo Guo

Wenjie Ma

Raaj S. Mehta

Fatma Mohamed Shebl

Erica T. Warner

Christina M. Astley

Jordi Merino

Benjamin Murray

Jonathan Wolf

Sebastien Ourselin

Claire J. Steves

Tim D. Spector

Jaime E. Hart

Mingyang Song

Trang VoPham

Andrew T. Chan

机构：

[1] Massachusetts General Hospital and Harvard Medical School,Clinical and Translational Epidemiology Unit

[2] Massachusetts General Hospital and Harvard Medical School,Division of Gastroenterology

[3] Harvard T.H. Chan School of Public Health,Department of Biostatistics

[4] University of Hong Kong,Department of Medicine, Li Ka Shing Faculty of Medicine

[5] Massachusetts General Hospital and Harvard Medical School,Medical Practice Evaluation Center

[6] Vulnerable Populations,Harvard/MGH Center on Genomics

[7] and Health Disparities,Division of Endocrinology and Computational Epidemiology

[8] Massachusetts General Hospital,Diabetes Unit, Center for Genomic Medicine

[9] Boston Children’s Hospital and Harvard Medical School,Programs in Metabolism and Medical & Population Genetics

[10] Broad Institute of MIT and Harvard,Department of Medicine

[11] Massachusetts General Hospital,School of Biomedical Engineering & Imaging Sciences

[12] Broad Institute of MIT and Harvard,Department of Twin Research and Genetic Epidemiology

[13] Harvard Medical School,Channing Division of Network Medicine, Department of Medicine

[14] King’s College London,Exposure, Epidemiology and Risk Program, Department of Environmental Health

[15] Zoe Limited,Department of Epidemiology

[16] King’s College London,Epidemiology Program, Division of Public Health Sciences

[17] Brigham and Hospital and Harvard Medical School,Department of Epidemiology

[18] Harvard T.H. Chan School of Public Health,Department of Immunology and Infectious Disease

[19] Harvard T.H. Chan School of Public Health,undefined

[20] Department of Nutrition,undefined

[21] Harvard T.H. Chan School of Public Health,undefined

[22] Fred Hutchinson Cancer Research Center,undefined

[23] University of Washington School of Public Health,undefined

[24] Harvard T.H. Chan School of Public Health,undefined

来源：

Nature Communications | / 12卷

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摘要：

Given the continued burden of COVID-19 worldwide, there is a high unmet need for data on the effect of social distancing and face mask use to mitigate the risk of COVID-19. We examined the association of community-level social distancing measures and individual face mask use with risk of predicted COVID-19 in a large prospective U.S. cohort study of 198,077 participants. Individuals living in communities with the greatest social distancing had a 31% lower risk of predicted COVID-19 compared with those living in communities with poor social distancing. Self-reported ‘always’ use of face mask was associated with a 62% reduced risk of predicted COVID-19 even among individuals living in a community with poor social distancing. These findings provide support for the efficacy of mask-wearing even in settings of poor social distancing in reducing COVID-19 transmission. Despite mass vaccination campaigns in many parts of the world, continued efforts at social distancing and face mask use remain critically important in reducing the spread of COVID-19.

引用

共 50 条

[1] Association of social distancing and face mask use with risk of COVID-19
Kwon, Sohee
Joshi, Amit D.
Lo, Chun-Han
Drew, David A.
Nguyen, Long H.
Guo, Chuan-Guo
Ma, Wenjie
Mehta, Raaj S.
Shebl, Fatma Mohamed
Warner, Erica T.
Astley, Christina M.
Merino, Jordi
Murray, Benjamin
Wolf, Jonathan
Ourselin, Sebastien
Steves, Claire J.
Spector, Tim D.
Hart, Jaime E.
Song, Mingyang
VoPham, Trang
Chan, Andrew T.
NATURE COMMUNICATIONS, 2021, 12 (01)
[2] Risk Compensation During COVID-19: The Impact of Face Mask Usage on Social Distancing
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[3] A mathematical framework for estimating risk of airborne transmission of COVID-19 with application to face mask use and social distancing
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[10] A Method to Estimate COVID-19 Contamination Risk Based on Social Distancing and Face Mask Detection Using Convolutional Neural Networks
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