5
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Abstract

          Estimating the effectiveness of COVID-19 control measures requires large prospective data including symptoms and personal risk factors. Here, the authors used data from smartphone-based application and found that individual face mask use was associated with a 64% reduced risk of COVID-19 symptoms.

          Related collections

          Most cited references21

          • Record: found
          • Abstract: found
          • Article: not found

          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found

            Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis

            Summary Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread person-to-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye protection on virus transmission in health-care and non-health-care (eg, community) settings. Methods We did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-to-person virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects meta-regressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047. Findings Our search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25 697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m (n=10 736, pooled adjusted odds ratio [aOR] 0·18, 95% CI 0·09 to 0·38; risk difference [RD] −10·2%, 95% CI −11·5 to −7·5; moderate certainty); protection was increased as distance was lengthened (change in relative risk [RR] 2·02 per m; p interaction=0·041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0·15, 95% CI 0·07 to 0·34, RD −14·3%, −15·9 to −10·7; low certainty), with stronger associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12–16-layer cotton masks; p interaction=0·090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0·22, 95% CI 0·12 to 0·39, RD −10·6%, 95% CI −12·5 to −7·7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings. Interpretation The findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance. Funding World Health Organization.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Real-time tracking of self-reported symptoms to predict potential COVID-19

              A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.
                Bookmark

                Author and article information

                Contributors
                achan@mgh.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 June 2021
                18 June 2021
                2021
                : 12
                : 3737
                Affiliations
                [1 ]GRID grid.38142.3c, ISNI 000000041936754X, Clinical and Translational Epidemiology Unit, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Gastroenterology, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [4 ]GRID grid.194645.b, ISNI 0000000121742757, Department of Medicine, Li Ka Shing Faculty of Medicine, , University of Hong Kong, ; Hong Kong, China
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Medical Practice Evaluation Center, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA
                [6 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Harvard/MGH Center on Genomics, , Vulnerable Populations, and Health Disparities, Massachusetts General Hospital, ; Boston, MA USA
                [7 ]GRID grid.2515.3, ISNI 0000 0004 0378 8438, Division of Endocrinology and Computational Epidemiology, , Boston Children’s Hospital and Harvard Medical School, ; Boston, MA USA
                [8 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [9 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Diabetes Unit, Center for Genomic Medicine, , Massachusetts General Hospital, ; Boston, MA USA
                [10 ]GRID grid.66859.34, Programs in Metabolism and Medical & Population Genetics, , Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [11 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Medicine, , Harvard Medical School, ; Boston, MA USA
                [12 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, School of Biomedical Engineering & Imaging Sciences, , King’s College London, ; London, UK
                [13 ]Zoe Limited, London, UK
                [14 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Department of Twin Research and Genetic Epidemiology, , King’s College London, ; London, UK
                [15 ]GRID grid.38142.3c, ISNI 000000041936754X, Channing Division of Network Medicine, Department of Medicine, , Brigham and Hospital and Harvard Medical School, ; Boston, MA USA
                [16 ]GRID grid.38142.3c, ISNI 000000041936754X, Exposure, Epidemiology and Risk Program, Department of Environmental Health, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [17 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Epidemiology, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [18 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Nutrition, Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [19 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Epidemiology Program, Division of Public Health Sciences, , Fred Hutchinson Cancer Research Center, ; Seattle, WA USA
                [20 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Epidemiology, , University of Washington School of Public Health, ; Seattle, WA USA
                [21 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Immunology and Infectious Disease, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0001-8202-4513
                http://orcid.org/0000-0002-5436-4219
                http://orcid.org/0000-0002-0657-473X
                http://orcid.org/0000-0002-5063-8470
                http://orcid.org/0000-0001-8312-1438
                http://orcid.org/0000-0002-0530-2257
                http://orcid.org/0000-0002-9795-0365
                http://orcid.org/0000-0001-7284-6767
                Article
                24115
                10.1038/s41467-021-24115-7
                8213701
                34145289
                a9213a16-9136-4842-ac53-cb3d3fb5ca07
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 November 2020
                : 28 May 2021
                Funding
                Funded by: ATC is the Stuart and Suzanne Steele MGH Research Scholar and Stand Up to Cancer scientist.
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                sars-cov-2,viral infection,lifestyle modification,epidemiology
                Uncategorized
                sars-cov-2, viral infection, lifestyle modification, epidemiology

                Comments

                Comment on this article