Mask wearing in community settings reduces SARS-CoV-2 transmission
Year: 2022
Type: article
Abstract: The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior (n= 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing.
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Funders: Bill and Melinda Gates Foundation, Alan Turing Institute, UK Research and Innovation, Open Philanthropy Project, DeepMind, National Institute for Health Research Health Protection Research Unit, Flu Lab, Cancer Research UK, National Institute for Health and Care Research, Research Councils UK, Academy of Medical Sciences, Imperial College London, Imperial College Healthcare NHS Trust, University of Oxford, European Commission, University of Bristol, Rural Development Administration, Novo Nordisk, Novo Nordisk Fonden, Foreign, Commonwealth and Development Office, Centers for Disease Control and Prevention, Medical Research Council, Engineering and Physical Sciences Research Council, European and Developing Countries Clinical Trials Partnership
Awards: G1052272520, G2366428187, MRC Centre for Global Infectious Disease Analysis, G3112244512, G3120849050, G3122335838, G31554954, G3166327464, G3363401584, G3644606522, G393184570, G4336306655, G4337640808, G4398504479, G49701852, Multiresolution predictive dynamics of COVID-19 risk and intervention effects, G551833079, EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems, UKRI Centre for Doctoral Training in Interactive Artificial Intelligence, Robust and interpretable machine learning for biomedicine and healthcare, G5975650167, G6452394617, G6520098237, G6554525099, G7052036733, G712396241, G7816555987, G8039111286, G8092665698, G8537180282
OpenAlex ID: W4282932527