Mask wearing in community settings reduces SARS-CoV-2 transmission

Gavin Leech; Charlie Rogers-Smith; Joshua Teperowski Monrad; Jonas B. Sandbrink; Benedict Snodin; Robert Zinkov; Benjamin Rader; John S. Brownstein; Yarin Gal; Samir Bhatt; Mrinank Sharma; Sören Mindermann; Jan M. Brauner; Laurence Aitchison

doi:10.1073/pnas.2119266119

Mask wearing in community settings reduces SARS-CoV-2 transmission

Gavin Leech^*
, Charlie Rogers-Smith
, Joshua Teperowski Monrad
, Jonas B. Sandbrink
, Benedict Snodin
, Robert Zinkov
, Benjamin Rader
, John S. Brownstein
, Yarin Gal
, Samir Bhatt^*
, Mrinank Sharma
, Sören Mindermann
, Jan M. Brauner
, Laurence Aitchison

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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291 Downloads (Pure)

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.

Original language	English
Article number	e2119266119
Journal	Proceedings of the National Academy of Sciences
Volume	119
Issue number	23
DOIs	https://doi.org/10.1073/pnas.2119266119
Publication status	Published - 31 May 2022

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. We thank Swapnil Mishra for cloud infrastructure and moral support; we thank Tomásˇ Gavencˇiak for help debugging and plotting. We thank Jan Kulveit for strategizing. G.L. was supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Interactive Artificial Intelligence

Funding Information:
Award(EP/S022937/1).C.R.-S.wassupportedbyagrantfromOpenPhilanthropy. M.S. was supported by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Autonomous Intelligent Machines and Systems Award (EP/S024050/1) and a grant from the Effective Altruism Funds program. S.M.’s funding for graduate studies was from Oxford University and DeepMind. S.B. acknowledges funding from the Medical Research Council (MRC) Centre for Global Infectious Disease Analysis Award (MR/R015600/1), jointly funded by the UK MRC and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the European and Developing Countries Clinical Trials Partnership program supported by the European Union; and acknowledges funding by Community Jameel, UKRI Award (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), the MRC Award (MR/R015600/1), the Bill and Melinda Gates Foundation Award (OPP1197730), Imperial College Healthcare NHS Trust-Biomedical Research Centre Funding Award (RDA02), the Novo Nordisk Young Investigator Award (NNF20OC0059309) and the National Institute for Health and Care Research Health Protection Research Unit in Modelling Methodology. J.M.B. was supported by the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems Award (EP/S024050/1) and by Cancer Research UK. J.B.S. and B.R. acknowledge funding from the Centers for Disease Control and Prevention Award (75D30120C07727), Flu Lab, and Ending Pandemics. S.B. thanks Microsoft AI for Health and AWS for compute credit.

Publisher Copyright:
Copyright © 2022 the Author(s).

Research Groups and Themes

Interactive Artificial Intelligence CDT

Keywords

COVID-19
epidemiology
Bayesian modeling
hierarchical modeling
face masks

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10.1073/pnas.2119266119

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Leech, G., Rogers-Smith, C., Monrad, J. T., Sandbrink, J. B., Snodin, B., Zinkov, R., Rader, B., Brownstein, J. S., Gal, Y., Bhatt, S., Sharma, M., Mindermann, S., Brauner, J. M., & Aitchison, L. (2022). Mask wearing in community settings reduces SARS-CoV-2 transmission. Proceedings of the National Academy of Sciences, 119(23), Article e2119266119. https://doi.org/10.1073/pnas.2119266119

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title = "Mask wearing in community settings reduces SARS-CoV-2 transmission",

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.",

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author = "Gavin Leech and Charlie Rogers-Smith and Monrad, \{Joshua Teperowski\} and Sandbrink, \{Jonas B.\} and Benedict Snodin and Robert Zinkov and Benjamin Rader and Brownstein, \{John S.\} and Yarin Gal and Samir Bhatt and Mrinank Sharma and S{\"o}ren Mindermann and Brauner, \{Jan M.\} and Laurence Aitchison",

note = "Funding Information: ACKNOWLEDGMENTS. We thank Swapnil Mishra for cloud infrastructure and moral support; we thank Tom{\'a}sˇ Gavencˇiak for help debugging and plotting. We thank Jan Kulveit for strategizing. G.L. was supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Interactive Artificial Intelligence Funding Information: Award(EP/S022937/1).C.R.-S.wassupportedbyagrantfromOpenPhilanthropy. M.S. was supported by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Autonomous Intelligent Machines and Systems Award (EP/S024050/1) and a grant from the Effective Altruism Funds program. S.M.{\textquoteright}s funding for graduate studies was from Oxford University and DeepMind. S.B. acknowledges funding from the Medical Research Council (MRC) Centre for Global Infectious Disease Analysis Award (MR/R015600/1), jointly funded by the UK MRC and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the European and Developing Countries Clinical Trials Partnership program supported by the European Union; and acknowledges funding by Community Jameel, UKRI Award (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), the MRC Award (MR/R015600/1), the Bill and Melinda Gates Foundation Award (OPP1197730), Imperial College Healthcare NHS Trust-Biomedical Research Centre Funding Award (RDA02), the Novo Nordisk Young Investigator Award (NNF20OC0059309) and the National Institute for Health and Care Research Health Protection Research Unit in Modelling Methodology. J.M.B. was supported by the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems Award (EP/S024050/1) and by Cancer Research UK. J.B.S. and B.R. acknowledge funding from the Centers for Disease Control and Prevention Award (75D30120C07727), Flu Lab, and Ending Pandemics. S.B. thanks Microsoft AI for Health and AWS for compute credit. Publisher Copyright: Copyright {\textcopyright} 2022 the Author(s).",

year = "2022",

month = may,

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doi = "10.1073/pnas.2119266119",

language = "English",

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T1 - Mask wearing in community settings reduces SARS-CoV-2 transmission

AU - Leech, Gavin

AU - Rogers-Smith, Charlie

AU - Monrad, Joshua Teperowski

AU - Sandbrink, Jonas B.

AU - Snodin, Benedict

AU - Zinkov, Robert

AU - Rader, Benjamin

AU - Brownstein, John S.

AU - Gal, Yarin

AU - Bhatt, Samir

AU - Sharma, Mrinank

AU - Mindermann, Sören

AU - Brauner, Jan M.

AU - Aitchison, Laurence

N1 - Funding Information: ACKNOWLEDGMENTS. We thank Swapnil Mishra for cloud infrastructure and moral support; we thank Tomásˇ Gavencˇiak for help debugging and plotting. We thank Jan Kulveit for strategizing. G.L. was supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Interactive Artificial Intelligence Funding Information: Award(EP/S022937/1).C.R.-S.wassupportedbyagrantfromOpenPhilanthropy. M.S. was supported by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Autonomous Intelligent Machines and Systems Award (EP/S024050/1) and a grant from the Effective Altruism Funds program. S.M.’s funding for graduate studies was from Oxford University and DeepMind. S.B. acknowledges funding from the Medical Research Council (MRC) Centre for Global Infectious Disease Analysis Award (MR/R015600/1), jointly funded by the UK MRC and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the European and Developing Countries Clinical Trials Partnership program supported by the European Union; and acknowledges funding by Community Jameel, UKRI Award (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), the MRC Award (MR/R015600/1), the Bill and Melinda Gates Foundation Award (OPP1197730), Imperial College Healthcare NHS Trust-Biomedical Research Centre Funding Award (RDA02), the Novo Nordisk Young Investigator Award (NNF20OC0059309) and the National Institute for Health and Care Research Health Protection Research Unit in Modelling Methodology. J.M.B. was supported by the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems Award (EP/S024050/1) and by Cancer Research UK. J.B.S. and B.R. acknowledge funding from the Centers for Disease Control and Prevention Award (75D30120C07727), Flu Lab, and Ending Pandemics. S.B. thanks Microsoft AI for Health and AWS for compute credit. Publisher Copyright: Copyright © 2022 the Author(s).

PY - 2022/5/31

Y1 - 2022/5/31

N2 - 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.

AB - 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.

KW - COVID-19

KW - epidemiology

KW - Bayesian modeling

KW - hierarchical modeling

KW - face masks

UR - https://doi.org/10.1073/pnas.2119266119

U2 - 10.1073/pnas.2119266119

DO - 10.1073/pnas.2119266119

M3 - Article (Academic Journal)

C2 - 35639701

SN - 0027-8424

VL - 119

JO - Proceedings of the National Academy of Sciences

JF - Proceedings of the National Academy of Sciences

IS - 23

M1 - e2119266119

ER -

Mask wearing in community settings reduces SARS-CoV-2 transmission

Abstract

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