The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories

CMMID COVID-19 Working Group, Yang Liu, Christian Morgenstern, James Kelly, Rachel Lowe, Mark Jit

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    Abstract

    Background: Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories. 

    Methods: We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission using data from January to June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number (Rt) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in Rt, levels of NPI intensity, time-varying changes in NPI effect, and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. 

    Results: There was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced Rt. Another three NPIs (workplace closure, income support, and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective, restrictions on < 10 people gathering were). Evidence about the effectiveness of the remaining NPIs (stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Effect sizes varied depending on whether or not we included data after peak NPI intensity. 

    Conclusion: Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects, and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many, although not all, actions policy-makers are taking to respond to the COVID-19 pandemic.

    Original languageEnglish
    Article number40
    Number of pages12
    JournalBMC Medicine
    Volume19
    Issue number1
    DOIs
    Publication statusPublished - 5 Feb 2021

    Bibliographical note

    Funding Information: Funding information for the Centre for Mathematical Modelling of Infectious Disease COVID-19 Working Group: James Munday (Wellcome Trust: 210758/Z/18/Z); Hamish Gibbs (UK DHSC/UK Aid/NIHR: ITCRZ 03010); Carl A B Pearson (BMGF: NTD Modelling Consortium OPP1184344, DFID/Wellcome Trust: 221303/Z/20/Z); Kiesha Prem (BMGF: INV-003174, European Commission: 101003688); Quentin J Leclerc (UK MRC: LID DTP MR/N013638/1); Sophie R Meakin (Wellcome Trust: 210758/Z/18/Z); W John Edmunds (European Commission: 101003688, UK MRC: MC_PC_19065, NIHR: PR-OD-1017-20002); Christopher I Jarvis (Global Challenges Research Fund: ES/P010873/1); Amy Gimma (Global Challenges Research Fund: ES/P010873/1, UK MRC: MC_PC_19065); Sebastian Funk (Wellcome Trust: 210758/Z/18/Z); Matthew Quaife (ERC Starting Grant: #757699, BMGF: INV-001754); Timothy W Russell (Wellcome Trust: 206250/Z/17/Z); Jon C Emery (ERC Starting Grant: #757699); Sam Abbott (Wellcome Trust: 210758/Z/18/Z); Joel Hellewell (Wellcome Trust: 210758/Z/18/Z); Rein M G J Houben (ERC Starting Grant: #757699); Kathleen O’Reilly (BMGF: OPP1191821); Georgia R Gore-Langton (UK MRC: LID DTP MR/ N013638/1); Adam J Kucharski (Wellcome Trust: 206250/Z/17/Z); Megan Auzenbergs (BMGF: OPP1191821); Billy J Quilty (NIHR: 16/137/109, NIHR: 16/136/46); Thibaut Jombart (Global Challenges Research Fund: ES/P010873/1, UK Public Health Rapid Support Team, NIHR: Health Protection Research Unit for Modelling Methodology HPRU-2012-10096, UK MRC: MC_PC_19065); Alicia Rosello (NIHR: PR-OD-1017-20002); Oliver Brady (Wellcome Trust: 206471/Z/17/Z); Kevin van Zandvoort (Elrha R2HC/UK DFID/Wellcome Trust/NIHR, DFID/Wellcome Trust: Epidemic Preparedness Coronavirus research
    programme 221303/Z/20/Z); James W Rudge (DTRA: HDTRA1-18-1-0051);
    Akira Endo (Nakajima Foundation, Alan Turing Institute); Kaja Abbas (BMGF:
    OPP1157270); Fiona Yueqian Sun (NIHR: 16/137/109); Simon R Procter (BMGF:
    OPP1180644); Samuel Clifford (Wellcome Trust: 208812/Z/17/Z, UK MRC:
    MC_PC_19065); Nicholas G. Davies (NIHR: Health Protection Research Unit for
    Immunisation NIHR200929, UK MRC: MC_PC_19065); Charlie Diamond (NIHR:
    16/137/109); Rosanna C Barnard (European Commission: 101003688); Rosalind
    M Eggo (HDR UK: MR/S003975/1, UK MRC: MC_PC_19065); Emily S
    Nightingale (BMGF: OPP1183986); David Simons (BBSRC LIDP: BB/M009513/1);
    Katharine Sherratt (Wellcome Trust: 210758/Z/18/Z); Graham Medley (BMGF:
    NTD Modelling Consortium OPP1184344); Gwenan M Knight (UK MRC: MR/
    P014658/1); Stefan Flasche (Wellcome Trust: 208812/Z/17/Z); Nikos I Bosse
    (Wellcome Trust: 210758/Z/18/Z); Petra Klepac (Royal Society: RP\EA\180004,
    European Commission: 101003688).
    CM and JK’s contribution to this work were supported by the Royal Society’s Rapid
    Assistance in Modelling the Pandemic (RAMP) scheme.
    We thank Richard Pebody (WHO European Region Office, Copenhagen), Katelijn Vandemaele (WHO, Geneva), and Helen Johnson (European Centre for Disease Prevention and Control, Stockholm) for helpful comments. YL and MJ are funded by the National Institute of Health Research (UK) (16/137/109), the Bill & Melinda Gates Foundation (INV-003174), and the European Commission project Epipose (101003688). This research was partly funded by the National Institute for Health Research (NIHR) (16/137/109) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care. This research is partly funded by the Bill & Melinda Gates Foundation (INV-003174). The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Bill &
    Melinda Gates Foundation. YL is also supported by the UK Medical Research
    Council (MC_PC_19065). CM and JK are employed by IPM Informed Portfolio
    Management. IPM is appreciative of the contributions of its employees above and beyond the scope of their work, their dedication to the community, and the world as a whole. The views expressed herein are made in a personal capacity and are not those necessarily made, sponsored, affiliated, or endorsed by IPM. RL was supported by a Royal Society Dorothy Hodgkin Fellow.
    Prof. Mark Jit is a member of the editorial board at BMC Medicine.

    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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.
    The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

    Publisher Copyright: © The Author(s). 2021.

    Citation: Liu, Y., Morgenstern, C., Kelly, J. et al. The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories. BMC Med 19, 40 (2021). https://doi.org/10.1186/s12916-020-01872-8

    DOI: https://doi.org/10.1186/s12916-020-01872-8

    Keywords

    • COVID-19
    • Health impact assessment
    • Longitudinal analysis
    • Non-pharmaceutical interventions
    • Pandemic
    • Policy evaluation
    • Public health intervention
    • Quantitative
    • SARS-CoV-2
    • TESTS

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