TY - JOUR
T1 - The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories
AU - Liu, Yang
AU - Morgenstern, Christian
AU - Kelly, James
AU - Lowe, Rachel
AU - CMMID COVID-19 Working Group
AU - Munday, James
AU - Villabona-Arenas, C. Julian
AU - Gibbs, Hamish
AU - Pearson, Carl A.B.
AU - Prem, Kiesha
AU - Leclerc, Quentin J.
AU - Meakin, Sophie R.
AU - Edmunds, W. John
AU - Jarvis, Christopher I.
AU - Gimma, Amy
AU - Funk, Sebastian
AU - Quaife, Matthew
AU - Russell, Timothy W.
AU - Emory, Jon C.
AU - Abbott, Sam
AU - Hellewell, Joel
AU - Tully, Damien C.
AU - Houben, Rein M.G.J.
AU - O’Reilly, Kathleen
AU - Gore-Langton, Georgia R.
AU - Kucharski, Adam J.
AU - Auzenbergs, Megan
AU - Quilty, Billy J.
AU - Jombart, Thibaut
AU - Rosello, Alicia
AU - Brady, Oliver
AU - Atkins, Katherine E.
AU - van Zandvoort, Kevin
AU - Rudge, James W.
AU - Endo, Akira
AU - Abbas, Kaja
AU - Sun, Fiona Yueqian
AU - Procter, Simon R.
AU - Clifford, Samuel
AU - Foss, Anna M.
AU - Davies, Nicholas G.
AU - Chan, Yung Wai Desmond
AU - Diamond, Charlie
AU - Barnard, Rosanna C.
AU - Eggo, Rosalind M.
AU - Deol, Arminder K.
AU - Nightingale, Emily S.
AU - Simons, David
AU - Sherratt, Katharine
AU - Medley, Graham
AU - Hué, Stéphane
AU - Knight, Gwenan M.
AU - Flasche, Stefan
AU - Bosse, Nikos I.
AU - Klepac, Petra
AU - Jit, Mark
N1 - Funding Information:
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.
Funding Information:
CM and JK’s contribution to this work were supported by the Royal Society’s Rapid Assistance in Modelling the Pandemic (RAMP) scheme.
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).
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/2/5
Y1 - 2021/2/5
N2 - 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.
AB - 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.
KW - COVID-19
KW - Health impact assessment
KW - Longitudinal analysis
KW - Non-pharmaceutical interventions
KW - Pandemic
KW - Policy evaluation
KW - Public health intervention
KW - Quantitative
KW - SARS-CoV-2
UR - http://www.scopus.com/inward/record.url?scp=85100537673&partnerID=8YFLogxK
U2 - 10.1186/s12916-020-01872-8
DO - 10.1186/s12916-020-01872-8
M3 - Article
C2 - 33541353
AN - SCOPUS:85100537673
VL - 19
JO - BMC Medicine
JF - BMC Medicine
SN - 1741-7015
IS - 1
M1 - 40
ER -