Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study.


Journal

The Lancet. Public health
ISSN: 2468-2667
Titre abrégé: Lancet Public Health
Pays: England
ID NLM: 101699003

Informations de publication

Date de publication:
07 2020
Historique:
received: 04 04 2020
revised: 11 05 2020
accepted: 18 05 2020
pubmed: 6 6 2020
medline: 9 7 2020
entrez: 6 6 2020
Statut: ppublish

Résumé

Non-pharmaceutical interventions have been implemented to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been crucial to support evidence-based policy making during the early stages of the epidemic. This study assesses the potential impact of different control measures for mitigating the burden of COVID-19 in the UK. We used a stochastic age-structured transmission model to explore a range of intervention scenarios, tracking 66·4 million people aggregated to 186 county-level administrative units in England, Wales, Scotland, and Northern Ireland. The four base interventions modelled were school closures, physical distancing, shielding of people aged 70 years or older, and self-isolation of symptomatic cases. We also modelled the combination of these interventions, as well as a programme of intensive interventions with phased lockdown-type restrictions that substantially limited contacts outside of the home for repeated periods. We simulated different triggers for the introduction of interventions, and estimated the impact of varying adherence to interventions across counties. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (ie, admission to the intensive care units [ICU]) treatment, and deaths, and compared the effect of each intervention on the basic reproduction number, R We projected a median unmitigated burden of 23 million (95% prediction interval 13-30) clinical cases and 350 000 deaths (170 000-480 000) due to COVID-19 in the UK by December, 2021. We found that the four base interventions were each likely to decrease R The characteristics of SARS-CoV-2 mean that extreme measures are probably required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs. Medical Research Council.

Sections du résumé

BACKGROUND
Non-pharmaceutical interventions have been implemented to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been crucial to support evidence-based policy making during the early stages of the epidemic. This study assesses the potential impact of different control measures for mitigating the burden of COVID-19 in the UK.
METHODS
We used a stochastic age-structured transmission model to explore a range of intervention scenarios, tracking 66·4 million people aggregated to 186 county-level administrative units in England, Wales, Scotland, and Northern Ireland. The four base interventions modelled were school closures, physical distancing, shielding of people aged 70 years or older, and self-isolation of symptomatic cases. We also modelled the combination of these interventions, as well as a programme of intensive interventions with phased lockdown-type restrictions that substantially limited contacts outside of the home for repeated periods. We simulated different triggers for the introduction of interventions, and estimated the impact of varying adherence to interventions across counties. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (ie, admission to the intensive care units [ICU]) treatment, and deaths, and compared the effect of each intervention on the basic reproduction number, R
FINDINGS
We projected a median unmitigated burden of 23 million (95% prediction interval 13-30) clinical cases and 350 000 deaths (170 000-480 000) due to COVID-19 in the UK by December, 2021. We found that the four base interventions were each likely to decrease R
INTERPRETATION
The characteristics of SARS-CoV-2 mean that extreme measures are probably required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs.
FUNDING
Medical Research Council.

Identifiants

pubmed: 32502389
pii: S2468-2667(20)30133-X
doi: 10.1016/S2468-2667(20)30133-X
pmc: PMC7266572
pii:
doi:

Types de publication

Evaluation Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e375-e385

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 208812/Z/17/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_19065
Pays : United Kingdom

Investigateurs

Thibaut Jombart (T)
Kathleen O'Reilly (K)
Akira Endo (A)
Joel Hellewell (J)
Emily S Nightingale (ES)
Billy J Quilty (BJ)
Christopher I Jarvis (CI)
Timothy W Russell (TW)
Petra Klepac (P)
Nikos I Bosse (NI)
Sebastian Funk (S)
Sam Abbott (S)
Graham F Medley (GF)
Hamish Gibbs (H)
Carl A B Pearson (CAB)
Stefan Flasche (S)
Mark Jit (M)
Samuel Clifford (S)
Kiesha Prem (K)
Charlie Diamond (C)
Jon Emery (J)
Arminder K Deol (AK)
Simon R Procter (SR)
Kevin van Zandvoort (K)
Yueqian Fiona Sun (YF)
James D Munday (JD)
Alicia Rosello (A)
Megan Auzenbergs (M)
Gwen Knight (G)
Rein M G J Houben (RMGJ)
Yang Liu (Y)

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Auteurs

Nicholas G Davies (NG)

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK. Electronic address: nicholas.davies@lshtm.ac.uk.

Adam J Kucharski (AJ)

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Rosalind M Eggo (RM)

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Amy Gimma (A)

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

W John Edmunds (WJ)

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

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