Evidence map›Paper›PMID 32502389›Full record

ArticleThe Lancet. Public health2020

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

Nicholas G Davies, Adam J Kucharski, Rosalind M Eggo, Amy Gimma, W John Edmunds, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 working group

Abstract readEvaluation Study
In one paragraph

Article in The Lancet. Public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 490 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
490citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

490 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  11. The Risk from Reopening International Travel During a Disease X Pandemic: A Case Study of SARS-CoV-2.Risk analysis : an official publication of the Society for Risk Analysis · 2026
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  17. Informed decisions about public health and social measures.Health research policy and systems · 2025
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430 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Nicholas G DaviesDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK. Electronic address: nicholas.davies@lshtm.ac.uk.
Adam J KucharskiDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Rosalind M EggoDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Amy GimmaDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
W John EdmundsDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Centre for the Mathematical Modelling of Infectious Diseases COVID-19 working group

Funding

Medical Research Council MC_PC_19065Wellcome Trust 208812/Z/17/Z
6 · The paper itself

Abstract

backgroundNon-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.

methodsWe 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

findingsWe 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

interpretationThe 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.

fundingMedical Research Council.

Indexed as

Coronavirus InfectionsCOVID-19EpidemicsHealth Services Needs and DemandHospitalizationHumansIntensive Care UnitsModels, TheoreticalPandemicsPneumonia, ViralProgram EvaluationUnited Kingdom

Identifiers

PMID32502389
PMCPMC7266572

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.