Evidence map›Paper›PMID 32220655›Full record

ArticleThe Lancet. Public health2020

The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study.

Kiesha Prem, Yang Liu, Timothy W Russell, Adam J Kucharski, Rosalind M Eggo, Nicholas Davies, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Mark Jit, Petra Klepac

Erratum issued Registry-linked trialAbstract read
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. An erratum has been issued. It is linked to trial NCT04487691 (Autologous Nebulized Platelet Lysate for Post COVID-19 Syndrome), which is not on this map. Cited by 927 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
927citing papers in PubMed, 2 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.

NCT04487691 naterminatednot on this map

Autologous Nebulized Platelet Lysate for Post COVID-19 Syndrome

TypeinterventionalSponsorRegenexx, LLCRan2020 to 2021Enrolled1ConditionsCovid19ArmsNebulized Platelet Lysate, Nebulized Sterile Saline
3 · Its place in the literature

Who cites it

927 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Pooled it
  2. Does medical waste research during COVID-19 meet the challenge induced by the pandemic to waste management?Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA · 2024
    Pooled it
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867 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Kiesha PremCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK. Electronic address: kiesha.prem@lshtm.ac.uk.
Yang LiuCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Timothy W RussellCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Adam J KucharskiCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Rosalind M EggoCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Nicholas DaviesCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group
Mark JitCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Petra KlepacCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Funding

Medical Research Council MR/S003975/1Wellcome Trust 206250/Z/17/ZWellcome Trust 208812/Z/17/ZWellcome Trust 210758/Z/18/Z
6 · The paper itself

Abstract

backgroundIn December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world.

methodsTo examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April).

findingsOur projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66-97) and 24% (13-90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic.

interpretationRestrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R

fundingBill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.

Indexed as

Communicable Disease ControlChinaCoronavirus InfectionsCOVID-19EpidemicsHumansModels, TheoreticalPandemicsPneumonia, Viral

Identifiers

PMID32220655
PMCPMC7158905

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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.