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The Effect of Control Strategies that Reduce Social Mixing on Outcomes of the COVID-19 Epidemic in Wuhan, China

27 Pages Posted: 24 Mar 2020

See all articles by Kiesha Prem

Kiesha Prem

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

Yang Liu

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

Timothy W. Russell

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

Adam J. Kucharski

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

Rosalind M. Eggo

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

Nicholas Davies

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group

Mark Jit

London School of Hygiene and Tropical Medicine - Department of Infectious Disease and Epidemiology

Petra Klepac

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

More...

Abstract

Background: In December 2019, a novel strain of SARS-CoV-2 emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures and efforts in response to the outbreak.  

Methods: We quantified the effects of control measures on population contact patterns in Wuhan, China, to assess their effects on the progression of the outbreak. We included the latest estimates of epidemic parameters from a transmission model fitted to data on local and internationally exported cases from Wuhan in the age-structured epidemic framework. Further, we looked at the age-distribution of cases. Lastly, we 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.  

Findings: Changes in mixing patterns may have contributed to reducing the number of infections in mid-2020 by 92% (interquartile range: 66–97%). There are benefits to sustaining these measures until April in terms of reducing the height of the peak, overall epidemic size in mid-2020 and probability that a second peak may occur after return to work. However, the modelled effects of social distancing measures vary by the duration of infectiousness and the role school children play in the epidemic.  

Interpretation: Restrictions on activities in Wuhan, if maintained until April, would likely contribute to the reduction and delay the epidemic size and peak, respectively. However, there are some limitations to the analysis, including large uncertainties around estimates of R0 and the duration of infectiousness.

Funding: KP, YL, MJ, and PK were funded by the Bill & Melinda Gates Foundation (grant number INV003174), YL and MJ were funded by the National Institute for Health Research (NIHR) (16/137/109), TWR and AJK were funded by the Wellcome Trust (grant number 206250/Z/17/Z), RME was funded by HDR UK (grant number MR/S003975/1), and ND was funded by NIHR (HPRU-2012-10096).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. We would like to acknowledge (in a randomised order) the other members of the London School of Hygiene & Tropical Medicine COVID-19 modelling group, who contributed to this work: Stefan Flasche, Samuel Clifford, Carl A B Pearson, James D Munday, Sam Abbott, Hamish Gibbs, Alicia Rosello, Billy J Quilty, Thibaut Jombart, Fiona Sun, Charlie Diamond, Amy Gimma, Kevin van Zandvoort, Sebastian Funk, Christopher I Jarvis, W John Edmunds, Nikos I Bosse, and Joel Hellewell. Their funding sources are as follows: Stefan Flasche and Sam Clifford (Sir Henry Dale Fellowship [grant number 208812/Z/17/Z]); Billy J Quilty, Fiona Sun, and Charlie Diamond (NIHR [grant number 16/137/109]); Joel Hellewell, Sam Abbott, James D Munday, and Sebastian Funk (Wellcome Trust [grant number 210758/Z/18/Z] ); Amy Gimma and Christopher I Jarvis (Global Challenges Research Fund [grant number ES/P010873/1]); Hamish Gibbs (Department of Health and Social Care [grant number ITCRZ 03010]); Alicia Rosello (NIHR [grant number PROD-1017-20002]); Thibaut Jombart (RCUK/ESRC [grant number ES/P010873/1], UK PH RST, NIHR HPRU Modelling Methodology); Kevin van Zandvoort (Elrha’s Research for Health in Humanitarian Crises (R2HC) Programme, UK Government (DFID), Wellcome Trust, NIHR).

Declaration of Interest: The authors declare no competing interests.

Keywords: COVID-19; novel coronavirus; modelling; social distancing interventions; Wuhan

Suggested Citation

Prem, Kiesha and Liu, Yang and Russell, Timothy W. and Kucharski, Adam J. and Eggo, Rosalind M. and Davies, Nicholas and Group, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working and Jit, Mark and Klepac, Petra, The Effect of Control Strategies that Reduce Social Mixing on Outcomes of the COVID-19 Epidemic in Wuhan, China (3/9/2020). Available at SSRN: https://ssrn.com/abstract=3552864 or http://dx.doi.org/10.2139/ssrn.3552864

Kiesha Prem (Contact Author)

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases ( email )

London
United Kingdom

Yang Liu

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

London
United Kingdom

Timothy W. Russell

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

London
United Kingdom

Adam J. Kucharski

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

London
United Kingdom

Rosalind M. Eggo

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

London
United Kingdom

Nicholas Davies

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

London
United Kingdom

Mark Jit

London School of Hygiene and Tropical Medicine - Department of Infectious Disease and Epidemiology

United Kingdom

Petra Klepac

London School of Hygiene and Tropical Medicine - Centre for Mathematical Modelling of Infectious Diseases

London
United Kingdom

No contact information is available for Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group

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