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

Centre for the Mathematical Modelling of Infectious Diseases COVID-19 working group

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Abstract

Background: In 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. Methods: To 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). Findings: Our 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. Interpretation: Restrictions 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 R0 and the duration of infectiousness. Funding: Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.

Original languageEnglish
Pages (from-to)e261-e270
JournalThe Lancet Public Health
Volume5
Issue number5
DOIs
Publication statusPublished - May 2020

Bibliographical note

Funding Information:
KP, YL, MJ, and PK were funded by the Bill & Melinda Gates Foundation (INV-003174). 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 (206250/Z/17/Z). RME was funded by Health Data Research UK (MR/S003975/1). ND was funded by NIHR (HPRU-2012-10096). This research was partly funded by the NIHR (16/137/109) using 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 the other members of the London School of Hygiene & Tropical Medicine COVID-19 modelling group, who contributed to this work. Their funding sources are as follows: Stefan Flasche and Sam Clifford (Sir Henry Dale Fellowship 208812/Z/17/Z); Billy J Quilty, Fiona Sun, and Charlie Diamond (NIHR 16/137/109); Joel Hellewell, Sam Abbott, James D Munday, and Sebastian Funk (Wellcome Trust 210758/Z/18/Z); Amy Gimma and Christopher I Jarvis (Global Challenges Research Fund ES/P010873/1); Hamish Gibbs (Department of Health and Social Care ITCRZ 03010); Alicia Rosello (NIHR PR-OD-1017-20002); Thibaut Jombart (Research Public Health Rapid Support Team, NIHR Health Protection Research Unit Modelling Methodology); Kevin van Zandvoort (Elrha's Research for Health in Humanitarian Crises [R2HC] Programme, UK Government [Department for International Development], Wellcome Trust, and NIHR).

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

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