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. 2020 May;5(5):e261-e270.
doi: 10.1016/S2468-2667(20)30073-6. Epub 2020 Mar 25.

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

Kiesha Prem  1 , Yang Liu  2 , Timothy W Russell  2 , Adam J Kucharski  2 , Rosalind M Eggo  2 , Nicholas Davies  2 ; Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group; Mark Jit  2 , Petra Klepac  2
Collaborators, Affiliations

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

Kiesha Prem et al. Lancet Public Health. 2020 May.

Erratum in

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.

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Figures

Figure 1
Figure 1
Age-structured SEIR model and details of the modelled physical distancing interventions According to infection status, we divided the population into susceptible (S), exposed (E), infected (I), and removed (R) individuals. An infected individual in an age group can be clinical (Ic) or subclinical (Isc), and ρi refers to the probability that an individual is symptomatic or clinical. The age-specific mixing patterns of individuals in age group i, Ci,j, alter their likelihood of being exposed to the virus given a certain number of infected individuals in the population. Younger individuals are more likely to be asymptomatic and less infectious, ie, subclinical. When ρi=0 for all i, the model simplifies to a standard SEIR. The force of infection φi,t is given by 1–(βΣjCi,jICj,t+αβΣjCi,jISCj,t), where β is the transmission rate and α is the proportion of transmission that resulted from a subclinical individual. SEIR= susceptible-exposed-infected-removed.
Figure 2
Figure 2
Synthetic age-specific and location-specific contact matrices for China under various physical distancing scenarios during the intense control period for China Synthetic age-specific contact patterns across all locations, at home, in the workplace, in school, and at other locations during normal circumstances (ie, under no intervention) are presented in panels A to E. Age-specific and location-specific contact matrices under the various physical distancing interventions are presented in panels F to T. Darker colour intensities indicate higher proclivity of making the age-specific contact.
Figure 3
Figure 3
Effects of different intervention strategies on cumulative incidence and new cases per day among individuals aged 55 to <60 years (A to D) and 10 to <15 years (E to H) from late 2019 to end-2020
Figure 4
Figure 4
Effects of different physical distancing measures on cumulative incidence (A) and new cases per day (B), and age-specific incidence per day (C to G) from late 2019 to end-2020 Results depicted here assume an infectious period of 7 days. Median cumulative incidence, incident cases per day, and age-specific incidence per day are represented as solid lines. Shaded areas around the coloured lines in panel A represent the IQR.
Figure 5
Figure 5
Modelled proportion of number of infections averted by end-2020 by age for different physical distancing measures, assuming the duration of infectiousness to be 3 days (A, B) or 7 days (C, D) The additional proportions of cases averted (compared with no intervention) are presented across age and by the different physical distancing measures.
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