Social Distancing Measures in China Reduced Magnitude of COVID-19 Peak

Researchers simulated the ongoing trajectory of the COVID-19 outbreak in Wuhan, China, and estimated that social distancing measures could reduce the rate of infection by 92% by mid-2020.

In a simulation model of the novel coronavirus (COVID-19) trajectory in Wuhan, China, researchers identified that interventions based on sustained social distancing have a strong potential to reduce the magnitude of the peak of the COVID-19 epidemic and lead to a reduced number of overall cases. Results of the study are published in the Lancet Public Health.

“We built an age-specific and location-specific transmission model to assess progression of the Wuhan outbreak under different scenarios of school and workplace closure,” wrote the investigators. “We found that changes to contact patterns are likely to have substantially delayed the epidemic peak and reduced the number of [COVID-19] cases in Wuhan. If these restrictions [were] lifted in March, 2020, a second peak of cases might occur in late August, 2020. Such a peak could be delayed by 2 months if the restrictions were relaxed a month later, in April, 2020.”

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first emerged in Wuhan, China, in late 2019. In mid-January 2020, schools and workplaces closed as part of the Lunar New Year holidays, which were then extended to prevent SARS-CoV-2 spread.

Researchers simulated the ongoing trajectory of an outbreak in Wuhan using a deterministic stage-structured susceptible-exposed-infected-removed (SEIR) model over the course of a 1-year period. The population was divided according to infection status: susceptible, exposed, infected, or quarantined. The model also divided the population into 5-year age groups up to age 70 years, and a single category for those aged ≥75 years.

To simulate the effects of interventions aimed at reducing social mixing, the researchers created synthetic contact matrices for each of 3 intervention scenarios: theoretical (assumed no change to social mixing patterns across all location types), no interventions (assumed no physical distancing control measures), and intense control measures (assumed school closure and 10% of workforce in place). For the third scenario, the model ended at the beginning of March or April and allowed for a staggered return to work while the schools remained closed.

The simulations found that control measures aimed at reducing social mixing in the population may be effective in reducing the magnitude and delaying the peak of the COVID-19 outbreak. For different control measures among individuals aged 55 to <60 years and aged 10 to <15 years, the standard school winter break and holidays for the Lunar New Year would have had little effect on progression of the outbreak had schools and workplaces reopened as normal.

Intense control measures of prolonged school closure and work holidays reduced the cumulative infections and peak incidence while also delaying the peak of the outbreak. The effects of these physical distancing strategies vary across age categories: the reduction in incidence is highest among school children and older individuals and lowest among working-age individuals.

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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% and 24% in mid-2020 and at the of end of 2020, respectively.

“Given what is known about the transmissibility and (the relatively long 5-6 days) incubation period of COVID-19, the efficacy of physical distancing in reducing these important attributes of any epidemic are no surprise,” the authors concluded.


Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study [published online March 25, 2020]. Lancet Public Health. doi:10.1016/S2468-2667(20)30073-6. Accessed March 27, 2020.

This article originally appeared on Clinical Advisor