Updated Review Available
This expert summary is for the non peer-reviewed preprint. We also summarized this paper after it underwent peer-review and was published in Science on April 29, 2020. You can find our updated review of the published article here.
Study population and setting
Authors evaluated the impact of social distancing on human mixing patterns using contact survey data from Wuhan (n=636) and Shanghai, China (n=557) before and during the outbreak, after cases had been identified and social distancing measures had been implemented in both cities. Survey participants in Wuhan were asked to describe their contact behavior on a regular weekday before the outbreak was recognized (late December 2019), and on the day before the interview (early February 2020, during the epidemic). Baseline contact behavior data for participants in Shanghai were obtained from a survey conducted in Shanghai in 2017–18 using similar methods and questions; participants in Shanghai were also asked to describe their contact behavior on the day before the interview (early February 2020, during the outbreak) using the same survey as Wuhan. Authors used contact tracing information from Hunan Provincial Centers for Disease Control and Prevention to calculate the age-specific relative risk of infection of 57 index cases, and estimate differences in susceptibility to infection and clinical disease by age. Using those estimates, authors modeled how SARS-CoV-2 population-level disease dynamics (e.g., transmission) are affected by differences in susceptibility by age and changes to social mixing patterns brought about by social distancing measures.
Summary of Main Findings
Wuhan was placed under total lockdown starting in late January 2020, including restricted travel, mass school closures, social distancing, and the closure of non-essential services and businesses. Although Shanghai implemented similar controls, as of February 17, 2020, it was only under semi-lockdown. This study found that the average daily number of contacts per participant was significantly reduced from 14.6 to 2.0 in Wuhan, and 20.6 to 2.3 in Shanghai. The largest number of contacts were recorded in school settings, and school closures eliminated contacts between school-aged individuals; contacts during the outbreak mostly occurred at home with household members. Individuals aged 0–14 years had a lower risk of infection during close contact with confirmed cases relative to individuals aged ≥65 years (OR=0.41; 95% CI: 0.18–0.93; p-value=0.03). Accounting for differences in susceptibility by age, authors then modeled the impact of various preemptive interventions (i.e., early in the epidemic) and showed that social distancing measures drastically reduced the R0 (reproductive number). Assuming a baseline R0 of 2.0–3.5, modeling suggests that school closures could reduce peak incidence by 64%, but would not completely stop transmission (i.e., reduce R0 to <1).
Differential infection risks by age are considered when assessing the impact of school closures. Contact mixing in Shanghai was determined using data from a previously conducted survey, so is not subject to recall bias. The transmission model was calibrated against survey data. Authors used 5.1 days for the model’s serial interval (the time between symptom onset in the source and symptom onset in the recipient infections), which is longer than some earlier estimates but consistent with values used in newer studies.
Contact mixing patterns are self-reported and may be subject to several biases. For example, contact mixing patterns in Wuhan may be affected by recall bias; recall of up to seven days is generally assumed to provide acceptable accuracy, and participants were asked to recall up to two months. Differences in susceptibility to infection and symptom development by age were estimated using only 57 primary confirmed cases. Uncertainty regarding the susceptibility profile of infections still exists. Modeling results may not be generalizable to other locations where social distancing measures may differ in strategy or magnitude. Model does not include individual behavior modifications that may have occurred simultaneously (e.g., using masks or maintaining physical distance while in contact), which may have resulted in an underestimation of the effects of social distancing.
This study estimated the independent population-level effects of social distancing measures on population mixing patterns and subsequent transmission. Authors also estimated susceptibility differences by age using age-stratified relative risk, which contrasts with previous work in Shenzhen indicating no difference in susceptibility by age.