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Social Distancing is Effective at Mitigating COVID-19 Transmission in the United States

Our take —

In this study available as a preprint and thus not yet peer reviewed, social distancing in the 25 U.S. counties with highest COVID-19 incidence was correlated with a decrease in the growth rate of COVID-19 cases, with an estimated 9 to 12 day lag period. Social distancing was measured via aggregated mobility data, and does not necessarily equate to a direct reduction in physical or social contact.

Study design


Study population and setting

Data from mobile phone records (via Teralytics) were aggregated from January 1 to April 20, 2020, and used to quantify social distancing within the 25 counties in the United States with the highest number of reported cases of COVID-19 as of April 16, 2020. Social distancing was measured using a ratio comparing the number of individual trips made per day in each county (incoming, outgoing, or within) after January 24, 2020 to a baseline period over the last three weeks of January 2020. Reported changes in COVID-19 cases were compared to the social distancing ratio in each county using a linear regression model.

Summary of Main Findings

There were varying levels of social distancing behavior across states and counties, with many southern states displaying travel patterns closer to the baseline. Social distancing was observed to occur prior to state directives, suggesting either an early implementation of local directives or other incentives driving this behavioral change. A “lag” period of 11 (9-12) days was identified as the difference in time between the beginning of social distancing and the resulting reduction in cases. Social distancing was significantly correlated with a decrease in COVID-19 incidence in 23 of the 25 counties.

Study Strengths

Use of mobility data and number of trips, as opposed to inferred movement based on travel distances or transmission rates, captured real-time movement of people from the selected counties. Authors used averages over a few days to estimate baseline mobility and the growth rate of new COVID-cases, which is helpful when dealing with volatility in reported data. The 11-day lag window is consistent with the 4-5 day median incubation period of COVID-19. Results from a sensitivity analysis, using aggregated case reports, were consistent with the main findings.


Mobile data is a coarse measure of movement, does not necessarily distinguish individual behavior, and does not capture the movement of non-mobile users. Links between reduced movement and changes in social or physical contacts remain unclear. Other mitigating factors in reducing case growth are ignored. The reported number of cases may have underestimated the actual prevalence within the counties due to testing limitations and reporting biases. Selected counties were those with the most cases in mid-April 2020, but may not be representative of later hotspots.

Value added

This study evaluates the impact of social distancing measures in a setting other than China, which has been the focus of most early literature surrounding the relationship between mobility and COVID-19.

This review was posted on: 17 June 2020