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A cluster of cases of pneumonia of unknown cause in Wuhan, China, was first reported on Dec 31, 2019,1 and a week later identified as the disease now called COVID-19.2 COVID-19 has since spread rapidly around the world, nearing 10 million confirmed cases and more than 500 000 deaths reported in 188 countries and regions as of June 25, 2020.3 The first case of COVID-19 in the USA was reported on Jan 20, 2020, in Snohomish County, WA, and as of June 25, COVID-19 has been reported in every US state and more than 3000 US counties.4, 5 Until the widespread availability of a vaccine, social distancing alongside personal protective measures, such as handwashing and wearing a mask, will remain the primary control mechanisms for mitigating the spread of COVID-19.

In China, a nationally coordinated effort limiting travel and social interaction effectively mitigated the spread of the disease.6, 7 Crucially, by contrast with the nationally mandated directives put in place in China, the US directives to shelter in place and temporarily close non-essential businesses and schools were made at the state and local level throughout March and April, 2020 (appendix pp 2, 12–13). This distributed decision-making process and variable enforcement resulted in an outbreak mitigation response that was highly variable in both space and time. Adding to this complexity is the varying intensities of the outbreak around the USA, with some counties nearing their peak and others remaining in the early stages of an epidemic.5 Together, these issues pose a significant challenge to evaluating the effectiveness of social distancing policies in the USA. To address this issue, we used real-time mobility data derived from cell (mobile) phone data to quantify the progression of social distancing within the USA. Subsequently, we examined the relationship of the data to the rate of emerging COVID-19 cases in 25 US counties, with the highest number of reported cases as of April 16, 2020 (table; appendix p 3).

Table The selected 25 US counties and the associated Pearson correlation coefficient and generalised linear model coefficients (intercept and slope) between 11-day lagged mobility ratio and growth rate ratio
Correlation coefficient Intercept Slope
Essex, NJ (34013) 0·90 0·86 0·26
New York City, NY* (36061) 0·86 0·94 0·14
Middlesex, NJ (34023) 0·85 0·89 0·24
Cook, IL (17031) 0·85 0·89 0·22
Hudson, NJ (34017) 0·84 0·90 0·23
Nassau, NY (36059) 0·84 0·89 0·22
Union, NJ (34039) 0·84 0·85 0·28
Middlesex, MA (25017) 0·83 0·90 0·24
Suffolk, NY (36103) 0·83 0·85 0·26
Miami-Dade, FL (12086) 0·83 0·86 0·28
Bergen, NJ (34003) 0·82 0·87 0·24
Passaic, NJ (34031) 0·81 0·87 0·26
Suffolk, MA (25025) 0·81 0·90 0·27
Philadelphia, PA (42101) 0·80 0·84 0·33
Wayne, MI (26163) 0·80 0·82 0·32
Westchester, NY (36119) 0·80 0·88 0·22
Monmouth, NJ (34025) 0·76 0·83 0·28
Rockland, NY (36087) 0·74 0·81 0·36
Jefferson, LA (22051) 0·71 0·77 0·38
Oakland, MI (26125) 0·71 0·86 0·27
Orange, NY (36071) 0·66 0·80 0·34
Los Angeles, CA (06037) 0·62 0·89 0·22
Fairfield, CT (09001) 0·61 0·85 0·27
Orleans, LA (22071) 0·61 0·84 0·26
Harris, TX (48201) 0·53 0·78 0·38
All counties 0·71 0·88 0·24

The list is presented by the correlation coefficient in descending order. The last row represents the results of a single model for all 25 US counties. All coefficients are statistically significant at 95% CI. Federal Information Processing Standards code for each county is given.

* To be consistent with the Johns Hopkins University Center for Systems Science and Engineering COVID-19 dashboard reporting,5 New York City is used to represent New York County, Queens County, Bronx County, Kings County, and Richmond County in one location.