| Abstract |
Importance: It is unknown how well cell phone location data portray social distancing strategies or if they are associated with the incidence of coronavirus disease 2019 (COVID-19) cases in a particular geographical area.
Objective: To determine if cell phone location data are associated with the rate of change in new COVID-19 cases by county across the US.
Design, setting, and participants: This cohort study incorporated publicly available county-level daily COVID-19 case data from January 22, 2020, to May 11, 2020, and county-level daily cell phone location data made publicly available by Google. It examined the daily cases of COVID-19 per capita and daily estimates of cell phone activity compared with the baseline (where baseline was defined as the median value for that day of the week from a 5-week period between January 3 and February 6, 2020). All days and counties with available data after the initiation of stay-at-home orders for each state were included.
Exposures: The primary exposure was cell phone activity compared with baseline for each day and each county in different categories of place.
Main outcomes and measures: The primary outcome was the percentage change in COVID-19 cases 5 days from the exposure date.
Results: Between 949 and 2740 US counties and between 22 124 and 83 745 daily observations were studied depending on the availability of cell phone data for that county and day. Marked changes in cell phone activity occurred around the time stay-at-home orders were issued by various states. Counties with higher per-capita cases (per 100 000 population) showed greater reductions in cell phone activity at the workplace (β, -0.002; 95% CI, -0.003 to -0.001; P < 0.001), areas classified as retail (β, -0.008; 95% CI, -0.011 to -0.005; P < 0.001) and grocery stores (β, -0.006; 95% CI, -0.007 to -0.004; P < 0.001), and transit stations (β, -0.003, 95% CI, -0.005 to -0.002; P < 0.001), and greater increase in activity at the place of residence (β, 0.002; 95% CI, 0.001-0.002; P < 0.001). Adjusting for county-level and state-level characteristics, counties with the greatest decline in workplace activity, transit stations, and retail activity and the greatest increases in time spent at residential places had lower percentage growth in cases at 5, 10, and 15 days. For example, counties in the lowest quartile of retail activity had a 45.5% lower growth in cases at 15 days compared with the highest quartile (SD, 37.4%-53.5%; P < .001).
Conclusions and relevance: Our findings support the hypothesis that greater reductions in cell phone activity in the workplace and retail locations, and greater increases in activity at the residence, are associated with lesser growth in COVID-19 cases. These data provide support for the value of monitoring cell phone location data to anticipate future trends of the pandemic.
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