mobile phone gps data and prevalence of covid-19 infections: quantifying parameters of social distancing in the u.s.

Background: to evaluate the association between social distancing quantified by mobile phone data and the current prevalence of covid-19 infections in the u.s. per capita. methods: data were accessed on april 4, 2020, from centers for disease control and prevention, google covid-19 community mobility report, and the united states census bureau to report prevalence of covid-19 infections, mobility data, and population per state, respectively. mobility data points were defined as daily length of visit or time spent in a single location based on mobile phone users shared locations from february 7 – march 29, 2020. multivariable linear regression was used to evaluate relationships between normalized per capita infection prevalence and six parameters of social distancing. results: mobility data indicated the following percent changes compared to median values of baseline activity: -50% in transit stations, -45% in retail/recreation, -36% in workplaces, -23% in grocery/pharmacy, -19% in parks, and +12% in residential living areas. multivariable linear regression revealed significant correlation between prevalence of infection per capita and parameters of social distancing (r= 0.604, p= 0.002). time at home was not an independent predictor for prevalence of infection per capita (beta= 0.016; 95% ci, -0.003 to 0.036; p= 0.09). conclusion: based on mobility reports from mobile phone gps data and six characteristics of social distancing, significant associations were identified between geographic activity and prevalence of covid-19 infections in the u.s. per capita. mobile phone data utilizing ‘location history’ may be warranted to monitor the effectiveness of social distancing parameters on reducing prevalence of covid-19 in the u.s.