Social Distancing Metrics and Estimates of SARS-CoV-2 Transmission Rates: Associations Between Mobile Telephone Data Tracking and R.

ABSTRACT

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). In the absence of robust preventive or curative strategies, the implementation of social distancing has been a key component of limiting the spread of the virus. METHODS: Daily estimates of R(t) were calculated and compared with measures of social distancing made publicly available by Unacast. Daily generated variables representing an overall grade for distancing, changes in distances traveled, encounters between individuals, and daily visitation, were modeled as predictors of average R value for the following week, using linear regression techniques for 8 counties surrounding the city of Syracuse, New York. Supplementary analysis examined differences between counties. RESULTS: A total of 225 observations were available across the 8 counties, with 166 meeting the mean R(t) < 3 outlier criterion for the regression models. Measurements for distance (ß = 1.002, P = .012), visitation (ß = .887, P = .017), and encounters (ß = 1.070, P = .001) were each predictors of R(t) for the following week. Mean R(t) drops when overall distancing grades move from D+ to C-. These trends were significant (P < .001 for each). CONCLUSIONS: Social distancing, when assessed by free and publicly available measures such as those shared by Unacast, has an impact on viral transmission rates. The scorecard may also be useful for public messaging about social distance, in hospital planning, and in the interpretation of epidemiological models.