A recent mathematical modelling study using mobile phone data found that individual decisions to reduce movement – even before state-wide stay-at-home policies were introduced – likely helped slow the spread of COVID-19 in the USA
The authors examined daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020. They used these data, which capture real-time trends in movement patterns for each US county, to generate a social distancing metric. They computed the COVID-19 growth rate ratio for a given county on a given day, and fit a statistical model for each county to evaluate how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020.
Their model shows a strong and statistically significant correlation between social distancing, quantified by mobility patterns, and reduction of COVID-19 case growth. Mobility patterns dropped by 35-63% relative to normal conditions. The model also revealed the effect of this social distancing on decreasing transmission is not likely to be perceptible for at least 9–12 days after implementation, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. Their model also indicates that behavioral changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message.