Geography Versus Sociodemographics As Predictors of Changes in Daily Mobility Across the USA During the COVID-19 Pandemic: a Two-Stage Regression Analysis Across 26 Metropolitan Areas

OBJECTIVE:We investigated whether a zip code's location or demographics are most predictive of changes in daily mobility throughout the course of the COVID-19 pandemic. DESIGN:We used a population-level study to examine the predictability of daily mobility during the COVID-19 pandemic using a two-stage regression approach, where generalised additive models (GAM) predicted mobility trends over time at a large spatial level, then the residuals were used to determine which factors (location, zip code-level features or number of non-pharmaceutical interventions (NPIs) in place) best predict the difference between a zip code's measured mobility and the average trend on a given date. SETTING:We analyse zip code-level mobile phone records from 26 metropolitan areas in the USA on 15 March-31 September 2020, relative to October 2020. RESULTS:While relative mobility had a general trend, a zip code's city-level location significantly helped to predict its daily mobility patterns. This effect was time-dependent, with a city's deviation from general mobility trends differing in both direction and magnitude throughout the course of 2020. The characteristics of a zip code further increased predictive power, with the densest zip codes closest to a city centre tended to have the largest decrease in mobility. However, the effect on mobility change varied by city and became less important over the course of the pandemic. CONCLUSIONS:The location and characteristics of a zip code are important for determining changes in daily mobility patterns throughout the course of the COVID-19 pandemic. These results can determine the efficacy of NPI implementation on multiple spatial scales and inform policy makers on whether certain NPIs should be implemented or lifted during the ongoing COVID-19 pandemic and when preparing for future public health emergencies.