Association Between Population Mobility Reductions and New COVID-19 Diagnoses in the United States Along the Urban–Rural Gradient, February–April, 2020

PEER REVIVEWED

Six maps of US counties (1 for each location type: Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, Workplaces, Residential), with each county colored according to whether it exhibits a significant positive or negative correlation or no significant correlation between changes in mobility and the percentage increase in new COVID-19 cases 11 days later. Each map has an associated catplot graph to its right, which displays the sign and the magnitude of the correlation across counties, where counties are stratified into 6 urban–rural classes: Large Central Metro, Large Fringe Metro, Medium Metro, Small Metro, Micropolitan, and Noncore. Maps are dominated by positive and significant correlations for nonresidential places, such as workplaces, and a significant negative correlation for residential places. Graphs show that the associations for each type of place are generally strongest in urban counties and weakest in rural counties.

Spatial distribution of the correlation between change in mobility and percentage increase in new COVID-19 cases 11 days later, from February 15 through April 26, 2020, by US county. Correlations are mapped for visits to 6 different types of places and plotted within 6 different urban–rural classifications. Significance is P < .05.  A decrease in visits to places outside the home, and an increase in time spent at home, are associated with reduced rates of new COVID-19 cases 11 days later in most counties, suggesting that restrictions on mobility can mitigate COVID-19 transmission. The association is stronger in more urban counties, suggesting that mobility restrictions may be most effective in urban areas. Abbreviation: metro, metropolitan.

Return to Article

Two catplot graphs displaying the correlation between the percentage increase in new COVID-19 cases and changes in visits to workplaces and residential places, each stratified by the 6 urban–rural classifications (Large Central Metro, Large Fringe Metro, Medium Metro, Small Metro, Micropolitan, and Noncore). Graphs show that the general patterns of correlations are similar to the initial analysis, where the growth in new cases is associated with more visits to workplaces and fewer visits to residential places, but that the strength of the patterns dissipates in the more rural counties to a greater extent.

Figure.
Post-hoc analysis of correlation between change in mobility and percentage increase in new COVID-19 cases 11 days later for February 15 through June 19, 2020, by US county. Correlations are shown for visits to workplaces and residential places and plotted within 6 different urban–rural classifications. Mobility data are from the Google Community Mobility Report, and confirmed COVID-19 case data are from the New York Times, Inc, Urban–rural classification data are from the National Center for Health Statistics. Significance is P< .05. The extended study period shows that the association between mobility change and new COVID-19 cases weakened somewhat as compared to the initial study period, particularly in more rural counties, reflecting the changing geographic pattern of disease dynamics occurring in May and June 2020. Abbreviation: metro, metropolitan.

Return to Article

Top