Cell Phone Data Shows Social Distancing Trends During COVID-19

An analysis of US cell phone data showed that social distancing practices increased by over nine times from late January through late March and then decreased by about half through mid-June, according to a study published in PLOS ONE.

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Since the coronavirus pandemic began spreading across the US, government and public health officials have implemented social distancing guidelines to reduce the rate of infection. The researchers noted that people engaged in social distancing practices voluntarily or because of government restrictions, or some combination of the two. Understanding the interplay between these two factors could help inform strategies to reduce disease spread, the team stated.

The group set out to develop a model of social distancing behaviors across the US to better understand practices across geographic areas. The model used cell phone tracking data to indicate the amount of time spent at home – a proxy for social distancing – in counties throughout the country.

The team used this model to explore how stay-at-home behaviors evolved over the first 21 weeks of the spread of COVID-19 in the US, from late January to June, 2020. Researchers examined how these behaviors changed in relation to governmental restrictions and demographic factors that could influence social distancing, including population density, the presence of children in households, education, race, and income.

The analysis showed that social distancing practices increased by over nine times from late January through late March, and then decreased by about half through mid-June. Demographic factors appear to have driven these changes to a substantially greater degree.

Additionally, there was also a tendency for behaviors to cluster, creating hotspots of counties with low social distancing.

“The strong tendency of social distances to cluster has a number of ramifications. Clustering of low social distance counties could show increasing levels of disease while clusters or coldspots of high social distance counties could show decreasing levels of disease,” the researchers said.

“Even if this leads to an aggregate decrease over some period, having clusters of low social distance counties with increasing incidence of disease may impede economic recovery, even in clusters of high social distance counties, because of the potential for contagion between low and high social distance county clusters.”

These findings indicate that encouraging voluntary distancing could potentially be an effective and lower-cost alternative to governmental restrictions. Such encouragement could boost acceptance of restrictions and increase compliance with distancing rules, leading to an even greater degree of distancing.

"Cell phone location and mobility data reveal that social distancing in the U.S. during the COVID-19 pandemic was initially voluntary rather than a response to governmental jurisdictional restrictions. As the pandemic progressed, both effects reinforced each other, increasing social distancing far more than what could be explained by the sum of the individual effects," said Rajesh Narayanan, a professor at Louisiana State University.

Cell phone location data has played a significant role in tracking and monitoring social distancing practices in different areas. A recent study published in JAMA Internal Medicine showed that county-level cell phone location data could help public health officials better monitor adherence to stay-at-home guidelines, as well as help identify areas at greatest risk for rapid COVID-19 spread.

“This study demonstrates that anonymized cell phone location can help researchers and public health officials better predict the future trends in the COVID-19 pandemic,” said corresponding author Shiv T. Sehra, MD, Director of the Internal Medicine Residency Program at Mount Auburn Hospital.

“To our knowledge, our study is among the first to evaluate the association of cell phone activity with the rate of growth in new cases of COVID-19, while considering regional confounding factors.”