It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US

Abstract

Restricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using 1 year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications.

Inter-prefectural Travel and Network Connectedness During the COVID-19 Pandemic in Japan

“…27,28 Notably, the negative correlation between closeness centrality and infection rates remained evident throughout the entirety of the pandemic in Japan, contrasting with prior research that suggests mobility is only a reliable predictor of infection rates during early stages of the pandemic. 26,29,30 Notably, Okinawa, Osaka, and Tokyo were consistent outliers in these analyses. Though outliers did not appear to markedly affect the association between closeness and COVID-19 positivity, the increase in cases nationwide naturally led to increased transmission, especially among those prefectures with the most "closeness."…”

Mobility was a significant determinant of reported COVID-19 incidence during the Omicron Surge in the most populous U.S. Counties

“…We relied upon the data on the six weekly Google mobility measures in the 111-county database, covering the 106-week period from the week ending February 24, 2020, through the week ending February 28, 2022, to compute the first principal component as a summary measure of mobility [ 20 , 21 ]. This summary measure, which we refer to here as our mobility indicator , represents the linear combination of the six individual mobility categories that captures the largest fraction of the overall variance of the data [ 28 ].…”

“…Mobility is a multidimensional concept that has been variously gauged by such diverse measures as smartphone visits to bars and restaurants [18], traffic patterns [7], and television watching as a proxy for time spent at home [19]. Here, following the lead of two key papers [20,21], we employ the statistical technique of principal component analysis to collapse the six-dimensional Google Mobility Reports [22] into a single mobility indicator. Further adhering to a recent study of reported case incidence and hospitalization in relation to vaccination rates during the Delta surge [23], we restrict our analysis to the most populous counties in the United States, together comprising approximately 44% of the total U.S. population.…”

Mobility was a Significant Determinant of Reported COVID-19 Incidence During the Omicron Surge in the Most Populous U.S. Counties

“…We relied upon the data on the six weekly Google mobility measures in the 111-county database, covering the 106-week period from the week ending February 24, 2020, through the week ending February 28, 2022, to compute the first principal component as a summary measure of mobility [23, 24]. This summary measure, which we refer to here as our mobility indicator , represents the linear combination of the six individual mobility categories that captures the largest fraction of the overall variance of the data [32].…”

“…Mobility is a multidimensional concept that has been variously gauged by such diverse measures as smartphone visits to bars and restaurants [21], traffic patterns [9], and television watching as a proxy for time spent at home [22]. Here, following the lead of two key papers [23, 24], we employ the statistical technique of principal component analysis to collapse the six-dimensional Google Mobility Reports [25] into a single mobility indicator. Further adhering to a recent study of reported case incidence and hospitalization in relation to vaccination rates during the Delta surge [26], we restrict our analysis to the most populous counties in the United States, together comprising approximately 44 percent of the total U.S. population.…”