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.
Highlights
In the hopes of better informing public health decision-making, researchers have developed many prediction models to forecast the COVID-19 pandemic
It is challenging to disentangle the effects of overlapping non-pharmaceutical interventions (NPIs), such as the rapid increase in mask-wearing in early April 2020 alongside widespread lockdowns in many parts of the United States
The primary aim of our study is to disentangle how the association between mobility and COVID-19 infection rates varies across time and space
Summary
In the hopes of better informing public health decision-making, researchers have developed many prediction models to forecast the COVID-19 pandemic. Effective forecasts capable of identifying reliable leading indicators of emerging outbreaks could improve policy recommendations. To this end, factors such as maskwearing[1,2], weather[3,4], and demography[5] have been found to be associated with rates of infection in the United States. Cell phone mobility data has emerged as an appealing surrogate of government mandates Since it is a directly observable measure of human movement, it contains more information than the duration of government orders. It may serve as a better proxy for the actual quantity that government actions are intended to reduce: the relative frequency of risky in-person interactions where transmissions may occur. The ubiquity of accessible mobility data, and the lack of alternative sources of data—such as contact tracing information—has made mobility an attractive proxy for interactions
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