The relationship between human mobility measures and SAR-Cov-2 transmission varies by epidemic phase and urbanicity: results from the United States

Abstract

Global efforts to prevent the spread of the SARS-COV-2 pandemic in early 2020 focused on non-pharmaceutical interventions like social distancing; policies that aim to reduce transmission by changing mixing patterns between people. As countries have implemented these interventions, aggregated location data from mobile phones have become an important source of real-time information about human mobility and behavioral changes on a population level. Human activity measured using mobile phones reflects the aggregate behavior of a subset of people, and although metrics of mobility are related to contact patterns between people that spread the coronavirus, they do not provide a direct measure. In this study, we use results from a nowcasting approach from 1,396 counties across the US between January 22nd, 2020 and July 9th, 2020 to determine the effective reproductive number (R(t)) along an urban/rural gradient. For each county, we compare the time series of R(t) values with mobility proxies from mobile phone data from Camber Systems, an aggregator of mobility data from various providers in the United States. We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties compared to baseline, but that the relationship weakens considerably after the initial 15 weeks of the epidemic, consistent with the emergence of a more complex ecosystem of local policies and behaviors including masking. Importantly, we highlight potential issues in the data generation process, representativeness and equity of access which must be addressed to allow for general use of these data in public health.

Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model

“…While mobility has a mechanistic relationship with disease transmission, the association between movement data and viral transmission is complex and variable across time and space, possibly because of changes in mask use and other non-pharmaceutical interventions. [ 24 , 25 ]…”

“…While mobility has a mechanistic relationship with disease transmission, the association between movement data and viral transmission is complex and variable across time and space, possibly because of changes in mask use and other non-pharmaceutical interventions. [24,25] In conclusion, the modeling approach described here provides a coherent framework for simultaneously estimating the trend in SARS-CoV-2 infections and the fraction of the population that has been infected previously, providing key information on the viral dynamics at…”

Mobility data as a proxy for epidemic measures

“…While mobility metrics can potentially behave as proxies for epidemiological measures, such as contact rate and effective reproductive number, the intensity and direction of this relationship can change by epidemic stage 9 . For example, early in the pandemic, before mask mandates were widely adopted, a measure of the proportion of individuals in a county who spent time outside their home was a useful proxy for potentially contagious contacts.…”