Review 1: "Characterizing Responsiveness to the COVID-19 Pandemic in the United States and Canada Using Mobility Data"

Michael Desjardins

doi:doi:10.1162/2e3983f5.72a97f69

This preprint utilizes mobile phone-derived human mobility data to characterize population responsiveness to rising Covid-19 case rates. Reviewers agree that the model and sensitivity analyses are valid, though the manuscript could have better addressed confounding.

by Michael Desjardins

Published onFeb 04, 2023

Characterizing responsiveness to the COVID-19 pandemic in the United States and Canada using mobility data

by Jean-Paul R. Soucy, David N. Fisman, Derek R. MacFadden, and Kevin A. Brown

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dx.doi.org

Description

AbstractBackgroundMobile phone-derived human mobility data are a proxy for disease transmission risk and have proven useful during the COVID-19 pandemic for forecasting cases and evaluating interventions. We propose a novel metric using mobility data to characterize responsiveness to rising case rates.MethodsWe examined weekly reported COVID-19 incidence and retail and recreation mobility from Google Community Mobility Reports for 50 U.S. states and nine Canadian provinces from December 2020 to November 2021. For each jurisdiction, we calculated the responsiveness of mobility to COVID-19 incidence when cases were rising. Responsiveness across countries was summarized using subgroup meta-analysis. We also calculated the correlation between the responsiveness metric and the reported COVID-19 death rate during the study period.FindingsResponsiveness in Canadian provinces (β= -1·45; 95% CI: -2·45, -0·44) was approximately five times greater than in U.S. states (β= -0·30; 95% CI: -0·38, -0·21). Greater responsiveness was moderately correlated with a lower reported COVID-19 death rate during the study period (Spearman’sρ= 0·51), whereas average mobility was only weakly correlated the COVID-19 death rate (Spearman’sρ= 0·20).InterpretationOur study used a novel mobility-derived metric to reveal a near-universal phenomenon of reductions in mobility subsequent to rising COVID-19 incidence across 59 states and provinces of the U.S. and Canada, while also highlighting the different public health approaches taken by the two countries.FundingThis study received no funding.Research in contextEvidence before the studyThere exists a wide body of literature establishing the usefulness of mobile phone-derived human mobility data for forecasting cases and other metrics during the COVID-19 pandemic. We performed a literature search to identify studies examining the opposite relationship, attempting to quantify the responsiveness of human mobility to changes in COVID-19 incidence. We searched PubMed on October 21, 2022 using the keywords “COVID-19”, “2019-nCoV”, or “SARS-CoV-2” in combination with “responsiveness” and one or more of “mobility”, “distancing”, “lockdown”, and “non-pharmaceutical interventions”. We scanned 46 published studies and found one that used a mobile phone data-derived index to measure the intensity of social distancing in U.S. counties from January 2020 to January 2021. The authors of this study found that an increase in cases in the last 7 days was associated with an increase in the intensity of social distancing, and that this effect was larger during periods of lockdown/shop closures.Added value of the studyOur study developed a metric of the responsiveness of mobility to rising case rates for COVID-19 and calculated it for 59 subnational jurisdictions in the United States and Canada. While nearly all jurisdictions displayed some degree of responsiveness, average responsiveness in Canada was nearly five times greater than in the United States. Responsiveness was moderately associated with the reported COVID-19 death rate during the study period, such that jurisdictions with greater responsiveness had lower death rates, and was more strongly associated with death rates than average mobility in a jurisdiction.Implications of all the available evidenceMobile phone-derived human mobility data has proven useful in the context of infectious disease surveillance during the COVID-19 pandemic, such as for forecasting cases and evaluating non-pharmaceutical interventions. In our study, we derived a metric of responsiveness to show that mobility data may be used to track the efficiency of public health responses as the pandemic evolves. This responsiveness metric was also correlated with reported COVID-19 death rates during the study period. Together, these results demonstrate the usefulness of mobility data for making broad characterizations of public health responses across jurisdictions during the COVID-19 pandemic and reinforce the value of mobility data as an infectious disease surveillance tool for answering present and future threats.

RR:C19 Evidence Scale rating by reviewer:

Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review:

This preprint titled “Characterizing responsiveness to the COVID-19 pandemic in the United States and Canada using mobility data” modeled the relationship between human mobility patterns from Google’s Community Mobility Reports and COVID-19 incidence rates at the provincial and state levels, respectively. No funding was provided to support this body of work. The authors note that there is a wide body of literature that examines the relationship between human mobility datasets and the impact on COVID-19 transmission for both retrospective and prospective analyses (despite their literature search only including papers between January 2020 to January 2021). They note that one paper found an increase in COVID-19 cases over a 7-day period resulted in increased social distancing and isolation behavior.

For this study, the authors utilized Google’s cell-phone based mobility data noted above to develop a metric of “responsiveness” to COVID-19 transmission trends; where responsiveness refers to the decline of a population’s mobility to curb the spread of the SARS-CoV-2 virus. The authors examined weekly-level mobility and COVID-19 case data in 59 political boundaries, specifically, US States and 9 Canadian provinces. The statistical modeling approach appears to be scientifically valid, where the outcome in change in mobility and the predictors are temporally-lagged COVID-19 case rates and temporally lagged “cases falling”. The hypothesis is that mobility will decline (i.e., higher responsiveness) when cases are rising. Sensitivity analyses were also conducted with models that considered different temporally lagged case rates and an alternative regression model to estimate responsiveness.

The authors found a negative relationship between reported COVID-19 incidence and changes in population-level mobility across nearly every state and province of the United States and Canada. Another key finding was that Canada exhibited approximately five times more responsiveness throughout the study period than the United States. Overall, this study is statistically rigorous and sound, while utilizing valid mobility and COVID-19 incidence data. The figures and results appear to be genuine and informative. One major concern is the lack of predictor variables that may address issues of confounding and minimizing potential spurious relationships. It would have been nice to see how mobility differed across various subpopulations, especially the most vulnerable groups, such as “essential” workers, ethnic minorities, and the impacts of vaccination. Furthermore, mobility is very heterogeneous within states and provinces, which was not examined with the current approach.

Review 1: "Characterizing Responsiveness to the COVID-19 Pandemic in the United States and Canada Using Mobility Data"

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