The relationship between human mobility measures and SAR-Cov-2 transmission varies by epidemic phase and urbanicity: results from the United States
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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.
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SciScore for 10.1101/2021.04.15.21255562: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Second, evaluating more parsimonious models allows us to infer some general rules of thumb regarding mobility metrics and their association with R(t) while identifying major limitations in the creation and interpretation of these metrics. Besides percent change in the number of individuals traveling into a county, entropy and dwell …
SciScore for 10.1101/2021.04.15.21255562: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Second, evaluating more parsimonious models allows us to infer some general rules of thumb regarding mobility metrics and their association with R(t) while identifying major limitations in the creation and interpretation of these metrics. Besides percent change in the number of individuals traveling into a county, entropy and dwell remain in our parsimonious model. As entropy, or the unpredictability of individuals movement increases, R(t) also increases. Anecdotally this process makes sense as individuals traveling to more locations than usual may be increasing their contact network. However, a decrease in the number of locations that a user visits can also be considered to be unpredictable and therefore result in higher entropy, underscoring the impact of appropriate baselines. Changes in entropy from week to week with no exogenous interventions should be interpreted in a different light than changes in entropy during the implementation of a travel restriction. Dwell, or the average number of locations an individual in a county spends at least 5 minutes, had an unexpected negative relationship with R(t) in our parsimonious model, likely due to artifacts of the way in which these data are collected on device. In following orders for travel restrictions or physical distancing, one might reasonably expect this metric to decrease and that individuals may travel to fewer locations. However, in our data, dwell increases almost universally in the aftermath of such interventions. T...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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