Novel mobility index tracks COVID-19 transmission following stay-at-home orders
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Abstract
Considering the emergence of SARS-CoV-2 variants and low vaccine access and uptake, minimizing human interactions remains an effective strategy to mitigate the spread of SARS-CoV-2. Using a functional principal component analysis, we created a multidimensional mobility index (MI) using six metrics compiled by SafeGraph from all counties in Illinois, Ohio, Michigan and Indiana between January 1 to December 8, 2020. Changes in mobility were defined as a time-updated 7-day rolling average. Associations between our MI and COVID-19 cases were estimated using a quasi-Poisson hierarchical generalized additive model adjusted for population density and the COVID-19 Community Vulnerability Index. Individual mobility metrics varied significantly by counties and by calendar time. More than 50% of the variability in the data was explained by the first principal component by each state, indicating good dimension reduction. While an individual metric of mobility was not associated with surges of COVID-19, our MI was independently associated with COVID-19 cases in all four states given varying time-lags. Following the expiration of stay-at-home orders, a single metric of mobility was not sensitive enough to capture the complexity of human interactions. Monitoring mobility can be an important public health tool, however, it should be modelled as a multidimensional construct.
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SciScore for 10.1101/2021.08.27.21262629: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study also has limitations. Although cell phone data was freely available and could help to predict trends during the pandemic, it is only a proxy for human contact. In this study we attempted to define a more robust definition of mobility, however it still remains a surrogate exposure. The association between mobility and COVID-19 cases may be underestimated, given our outcome is dependent on testing. Testing capacity has …
SciScore for 10.1101/2021.08.27.21262629: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study also has limitations. Although cell phone data was freely available and could help to predict trends during the pandemic, it is only a proxy for human contact. In this study we attempted to define a more robust definition of mobility, however it still remains a surrogate exposure. The association between mobility and COVID-19 cases may be underestimated, given our outcome is dependent on testing. Testing capacity has significantly changed throughout the pandemic in the United States. Seroprevalence studies estimate case detection is underrepresented by a factor of three times [39]. Although we do not believe this underrepresentation to be differential, outcomes such as COVID-19 related deaths and hospitalizations may be less bias. While the advantage is clear, the utility of these outcomes as a “real-time” public health tool is debatable as the latency period (time of infection to outcome) is long (>21 days). As with all observational studies, associations should not be interpreted causally. Our model does not take into consideration confounding interventions that could also increase or mitigate transmission such as the proportion of the population adhering to physical distancing guidelines, wearing masks, interactions outside vs inside or air quality. To effectively measure social distancing patterns individual wearable technology or trackers, would be more sensitive compared to aggregate data, but this raises ethical and privacy concerns [40]. Recent reports have ...
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.
Results from scite Reference Check: We found no unreliable references.
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