Can Tracking Mobility be used as a Public Health Tool Against COVID-19 Following the Expiration of Stay-at-Home Mandates?

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 1st 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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Can tracking mobility be used as a public health tool against COVID-19 following the expiration of stay-at-home mandates?

10.1101/2021.08.27.21262629 ◽

2021 ◽

Author(s):

Peter Hyunwuk Her ◽

Sahar Saeed ◽

Khai Hoan Tram ◽

Sahir Rai Bhatnagar

Keyword(s):

Public Health ◽

Additive Model ◽

Principal Component ◽

Human Interaction ◽

Functional Principal Component Analysis ◽

Time Lags ◽

Calendar Time ◽

Mobility Index ◽

Human Interactions ◽

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Background: 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. The aim of this study was to create a novel multidimensional mobility index to capture the complexity of human interaction and evaluate its utility as an early indicator of surges in COVID-19 cases. Methods: We used publicly available anonymous cell phone data compiled by SafeGraph, from all counties in Illinois, Ohio, Michigan and Indiana between January 1st to December 8, 2020. Six metrics of mobility were extracted for each county. Changes in mobility were defined as a time-updated 7-day rolling average. We used an unsupervised machine learning method known as functional principal component analysis (fPCA) to construct the latent mobility index (MI) using the six metrics of mobility. 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. Results: 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. Following the expiration of stay-at-home orders, mobility increased across all counties and this was particularly evident on weekends. 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. Conclusion: 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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