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Clustering Patterns Connecting COVID-19 Dynamics and Human Mobility Using Optimal Transport

Author

Listed:
  • Frank Nielsen

    (Sony Computer Science Laboratories Inc)

  • Gautier Marti

    (Independent Researcher)

  • Sumanta Ray

    (Centrum Wiskunde, Informatica)

  • Saumyadipta Pyne

    ((1) Public Health Dynamics Lab; and Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh; (2) Health Analytics Network)

Abstract

Social distancing and stay-at-home are among the few measures that are known to be effective in checking the spread of a pandemic such as COVID-19 in a given population. The patterns of dependency between such measures and their effects on disease incidence may vary dynamically and across different populations. We described a new computational framework to measure and compare the temporal relationships between human mobility and new cases of COVID-19 across more than 150 cities of the United States with relatively high incidence of the disease. We used a novel application of Optimal Transport for computing the distance between the normalized patterns induced by bivariate time series for each pair of cities. Thus, we identified 10 clusters of cities with similar temporal dependencies, and computed the Wasserstein barycenter to describe the overall dynamic pattern for each cluster. Finally, we used city-specific socioeconomic covariates to analyze the composition of each cluster.

Suggested Citation

  • Frank Nielsen & Gautier Marti & Sumanta Ray & Saumyadipta Pyne, 2021. "Clustering Patterns Connecting COVID-19 Dynamics and Human Mobility Using Optimal Transport," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 167-184, May.
    • Handle: RePEc:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-021-00255-0
    DOI: 10.1007/s13571-021-00255-0
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    References listed on IDEAS

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    1. James H. Fowler & Seth J. Hill & Remy Levin & Nick Obradovich, 2020. "The effect of stay-at-home orders on COVID-19 cases and fatalities in the United States," Papers 2004.06098, arXiv.org, revised May 2020.
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    Cited by:

    1. Alessia Benevento & Fabrizio Durante, 2023. "Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information," Mathematics, MDPI, vol. 12(1), pages 1-15, December.

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