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The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling

Nature Computational Science volume 4, pages 398–411 (2024)Cite this article

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Abstract

Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges—some related to accessing and processing these data, and some related to data quality—and propose several research directions to address them moving forward.

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Fig. 1: Broad overview of the data-processing pipeline.
Fig. 2: Fraction of the population in Philadelphia staying at home all day for 2019–2020 (7-day rolling average).
Fig. 3: Sources of error in the estimation of metrics from GPS location data.
Fig. 4: Ratio of DAU to MAU for five samples of GPS human-mobility data.
Fig. 5: Effect of stop-detection algorithm parameters on a synthetic trajectory.

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Data availability

Mobile phone mobility data from SafeGraph depicted in Fig. 2 were made available to the research community through SafeGraph’s COVID-19 Data Consortium (https://www.safegraph.com/covid-19-data-consortium). The data for Fig. 4 can be accessed from the following sources: SafeGraph, https://www.eff.org/deeplinks/2021/08/illinois-bought-invasive-phone-location-data-banned-broker-safegraph; X-mode57; Kochava, https://www.ftc.gov/news-events/news/press-releases/2022/08/ftc-sues-kochava-selling-data-tracks-people-reproductive-health-clinics-places-worship-other; Factori, https://datarade.ai/data-providers/lifesight/profile; Quadrant, https://www.quadrant.io/solutions/data-quality-dashboard. We have archived the websites referenced in this paper for future reference (Supplementary Table 1).

Code availability

Code for the evaluation of stop-detection on synthetic trajectories as shown in Fig. 5, as well as the visualization in Fig. 2, is available at https://github.com/Watts-Lab/stop-detection-validation.

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Acknowledgements

We thank B. Lake, M. Whiting, H. Hosseinmardi, and B. Hsiao for helpful discussions, and SafeGraph for making their data easily available. This work was supported by the National Science Foundation XSEDE program (grants SBE200005 and CIS210096 awarded to D.J.W.) as part of the COVID-19 HPC Consortium (https://covid19-hpc-consortium.org/).

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  1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA

    Francisco Barreras & Duncan J. Watts

  2. Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Duncan J. Watts

  3. Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA

    Duncan J. Watts

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F.B. and D.J.W. conceived the overall structure of the paper, wrote the paper and approved the final version of the paper. F.B. reviewed the literature, contributed to figures and coded the experiments.

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Barreras, F., Watts, D.J. The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling. Nat Comput Sci 4, 398–411 (2024). https://doi.org/10.1038/s43588-024-00637-0

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