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The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology

Author

Listed:
  • Kyra H. Grantz

    (Johns Hopkins Bloomberg School of Public Health)

  • Hannah R. Meredith

    (Johns Hopkins Bloomberg School of Public Health)

  • Derek A. T. Cummings

    (University of Florida)

  • C. Jessica E. Metcalf

    (Princeton University)

  • Bryan T. Grenfell

    (Princeton University)

  • John R. Giles

    (Johns Hopkins Bloomberg School of Public Health)

  • Shruti Mehta

    (Johns Hopkins Bloomberg School of Public Health)

  • Sunil Solomon

    (Johns Hopkins Bloomberg School of Public Health)

  • Alain Labrique

    (Johns Hopkins Bloomberg School of Public Health)

  • Nishant Kishore

    (Harvard TH Chan School of Public Health)

  • Caroline O. Buckee

    (Harvard TH Chan School of Public Health)

  • Amy Wesolowski

    (Johns Hopkins Bloomberg School of Public Health)

Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.

Suggested Citation

  • Kyra H. Grantz & Hannah R. Meredith & Derek A. T. Cummings & C. Jessica E. Metcalf & Bryan T. Grenfell & John R. Giles & Shruti Mehta & Sunil Solomon & Alain Labrique & Nishant Kishore & Caroline O. B, 2020. "The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18190-5
    DOI: 10.1038/s41467-020-18190-5
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    Cited by:

    1. Sparks, Kevin & Moehl, Jessica & Weber, Eric & Brelsford, Christa & Rose, Amy, 2022. "Shifting temporal dynamics of human mobility in the United States," Journal of Transport Geography, Elsevier, vol. 99(C).
    2. Yulin Hswen & Ulrich Nguemdjo & Elad Yom-Tov & Gregory M Marcus & Bruno Ventelou, 2022. "Individuals’ willingness to provide geospatial global positioning system (GPS) data from their smartphone during the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    3. Huy Quan Vu & Shah Jahan Miah & Haiyang Xia & Gang Li & Birgit Muskat & Rob Law, 2023. "Advancing reliability assessment of venue-reference social media data for enhanced domestic tourism development," Information Technology & Tourism, Springer, vol. 25(3), pages 433-451, September.
    4. Yanchao Li & Ziyu Ran & Lily Tsai & Sarah Williams, 2023. "Using call detail records to determine mobility patterns of different socio-demographic groups in the western area of Sierra Leone during early COVID-19 crisis," Environment and Planning B, , vol. 50(5), pages 1298-1312, June.
    5. Lee, Wang-Sheng & Tran, Trang My & Yu, Lamont Bo, 2022. "Dual Circulation and Population Mobility during the Pandemic in China," IZA Discussion Papers 15269, Institute of Labor Economics (IZA).
    6. David J. Haw & Christian Morgenstern & Giovanni Forchini & Robert Johnson & Patrick Doohan & Peter C. Smith & Katharina D. Hauck, 2022. "Data needs for integrated economic-epidemiological models of pandemic mitigation policies," Papers 2209.01487, arXiv.org.

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