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A population-based controlled experiment assessing the epidemiological impact of digital contact tracing

Author

Listed:
  • Pablo Rodríguez

    (Member, Association for Computing Machinery (ACM))

  • Santiago Graña

    (Secretaría de Estado de Digitalización e Inteligencia Artificial (SEDIA), Secretaría General de Administración Digital, Ministerio de Asuntos Económicos y Transformación Digital)

  • Eva Elisa Alvarez-León

    (Dirección General de Salud Pública, Servicio Canario de la Salud, Gobierno de Canarias)

  • Manuela Battaglini

    (Transparent Internet)

  • Francisco Javier Darias

    (Dirección General de Salud Pública, Servicio Canario de la Salud, Gobierno de Canarias)

  • Miguel A. Hernán

    (Department of Epidemiology, Harvard TH Chan School of Public Health
    Department of Biostatistics, Harvard TH Chan School of Public Health
    Harvard-MIT Division of Health Sciences and Technology)

  • Raquel López

    (User Experience, INDRA)

  • Paloma Llaneza

    (Razona LegalTech)

  • Maria Cristina Martín

    (User Experience, INDRA)

  • Oriana Ramirez-Rubio

    (Centro de Coordinación de Alertas y Emergencias Sanitarias. Dirección General de Salud Pública, Calidad e Innovación. Ministerio de Sanidad)

  • Adriana Romaní

    (Centro de Coordinación de Alertas y Emergencias Sanitarias. Dirección General de Salud Pública, Calidad e Innovación. Ministerio de Sanidad)

  • Berta Suárez-Rodríguez

    (Centro de Coordinación de Alertas y Emergencias Sanitarias. Dirección General de Salud Pública, Calidad e Innovación. Ministerio de Sanidad)

  • Javier Sánchez-Monedero

    (School of Journalism, Media and Culture, Cardiff University)

  • Alex Arenas

    (Departament d’Enginyeria Informática i Matemátiques, Universitat Rovira i Virgili)

  • Lucas Lacasa

    (School of Mathematical Sciences, Queen Mary University of London
    Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), E-07122)

Abstract

While Digital contact tracing (DCT) has been argued to be a valuable complement to manual tracing in the containment of COVID-19, no empirical evidence of its effectiveness is available to date. Here, we report the results of a 4-week population-based controlled experiment that took place in La Gomera (Canary Islands, Spain) between June and July 2020, where we assessed the epidemiological impact of the Spanish DCT app Radar Covid. After a substantial communication campaign, we estimate that at least 33% of the population adopted the technology and further showed relatively high adherence and compliance as well as a quick turnaround time. The app detects about 6.3 close-contacts per primary simulated infection, a significant percentage being contacts with strangers, although the spontaneous follow-up rate of these notified cases is low. Overall, these results provide experimental evidence of the potential usefulness of DCT during an epidemic outbreak in a real population.

Suggested Citation

  • Pablo Rodríguez & Santiago Graña & Eva Elisa Alvarez-León & Manuela Battaglini & Francisco Javier Darias & Miguel A. Hernán & Raquel López & Paloma Llaneza & Maria Cristina Martín & Oriana Ramirez-Rub, 2021. "A population-based controlled experiment assessing the epidemiological impact of digital contact tracing," Nature Communications, Nature, vol. 12(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20817-6
    DOI: 10.1038/s41467-020-20817-6
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    Cited by:

    1. Atul Pokharel & Robert Soulé & Avi Silberschatz, 2021. "A case for location based contact tracing," Health Care Management Science, Springer, vol. 24(2), pages 420-438, June.
    2. Shoji, Masahiro & Cato, Susumu & Ito, Asei & Iida, Takashi & Ishida, Kenji & Katsumata, Hiroto & McElwain, Kenneth Mori, 2022. "Mobile health technology as a solution to self-control problems: The behavioral impact of COVID-19 contact tracing apps in Japan," Social Science & Medicine, Elsevier, vol. 306(C).

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