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Individual level analysis of digital proximity tracing for COVID-19 in Belgium highlights major bottlenecks

Author

Listed:
  • Caspar Geenen

    (KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology)

  • Joren Raymenants

    (KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology
    The University of Melbourne at the Peter Doherty Institute for Infection and Immunity)

  • Sarah Gorissen

    (KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology)

  • Jonathan Thibaut

    (KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology)

  • Jodie McVernon

    (The University of Melbourne at the Peter Doherty Institute for Infection and Immunity
    Victorian Infectious Diseases Laboratory Epidemiology Unit, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity)

  • Natalie Lorent

    (University Hospitals Leuven, Respiratory Diseases
    KU Leuven, Dept of CHROMETA, Laboratory of Thoracic Surgery and Respiratory Diseases (BREATHE))

  • Emmanuel André

    (KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology
    University Hospitals Leuven, Laboratory Medicine)

Abstract

To complement labour-intensive conventional contact tracing, digital proximity tracing was implemented widely during the COVID-19 pandemic. However, the privacy-centred design of the dominant Google-Apple exposure notification framework has hindered assessment of its effectiveness. Between October 2021 and January 2022, we systematically collected app use and notification receipt data within a test and trace programme targeting around 50,000 university students in Leuven, Belgium. Due to low success rates in each studied step of the digital notification cascade, only 4.3% of exposed contacts (CI: 2.8-6.1%) received such notifications, resulting in 10 times more cases detected through conventional contact tracing. Moreover, the infection risk of digitally traced contacts (5.0%; CI: 3.0–7.7%) was lower than that of conventionally traced non-app users (9.8%; CI: 8.8-10.7%; p = 0.002). Contrary to common perception as near instantaneous, there was a 1.2-day delay (CI: 0.6–2.2) between case PCR result and digital contact notification. These results highlight major limitations of a digital proximity tracing system based on the dominant framework.

Suggested Citation

  • Caspar Geenen & Joren Raymenants & Sarah Gorissen & Jonathan Thibaut & Jodie McVernon & Natalie Lorent & Emmanuel André, 2023. "Individual level analysis of digital proximity tracing for COVID-19 in Belgium highlights major bottlenecks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42518-6
    DOI: 10.1038/s41467-023-42518-6
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    References listed on IDEAS

    as
    1. Marco Mancastroppa & Claudio Castellano & Alessandro Vezzani & Raffaella Burioni, 2021. "Stochastic sampling effects favor manual over digital contact tracing," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    2. Joren Raymenants & Caspar Geenen & Jonathan Thibaut & Klaas Nelissen & Sarah Gorissen & Emmanuel Andre, 2022. "Empirical evidence on the efficiency of backward contact tracing in COVID-19," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Simon Munzert & Peter Selb & Anita Gohdes & Lukas F. Stoetzer & Will Lowe, 2021. "Tracking and promoting the usage of a COVID-19 contact tracing app," Nature Human Behaviour, Nature, vol. 5(2), pages 247-255, February.
    4. Chris Wymant & Luca Ferretti & Daphne Tsallis & Marcos Charalambides & Lucie Abeler-Dörner & David Bonsall & Robert Hinch & Michelle Kendall & Luke Milsom & Matthew Ayres & Chris Holmes & Mark Briers , 2021. "The epidemiological impact of the NHS COVID-19 app," Nature, Nature, vol. 594(7863), pages 408-412, June.
    5. William J. Bradshaw & Ethan C. Alley & Jonathan H. Huggins & Alun L. Lloyd & Kevin M. Esvelt, 2021. "Bidirectional contact tracing could dramatically improve COVID-19 control," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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    Cited by:

    1. Zengmiao Wang & Peng Yang & Ruixue Wang & Luca Ferretti & Lele Zhao & Shan Pei & Xiaoli Wang & Lei Jia & Daitao Zhang & Yonghong Liu & Ziyan Liu & Quanyi Wang & Christophe Fraser & Huaiyu Tian, 2024. "Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data," Nature Communications, Nature, vol. 15(1), pages 1-8, December.

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