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Bidirectional contact tracing could dramatically improve COVID-19 control

Author

Listed:
  • William J. Bradshaw

    (Max Planck Institute for Biology of Ageing
    Alt. Technology Labs)

  • Ethan C. Alley

    (Alt. Technology Labs
    Massachusetts Institute of Technology)

  • Jonathan H. Huggins

    (Boston University)

  • Alun L. Lloyd

    (North Carolina State University)

  • Kevin M. Esvelt

    (Massachusetts Institute of Technology)

Abstract

Contact tracing is critical to controlling COVID-19, but most protocols only “forward-trace” to notify people who were recently exposed. Using a stochastic branching-process model, we find that “bidirectional” tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in effective reproduction number (Reff) achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realised by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20325-7
    DOI: 10.1038/s41467-020-20325-7
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    Cited by:

    1. Andrew Bo Liu & Daniel Lee & Amogh Prabhav Jalihal & William P. Hanage & Michael Springer, 2023. "Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Hakan Yilmazkuday, 2022. "COVID-19 and Exchange Rates: Spillover Effects of U.S. Monetary Policy," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 50(1), pages 67-84, June.
    3. 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.
    4. 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.
    5. Wang, Haiying & Moore, Jack Murdoch & Small, Michael & Wang, Jun & Yang, Huijie & Gu, Changgui, 2022. "Epidemic dynamics on higher-dimensional small world networks," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    6. 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.
    7. Elías, L. Llamazares & Elías, S. Llamazares & del Rey, A. Martín, 2022. "An analysis of contact tracing protocol in an over-dispersed SEIQR Covid-like disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).

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