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Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics

Author

Listed:
  • Andrew Bo Liu

    (Harvard Medical School
    Harvard Medical School)

  • Daniel Lee

    (Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Amogh Prabhav Jalihal

    (Harvard Medical School)

  • William P. Hanage

    (Harvard T.H. Chan School of Public Health)

  • Michael Springer

    (Harvard Medical School)

Abstract

Researchers and policymakers have proposed systems to detect novel pathogens earlier than existing surveillance systems by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases (standard error: 76 cases) compared to 3,400 (standard error: 161 cases). Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Air travel monitoring does not accelerate outbreak detection in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44199-7
    DOI: 10.1038/s41467-023-44199-7
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    References listed on IDEAS

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