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

Andrew Bo Liu (), Daniel Lee, Amogh Prabhav Jalihal, William P. Hanage and Michael Springer ()
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Andrew Bo Liu: Harvard Medical School
Daniel Lee: Harvard Medical School
Amogh Prabhav Jalihal: Harvard Medical School
William P. Hanage: Harvard T.H. Chan School of Public Health
Michael Springer: Harvard Medical School

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-44199-7

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