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Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge

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  • Edward H. Kaplan

    (Yale School of Management
    Yale University
    Yale University)

  • Dennis Wang

    (Yale School of Medicine)

  • Mike Wang

    (Yale University)

  • Amyn A. Malik

    (Yale University)

  • Alessandro Zulli

    (Yale University)

  • Jordan Peccia

    (Yale University)

Abstract

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ± 0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.

Suggested Citation

  • Edward H. Kaplan & Dennis Wang & Mike Wang & Amyn A. Malik & Alessandro Zulli & Jordan Peccia, 2021. "Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge," Health Care Management Science, Springer, vol. 24(2), pages 320-329, June.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-020-09525-1
    DOI: 10.1007/s10729-020-09525-1
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    References listed on IDEAS

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    1. Edward H. Kaplan, 2020. "Containing 2019-nCoV (Wuhan) coronavirus," Health Care Management Science, Springer, vol. 23(3), pages 311-314, September.
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    Cited by:

    1. Xuan Li & Huan Liu & Li Gao & Samendra P. Sherchan & Ting Zhou & Stuart J. Khan & Mark C. M. Loosdrecht & Qilin Wang, 2023. "Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Alec Morton & Ebru Bish & Itamar Megiddo & Weifen Zhuang & Roberto Aringhieri & Sally Brailsford & Sarang Deo & Na Geng & Julie Higle & David Hutton & Mart Janssen & Edward H Kaplan & Jianbin Li & Món, 2021. "Introduction to the special issue: Management Science in the Fight Against Covid-19," Health Care Management Science, Springer, vol. 24(2), pages 251-252, June.

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