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Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Author

Listed:
  • Xuan Li

    (University of Technology Sydney)

  • Huan Liu

    (University of Technology Sydney)

  • Li Gao

    (South East Water)

  • Samendra P. Sherchan

    (Morgan State University
    Tulane University)

  • Ting Zhou

    (University of Technology Sydney)

  • Stuart J. Khan

    (University of New South Wales)

  • Mark C. M. Loosdrecht

    (Delft University of Technology)

  • Qilin Wang

    (University of Technology Sydney)

Abstract

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40305-x
    DOI: 10.1038/s41467-023-40305-x
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    References listed on IDEAS

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    1. 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.
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