Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties
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 M.C. van Loosdrecht (TU Delft - BT/Environmental Biotechnology)
Qilin Wang (University of Technology Sydney)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.
help_outline