A modeling pipeline to relate municipal wastewater surveillance and regional public health data

Leisman, Katelyn Plaisier; Owen, Christopher; Warns, Maria M.; Tiwari, Anuj; Bian, George (Zhixin); Owens, Sarah M.; Catlett, Charlie; Shrestha, Abhilasha; Poretsky, Rachel; Packman, Aaron I.; Mangan, Niall M.

doi:10.1016/j.watres.2024.121178

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

Journal Article · Wed Jan 24 00:00:00 EST 2024 · Water Research

DOI:https://doi.org/10.1016/j.watres.2024.121178· OSTI ID:2467434

^[1]; ^[2]; ^[1]; ^[2]; Bian, George (Zhixin) ^[1]; ^[3]; Catlett, Charlie ^[4]; ^[2]; ^[2]; Packman, Aaron I. ^[1]; ^[1]

Northwestern University, Evanston, IL (United States)
University of Illinois, Chicago, IL (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)
University of Illinois, Chicago, IL (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

As COVID-19 becomes endemic, public health departments require improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging. We have developed a generalized methodology to improve the predictive power of wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but all model estimates of prevalence significantly improved correlation compared to doing no correction. We also investigate the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We do this in a rolling manner for corrected wastewater data and for other prevalence indicators, and statistically compare the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We find that wastewater trends often lead other COVID-19 indicators in predicting new surges.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: Illinois Department of Public Health; National Science Foundation (NSF); The Walder Foundation; USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2467434

Journal Information:: Water Research, Journal Name: Water Research Vol. 252; ISSN 0043-1354

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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60 APPLIED LIFE SCIENCES
Model selection
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A modeling pipeline to relate municipal wastewater surveillance and regional public health data

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