Abstract
As COVID-19 becomes endemic, public health departments benefit from 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 due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in 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 the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.
| Original language | English (US) |
|---|---|
| Article number | 121178 |
| Journal | Water Research |
| Volume | 252 |
| DOIs | |
| State | Published - Mar 15 2024 |
Funding
All authors were supported by the Illinois Department of Public Health and the Chicago Department of Public Health (CDPH) . K.P.L., C.O., M.M.W., A.T., S.M.O., C.C., A.S., R.P., A.I.P., and N.M.M. were also supported by the Walder Foundation Coronavirus Assessment Network and the University of Illinois system Discovery Partners Institute . M.M.W. was also supported by NSF research training grant DMS-1547394 . The conclusions, opinions, or recommendations in this paper are those of the authors and not of IDPH, CDPH, or MWRD. We thank the Illinois Department of Public Health (IDPH) for their partnership with the University of Illinois and Northwestern in the work discussed in this publication. We also thank the Metropolitan Water Reclamation District of Greater Chicago (MWRD) for their assistance in sample collection. All authors approved the final copy of the manuscript. We also thank David Morton for helpful conversations and feedback. All authors were supported by the Illinois Department of Public Health and the Chicago Department of Public Health (CDPH). K.P.L. C.O. M.M.W. A.T. S.M.O. C.C. A.S. R.P. A.I.P. and N.M.M. were also supported by the Walder Foundation Coronavirus Assessment Network and the University of Illinois system Discovery Partners Institute. M.M.W. was also supported by NSF research training grant DMS-1547394. The conclusions, opinions, or recommendations in this paper are those of the authors and not of IDPH, CDPH, or MWRD.
Keywords
- Model selection
- Normalization model
- Pandemic intelligence
- SARS-CoV-2
- Trend prediction
- Wastewater-based epidemiology
ASJC Scopus subject areas
- Environmental Engineering
- Civil and Structural Engineering
- Ecological Modeling
- Water Science and Technology
- Waste Management and Disposal
- Pollution
