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Leisman KP, Owen C, Warns MM, Tiwari A, Bian GZ, Owens SM, Catlett C, Shrestha A, Poretsky R, Packman AI, Mangan NM. A modeling pipeline to relate municipal wastewater surveillance and regional public health data. Water Res 2024; 252:121178. [PMID: 38309063 DOI: 10.1016/j.watres.2024.121178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/18/2023] [Accepted: 01/22/2024] [Indexed: 02/05/2024]
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.
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Affiliation(s)
- Katelyn Plaisier Leisman
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Christopher Owen
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Maria M Warns
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Anuj Tiwari
- Discovery Partners Institute, University of Illinois Chicago, Chicago, IL, USA
| | - George Zhixin Bian
- Department of Computer Science, Northwestern University, Evanston, IL, USA
| | - Sarah M Owens
- Biosciences, Argonne National Laboratory, Lemont, IL, USA
| | - Charlie Catlett
- Discovery Partners Institute, University of Illinois Chicago, Chicago, IL, USA; Computing, Environment, and Life Sciences, Argonne National Laboratory, Lemont, IL, USA
| | - Abhilasha Shrestha
- Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois Chicago, Chicago, IL, USA
| | - Rachel Poretsky
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Aaron I Packman
- Center for Water Research, Northwestern University, Evanston, IL, USA; Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA
| | - Niall M Mangan
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA; Center for Water Research, Northwestern University, Evanston, IL, USA.
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