Relating SARS-CoV-2 RNA Measured in Wastewater Treatment Plants in Illinois to COVID-19 Public Health Data
Abstract
Estimating the prevalence of infectious disease in a community is useful for public health resource allocation, policy making, and messaging. When diseases such as COVID-19 become endemic in the community it is essential to have passive indicators that do not depend on voluntary testing data. Our team is working with public health departments to use wastewater to inform our understanding of COVID-19 prevalence in communities throughout Illinois. We have developed a generalized methodology to improve the predictive power of wastewater from treatment plants in the Chicago area. Connecting measured SARS-CoV-2 RNA to community prevalence is challenging, due to variations in the contributing population, the variable rate of wastewater flow, and the complexity of wastewater media, which impacts RNA decay rates and lab measurement accuracy. To quantify the impact of these factors we also track other viruses including pepper mild mottle virus (PMMoV), a biomarker for the number of people contributing to the wastewater, and bovine coronavirus (BCoV), a lab process recovery control. We build and compare a set of multi-linear regression models, which incorporate PMMoV, BCoV, and flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. Laboratory methods evolved rapidly during the COVID-19 pandemic, and we show that correction terms differ depending on the laboratory procedures used in analyzing the samples. Nonetheless, in all cases a correction model provides a significant improvement over doing no correction. We also determine optimal ways to both compare and aggregate RNA data across different sampling locations - whether multiple sample locations covering the same or overlapping service areas, or samples from multiple service areas in a larger region. For regions where we have trained our correction model, locations can be aggregated via a population weighted average, but we also discuss a simpler method using both the served population and either PMMoV or the flow rate when training more complicated models is difficult or impossible. This simpler method could be applied even if clinical prevalence data is unavailable, and can also be extended beyond SARS-CoV-2.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2023
- Bibcode:
- 2023AGUFMGH41B1108L