Modeling and Analysis of Daily SARS-CoV-2 Concentrations in Wastewater in Ontario, Canada Using N1 and N2 Gene Targets and Random Forest Regression

This paper explores the potential of wastewater-based epidemiology to serve as a tool for real-time monitoring of SARS-CoV-2 concentrations, providing an alternative to traditional case reporting methods. By applying Random Forest Regression to model viral loads in wastewater, the study offers a more robust and accurate method for tracking COVID-19 trends, especially in the face of underreporting or delays in clinical data. The findings emphasize the critical role of wastewater surveillance in public health research, offering a non-invasive, scalable approach for early detection, trend analysis, and effective policy planning, which can be applied to future infectious disease outbreaks.