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Wastewater Surveillance (WS) is a crucial tool in the management of COVID-19 pandemic. The surveillance is based on enumerating SARS-CoV-2 RNA concentrations in the community's sewage. In this study, we used WS data to develop a regression model for estimating the number of active COVID-19 cases on a university campus. Eight univariate and multivariate regression model types i.e. Linear Regression (LM), Polynomial Regression (PR), Generalised Additive Model (GAM), Locally Estimated Scatterplot Smoothing Regression (LOESS), K Nearest Neighbours Regression (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Random Forest (RF) were developed and compared. We found that the multivariate RF regression model, was the most appropriate for predicting the prevalence of COVID-19 infections at both a campus level and hostel-level. We also found that smoothing the normalised SARS-CoV-2 data and employing multivariate modelling, using student population as a second independent variable, significantly improved the performance of the models. The final RF campus level model showed good accuracy when tested using previously unseen data; correlation coefficient of 0.97 and a mean absolute error (MAE) of 20 %. In summary, our non-intrusive approach has the ability to complement projections based on clinical tests, facilitating timely follow-up and response., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

In this study, we report the implementation of a comprehensive wastewater surveillance testing program at a university campus in Singapore to identify Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected individuals and the usage of pharmaceuticals and personal care products (PPCPs) as well as other emerging contaminants (ECs). This unique co-monitoring program simultaneously measured SARS-CoV-2 with chemical markers/contaminants as the COVID-19 situation evolved from pandemic to endemic stages, following a nationwide mass vaccination drive. SARS-CoV-2 RNA concentrations in wastewater from campus dormitories were measured using real-time reverse transcription-polymerase chain reaction (RT-qPCR) and corroborated with the number of symptomatic COVID-19 cases confirmed with the antigen rapid test (ART). Consistent results were observed where the concentrations of SARS-CoV-2 RNA detected in wastewater increased proportionately with the number of COVID-19 infected individuals residing on campus. Similarly, a wide range of ECs, including disinfectants and antibiotics, were detected through sensitive liquid chromatography with tandem mass spectrometry (LC-MS/MS) techniques to establish PPCPs consumption patterns during various stages of the COVID-19 pandemic in Singapore. Statistical correlation of SARS-CoV-2 RNA was observed with few ECs belonging to disinfectants, PCPs and antibiotics. A high concentration of disinfectants and subsequent positive correlation with the number of reported cases on the university campus indicates that disinfectants could serve as a chemical marker during such unprecedented times., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)