Monitoring COVID-19 through Wastewater Based Epidemiology

: A New Analytical Framework and Technological Approaches Towards Infectious Disease Outbreak Tracking.

Abstract

An analytical framework has been developed for generating and interrogating epidemiological data in wastewater based epidemiology (WBE). Data processing methodologies were created to guide decisions regarding methodological characterisation and standardisation. 2,088 nucleic acid samples were analysed from 4 wastewater treatment plants (WWTPs) over 24 months, yielding 20,124 data points for developing and characterising methodologies for data generation and interrogation.

Wastewater storage at -20 °C was found to reduce precision from 97.5% to 40.0% (SARS-CoV-2 N1, gc/L). It was found that (i) pasteurisation significantly impacted control recovery, (ii) suspended particulate matter removal offered minimal benefit and (iii) kit-based nucleic acid clean-up provided no benefit over TRIzolᵀᴹ extraction. A methodology is presented using the geometric mean and non-detect censoring with multiple imputation by partner replicate values to avoid bias upon data processing.

Method performance was compared with COVID-19 clinical data, with Pearson's correlation coefficients between 0.822 and 0.875 for different WWTPs, while combined data showed 94.6% specificity, 97.5% precision, and 85.7% sensitivity (SARS-CoV-2 N1, gc/L) for detecting COVID-19 above 250 cases per million people. WBE data variability analysis determined Process Limits of Decision at 4.9×10³ and 6.0×10³ gc/L for SARS-CoV-2 N1 and E-Sarbeco, respectively. Flow-and-population normalisation offered minimal benefit, while flow-normalisation and CrAssphage-normalisation negatively impacted performance. SARS-CoV-2 N1 outperformed E-Sarbeco by both variability and native RNA recovery.

WBE data was a leading indicator of clinical data by 7 days, aligning with viral shedding profiles. Time-series analysis showed strong alignment of WBE data with clinical data, tracking the COVID-19 case-load profile. SARS-CoV-2 RNA removal at wastewater treatment was also assessed, showing significant reduction at Radstock WWTP and no significant increase in SARS-CoV-2 RNA in river water.

Date of Award	25 Jun 2025
Original language	English
Awarding Institution	University of Bath
Supervisor	Barbara Kasprzyk-Hordern (Supervisor), Edward Feil (Supervisor), Pedro Estrela (Supervisor) & Ruth Barden (Supervisor)