Determinants and spatio-temporal structure of variability in wastewater SARS-CoV-2 viral load measurements in Switzerland: key insights for future surveillance efforts

BackgroundWastewater-based surveillance (WBS) has emerged as a valuable tool for monitoring the circulation of infectious pathogens in the population, offering a complement to classical surveillance methods. However, the interpretation of WBS data is often challenged by substantial variability and bias in viral load measurements, which can stem from differences in laboratory protocols, population demographics, and in-sewer fate and transport processes. Methods and FindingsWe analysed 23,025 wastewater samples collected between February 2022 and November 2023 from 118 wastewater treatment plants (WWTPs) across Switzerland. Samples of influent wastewater were processed by 8 independent laboratories using distinct concentration, extraction and quantification methods to estimate SARS-CoV-2 concentrations. Concentrations were converted to daily viral loads using wastewater flow rates, and normalized by the size of the population served by the treatment plant. To characterise the contributions of different sources of variation, we applied a Bayesian modelling framework, incorporating fixed effects and spatio-temporal random effects to separate the contributions of laboratory protocols, demographic factors, and geographic structure to the observed variability in SARS-CoV-2 viral loads. Our analysis revealed that laboratory-specific biases were substantial, and that local demographic characteristics (particularly the age structure of the catchment population) also influenced viral load estimates. Adjusting for these sources of bias improved the reliability of interpretations based on viral loads, as indicated by an increased correlation with regional COVID-19 hospitalization data (from 0.55 for raw data to 0.77 for adjusted temporal trends). Dynamic time warping clustering of the adjusted temporal trends uncovered distinct geographic clusters, highlighting persistent spatial structures that evolved over successive epidemic phases. ConclusionsOur study demonstrates that variability in WBS data during the 2022-2023 Swiss national SARS-CoV-2 surveillance campaign is driven by a complex interplay of laboratory methods, population demographics, and spatio-temporal dynamics. Standardization of laboratory protocols and the implementation of robust statistical adjustment methods such as the one demonstrated here can enhance the reliability of WBS as a public health surveillance tool. These findings provide underscore the importance of advanced data processing methods for enhancing future surveillance efforts and for the effective integration of wastewater data into public health decision-making frameworks.