Optimizing wastewater surveillance data by normalization to monitor pathogen circulation in dynamic populations for public health decision-making

Wastewater surveillance is increasingly applied to monitor infectious disease trends. However, interpreting pathogen concentrations in wastewater is challenging because wastewater dilution and population size fluctuations can obscure true infection trends. To adjust for this, normalization using the human biomarkers Pepper Mild Mottle Virus (PMMoV), crAssphage, or flow-based approaches are commonly applied. Yet, the ability of these strategies to capture within-week and seasonal population changes, such as those caused by commuting and tourism, and their comparative effectiveness remains unclear. We conducted a longitudinal study at 59 wastewater treatment plants across the Netherlands, with varying levels of tourism and commuting. Between December 2020 and January 2021, and July 2023 to January 2024, 3829 wastewater samples were collected. SARS-CoV-2 (target pathogen), PMMoV, and crAssphage concentrations were quantified by RT-qPCR and flow data was retrieved from operational records. The different normalization strategies were evaluated by their effects on viral load outcomes in relation to COVID-19 case data and trend stability. PMMoV and crAssphage both reflect seasonal holiday-related dynamics and within-week changes due to commuting. Biomarker variability decreases as population size increases, indicating greater sensitivity in smaller catchments. Normalization of SARS-CoV-2 trends with biomarkers or flow leads to reduced residual variance, facilitating trend recognition. Normalization using human biomarkers or flow can substantially improve the reliability of wastewater-based pathogen surveillance, especially in areas with dynamic populations. These findings guide selection of normalization strategies to improve detection of pathogen trends, thereby enhancing the value of wastewater surveillance for public health decision-making.