Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

Surface waters are widely used as drinking water sources and hence their quality needs to be continuously monitored. However, current routine monitoring programs are not comprehensive as they generally cover only a limited number of known pollutants and emerging contaminants. This study presents a risk-based approach combining suspect and non-target screening (NTS) to help extend the coverage of current monitoring schemes. In particular, the coverage of NTS was widened by combining three complementary separations modes: Reverse phase (RP), Hydrophilic interaction liquid chromatography (HILIC) and Mixed-mode chromatography (MMC). Suspect lists used were compiled from databases of relevant substances of very high concern (e.g., SVHCs) and the concentration of detected suspects was evaluated based on ionization efficiency prediction. Results show that suspect candidates can be prioritized based on their potential risk (i.e., hazard and exposure) by combining ionization efficiency-based concentration estimation, in vitro toxicity data or, if not available, structural alerts and QSAR.based toxicity predictions. The acquired information shows that NTS analyses have the potential to complement target analyses, allowing to update and adapt current monitoring programs, ultimately leading to improved monitoring of drinking water sources.

Analysing wastewater can be used to track infectious disease agents that are shed via stool and urine. Sewage surveillance of SARS-CoV-2 has been suggested as a tool to determine the extent of COVID-19 in cities and serve as an early warning for (re-)emergence of SARS-CoV-2 circulation in communities. The focus of this review is on the strength of evidence, opportunities and challenges for the application of sewage surveillance to inform public health decision making. Considerations for undertaking sampling programs are reviewed including sampling sites, strategies, sample transport, storage and quantification methods; together with the approach and evidence base for quantifying prevalence of infection from measured wastewater concentration. Published SARS-CoV-2 sewage surveillance studies (11 peer reviewed and 10 preprints) were reviewed to demonstrate the current status of implementation to support public health decisions. Although being very promising, a number of areas were identified requiring additional research to further strengthen this approach and take full advantage of its potential. In particular, design of adequate sampling strategies, spatial and temporal resolution of sampling, sample storage, replicate sampling and analysis, controls for the molecular methods used for the quantification of SARS-CoV-2 RNA in wastewater. The use of appropriate prevalence data and methods to correlate or even translate SARS-CoV-2 concentrations in wastewater to prevalence of virus shedders in the population is discussed.