Wastewater-based epidemiology (WBE) has emerged as a valuable tool for monitoring the prevalence of SARS-CoV-2 in communities. By analyzing viral RNA in wastewater, WBE can provide early warning of outbreaks, track the spread of the virus, and inform public health decision-making. However, the effectiveness of WBE depends on the strategic selection of sampling points within wastewater networks.

This study aims to identify optimal sampling points for SARS-CoV-2 surveillance using the Storm Water Management Model (SWMM). SWMM is a widely used hydraulic and hydrologic modeling software that can simulate the flow of wastewater through networks. By incorporating information on wastewater flow rates, pipe networks, and potential sources of contamination, SWMM can help identify locations where the concentration of SARS-CoV-2 is likely to be highest.

To optimize sampling point selection, we developed a modeling framework that integrates SWMM with data on wastewater flow rates, population density, and land use patterns. We then evaluated various sampling strategies, including random sampling, targeted sampling based on population density, and sampling at key infrastructure points (e.g., wastewater treatment plants, pumping stations).

Our results demonstrate that targeted sampling based on population density and key infrastructure points is most effective in capturing the spatial variability of SARS-CoV-2 in wastewater networks. By strategically placing sampling points in these locations, public health authorities can maximize the efficiency and effectiveness of wastewater-based surveillance efforts.

In conclusion, this study provides a valuable framework for identifying optimal sampling points in wastewater networks for SARS-CoV-2 surveillance. By leveraging SWMM and integrating relevant data, public health agencies can enhance their ability to detect and monitor outbreaks, inform public health decision-making, and protect the health of communities.