Wastewater surveillance is an evolving frontier, gaining prominence due to the impact of the COVID-19 pandemic, for example. This field involves analyzing wastewater samples from treatment plants and sewers to understand the health status of communities within sewer network catchments. Researchers emphasize the need for finer spatial resolution when studying health status markers like antimicrobial resistance genes, SARS-CoV-2, and indicators of illicit drug use. This precision enables study of fate and transport and targeted interventions in areas with greater health burdens. However, this finer granularity raises ethical concerns regarding geoprivacy. Public release of data can lead to biases and potential social and economic harm to specific populations. There is also a risk of function creep, where well-intentioned research may be misused by other institutions. To address these challenges, we propose a method to apply the k-anonymity concept (i.e., aggregating data into larger, anonymized k-sized groups) to protect individual and community privacy on sewer networks, utilizing data such as U.S. Census and sewer network information to identify areas where sampling should be restricted. Our project "maskynet" will provide a Python package for identifying sampling sites with optimal geoprivacy protection, addressing the trade-off between geoprivacy and modeling error. Future developments could include automating subcatchment delineation and incorporating decay models for health status markers by using sewer travel time (e.g., estimated using EPA SWMM). In summary, wastewater surveillance offers vital insights for wastewater resources and public health researchers and “maskynet” will enable researchers to develop ethical sampling designs that safeguard privacy.
