From wastewater to GIS-based reporting: the ANNA-WES data model for reliable biomarker tracking in wastewater and environmental surveillance

Since the COVID-19 pandemic, wastewater-based epidemiology (WBE) has emerged as a useful additional diagnostic tool for public health management. For rapid reporting of results and an automated implementation of WBE as a monitoring tool, a digital work flow of data is crucial. Here, we present the Automated Network for Normalization, Analysis, and Visualization of Wastewater and Environmental Surveillance (ANNA-WES) – a comprehensive workflow integrating GIS-based data entry, Python-driven data processing, and ArcGIS-supported visualization. ANNA-WES streamlines data transfer between wastewater treatment plant operators, decision-makers, and the public while ensuring harmonized data processing for transferability, precise georeferencing of index cases, and near real-time SARS-CoV-2 biomarker reporting. To enhance data reliability, we embedded an unsupervised quality control algorithm that filters outliers based on gene ratios, surrogate viruses, water quality parameters, and theoretical reproductive value thresholds. Designed for scalability, ANNA-WES integrates into public dashboards and can be combined with regional or national health data, providing a robust decision-support system for infectious disease surveillance. The workflow is adaptable to various pathogens or biomarkers, advancing WBE as a continuous, quality-controlled public health monitoring tool.