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 and environmental surveillance (WES) has emerged as a useful additional diagnostic tool for public health management. For rapid reporting of results and an automated implementation of WES as a monitoring tool, a digital workflow of the 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 Geographic Information System (GIS)-based data entry, Python-driven data processing, and ArcGIS-supported visualization. ANNA-WES streamlines data transfer among 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 WES as a continuous, quality-controlled public health monitoring tool.