A quantitative decision-support framework for assessing the feasibility and sensitivity of wastewater-based epidemiology of respiratory virus surveillance

Wastewater-based epidemiology (WBE) has emerged as a cost-effective and non-invasive tool for monitoring infectious diseases. However, its practical implementation for virus surveillance remains challenged by uncertainty in detection sensitivity, which depends on both pathogen-specific and methodological factors. This study applies a quantitative decision-support framework to prospectively evaluate the feasibility and sensitivity of WBE, with a specific focus on estimating the minimum number of infected individuals required for reliable detection, for SARS-CoV-2, respiratory syncytial virus (RSV), influenza A virus (IAV), and rhinovirus (RhV), using year-round weekly wastewater data. SARS-CoV-2 was detected throughout the year, and RSV exhibited seasonal detection, whereas IAV and RhV were undetected despite concurrent clinical circulation. To interpret these observations, a Monte Carlo simulation framework incorporating fecal shedding rates, recovery efficiency, and RNA decay was used to assess detection feasibility prior to large-scale deployment. The model indicated that SARS-CoV-2 required the lowest infections to surpass a 50% detection threshold (0.85 per 100,000 population), followed by RSV and RhV, while IAV had the highest threshold (1177.02 per 100,000), primarily due to low fecal shedding. Sensitivity analysis identified viral shedding and recovery efficiency as the most influential parameters. Simulations further suggested that droplet digital PCR combined with recovery efficiencies ≥35% could enable reliable detection of all four viruses under urban conditions. Overall, these findings demonstrate that WBE sensitivity is jointly governed by virus-specific characteristics and methodological performance. The proposed framework, informed by field observations, provides a transparent and data-driven approach for feasibility-oriented planning and methodological optimization of WBE surveillance programs.