Wastewater-based surveillance and early warning–forecasting framework for norovirus: a two-year longitudinal study in Shenzhen, China

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Objective: Norovirus (NoV) is a leading cause of acute gastroenteritis globally; however, traditional clinical surveillance underestimates its true infection burden. Wastewater-based epidemiology (WBE) offers a novel approach for comprehensive viral monitoring. This study aimed to develop and validate a practical WBE framework integrating a two-tiered early warning system and trend forecasting to support public health interventions against NoV. Methods: A two-year (August 2022-August 2024) WBE study was conducted in Shenzhen. NoV in influent wastewater samples from five wastewater treatment plants was monitored using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). We employed a clinical data-calibrated approach to derive estimates of NoV cases from reported infectious diarrhea data; these estimates served as the gold standard. Using this gold standard, we employed the Moving Epidemic Method (MEM) to establish and validate a two-tiered early warning system based on wastewater NoV concentrations. In addition, we developed a Poisson regression model (PRM) to forecast NoV infection trends. Results: < 0.01). The MEM-based scheme achieved 100% detection of early stage moderate-level epidemics (sensitivity = 89.6%, specificity = 79.3%) and a 60.0% early warning rate for high-level epidemics, with a negative predictive value of 95.2%. Additionally, the PRM enabled one-week-ahead forecasts of NoV infection trends. Conclusion: WBE monitoring effectively captures the seasonal fluctuations of NoV infections in the population. This study provides a practical WBE framework integrating a two-tiered early warning system with one-week-ahead trend forecasting, thereby enabling the transformation of passive monitoring into an actionable public health tool for NoV.

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