Journal of water and health · 2026
A dual-branch deep learning framework for tiered early warning of COVID-19 utilizing wastewater data.
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Li Xiaoqiang, Wu Changqian, Jiang Jingyi, Wu Shufan, Zhu Cheng, Yang Minhui, Chen Zhiyong, Chen Xiaoyue, Tan Lifeng
Аннотация
BACKGROUND: Wastewater offers earlier, population-level signals, yet few models integrate environmental drivers for reliable routine COVID-19 alerts. We hypothesized that combining wastewater and environmental covariates in a dual-branch deep model leveraging FFT would improve forecasting and alerting.
METHODS: Using weekly wastewater, meteorological, and case data from Changzhou, China (Jan 29-Dec 10, 2024), we developed a framework that forecasts case trajectories and triggers tiered yellow/red alerts at predefined thresholds.
RESULTS: On 2-week-ahead internal tests, performance was: RMSE 1.40 (1.13-1.67), MAE 1.23 (0.99-1.48), MAPE 10.44% (5.20-16.40), and R2 0.99 (0.99-0.99). On an external test, both yellow and red alerts were correctly predicted 3 weeks ahead. Versus a naive baseline and the COVID-19 Forecast Hub model, our approach reduced missed alerts, whereas the Forecast Hub model reduced false declarations. Ablation showed necessity of the dual-branch architecture and covariates.
CONCLUSIONS: The novel framework delivers accurate, timely forecasts and reliable early warnings from multi-source data, supporting proactive public health response to COVID-19. It may also be a promising approach for the prediction of other infectious diseases. However, validating and adapting the approach across locations, and epidemic patterns is a key next step to establish robustness, generalizability, and operational value.
METHODS: Using weekly wastewater, meteorological, and case data from Changzhou, China (Jan 29-Dec 10, 2024), we developed a framework that forecasts case trajectories and triggers tiered yellow/red alerts at predefined thresholds.
RESULTS: On 2-week-ahead internal tests, performance was: RMSE 1.40 (1.13-1.67), MAE 1.23 (0.99-1.48), MAPE 10.44% (5.20-16.40), and R2 0.99 (0.99-0.99). On an external test, both yellow and red alerts were correctly predicted 3 weeks ahead. Versus a naive baseline and the COVID-19 Forecast Hub model, our approach reduced missed alerts, whereas the Forecast Hub model reduced false declarations. Ablation showed necessity of the dual-branch architecture and covariates.
CONCLUSIONS: The novel framework delivers accurate, timely forecasts and reliable early warnings from multi-source data, supporting proactive public health response to COVID-19. It may also be a promising approach for the prediction of other infectious diseases. However, validating and adapting the approach across locations, and epidemic patterns is a key next step to establish robustness, generalizability, and operational value.