ArticleJournal of water and health2026
A dual-branch deep learning framework for tiered early warning of COVID-19 utilizing wastewater data.
Article in Journal of water and health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundWastewater 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.
methodsUsing 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.
resultsOn 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
conclusionsThe 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.
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