CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004–2027)

Background: Hepatitis B remains a leading notifiable infection in mainland China, with a persistent burden shaped by chronic reservoirs, varied immunity, and shifting surveillance practices. Reliable short- to medium-term forecasts of reported hepatitis B cases are therefore valuable for planning diagnostics, care pathways, antiviral supply, and targeted prevention. Methods: , Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and regression diagnostics across training, validation, and test splits. Results: . Forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence. Conclusions: The findings demonstrate that integrating CEEMDAN with KOA optimised machine learning models delivers highly reliable short-term forecasts. This hybrid framework establishes a resilient continuum from epidemiological surveillance to predictive modelling and public health decision making, thereby providing essential foresight to guide optimal resource allocation and proactive intervention strategies for Hepatitis B control.