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Updated: Sep 19, 2026
Real-Time Polymerase Chain Reaction-Based Detection and Quantification of Hepatitis B Virus DNA
Published on: December 15, 2023
CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a
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:
We compiled a national monthly series of hepatitis B notifications from January 2004 to December 2025 and applied Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to isolate multiscale temporal components. Four modelling approaches - gated recurrent units (GRU), convolutional neural networks (CNN), support vector machines (SVM), and a Transformer encoder - were trained on CEEMDAN-derived features using a sliding 12-month window and recursively extended to 24-month horizons. Hyperparameters were optimised via the Kepler Optimization Algorithm (KOA), while performance was assessed through R2, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and regression diagnostics across training, validation, and test splits.
Results:
All models captured dominant trends and seasonality; on the held-out test split, Transformer again delivered the best out-of-sample fit (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, and R2 = 0.928), while SVM ranked second with MAE, MAPE and RMSE values that were 11.317%, 11.615%, and 9.964% higher than the Transformer model's. CNN performed better than GRU but worse than SVM on the test set, achieving MAE, MAPE and RMSE reductions of 21.985%, 22.178%, and 14.944% relative to GRU, alongside a 6.158% larger R2. 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.
