Public articles linked to the same research event.
Journal of global health This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.
This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.
This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.
This study compiled a national monthly series of hepatitis B notifications in mainland China from January 2004 to December 2025, applied CEEMDAN to isolate multiscale temporal components, and trained four models (GRU, CNN, SVM, and a Transformer encoder) with KOA-optimised hyperparameters on a sliding 12-month window recursively extended to 24-month horizons; the Transformer delivered the best out-of-sample fit on the held-out test split (MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, R² = 0.928), SVM ranked second, CNN outperformed GRU but not SVM, and forecasts for 2026-2027 remain elevated, signalling little improvement and even possible resurgence.