---
title: "CEEMDAN + KOA-Optimised Deep Learning Forecasts for Reported Hepatitis B in Mainland China (2004–2"
id: "pubmed-42757779"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42757779"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42757779/"
doi: "10.7189/jogh.16.04200"
published_at: "2026-09-18T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# CEEMDAN + KOA-Optimised Deep Learning Forecasts for Reported Hepatitis B in Mainland China (2004–2
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42757779
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42757779/)
- **DOI:** [10.7189/jogh.16.04200](https://doi.org/10.7189%2Fjogh.16.04200)
- **Published At:** 2026-09-18T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- A national monthly series of reported **hepatitis B** cases in mainland China from January 2004 to December 2025 was compiled for short- to medium-term forecasting. - The study used **CEEMDAN** (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the time series into multiscale temporal components for modelling. - Four modelling approaches were evaluated on CEEMDAN-derived features: **Transformer** encoder, gated recurrent units (**GRU**), convolutional neural networks (**CNN**), and support vector machines (**SVM**). - Models were trained with a sliding 12-month input window and forecasts recursively extended to 24-month horizons (up to 2027). - Hyperparameter tuning for all models used the **Kepler Optimization Algorithm (KOA)**. - Model performance was assessed on training, validation, and a held-out test split using R2, RMSE, MAE, MAPE, and regression diagnostics. - All models recovered dominant long-term trends and seasonal patterns in the notification series. - On the held-out test set the **Transformer** achieved the best out-of-sample fit: MAE = 3105.508, MAPE = 0.024, RMSE = 4071.901, and R2 = 0.928. - **SVM** ranked second, with MAE, MAPE and RMSE values 11.317%, 11.615%, and 9.964% higher than the Transformer, respectively. - **CNN** outperformed **GRU** on the test set, reducing MAE, MAPE and RMSE relative to GRU by 21.985%, 22.178%, and 14.944% and producing a 6.158% larger R2 than GRU. - Forecasts for 2026–2027 produced by the hybrid CEEMDAN + KOA-optimised models remain elevated, indicating limited near-term improvement and potential resurgence. - The authors conclude that integrating **CEEMDAN** with KOA-optimised machine learning models yields highly reliable short-term forecasts and supports public health planning and resource allocation for Hepatitis B control.
## Clinical Analysis & Structured Key Points
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J Glob Health. 2026. Show details Display options Display options Format Abstract PubMed PMID J Glob Health Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22J+Glob+Health%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22J+Glob+Health%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42757779/) . 2026 Sep 18:16:04200. doi: 10.7189/jogh.16.04200. ### Authors [Zhende Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+Z&cauthor_id=42757779), [Fengying Bi](https://pubmed.ncbi.nlm.nih.gov/?term=Bi+F&cauthor_id=42757779), [Zhichao Duan](https://pubmed.ncbi.nlm.nih.gov/?term=Duan+Z&cauthor_id=42757779), [Li Ding](https://pubmed.ncbi.nlm.nih.gov/?term=Ding+L&cauthor_id=42757779), [Bin Song](https://pubmed.ncbi.nlm.nih.gov/?term=Song+B&cauthor_id=42757779), [Ke Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+K&cauthor_id=42757779), [Jinyu Zhao](https://pubmed.ncbi.nlm.nih.gov/?term=Zhao+J&cauthor_id=42757779), [Haiying Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+H&cauthor_id=42757779) * PMID: **42757779** * DOI: [ 10.7189/jogh.16.04200 ](https://doi.org/10.7189/jogh.16.04200) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **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. **Keywords:** epidemiology; hepatitis B; modelling; prediction. © 2026 The Authors. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests. ## Similar articles * [ Forecasting monthly AIDS incidence in China via LSTM-CNN parallel fusion: a comparative study of 10 predictive models. ](https://pubmed.ncbi.nlm.nih.gov/42591381/) Li C, Li J, Pang S, Rong C, Ye X, Lao X, Chen M.Li C, et al.Front Public Health. 2026 Jul 29;14:1873637. doi: 10.3389/fpubh.2026.1873637. eCollection 2026.Front Public Health. 2026.PMID: 42591381Free PMC article. * [ ChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis. ](https://pubmed.ncbi.nlm.nih.gov/40577658/) Huang W, Wei W, He X, Zhan B, Xie X, Zhang M, Lai S, Yuan Z, Lai J, Chen R, Jiang J, Ye L, Liang H.Huang W, et al.J Med Internet Res. 2025 Jun 27;27:e74423. doi: 10.2196/74423.J Med Internet Res. 2025.PMID: 40577658Free PMC article. * [ Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. ](https://pubmed.ncbi.nlm.nih.gov/34221854/) Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Khosravi A, Nahavandi S, Gholamzadeh Chofreh A, Goni FA, Klemeš JJ, Mosavi A.Ayoobi N, et al.Results Phys. 2021 Aug;27:104495. doi: 10.1016/j.rinp.2021.104495. Epub 2021 Jun 26.Results Phys. 2021.PMID: 34221854Free PMC article. * [ Personality Theories. ](https://pubmed.ncbi.nlm.nih.gov/42475469/) Gallios JM, Iyer V, Kaylor LE.Gallios JM, et al.2026 Jun 20. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–.2026 Jun 20. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–.PMID: 42475469Free Books & Documents. * [ A review of AI/ML approaches in wastewater surveillance advancement. ](https://pubmed.ncbi.nlm.nih.gov/41576591/) Ali M, Younis AB, Duru CI, Sherchan SP.Ali M, et al.Sci Total Environ. 2026 Feb 10;1015:181364. doi: 10.1016/j.scitotenv.2026.181364. Epub 2026 Jan 22.Sci Total Environ. 2026.PMID: 41576591Review. 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