CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004-2027).
- DOI
- 10.7189/jogh.16.04200
- Published
- 2026 Sep 18
- Container
- Journal of global health
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.7189/jogh.16.04200,
title = {CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004-2027).},
author = {Wang Z and Bi F and Duan Z and Ding L and Song B and Wang K and Zhao J and Li H},
year = {2026},
journal = {Journal of global health},
doi = {10.7189/jogh.16.04200},
url = {https://doi.org/10.7189/jogh.16.04200}
}RIS
TY - JOUR TI - CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004-2027). AU - Wang Z AU - Bi F AU - Duan Z AU - Ding L AU - Song B AU - Wang K AU - Zhao J AU - Li H PY - 2026 JO - Journal of global health DO - 10.7189/jogh.16.04200 UR - https://doi.org/10.7189/jogh.16.04200 ER -
APA
Z, W., F, B., Z, D., L, D., B, S., K, W., J, Z., & H, L. (2026). CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004-2027).. Journal of global health. https://doi.org/10.7189/jogh.16.04200
Source records
- pubmed · retrieved 2026-09-24T18:58:06.031Z
- europe-pmc · retrieved 2026-09-24T18:58:06.051Z