Probability-Based Early Warning for Seasonal Influenza in China: Model Development Study
This study presented a novel probability-based machine learning model essential for early warning signals of influenza, demonstrating superior accuracy, flexibility, and practical applicability compared to other techniques. This approach enhances preparedness for influenza among the population and promotes the use of automated artificial intelligence-driven public health responses by replacing binary warnings with probability-driven risk assessments. Future research should integrate real-time surveillance data and dynamic transmission models to improve the precision of early warning.
Authors
- Qiang Huang
- Liuyang Yang (ORCID: https://orcid.org/0000-0001-6140-6846)
- Xuefeng Huang (ORCID: https://orcid.org/0000-0003-0773-9601)
- Weizhong Yang (ORCID: https://orcid.org/0000-0002-6599-825X)
- Yang Yu (ORCID: https://orcid.org/0000-0002-1732-9545)
- Xiaoli Wang (ORCID: https://orcid.org/0000-0002-3128-649X)
- Ting Zhang (ORCID: https://orcid.org/0000-0002-9569-9357)
- Zhongjie Li (ORCID: https://orcid.org/0000-0002-0356-0463)
- Yifeng Shen (ORCID: https://orcid.org/0000-0001-7303-0348)
- Jinzhao Cui (ORCID: https://orcid.org/0009-0007-5095-3722)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Fudan University (CN)
- Peking Union Medical College Hospital (CN)
- Beijing Center for Disease Prevention and Control (CN)
- Pudong New Area People's Hospital (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-06-30
- DOI
- https://doi.org/10.2196/73631
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00