A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis
This study shows strong potential for influenza prediction by combining Baidu index data with traditional surveillance and specific keywords for epidemic and nonepidemic seasons. It provides a new perspective for public health preparedness. This research is expected to support early warning systems for influenza and other diseases. Future work will further optimize these models for more timely and accurate predictions, enhancing public health responses.
Authors
- Chunxia Cao (ORCID: https://orcid.org/0000-0003-4857-425X)
- Xiangyu Yan (ORCID: https://orcid.org/0000-0002-1496-0358)
- Xiaohan Sun (ORCID: https://orcid.org/0009-0005-6488-548X)
- Z R Liu
- Yue Li (ORCID: https://orcid.org/0000-0002-9795-5668)
- Xuliang Chu
- Ji Li (ORCID: https://orcid.org/0009-0001-5881-0845)
- Ying Zhang (ORCID: https://orcid.org/0000-0002-6936-2320)
- Guoliang Liu
- Jianming Yuan (ORCID: https://orcid.org/0009-0001-1931-1576)
- Zihan Mei
- Xiaochun Dong (ORCID: https://orcid.org/0000-0003-1331-0924)
- Lin Li
Institutions
- Tianjin University (CN)
- Nankai University (CN)
- Tianjin Centers for Disease Control and Prevention (CN)
- Hebei Provincial Center for Disease Control and Prevention (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-08-11
- DOI
- https://doi.org/10.2196/71786
- Citations
- 4
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 5.42