Predicting influenza trends in the context of post-COVID immunity gaps in Macao, China
We used Google and Baidu search data with machine learning to predict flu trends in Macao. ‘Cold’ was the strongest early indicator, with XGBoost showing the best performance. Findings support enhanced flu surveillance amid rising risks from post-COVID immunity debt and growing tourist inflow.
Authors
- Qian Hong Ye (ORCID: https://orcid.org/0000-0003-1086-2248)
- Hwei Jene Ng (ORCID: https://orcid.org/0000-0002-9077-2367)
- Henry H. Y. Tong (ORCID: https://orcid.org/0000-0003-2687-741X)
- Kefeng Li (ORCID: https://orcid.org/0000-0002-7233-4347)
- Ying Liu
Institutions
- Xiamen University (CN)
- Kiang Wu Hospital (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- Journal of Travel Medicine
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1093/jtm/taaf035
- Citations
- 1
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.35