A hybrid model for influenza forecasting in Xinjiang: adapting to complex seasonality and post-COVID-19 structural breaks
The proposed LSTM-GBR model provides a validated, robust, high-accuracy forecasting tool specifically for Xinjiang. It successfully addresses the failures of general-purpose models, which are unable to capture the region's complex local dynamics and emergent structural breaks. This localized feature-fusion approach is essential to strengthen public health surveillance in Xinjiang.
Authors
- Xubin Zheng (ORCID: https://orcid.org/0000-0003-2322-857X)
- Yongmei Ge
- Yanran Liu (ORCID: https://orcid.org/0000-0003-0062-4473)
- Xiaoming Peng
- Jingxia Guo
- Di Wang
Institutions
- City University of Hong Kong (HK)
- Dongguan University of Technology (CN)
- Xinjiang Production and Construction Corps (CN)
- Xinjiang Uygur Autonomous Region Disease Prevention and Control Center (CN)
- Great Bay University
Publication Details
- Journal
- BMC Public Health
- Published
- 2026-02-16
- DOI
- https://doi.org/10.1186/s12889-026-26562-7
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00