Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China
For cities with nonlinear waning epidemics and seasonally contracting amplitude, machine-learning-based XGBoost offers superior extrapolation robustness over traditional ARIMA or Prophet approaches, providing an evidence-based tool for monthly PTB early-warning, precise resource pre-positioning and targeted control in comparable high-density, coastal urban settings.
Authors
- Yan Xu (ORCID: https://orcid.org/0000-0003-0379-8866)
- J. Joshua Yang (ORCID: https://orcid.org/0000-0001-8242-7531)
- Xiaoyang Zhang (ORCID: https://orcid.org/0000-0001-8456-0547)
- Jianhui Chen (ORCID: https://orcid.org/0000-0002-5080-1880)
- J. Pan
Institutions
- Fujian Medical University (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Tropical Medicine and Health
- Published
- 2026-03-08
- DOI
- https://doi.org/10.1186/s41182-026-00934-6
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00