A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data
Yilin Zhang, Hongbo Song, Shuangxueer Zhang, Xiaoying Wang, Junjie Tang · 2025
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Description
Yilin Zhang and colleagues build a hybrid forecasting model for lung tuberculosis in Chongqing, China, and feed it data from nearby regions as well. Tuberculosis spreads through the air, so case counts in one city echo its neighbours. Their two-stage model runs a seasonal time-series model and a support vector model side by side, then blends them with an extreme learning machine tuned by a sparrow search method.
Against older hybrid models, the errors fell by roughly a fifth to over three quarters. Adding neighbour-region data cut errors further still, by about a fifth to two thirds. The authors offer the tool for earlier warning and planning.
Catalog record
| CREATOR | Yilin Zhang, Hongbo Song, Shuangxueer Zhang, Xiaoying Wang, Junjie Tang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |