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Construction of an early warning model for imported dengue fever: a multi-fusion machine learning approach
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Jie ZHAO1, Dayong GU2, Jianqing ZHANG3, Yufan WU4, Jie WEN5, Tingting JIANG1, Yuping WANG1, 3
Chinese Journal of Public Health | 2026, 42(5) : 622 - 627
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Chinese Journal of Public Health | 2026, 42(5): 622-627
• Survey and Report •
Construction of an early warning model for imported dengue fever: a multi-fusion machine learning approach
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Jie ZHAO1, Dayong GU2, Jianqing ZHANG3, Yufan WU4, Jie WEN5, Tingting JIANG1, Yuping WANG1, 3
Affiliations
  • 1School of Public Health, Fujian Medical University, Fuzhou 350004, China
  • 2Shenzhen University, Shenzhen 518000, China
  • 3Fuzhou International Travel Healthcare Center, Fuzhou 350001, China
  • 4Shenzhen Second People’s Hospital, Shenzhen 518000, China
  • 5Guangxi University of Chinese Medicine, Nanning 530200, China
Published: 2026-05-10 doi: 10.11847/zgggws1148778
Outline
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Objective

To develop an imported dengue fever prediction model based on multi-fusion machine learning, aiming to enhance the scientific basis and foresight of infectious disease prevention and control at Guangzhou port.

Methods

Data on imported dengue fever cases at Guangzhou port from 2015 to 2019 were collected, along with meteorological indicators, inbound passenger volume, flight traffic, vegetation index, and vector density. After data cleaning and standardization, three base models (BP neural network, random forest, and CatBoost) were constructed. Weighted averaging and stacking ensemble strategies were then adopted to build dual-fusion and triple-fusion models. Model performance was evaluated based on mean squared error (MSE) and coefficient of determination (R2).

Results

The fusion models outperformed single models overall, demonstrating higher predictive accuracy and stability. Among them, the stacked ensemble of random forest and CatBoost achieved the best performance, with an MSE of 4.749 and an R2 of 0.601, significantly improving upon the best single model. SHAP analysis further revealed that humidity, sunshine duration, and diurnal temperature variation were the main driving factors, while inbound passenger volume also contributed substantially to predictions.

Conclusions

Multi-fusion machine learning models provide significant advantages in predicting imported dengue cases, offering strong support for early warning and the formulation of prevention strategies in port public health.

dengue fever  /  machine learning  /  prediction model  /  public health  /  Guangzhou
赵婕, 顾大勇, 张建庆, 吴育凡, 文洁, 江婷婷, 王宇平. 输入性登革热预警模型构建:基于多融合机器学习法. 中国公共卫生, 2026 , 42 (5) : 622 -627 . DOI: 10.11847/zgggws1148778
Jie ZHAO, Dayong GU, Jianqing ZHANG, Yufan WU, Jie WEN, Tingting JIANG, Yuping WANG. Construction of an early warning model for imported dengue fever: a multi-fusion machine learning approach[J]. Chinese Journal of Public Health, 2026 , 42 (5) : 622 -627 . DOI: 10.11847/zgggws1148778
Year 2026 volume 42 Issue 5
76
33
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Article Info
doi: 10.11847/zgggws1148778
  • Receive Date:2025-09-01
  • Online Date:2026-10-23
  • Published:2026-05-10
Article Data
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History
  • Received:2025-09-01
  • Revised:2025-11-26
  • Accepted:2026-03-07
Affiliations
    1School of Public Health, Fujian Medical University, Fuzhou 350004, China
    2Shenzhen University, Shenzhen 518000, China
    3Fuzhou International Travel Healthcare Center, Fuzhou 350001, China
    4Shenzhen Second People’s Hospital, Shenzhen 518000, China
    5Guangxi University of Chinese Medicine, Nanning 530200, China

Corresponding:

WANG Yuping, E-mail:
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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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