基于气象特征和深度学习模型的登革热预测
Yunyun Cheng1,2, Rong Cheng3, Ting Xu3
1Shanxi University of Electronic Science and Technology, Linfen, 041000, China.
Infectious Disease Modelling
|January 15, 2026
概括
这项研究引入了一种用于登革热预测的新型混合模型,通过整合气象数据来提高准确性. 该模型有效预测流行病趋势,这对于公共卫生监测至关重要.
科学领域:
- 流行病学 流行病学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 登革热疫情对全球健康构成重大挑战,需要准确预测流行病学趋势.
- 已知温度,湿度和降水等气象因素会影响登革热的发生和流行.
- 现有的预测模型可能无法完全捕捉多维气象特征的复杂相互作用.
研究的目的:
- 提出一种有效的混合模型,以提高登革热预测性能.
- 将多维气象特征纳入登革热趋势预测.
- 解决流行病学数据集中的数据稀缺问题.
主要方法:
- 使用时间序列生成对抗网络 (TimeGAN) 的数据增强.
- 气象数据通过Symplectic Geometry Mode Decomposition (SGMD) 进行分解,并使用样本 (SE) 进行重建.
- 使用双向时间卷积网络 (BiTCN) 和基于注意力的双向长期和短期记忆网络 (BiLSTM) 的特征提取和融合.
主要成果:
- 拟议的混合模型证明了对中国广东省登革热流行趋势的准确预测.
- 获得了192.98759的平均绝对误差 (MAE) 和2.492.492的平均绝对百分比误差 (MAPE).
- 整合TimeGAN,SGMD,SE,BiTCN和BiLSTM显著提高了预测的准确性.
结论:
- 开发的混合模型为预测登革热流行病提供了强大的方法.
- 准确预测登革热趋势对于有效的公共卫生干预和资源分配至关重要.
- 这种方法强调了先进的机器学习技术在分析复杂的环境和流行病学数据方面的潜力.
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