在泰国利用通用和转移学习模型进行流感预测
Pitiwat Lueangwitchajaroen1, Suparinthon Anupong2, Chanidapa Winalai3
1Department of Physics and Astronomy, University of Nottingham, Nottingham, UK.
Scientific reports
|January 30, 2026
概括
深度学习模型准确地预测了泰国的流感发病率. 转移学习提高了数据有限的地区的预测,改善了对流感疫情的公共卫生准备.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 流感对全球健康造成重大负担,需要准确的发病率预测,以有效应对公共卫生.
- 在数据有限的地区,预测流感是具有挑战性的,阻碍了准备和资源分配.
- 准确的流感预测模型对于监测疫情和优化公共卫生干预至关重要.
研究的目的:
- 开发通用深度学习 (DL) 模型,用于在多个泰国省份预测流感发病率.
- 通过特征选择和转移学习 (TL) 提高模型通用性和性能.
- 在数据稀缺的地区提高流感预测的准确性.
主要方法:
- 开发了通用深度学习模型用于流感发病率预测 (2010-2019).
- 实施特征选择,以确保从时间序列数据中获得平衡的贡献.
- 使用预先训练的模型进行应用转移学习,为缺乏气象和PM10数据的省份进行微调.
主要成果:
- 具有128个节点的单个隐藏层DL模型在通用框架中表现出最佳性能.
- 与数据有限领域的基线模型相比,转移学习显著提高了预测准确性.
- 微调的TL模型实现了流感发病率预测的最高准确度.
结论:
- 通用深度学习和转移学习框架显示了流感趋势预测的巨大潜力.
- 这些模型即使在特征数据有限的地区也有效,解决了关键的公共卫生挑战.
- 整合特定领域的知识对于强大的流行病管理策略和准确的流感预测至关重要.
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