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基于物理的深度学习用于传染病预测
Ying Qian1, Éric Marty2, Avranil Basu2
1School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, GA 30602.
ArXiv
|January 29, 2025
概括
一个新的物理信息神经网络 (PINN) 模型通过将流行病学理论与数据集成来改善传染病预测. 这种方法提高了病例,死亡和住院预测的准确性,优于现有方法.
科学领域:
- 流行病学 流行病学
- 科学机器学习科学机器学习
- 计算生物学 计算生物学
背景情况:
- 准确预测传染病对于公共卫生政策和流行病准备至关重要.
- 现有的深度学习模型在仅以观察数据进行训练时,往往会出现过度拟合.
- 改善预测方法对于减轻未来流行病影响至关重要.
研究的目的:
- 提出和评估一种使用物理信息的神经网络 (PINNs) 的新型传染病预测模型.
- 将流行病学理论集成到机器学习框架中,以提高预测准确性并防止过度匹配.
- 通过使用现实世界COVID-19数据,评估模型的性能与已建立的基准.
主要方法:
- 开发了一种PINN模型,将疾病传播的动态系统纳入神经网络的损失函数.
- 集成了一个子网络,以考虑影响疾病传播率的移动性和疫苗接种等共同变量.
- 通过加利福尼亚州的州级COVID-19病例,死亡和住院数据验证了该模型.
主要成果:
- 该PINN模型展示了对COVID-19病例,死亡和住院治疗的准确预测.
- PINN模型的性能超过了基本的神经网络和天真预测的基线.
- PINN模型实现了与复杂的高斯感染状态空间与时间依赖 (GISST) 模型相提并论的性能,提供了更简单的实现.
结论:
- 拟议的PINN模型提供了一个强大的和可实施的计算工具,用于增强传染病预测能力.
- 将物理原理 (流行病学动态) 与机器学习相结合,有效地解决了过度装配问题,并提高了预测准确性.
- 这种方法在改善公共卫生准备和应对传染病爆发方面具有重大潜力.
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