基于物理的深度学习用于传染病预测
Ying Qian1, Kui Zhang1, Eric Marty2
1School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, GA, USA.
Journal of the Royal Society, Interface
|November 25, 2025
概括
基于物理学的神经网络 (PINNs) 通过将流行病学理论集成到深度学习模型中来改善传染病预测. 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
- 计算科学 计算科学
- 机器学习 机器学习
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.
- 当前的预测方法面临挑战,例如仅依赖观测数据时的模型过拟合.
研究的目的:
- 实施和评估用于传染病预测的物理信息神经网络 (PINNs).
- 提高预测准确度,防止流行病学模型过拟合.
主要方法:
- 使用PINNs,将疾病传播的动态系统集成到神经网络的损失函数中.
- 一个子网络被用来结合诸如流动性和疫苗接种率等共变量.
- 该模型使用加利福尼亚州的州级COVID-19数据进行了验证.
主要成果:
- PINNs显示了对COVID-19病例,死亡和住院治疗的准确预测.
- 该模型的表现超过了基线预测和各种序列深度学习模型 (RNN,LSTM,GRU,变压器).
- PINNs的性能与复杂的高斯感染状态预测模型相当,但结构更简单.
结论:
- PINNs提供了一个强大而高效的计算工具,用于增强传染病预测能力.
- 在机器学习框架中整合流行病学理论可以减轻过度拟合,提高预测准确度.
- 拟议的PINNs模型显示了改善公共卫生准备和应对未来流行病的巨大潜力.
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