改善流行病预测:整合物理信息神经网络和象征性回归用于COVID-19建模
Shila Rezvani1, Mostafa Abbaszadeh1, Mehdi Dehghan1
1Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Computer methods in biomechanics and biomedical engineering
|February 5, 2026
概括
这项研究使用混合方法增强了意大利COVID-19的SIDARTHE模型. 物理信息神经网络和符号回归提高了准确性,减少了公共卫生规划的预测不确定性.
科学领域:
- 流行病学 流行病学
- 计算建模计算建模
- 数据科学是数据科学.
背景情况:
- 由于COVID-19的流行,需要准确的流行病学模型.
- 像SIDARTHE这样的现有模型需要对现实世界的数据进行改进.
- 数据驱动的方法提供了提高预测准确性的潜力.
研究的目的:
- 完善意大利COVID-19疫情的SIDARTHE流行病学模型.
- 为了比较最大概率估计 (MLE) 与物理信息的神经网络 (PINNs) 的参数估计.
- 使用符号回归优化模型的管理方程.
主要方法:
- 开发了一个混合,数据驱动的框架.
- 一个两阶段的方法涉及参数估计和方程优化.
- 进行了最大概率估计 (MLE) 和物理信息神经网络 (PINNs) 的比较.
- 使用gplearn和PySR的符号回归被应用来优化方程.
主要成果:
- 基于物理学的神经网络 (PINNs) 显示出高于最大概率估计 (MLE) 的精度.
- PySR提供了比gplearn更强大的象征性表达.
- 综合模型显著降低了预测不确定性.
- 改进后的模型与意大利的COVID-19数据密切一致.
结论:
- 混合框架,结合PINNs和符号回归,提供了一个更具弹性和准确的流行病学建模工具.
- 这种精细的模型可以在传染病爆发期间帮助公共卫生规划和决策.
- 该研究强调了将机器学习与传统建模技术相结合的有效性.
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