用强化学习自动推导传染病模型的数学表达式，兼顾准确性和可解释性。
Learning Epidemiological Dynamics via the Finite Expression Method
- 通过强化学习搜索显式数学表达式，替代传统人工建模
- 在真实和合成数据上均实现高精度预测，捕捉变量间关系
- 适合需要可解释性的公共卫生决策场景
传染病传播建模与预测对公共健康决策至关重要。传统流行病学模型依赖专家定义的框架描述复杂动态，而神经网络虽具强大预测能力，却因‘黑箱’特性缺乏可解释性。本文提出有限表达法（Finite Expression Method, FEX），一种符号学习框架，利用强化学习自动推导流行病动力学的显式数学表达式。在合成数据和真实世界数据集上的数值实验表明，FEX 在建模与预测疾病传播方面表现出高精度，同时揭示了流行病学变量间的明确关系。结果表明，FEX 是一种兼具可解释性与强预测性能的强大工具，可支持公共卫生中的实际应用。
原文摘要 · Abstract (English)
Modeling and forecasting the spread of infectious diseases is essential for effective public health decision-making. Traditional epidemiological models rely on expert-defined frameworks to describe complex dynamics, while neural networks, despite their predictive power, often lack interpretability due to their ``black-box" nature. This paper introduces the Finite Expression Method, a symbolic learning framework that leverages reinforcement learning to derive explicit mathematical expressions for epidemiological dynamics. Through numerical experiments on both synthetic and real-world datasets, FEX demonstrates high accuracy in modeling and predicting disease spread, while uncovering explicit relationships among epidemiological variables. These results highlight FEX as a powerful tool for infectious disease modeling, combining interpretability with strong predictive performance to support practical applications in public health.
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