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.

翻译：传染病传播的建模与预测对于有效的公共卫生决策至关重要。传统的流行病学模型依赖专家定义的框架来描述复杂动力学，而神经网络尽管具有预测能力，却常因其"黑箱"性质而缺乏可解释性。本文提出有限表达式方法，一种利用强化学习推导流行病动力学显式数学表达式的符号学习框架。通过在合成数据集和真实数据集上的数值实验，FEX 在疾病传播建模与预测中展现出高精度，同时揭示了流行病学变量间的显式关系。这些结果表明，FEX 作为一种强大的传染病建模工具，将可解释性与强大的预测性能相结合，能够支持公共卫生领域的实际应用。