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arXiv · 2412.21049

Learning Epidemiological Dynamics via the Finite Expression Method

Jianda Du·Senwei Liang·Chunmei Wang
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Abstract

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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Jianda Du, Senwei Liang, Chunmei Wang. 2024-12-30. Learning Epidemiological Dynamics via the Finite Expression Method. https://arxiv.org/abs/2412.21049

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