Abstract
Forecasting the transmission patterns of infectious diseases is of paramount importance in gaining valuable insights into outbreak growth and optimizing the allocation of medical resources. In this paper, we conduct a comparative study between a physics-based model and a machine learning (ML) model for epidemic forecasting and countermeasures, considering criteria such as accuracy and practicality. We develop four ML models: back-propagation (BP), long short-term memory, support vector machine, and extreme learning machine. In addition, we propose a reaction–diffusion (R–D) model that incorporates factors such as susceptibility heterogeneity, lockdown measures, population movement, and dynamically dependent rates. The experimental results highlight the superior accuracy of the BP model for forecasting, while the R–D model provides comprehensive insights into disease dynamics, including stability and potential control strategies.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China (12201577, U23A2065, 52071298), Key Scientific and Technological Project of Henan Province (232102320136).
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Yiwen Tao: formal analysis, data curation, methodology, writing. Huaiping Zhu: conceptualization, validation, writing. Jingli Ren: supervision, conceptualization, formal analysis, methodology, validation, and writing.
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Tao, Y., Zhu, H. & Ren, J. Comparative study of physics-based model and machine learning model for epidemic forecasting and countermeasure. Comp. Appl. Math. 43, 148 (2024). https://doi.org/10.1007/s40314-024-02654-1
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DOI: https://doi.org/10.1007/s40314-024-02654-1