Abstract
The SEIR is a crucial mathematical model for solving infectious disease prediction and other problems in the field of artificial intelligence. It is used to effectively prevent and control infectious diseases by studying the infectious diseases’ propagation speed, spatial range, transmission route, dynamic mechanism and other issues. In order to improve the prediction of infectious diseases in a certain area, a SEIR model optimization approach based on differential evolution (DE) algorithm is proposed in this paper. In this method, the differential evolution is used to optimization the related variables in the model. The overall prediction of the adjusted and optimized SEIR model algorithm is conformed to the regional development laws. The experimental results show that the SEIR infectious disease model optimized by DE algorithm is accurate and reliable in the analysis of COVID-19 propagation situation, and the model can be used to provide certain theoretical methods and technical support for future outbreak policy formulation.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61903089), the Jiangxi Provincial Natural Science Foundation (Grant No. 20202BAB202014).
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Wang, D., Sun, Y., Song, J., Huang, Y. (2020). A SEIR Model Optimization Using the Differential Evolution. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_34
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DOI: https://doi.org/10.1007/978-3-030-62460-6_34
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