Abstract
In this study, based on the characteristics and the transmission mechanism of COVID-19, SEIR epidemiological model is employed for modeling and analysis, utilizing the data of Hubei Province. To optimize the key epidemic parameters of the proposed SEIR model, a stochastic computational intelligence approach, the Particle Swarm Optimization (PSO) is introduced. To better analyze the epidemic, the data between January 20, 2020 to March 25, 2020 is selected and divided into four stages. The parameters are dynamically changeable at different stages of the epidemic, which shows the effectiveness of public health prevention and control measures. Moreover, the Genetic Algorithm (GA) and the Bacterial Foraging Optimization (BFO) are also executed for comparison. The experimental results demonstrate that all swarm intelligence algorithms mentioned above can help forecast COVID-19, and PSO shows the advantages of faster convergence speed and the capability of finding a better set of solutions in fewer iterations, particularly.
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Acknowledgement
The work described in this paper was supported by The Natural Science Foundation of China (Grant No.71971143, 71571120); Natural Science Foundation of Guangdong Province (Grant No. 2020A1515010749); Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau (Grant No. 2019KZDXM030), and Guangdong Province Postgraduate Education Innovation Research Project (Grant No. 2019SFKC46).
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Qiu, H., Chen, J., Niu, B. (2021). Multi-stage COVID-19 Epidemic Modeling Based on PSO and SEIR. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_24
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DOI: https://doi.org/10.1007/978-3-030-78743-1_24
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