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An Epidemiological Control Strategy Model of SVEIMQR

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Artificial Intelligence and Machine Learning (IAIC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2058))

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Abstract

A new epidemiological model SVEIMQR (Susceptible–Vaccinated–Exposed–Infected-Mutant–Quarantined–Recovered) is proposed to explore COVID-19 transmission mechanism. Based on this model, this paper puts forward a hybrid control strategy model by considering “protection strategy” and “blocking strategy” with the purpose to reduce the number of infected. By comparing the proposed hybrid control strategy model with other strategies, the effectiveness of the hybrid control strategy model is verified. In order to reduce the number of infected and the cost as much as possible, we analyze the optimal control of the hybrid control model, prove the existence of the optimal control by using the Pontryagin's minimum principle, and get the optimal control system. The experimental results show that with the help of optimal control theory can minimize the cost of the hybrid control strategy and achieve the optimal control effect.

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Authors and Affiliations

  1. Institute of Systems Security and Control, College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054, China

    Jingmeng Zhang, Yulong An & Shixing Wu

Authors
  1. Jingmeng Zhang
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  2. Yulong An
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  3. Shixing Wu
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Corresponding author

Correspondence to Jingmeng Zhang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Hai Jin

  2. Chinese Academy of Science, Shenzhen, China

    Yi Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Jianfeng Lu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, J., An, Y., Wu, S. (2024). An Epidemiological Control Strategy Model of SVEIMQR. In: Jin, H., Pan, Y., Lu, J. (eds) Artificial Intelligence and Machine Learning. IAIC 2023. Communications in Computer and Information Science, vol 2058. Springer, Singapore. https://doi.org/10.1007/978-981-97-1277-9_30

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  • DOI: https://doi.org/10.1007/978-981-97-1277-9_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1276-2

  • Online ISBN: 978-981-97-1277-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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