JOURNAL ARTICLE

Optimal control and bifurcation analysis of a delayed fractional-order epidemic model for the COVID-19 pandemic.

  • Published In: International Journal of Biomathematics, 2025, v. 18, n. 8. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Xu, Conghui; Yu, Yongguang; Ren, Guojian; Si, Xinhui 3 of 3

Abstract

In this paper, a delayed fractional-order epidemic model with general incidence rate and incubation period is proposed for the Corona Virus Disease 2019 (COVID-19) pandemic. The corresponding sufficient conditions are established to analyze the existence and stability of disease-free equilibrium and endemic equilibrium of the proposed model. The conditions for the existence of Hopf bifurcation are obtained by selecting the time delay as the bifurcation parameter. The control strategies for the COVID-19 pandemic are designed, and the corresponding delay fractional order optimal control problem (DFOCP) is analyzed based on the generalized Euler–Lagrange equation. The parameters of the model are identified based on the data of multiple types of the COVID-19 pandemic. Further, the effectiveness of the model in describing the trend of the COVID-19 pandemic is verified. Based on the results of parameter identification, the influence of incubation period on the COVID-19 pandemic is discussed. The forward–backward sweep method (FBSM) is adopted to numerically solve DFOCP, and the control effects under different control measures are analyzed. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Biomathematics. 2025/11, Vol. 18, Issue 8, p1
  • Document Type:Article
  • Subject Area:Consumer Health
  • Publication Date:2025
  • ISSN:1793-5245
  • DOI:10.1142/S1793524524500360
  • Accession Number:188631759
  • Copyright Statement:Copyright of International Journal of Biomathematics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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