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Najari M, Balochian S, Alishahi M. An Intelligent Controller for a Fractional-Order SEIR Model of the COVID-19 Epidemic. Intern Med Today 2024; 31 (1)
URL: http://imtj.gmu.ac.ir/article-1-4135-en.html
URL: http://imtj.gmu.ac.ir/article-1-4135-en.html
1- Department of Electrical Engineering, Gon.C., Islamic Azad University, Gonabad, Iran
2- Department of Engineering, Ma.C., Islamic Azad University, Mashhad, Iran ,saeed.balochian@iau.ac.ir
3- Department of Computer Engineering, Ma.C., Islamic Azad University, Mashhad, Iran
2- Department of Engineering, Ma.C., Islamic Azad University, Mashhad, Iran ,
3- Department of Computer Engineering, Ma.C., Islamic Azad University, Mashhad, Iran
Abstract: (10 Views)
Background: The management of epidemic diseases, particularly following the COVID-19 pandemic, has emerged as a critical challenge for global healthcare systems. Consequently, developing accurate epidemiological models and implementing effective control strategies are crucial to mitigating the spread of such diseases. To address this, an intelligent adaptive predictive control scheme is designed for a COVID-19 model, incorporating time-delay dynamics.
Methods: In this paper, a fractional-order SEIR epidemic model with time delay was considered to better represent the system dynamics. To control this model, a fractional-order adaptive predictive controller was designed. The performance and efficiency of the proposed scheme were evaluated and verified through simulations in MATLAB.
Results: The simulation results show that the proposed fractional-order adaptive predictive controller outperforms its integer-order counterpart, achieving faster convergence of system states to the target values. Notably, the susceptible individuals approached the desired threshold in a much shorter timeframe (around 100 days) under the proposed scheme, compared to over 300 days under the integer-order control. Additionally, the sizes of both exposed and infectious compartments decreased more rapidly, and the estimated parameters (α and β) stabilized sooner with reduced oscillation amplitudes.
Conclusion: The findings of this study indicate that the proposed fractional-order adaptive predictive controller can manage epidemic diseases more effectively than existing control schemes, thereby reducing disease transmission, enhancing public health, and mitigating pressure on healthcare systems. Overall, the simulation results demonstrate that the proposed controller outperforms alternative approaches in regulating both susceptible and infectious populations.
Methods: In this paper, a fractional-order SEIR epidemic model with time delay was considered to better represent the system dynamics. To control this model, a fractional-order adaptive predictive controller was designed. The performance and efficiency of the proposed scheme were evaluated and verified through simulations in MATLAB.
Results: The simulation results show that the proposed fractional-order adaptive predictive controller outperforms its integer-order counterpart, achieving faster convergence of system states to the target values. Notably, the susceptible individuals approached the desired threshold in a much shorter timeframe (around 100 days) under the proposed scheme, compared to over 300 days under the integer-order control. Additionally, the sizes of both exposed and infectious compartments decreased more rapidly, and the estimated parameters (α and β) stabilized sooner with reduced oscillation amplitudes.
Conclusion: The findings of this study indicate that the proposed fractional-order adaptive predictive controller can manage epidemic diseases more effectively than existing control schemes, thereby reducing disease transmission, enhancing public health, and mitigating pressure on healthcare systems. Overall, the simulation results demonstrate that the proposed controller outperforms alternative approaches in regulating both susceptible and infectious populations.
Keywords: Adaptive predictive controller, COVID-19, Epidemic diseases, Fractional calculus, SEIR model
Type of Study: Original |
Subject:
Diseases
Received: 2024/04/18 | Accepted: 2024/06/30 | Published: 2024/12/8
Received: 2024/04/18 | Accepted: 2024/06/30 | Published: 2024/12/8
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