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Parameters and order identification of fractional-order epidemiological systems by Lévy-PSO and its application for the spread of COVID-19

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  • Xie, Bing
  • Ge, Fudong

Abstract

To identify the knowledge about parameters and order is very important for the modeling of fractional-order epidemiological systems. In this paper, such an identification problem is formulated as a nonlinear optimization problem. For solving this, the Lévy-PSO algorithm, which is obtained by applying Lévy flight to generalize the classical particle swarm optimization (PSO), is used. More precisely, we first utilize Lévy-PSO to identify the constant parameters and the order of fractional-order SIR, SEIR systems with simulated data to show the effectiveness of our proposed identification strategy. Then, we continue employing Lévy-PSO to solve the parameter estimation problem of fractional-order SEAIR model under the real data of COVID-19 in Shanghai from 2/26/2022 to 4/27/2022. Numerical examples and associated comparisons with other existing methods allow us to achieve that our proposed identification strategy can generate a good performance with high accuracy and rapid convergence.

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  • Xie, Bing & Ge, Fudong, 2023. "Parameters and order identification of fractional-order epidemiological systems by Lévy-PSO and its application for the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000644
    DOI: 10.1016/j.chaos.2023.113163
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    References listed on IDEAS

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    Cited by:

    1. Yao Lu, 2023. "The Maximum Correntropy Criterion-Based Identification for Fractional-Order Systems under Stable Distribution Noises," Mathematics, MDPI, vol. 11(20), pages 1-18, October.

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