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A finite difference scheme to solve a fractional order epidemic model of computer virus

  • Received: 15 May 2022 Revised: 01 October 2022 Accepted: 16 October 2022 Published: 01 November 2022
  • MSC : 65P99, 65Z05, 37M15

  • In this article, an analytical and numerical analysis of a computer virus epidemic model is presented. To more thoroughly examine the dynamics of the virus, the classical model is transformed into a fractional order model. The Caputo differential operator is applied to achieve this. The Jacobian approach is employed to investigate the model's stability. To investigate the model's numerical solution, a hybridized numerical scheme called the Grunwald Letnikov nonstandard finite difference (GL-NSFD) scheme is created. Some essential characteristics of the population model are scrutinized, including positivity boundedness and scheme stability. The aforementioned features are validated using test cases and computer simulations. The mathematical graphs are all detailed. It is also investigated how the fundamental reproduction number $ \mathfrak{R}_0 $ functions in stability analysis and illness dynamics.

    Citation: Zafar Iqbal, Muhammad Aziz-ur Rehman, Muhammad Imran, Nauman Ahmed, Umbreen Fatima, Ali Akgül, Muhammad Rafiq, Ali Raza, Ali Asrorovich Djuraev, Fahd Jarad. A finite difference scheme to solve a fractional order epidemic model of computer virus[J]. AIMS Mathematics, 2023, 8(1): 2337-2359. doi: 10.3934/math.2023121

    Related Papers:

  • In this article, an analytical and numerical analysis of a computer virus epidemic model is presented. To more thoroughly examine the dynamics of the virus, the classical model is transformed into a fractional order model. The Caputo differential operator is applied to achieve this. The Jacobian approach is employed to investigate the model's stability. To investigate the model's numerical solution, a hybridized numerical scheme called the Grunwald Letnikov nonstandard finite difference (GL-NSFD) scheme is created. Some essential characteristics of the population model are scrutinized, including positivity boundedness and scheme stability. The aforementioned features are validated using test cases and computer simulations. The mathematical graphs are all detailed. It is also investigated how the fundamental reproduction number $ \mathfrak{R}_0 $ functions in stability analysis and illness dynamics.



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