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Approximation treatment for linear fuzzy HIV infection model by variational iteration method

Jameel, Ali Fareed (2021) Approximation treatment for linear fuzzy HIV infection model by variational iteration method. Mathematics and Statistics, 9 (3). pp. 342-349. ISSN 2332-2071

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There has recently been considerable focus on finding reliable and more effective approximate methods for solving biological mathematical models in the form of differential equations. One of the well-known approximate or semi-analytical methods for solving linear, nonlinear differential well as partial differential equations within various fields of mathematics is the Variational Iteration Method (VIM). This paper looks at the use of fuzzy differential equations in human immunodeficiency virus (HIV) infection modeling. The main advantage of the method lies in its flexibility and ability to solve nonlinear equations easily. VIM is introduced to provide approximate solutions for linear ordinary differential equation system including the fuzzy HIV infection model. The model explains the amount of undefined immune cells, and the immune system viral load intensity intrinsic that will trigger fuzziness in patients infected by HIV. CD4+T-cells and cytototoxic T-lymphocytes (CTLs) are known for the immune cells concerned. The dynamics of the immune cell level and viral burden are analyzed and compared across three classes of patients with low, moderate and high immune systems. A modification and formulation of the VIM in the fuzzy domain based on the use of the properties of fuzzy set theory are presented. A model was established in this regard, accompanied by plots that demonstrate the reliability and simplicity of the methods. The numerical results of the model indicate that this approach is effective and easily used in fuzzy domain.

Item Type: Article
Uncontrolled Keywords: Fuzzy Set Theory, Fuzzy Differential Equations, Fuzzy HIV Infection Model, Analytical Solution, Variational Iteration Method (VIM)
Subjects: Q Science > Q Science (General)
Divisions: School of Quantitative Sciences
Depositing User: Mrs Nurin Jazlina Hamid
Date Deposited: 13 Mar 2022 15:11
Last Modified: 19 Jun 2023 10:00

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