Global dynamics of deterministic-stochastic dengue infection model including multi specific receptors via crossover effects

Saima Rashid; Fahd Jarad; Sobhy A. A. El-Marouf; Sayed K. Elagan; Saima Rashid; Fahd Jarad; Sobhy A. A. El-Marouf; Sayed K. Elagan

doi:10.3934/math.2023327

AIMS Mathematics

2023, Volume 8, Issue 3: 6466-6503. doi: 10.3934/math.2023327

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Global dynamics of deterministic-stochastic dengue infection model including multi specific receptors via crossover effects

1.
Department of Mathematics, Government College University, Faislabad, Pakistan
2.
Department of Mathematics, Çankaya University, Ankara, Turkey
3.
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
4.
Department of Mathematics, King Abdulaziz University, Jeddah, Saudi Arabia
5.
Department of Mathematics, Faculty of Science, Taibah University, Saudi Arabia
6.
Department of Mathematics and Computer Sciences, Faculty of Science Menoufia University, Shebin Elkom, Egypt
7.
Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Received: 22 August 2022 Revised: 25 November 2022 Accepted: 12 December 2022 Published: 04 January 2023
MSC : 46S40, 47H10, 54H25

Dengue viruses have distinct viral regularities due to the their serotypes. Dengue can be aggravated from a simple fever in an acute infection to a presumably fatal secondary pathogen. This article investigates a deterministic-stochastic secondary dengue viral infection (SDVI) model including logistic growth and a nonlinear incidence rate through the use of piecewise fractional differential equations. This framework accounts for the fact that the dengue virus can penetrate various kinds of specific receptors. Because of the supplementary infection, the system comprises both heterologous and homologous antibody. For the deterministic case, we determine the invariant region and threshold for the aforesaid model. Besides that, we demonstrate that the suggested stochastic SDVI model yields a global and non-negative solution. Taking into consideration effective Lyapunov candidates, the sufficient requirements for the presence of an ergodic stationary distribution of the solution to the stochastic SDVI model are generated. This report basically utilizes a novel idea of piecewise differentiation and integration. This method aids in the acquisition of mechanisms, including crossover impacts. Graphical illustrations of piecewise modeling techniques for chaos challenges are demonstrated. A piecewise numerical scheme is addressed. For various cases, numerical simulations are presented.
- dengue viral model,
- stochastic-deterministic models,
- numerical solutions,
- Itô derivative,
- chaotic attractor
Citation: Saima Rashid, Fahd Jarad, Sobhy A. A. El-Marouf, Sayed K. Elagan. Global dynamics of deterministic-stochastic dengue infection model including multi specific receptors via crossover effects[J]. AIMS Mathematics, 2023, 8(3): 6466-6503. doi: 10.3934/math.2023327

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Abstract

Dengue viruses have distinct viral regularities due to the their serotypes. Dengue can be aggravated from a simple fever in an acute infection to a presumably fatal secondary pathogen. This article investigates a deterministic-stochastic secondary dengue viral infection (SDVI) model including logistic growth and a nonlinear incidence rate through the use of piecewise fractional differential equations. This framework accounts for the fact that the dengue virus can penetrate various kinds of specific receptors. Because of the supplementary infection, the system comprises both heterologous and homologous antibody. For the deterministic case, we determine the invariant region and threshold for the aforesaid model. Besides that, we demonstrate that the suggested stochastic SDVI model yields a global and non-negative solution. Taking into consideration effective Lyapunov candidates, the sufficient requirements for the presence of an ergodic stationary distribution of the solution to the stochastic SDVI model are generated. This report basically utilizes a novel idea of piecewise differentiation and integration. This method aids in the acquisition of mechanisms, including crossover impacts. Graphical illustrations of piecewise modeling techniques for chaos challenges are demonstrated. A piecewise numerical scheme is addressed. For various cases, numerical simulations are presented.

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