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• W4210342175 abstract "Cancer is a common term for many diseases that can affect anybody. A worldwide leading cause of death is cancer, according to the World Health Organization (WHO) report. In 2020, ten million people died from cancer. This model identifies the interaction of cancer cells, viral therapy, and immune response. In this model, the cell population has four parts, namely uninfected cells (x), infected cells (y), virus-free cells (v), and immune cells (z). This study presents the analysis of the stochastic cancer virotherapy model in the cell population dynamics. The model results have restored the properties of the biological problem, such as dynamical consistency, positivity, and boundedness, which are the considerable requirements of the models in these fields. The existing computational methods, such as the Euler Maruyama, Stochastic Euler, and Stochastic Runge Kutta, fail to restore the abovementioned properties. The proposed stochastic nonstandard finite difference method is efficient, cost-effective, and accommodates all the desired feasible properties. The existing standard stochastic methods converge conditionally or diverge in the long run. The solution by the nonstandard finite difference method is stable and convergent over all time steps." @default.
• W4210342175 created "2022-02-08" @default.
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• W4210342175 date "2022-01-25" @default.
• W4210342175 modified "2023-10-18" @default.
• W4210342175 title "Stochastic Analysis of Nonlinear Cancer Disease Model through Virotherapy and Computational Methods" @default.
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• W4210342175 doi "https://doi.org/10.3390/math10030368" @default.
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