Open Access

ARTICLE

Computational Algorithms for the Analysis of Cancer Virotherapy Model

Ali Raza^1,2,*, Dumitru Baleanu^3,4, Muhammad Rafiq⁵, Syed Zaheer Abbas⁶, Abubakar Siddique⁶, Umer Javed⁸, Mehvish Naz⁷, Arooj Fatima⁶, Tayyba Munawar⁶, Hira Batool⁶, Zaighum Nazir⁶

1 Department of Mathematics, Govt. Maulana Zafar Ali Khan Graduate College Wazirabad, 52000, Punjab Higher Education Department (PHED), Lahore, 54000, Pakistan
2 Department of Mathematics, University of Sialkot, Sialkot, 51310, Pakistan
3 Department of Mathematics, Cankaya University, Balgat, Ankara, 06530, Turkey
4 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan
5 Department of Mathematics, Faculty of Sciences, University of Central Punjab, Lahore, 54500, Pakistan
6 Department of Mathematics, National College of Business Administration and Economics, Lahore, 54660, Pakistan
7 Department of Mathematics, COMSATS University Islamabad, Wah Campus, Quaid Avenue, Wah Cantonment, 47040, Pakistan
8 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Quaid Avenue, Wah Cantonment, 47040, Pakistan

* Corresponding Author: Ali Raza. Email:

Computers, Materials & Continua 2022, 71(2), 3621-3634. https://doi.org/10.32604/cmc.2022.023286

Received 02 September 2021; Accepted 09 October 2021; Issue published 07 December 2021

Abstract

Cancer is a common term for many diseases that can affect any part of the body. In 2020, ten million people will die due to cancer. A worldwide leading cause of death is cancer by the World Health Organization (WHO) report. Interaction of cancer cells, viral therapy, and immune response are identified in this model. Mathematical and computational modeling is an effective tool to predict the dynamics of cancer virotherapy. The cell population is categorized into three parts like uninfected cells (x), infected cells (y), virus-free cells (v), and immune cells (z). The modeling of cancer-like diseases is based on the law of mass action (the rate of change of reacting substances is directly proportional to the product of interacting substances). Positivity, boundedness, equilibria, threshold analysis, are part of deterministic modeling. Later on, a numerical analysis is designed by using the standard and non-standard finite difference methods. The non-standard finite difference method is developed to study the long-term behavior of the cancer model. For its efficiency, a comparison of the methods is investigated.

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