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Computational Algorithms for the Analysis of Cancer Virotherapy Model

Ali Raza1,2,*, Dumitru Baleanu3,4, 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.

Keywords

Cancer disease; epidemic model; algorithms; stability analysis

Cite This Article

A. Raza, D. Baleanu, M. Rafiq, S. Zaheer Abbas, A. Siddique et al., "Computational algorithms for the analysis of cancer virotherapy model," Computers, Materials & Continua, vol. 71, no.2, pp. 3621–3634, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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