TY - EJOU
AU - Raza, Ali
AU - Baleanu, Dumitru
AU - Rafiq, Muhammad
AU - Abbas, Syed Zaheer
AU - Siddique, Abubakar
AU - Javed, Umer
AU - Naz, Mehvish
AU - Fatima, Arooj
AU - Munawar, Tayyba
AU - Batool, Hira
AU - Nazir, Zaighum
TI - Computational Algorithms for the Analysis of Cancer Virotherapy Model
T2 - Computers, Materials \& Continua
PY - 2022
VL - 71
IS - 2
SN - 1546-2226
AB - 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.
KW - Cancer disease; epidemic model; algorithms; stability analysis
DO - 10.32604/cmc.2022.023286