This study presents a mathematical model to analyze the dynamics of cancer cell growth and interaction with healthy cells. The model also incorporates the body's immune response to cancer, providing insights into how treatment strategies, particularly chemotherapy, can influence disease progression. Our findings highlight the critical role of timely intervention and a strong immune system in controlling cancer growth. By employing and analyzing SIR (Susceptible-Infected-Recovered) models, we identify key factors that determine treatment success and disease outcomes. A deeper understanding of cancer cell dynamics through this approach offers valuable guidance for developing more effective treatment strategies.
Tools: MATLAB tools like Ode45, Euler and NSFD Schemes
Department: Department of Mathematics
Project Poster
