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Investigation of Lung Cancer by Early Detection and Anticancer Cell’s Measures with Control Strategies Utilizing Mathematical Modeling Approach |
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PP: 323-340 |
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doi:10.18576/pfda/120206
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Author(s) |
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Muhammad Farman,
Aqeel Ahmad,
Khurram Faiz,
Aseel Smerat,
Muhammad Manan Akram,
Mohamed Hafez,
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Abstract |
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| Lung cancer remains one of the most prevalent and life-threatening chronic respiratory diseases worldwide, demanding effective diagnostic and therapeutic strategies. In this study, we propose a novel deterministic mathematical model, denoted as TCDIL2, to investigate the impact of IL2 cytokine therapy on the immune system’s anticancer response. The model is structured into four epidemiological compartments and is analyzed through both qualitative and quantitative approaches. Local stability analysis is performed under limited data conditions, while equilibrium points, the basic reproduction number (R0), and sensitivity indices are systematically examined. The next-generation method is employed to compute R0, offering insight into disease progression across compartments, whereas sensitivity analysis highlights the influence of key parameters on system dynamics. Global stability is assessed via Lyapunov functions, establishing conditions under which IL2 therapy benefits immunocompromised individuals. For numerical simulations, a nonstandard finite difference (NSFD) scheme based on an implicit method is developed and benchmarked against Euler’s method and the fourth-order Runge–Kutta scheme, ensuring improved accuracy. Simulations conducted in MATLAB illustrate disease trajectories for both early- and mid-stage detection and evaluate the effectiveness of immune-based interventions. The results enhance understanding of cancer dynamics, provide public health perspectives on immunotherapy outcomes, and contribute to evidence-based strategies for controlling cancer.
Keywords: Mathematical Modeling; Stability analysis; Reproductive number; Sensitivity analysis; Non-standard finite difference (NSFD).Lung cancer remains one of the most prevalent and life-threatening chronic respiratory diseases worldwide, demanding effective diagnostic and therapeutic strategies. In this study, we propose a novel deterministic mathematical model, denoted as TCDIL2, to investigate the impact of IL2 cytokine therapy on the immune system’s anticancer response. The model is structured into four epidemiological compartments and is analyzed through both qualitative and quantitative approaches. Local stability analysis is performed under limited data conditions, while equilibrium points, the basic reproduction number (R0), and sensitivity indices are systematically examined. The next-generation method is employed to compute R0, offering insight into disease progression across compartments, whereas sensitivity analysis highlights the influence of key parameters on system dynamics. Global stability is assessed via Lyapunov functions, establishing conditions under which IL2 therapy benefits immunocompromised individuals. For numerical simulations, a nonstandard finite difference (NSFD) scheme based on an implicit method is developed and benchmarked against Euler’s method and the fourth-order Runge–Kutta scheme, ensuring improved accuracy. Simulations conducted in MATLAB illustrate disease trajectories for both early- and mid-stage detection and evaluate the effectiveness of immune-based interventions. The results enhance understanding of cancer dynamics, provide public health perspectives on immunotherapy outcomes, and contribute to evidence-based strategies for controlling cancer. |
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