A Mathematical Model for COVID-19 Severity and Lung Cancer Coinfection: Analysis of Threshold Behavior

This study develops a novel mathematical model to examine the coinfection dynamics of COVID-19 and lung cancer. We develop a seven-compartment deterministic model that combines the acute viral transmission with the long-term oncogenic progression via modified susceptibility parameters, enabling to model the clinically observed synergistic exacerbation between the two diseases. We obtain disease-specific epidemic thresholds and demonstrate that the overall reproduction number is determined by the maximum of these individual thresholds. Stability analysis shows that the disease-free state is globally stable if the threshold is less than unity, and a unique endemic state appears if it is greater than unity. The sensitivity analysis consists of combining the analytical indices for lung cancer and the global statistical method for COVID-19 parameters. The analysis results show that the dominant factors affecting the epidemic outcomes are the rates of disease transmission and recovery. Numerical simulations show that coinfection results in an advance of epidemic peaks by 15–30 days, and in an increase of the equilibrium prevalence up to 47% when compared to single-disease scenarios. Integrated interventions are 40%–65% more effective than isolated strategies in reducing the coinfection burden. The work provides a rigorous mathematical framework for modeling multiple diseases with implications for public health policy and clinical management of high-risk populations.