Abstract:
Lung disease ranks as the leading cause of infection-related mortality in children, men, and women, making it the third most significant cause of death globally, following Stroke and Coronary Heart Disease. This research presents a mathematical model that addresses the impact of hereditary genetics, air pollution and drug consumption on the population's health, with a particular focus on individuals with lung infections. The model considers the dynamics of individuals affected by lung infections and examines the progression of lung disease through the lens of biological phenomena, utilizing linear differential equations. Both analytical and numerical analyses were conducted. The study explores various factors contributing to the recovery of individuals with lung infections, such as self-healing, increased social awareness, hospitalization, and diverse medication options. In the biological sciences, evolutionary epidemiological models have been instrumental in the analysis of various infectious diseases and intervention strategies. The linear SIHR epidemic model framework for disease-free and endemic equilibrium is theoretically investigated to demonstrate stable conditions. Next, through the use of complex evolutionary game theory elements, the embedded susceptible and susceptible for drug consumption strategies are present among the members of society through an absurd phase diagram. The numerical simulations were performed using the Kermack-McKendrick SIR epidemic model, and the research findings have been documented. The hope is that this research will contribute to the development of treatments for lung diseases. Finally, A clear understanding of the impact of air pollution on the troposphere and increased drug efficacy will be obtained and encouraged a decision to reduce lung infection. This study also shows that increased awareness among the population may lead to hospitalization of infected individuals and reduce mortality.