A mathematical framework for analyzing co-infection of COVID-19 with lung cancer
Year: 2025
Type: article
Abstract: This study introduces a novel mathematical framework to explore the complex relationship between COVID-19 and lung cancer, addressing a critical gap in the existing literature. While various co-infection models have been discussed, no prior work has focused explicitly on the mathematical modeling of COVID-19 and lung cancer co-infection, positioning this research as pioneering. The dynamical model incorporates sub-models to analyze two distinct scenarios: one focusing exclusively on lung cancer and the other on COVID-19. The basic reproduction number determines stability conditions for the disease-free and endemic equilibriums. The model also integrates intervention strategies, including lung cancer preventive measures and COVID-19-specific treatments, to evaluate their impact on co-infection dynamics. Stability analysis and numerical simulations identify critical factors influencing the severity and progression of the co-infection, highlighting the substantial role of lung cancer prevention in reducing COVID-19 co-infections. The results underscore the importance of targeted interventions in managing co-infections effectively. This framework provides valuable insights for researchers and healthcare professionals as a comprehensive tool for understanding co-infection dynamics. By addressing the complexities of COVID-19 and lung cancer co-infection, this study goes beyond previous efforts, offering actionable strategies and advancing the understanding of this critical public health issue.
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,470
OpenAlex ID: W4409479730