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<p>Co-infection with COVID-19 and other respiratory pathogens presents a significant challenge for health management, as it complicates both diagnosis and treatment. This research develops a novel mathematical model for the dynamics of co-infection, including COVID-19 and tuberculosis (TB). It includes a Holling type-II nonlinear treatment rate. The analysis of the model begins by investigating the submodels for COVID-19, TB, and the overall COVID-19-Tuberculosis model. The equilibrium points for both disease-free and endemic equilibrium points for each sub-model and the overall COVID-19-TB co-infection model have been examined and analysed. The proposed model uses real-world data on all the COVID-19 cases in four South Asian countries: India, Pakistan, Sri Lanka, and Bangladesh. This data is used to estimate the model parameters and show how treatment strategies and limited resources affect the spread of disease. Additionally, numerical simulations are used to look at how treating both diseases and not treating all tuberculosis patients completely affects the overall number of people who are infected. This study also examines the effect of co-infection on mortality rates associated with disease.</p>

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Dynamics of COVID-19 and tuberculosis co-infection: a mathematical model incorporating nonlinear treatment rate

  • 期刊论文

  • 发表日期：

    2025年7月4日

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  • Sunil Singh Negi,
  • Nitin Sharma,
  • Anupam Priyadarshi

摘要

Co-infection with COVID-19 and other respiratory pathogens presents a significant challenge for health management, as it complicates both diagnosis and treatment. This research develops a novel mathematical model for the dynamics of co-infection, including COVID-19 and tuberculosis (TB). It includes a Holling type-II nonlinear treatment rate. The analysis of the model begins by investigating the submodels for COVID-19, TB, and the overall COVID-19-Tuberculosis model. The equilibrium points for both disease-free and endemic equilibrium points for each sub-model and the overall COVID-19-TB co-infection model have been examined and analysed. The proposed model uses real-world data on all the COVID-19 cases in four South Asian countries: India, Pakistan, Sri Lanka, and Bangladesh. This data is used to estimate the model parameters and show how treatment strategies and limited resources affect the spread of disease. Additionally, numerical simulations are used to look at how treating both diseases and not treating all tuberculosis patients completely affects the overall number of people who are infected. This study also examines the effect of co-infection on mortality rates associated with disease.