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<p>This study investigates the transmission dynamics of COVID-19 and pneumonia co-infection through a structured compartmental mathematical model that partitions the human population into eleven epidemiologically distinct classes. The model incorporates key public-health and clinical factors, including vaccination, misdiagnosis, and treatment failure, to capture realistic disease progression and intervention scenarios. We analyze the influence of vaccination rates, diagnostic accuracy, and treatment efficacy on disease spread and population-level outcomes. Analytical and numerical results reveal that transmission intensity, treatment effectiveness, and diagnostic reliability play decisive roles in determining outbreak severity. In particular, reduced treatment efficacy substantially increases disease burden and mortality, while elevated misdiagnosis rates undermine timely case detection and appropriate clinical management. These findings highlight the critical importance of accurate diagnosis and effective treatment in controlling co-infectious respiratory diseases. Overall, the study underscores the need for integrated intervention strategies that combine improved diagnostic capacity, effective treatment protocols, and sustained vaccination efforts. Continued investment in research on innovative therapies and vaccines, together with strengthened public-health infrastructure and awareness programs, is essential for mitigating the impact of co-infectious diseases and enhancing global health resilience.</p>

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Misdiagnosis challenges in identification of COVID-19 and pneumonia infection in their co-existence: mathematical modeling approach

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • Belela Samuel Kotola,
  • Shewaferaw Wondimagegnaw Teklu,
  • Haileyesus Tessema,
  • Oluwole Daniel Makinde

摘要

This study investigates the transmission dynamics of COVID-19 and pneumonia co-infection through a structured compartmental mathematical model that partitions the human population into eleven epidemiologically distinct classes. The model incorporates key public-health and clinical factors, including vaccination, misdiagnosis, and treatment failure, to capture realistic disease progression and intervention scenarios. We analyze the influence of vaccination rates, diagnostic accuracy, and treatment efficacy on disease spread and population-level outcomes. Analytical and numerical results reveal that transmission intensity, treatment effectiveness, and diagnostic reliability play decisive roles in determining outbreak severity. In particular, reduced treatment efficacy substantially increases disease burden and mortality, while elevated misdiagnosis rates undermine timely case detection and appropriate clinical management. These findings highlight the critical importance of accurate diagnosis and effective treatment in controlling co-infectious respiratory diseases. Overall, the study underscores the need for integrated intervention strategies that combine improved diagnostic capacity, effective treatment protocols, and sustained vaccination efforts. Continued investment in research on innovative therapies and vaccines, together with strengthened public-health infrastructure and awareness programs, is essential for mitigating the impact of co-infectious diseases and enhancing global health resilience.