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A modeling investigation of the disease severity driven by COVID-19-induced diabetic patients

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  • Hoque, Ashabul
  • Sultana, Razia
  • Islam, Hamidul
  • Malek, Abdul

Abstract

In this study, we introduce a novel SEICHR compartmental model to explore the transmission dynamics of COVID-19 among diabetic and non-diabetic patients, with a particular focus on the impact of COVID-19-induced diabetes on disease progression. The well-posedness of the model and the stability of the equilibrium points are confirmed through rigorous mathematical analysis. Mathematical results are validated using numerical simulations. Sensitivity and bifurcation analyses are conducted to determine the most sensitive parameters in the proposed model. The sensitivity of the model parameters is examined using the partial rank correlation coefficients (PRCC) analysis. The results show that the disease progression rate is higher among COVID-19 infected diabetic patients than non-diabetic patients. Numerical simulations further indicate that the forward bifurcation region expands progressively with increasing rates of disease development. The role of COVID-19-induced diabetic patients in exacerbating disease severity is examined through both constant and progressive delays in hospital isolation. Notably, the progressive waiting time exerts a significantly greater impact on transmission dynamics than the constant waiting time.

Suggested Citation

  • Hoque, Ashabul & Sultana, Razia & Islam, Hamidul & Malek, Abdul, 2025. "A modeling investigation of the disease severity driven by COVID-19-induced diabetic patients," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 237(C), pages 213-230.
    • Handle: RePEc:eee:matcom:v:237:y:2025:i:c:p:213-230
    DOI: 10.1016/j.matcom.2025.04.011
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    References listed on IDEAS

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    1. Alexander F. Siegenfeld & Pratyush K. Kollepara & Yaneer Bar-Yam & Toshikazu Kuniya, 2022. "Modeling Complex Systems: A Case Study of Compartmental Models in Epidemiology," Complexity, Hindawi, vol. 2022, pages 1-12, October.
    2. Leon Arriola & James M. Hyman, 2009. "Sensitivity Analysis for Uncertainty Quantification in Mathematical Models," Springer Books, in: Gerardo Chowell & James M. Hyman & Luís M. A. Bettencourt & Carlos Castillo-Chavez (ed.), Mathematical and Statistical Estimation Approaches in Epidemiology, pages 195-247, Springer.
    3. Masud M A & Md Hamidul Islam & Khondaker A. Mamun & Byul Nim Kim & Sangil Kim, 2020. "COVID-19 Transmission: Bangladesh Perspective," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    4. Kouidere, Abdelfatah & Youssoufi, Lahcen EL & Ferjouchia, Hanane & Balatif, Omar & Rachik, Mostafa, 2021. "Optimal Control of Mathematical modeling of the spread of the COVID-19 pandemic with highlighting the negative impact of quarantine on diabetics people with Cost-effectiveness," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    5. Anand, Monalisa & Danumjaya, P. & Rao, P. Raja Sekhara, 2023. "A nonlinear mathematical model on the Covid-19 transmission pattern among diabetic and non-diabetic population," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 210(C), pages 346-369.
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