Journal of Cardio-Thoracic Medicine

Journal of Cardio-Thoracic Medicine

Diabetes‎, ‎Hypertension‎, ‎Obesity‎, ‎and Age as Predictors of Severe COVID-19‎: ‎A Mathematical Modeling Study

Document Type : Original Article

Authors
1 Esfarayen University of Technology, Esfarayen, North Khorasan, Iran.
2 Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
3 Department of Applied Mathematics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.
4 Department of Statistics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Abstract
Objective: To analyze an epidemiological model of coronavirus disease 2019 (COVID-19) transmission and identify patient characteristics associated with disease severity‎.
Materials and Methods: An extended SIR-based compartmental model was developed‎, ‎incorporating asymptomatic/symptomatic classes‎, ‎hospitalization‎, ‎ intensive care unit (ICU) admission‎, ‎mortality‎, ‎reinfection‎, ‎and environmental transmission‎. ‎The basic reproduction number ‎ ) was derived‎, ‎and stability analysis was performed using Routh-Hurwitz and Castillo-Chavez criteria‎. ‎Numerical simulations were conducted using MATLAB with data from Mashhad University of Medical Sciences‎.
Results: The disease-free equilibrium is stable when ‎ <1‎. ‎Clinical findings show‎: ‎diabetic patients are twice as likely to develop severe symptoms; hypertensive patients have a 3.5-fold higher risk (20% mortality vs‎. ‎0.7%); men have seven-fold higher mortality than women; older adults (>40 years) show increased severity; and obese patients (BMI ≥30) have worse outcomes‎.
Conclusion: is a critical parameter for disease-free equilibrium stability‎. ‎Comorbidities (diabetes‎, ‎hypertension‎, ‎obesity) and demographic factors (older age‎, ‎male gender) significantly increase the risk of severe COVID-19 outcomes‎.
Keywords

  1. Ahmadi A, Fadaei Y, Shirani M, Rahmani F. Modeling and forecasting trend of COVID-19 epidemic in Iran until May 13, 2020. Medical Journal of the Islamic Republic of Iran. 2020 Mar 31;34:27.
  2. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The lancet. 2020 Feb 29;395(10225):689-97.
  3. Ngonghala CN, Iboi E, Eikenberry S, Scotch M, MacIntyre CR, Bonds MH, et al. Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus. Mathematical biosciences. 2020 Jul 1;325:108364.
  4. Okuonghae D, Omame A. Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria. Chaos, Solitons & Fractals. 2020 Oct 1;139:110032.
  5. Eikenberry SE, Mancuso M, Iboi E, Phan T, Eikenberry K, Kuang Y, et al. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infectious disease modelling. 2020 Jan 1;5:293-308.
  6. Perkins TA, España G. Optimal control of the COVID-19 pandemic with non-pharmaceutical interventions. Bulletin of mathematical biology. 2020 Oct 7;82(9):118.
  7. Tsay C, Lejarza F, Stadtherr MA, Baldea M. Modeling, state estimation, and optimal control for the US COVID-19 outbreak. Scientific reports. 2020 Jul 1;10(1):10711.
  8. Viceconte G, Petrosillo N. COVID-19 R0: Magic number or conundrum?. Infectious disease reports. 2020 Feb 24;12(1):8516.
  9. Mwalili S, Kimathi M, Ojiambo V, Gathungu D, Mbogo R. SEIR model for COVID-19 dynamics incorporating the environment and social distancing. BMC research notes. 2020 Jul 23;13(1):352.
  10. Mekonen KG, Habtemicheal TG, Balcha SF. Modeling the effect of contaminated objects for the transmission dynamics of COVID-19 pandemic with self protection behavior changes. Results in Applied Mathematics. 2021 Feb 1;9:100134.
  11. Olfatifar M, Alali WQ, Houri H, Pourhoseingholi MA, Babaee E, Seifollahi R, et al. Early estimation of the epidemiological parameters of novel coronavirus disease (COVID-2019) outbreak in Iran: 19 Feb-15 March, 2020. Gastroenterology and hepatology from bed to bench. 2020;13(Suppl1):S134.
  12. Ghasemabadi A. Mathematical modeling and control of Covid‐19. Mathematical Methods in the Applied Sciences. 2024 Aug;47(12):10478-89.
  13. Castillo-Chavez C, Song B. Dynamical models of tuberculosis and their applications. Mathematical biosciences and engineering. 2004;1(2):361.