A mathematical model for COVID-19 transmission dynamics in a population with treated and untreated type 2 diabetes

Abstract

The COVID-19 disease posed critical challenges to global health, particularly among individuals with pre-existing conditions such as Type 2 diabetes. While evidence suggested that Type 2 diabetes worsened COVID-19 outcomes, the e ect of Type 2 diabetes treatment on the disease dynamics remained unclear. This study formulates and analyzes a deterministic SEIR mathematical model of COVID-19 transmission in a population with and without Type 2 diabetes, subdividing diabetics into untreated and treated groups. The study derives the basic reproduction number (R), assesses equilibrium stability, and conducts sensitivity analysis to identify key parameters that in uence infection dynamics. Numerical simulations indicate that treatment of Type 2 diabetes reduces the infectious diabetic population, increases recovery, and lowers the basic reproduction number under appropriate parameter values.