Analyzing the role of comorbidity on COVID-19 infections by mathematical modeling

Abstract

This study develops a deterministic mathematical model to quantify the role played by comorbidity among humans as a risk factor for driving COVID-19 epidemic. The newly developed model is confined to those comorbidities that are not infectious and thus there is no transmission of comorbidities among individuals within the compartments of the model. Firstly, we carry out the quantitative and qualitative analysis of the model appertaining positivity, boundedness, equilibrium points, reproduction number, stability of the equilibrium points and bifurcation analysis. Secondly, we fit the model to real COVID-19 data of the Republic of South Africa to assure the validity of the model. Finally, we compute sensitivity indices with respect to the parameters of interest for the purpose of sensitivity interpretation and perform numerical simulations to assess the trajectory of COVID-19 infections as the parameters of interest vary. Results indicate that comorbidity contributes significantly in increasing the number of the COVID-19 infections.