A Mathematical Model of Transmission Dynamics of SARS-Cov-2 (Covid-19) with an Underlying Condition of Diabetes

TL;DR

This paper develops a mathematical model to understand COVID-19 transmission dynamics in individuals with diabetes, analyzing stability, sensitivity, and control strategies, and validates findings with Ghanaian data, emphasizing higher risks for diabetic patients.

Contribution

It introduces a deterministic mathematical model incorporating diabetes as a comorbidity in COVID-19 transmission, including optimal control strategies and validation with real-world data.

Findings

COVID-19 is endemic in Ghana with R0=1.4722.

Diabetic individuals have a higher risk of death from COVID-19.

Both lockdown and vaccination effectively reduce COVID-19 spread.

Abstract

It is well established that people with diabetes are more likely to have serious complications from COVID-19. Nearly 1 in 5 COVID-19 deaths in the African region are linked to diabetes. World Health Organization (WHO) finds that 18.3% of COVID-19 deaths in Africa are among people with diabetes. In this paper, we have formulated and analysed a mathematical comorbidity model of diabetes - COVID-19 of the deterministic type. The basic properties of the model were explored. The basic reproductive number, equilibrium points and stability of the equilibrium points were examined. Sensitivity analysis of the model was carried on to determine the impact of the model parameters on the basic reproduction number of the model. The model had a unique endemic equilibrium point, which was stable for R_0>1. Time-dependent optimal controls were incorporated into the model with the sole aim of determining the best strategy for curtailing the spread of the disease. COVID-19 cases from March to September 2020 in Ghana were used to validate the model. Results of the numerical simulation suggest a greater number of individuals deceased when the infected individual had an underlying condition of diabetes. More so COVID-19 is endemic in Ghana with the basic reproduction number found to be R_0=1.4722. The numerical simulation of the optimal control model reveals the lockdown control minimized the rate of decay of the susceptible individuals whereas the vaccination led to a number of susceptible individuals becoming immune to COVID-19 infections. In all the two preventive control measures were both effective in curbing the spread of the COVID-19 disease as the number of COVID-19 infections was greatly reduced. We conclude that more attention should be paid to COVID-19 patients with an underlying condition of diabetes as the probability of death in this population was significantly higher.