(untitled) (Journal of Nonlinear Modeling and Analysis, 2025)
This study presents the global post-effects of COVID-19 through a mathematical modeling approach. A compartmental model, dividing the total population into six epidemiological compartments, is developed to simulate the dynamics of the disease. These compartments include susceptible humans (S), exposed humans (E), infected humans (I), deceased humans (D), individuals in treatment class (T), and recovered humans (R). The study extensively discusses the post-effects of COVID-19 in Africa and the sensitivity analysis reveals that the contact rate of infection exhibits a positive sensitivity index, indicating that interventions aimed at reducing contact rate ϕ would significantly diminish the spread of the virus within the population. Conversely, the treatment rate ω shows a negative sensitivity index, suggesting that promoting higher treatment rates would lead to an increased recovery rate, thereby effectively controlling the spread of the disease. Numerical simulations carried out using MATLAB further confirms that a high treatment rate, avoidance of contact rate with any infected person or infectious surface, coupled with adherence to COVID-19 control measures, could help prevent the spread and outbreaks of COVID-19 in the future. The implications of these findings extend to healthcare workers, policymakers, and the general public, offering valuable insights into disease transmission dynamics and informing preparedness for future pandemics. Overall, this study considers the importance of proactive measures and effective healthcare interventions in mitigating the impact of COVID-19 and preventing the resurgence of infectious diseases in Africa.