The modeling and analysis of the COVID-19 pandemic with vaccination and treatment control: a case study of Maharashtra, Delhi, Uttarakhand, Sikkim, and Russia in the light of pharmaceutical and non-pharmaceutical approaches.

Nonlinear dynamics is an exciting approach to describe the dynamical practices of COVID-19 disease. Mathematical modeling is a necessary method for investigating the dynamics of epidemic diseases. In the current article, an effort has been made to cultivate a novel COVID-19 compartment mathematical model by incorporating vaccinated populations. Primarily, the fundamental characteristics of the model, such as positivity and boundedness of solutions, are established. Thereafter, equilibrium analysis of steady states has been illustrated through vaccine reproduction number. Further, a nonlinear least square curve fitting technique has been employed to recognize the best fitted model parameters from the COVID-19 mortality data of five regions, namely Maharashtra, Delhi, Uttarakhand, Sikkim, and Russia. The numerical framework of the model has been added to interpret the consequence of various control schemes (pharmaceutical or non-pharmaceutical) on COVID-19 dynamics, and it has been ascertained that all the control protocols have a positive influence on curtailing the COVID-19 transference in the aforementioned regions. In addition, the essence of vaccine efficacy and vaccine-induced immunity are examined by considering different scenarios. Our analysis demonstrates that the disease will be wiped off from the Maharashtra, Delhi, Uttarakhand and Sikkim regions of India, while it shall persist in Russia for some more time. It is also found that, if a vaccine calamity arises, the government should majorly focus on permanent drug treatment of hospitalized individuals rather than vaccination.