A Fuzzy Dynamic Optimal Model for COVID-19 Epidemic in India Based on Granular Differentiability

Abstract

The pandemic SARS-CoV-2 has become an undying virus to spread sustainable disease named COVID-19 for upcoming few years. The mortality rate of the disease is increasing rapidly as the approved drug is not available yet. Isolation from the infected individual or community is the recommended choice to save our existence. As the human is the only carrier so in that case if the host carriers are isolated from each other then it might be possible to control the spread or positive rates of infected population. Whereas only isolation might not be the only recommended solution. These are the resolutions of previous research work carried out on COVID-19 throughout the world. The present scenario of the world and public health is knocking hard with a big question of critical uncertainty of COVID-19 because of its imprecise database as per daily positive cases recorded all over the world and in India as well. In this research work we have presented an optimal control model for COVID-19 by using fuzzy dynamical system based granular differentiability. In the first step, we have formulated the fuzzy SEIAHRD model for COVID-19, analysed using granular differentiability and reported the disease dynamics for time independent disease control parameter. In the second step, we have upgraded the concerned fuzzy dynamical system and granular differentiability model up to an optimal control problem invader with time dependent control parameter. The theoretical findings are validated graphically with some realistic data for pandemic COVID-19 with respect to India’s perspective.

Note: Funding: None to declare

Declaration of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Keywords: COVID-19, Asymptomatic, Susceptible, Fuzzy dynamical system, Granular(gr) differentiability