Bio-Inspired Numerical Analysis of COVID-19 with Fuzzy Parameters

Fuzziness or uncertainties arise due to insufficient knowledge, experimental errors, operating conditions and parameters that provide inaccurate information. The concepts of susceptible, infectious recovered are uncertain the different degrees in susceptibility, infectivity recovery among individuals population. differences can arise, when population groups under consideration having distinct habits, customs age have resistance, etc. More realistic models needed which consider these susceptibility individuals. In this paper, a Susceptible, Infected Recovered (SIR) epidemic model with fuzzy is discussed. infection, death rates disease considered as numbers. Fuzzy basic reproduction number equilibrium points been derived for studied model. then solved numerically three techniques, forward Euler, Runge-Kutta fourth order method RK-4) nonstandard finite difference (NSFD) methods respectively. NSFD technique becomes more efficient reliable others preserves all essential features continuous dynamical system.