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

The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance. The handling strategies of real-world problems are artificial neural networks (ANN), evolutionary computing (EC), and many more. An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually, with only 25% to 45% reported to the World Health Organization (WHO). It remains one of the top parasitic diseases with outbreak and mortality potential. In 2020, more than ninety percent of new cases reported to World Health Organization (WHO) occurred in ten countries: Brazil,