Chaotic system analysis with genetic algorithm approach

Dynamical analysis of epidemiological models gained significant attention from applied mathematicians after the Covid-19 pandemic caused a global catastrophe. Although studies involving the decision of the suitable model to describe the epidemiologic data and the predictive analysis of the disease spread have been carried out in-depth during this phase, studies that will expose the unrecognized dynamics through predictive analysis by investigating the algebraic properties of the models such as the invariant spaces, the center manifolds, and the Lyapunov functions in the general parameter space, still have the potential to contribute vastly to comprehending the spread dynamics. This work implements the triangular probabilistic distribution to an epidemic delay differential equation as a distributed continuous delay to determine the recovery rate based on characteristic model parameters. In this way, we propose a way to reduce the model parameters that need to be obtained during model fitting by defining generic coefficients derivable from field research and population characteristics.