A hybrid structure for influenza disease model along special cases

In this study, stochastic artificial neural network (ANN) methods backed by Levenberg-Marquardt back-propagation (LMBP), also known as ANN-LMBP (hybrid model), are used to numerically simulate the influenza disease system (IDS). The stiff ordinary differential system is built with three classes: susceptible s(t), infected i(t), and recovered r(t). For the purpose of solving three different IDS variations, the hybrid model was used to do the numerical computations. In order to reduce mean square error (MSE) from data-based reference solutions, the generated numerical solutions through the hybrid model for solving the IDS have been given. An extensive analysis is presented using the correlation studies, MSE information to observe the correctness, efficiency, competence, and proficiency of the created computing paradigm hybrid model. The value and significance of hybrid model are supported by comparisons of the findings.