A design of computational stochastic framework for the mathematical severe acute respiratory syndrome coronavirus model

Abstract

This study presents the comprehensive investigations into the dynamics of a novel coronavirus infection within a population, which accounts for all potential interactions in the disease’s spread. The solutions of the novel nonlinear infectious disease system are performed stochastically by using the Levenberg-Marquardt Backpropagation neural network. This process contains ten neurons and log-sigmoid transfer function in the hidden layers. The training data is taken as 74%, while the testing and authentication statics are used as 14% and 12%. To assess the precision of the designed solver, a comparison based on the obtained and reference results along with the negligible absolute error up to order fourth to seventh decimal places is performed for each case of the model. Stability and sensitivity analyses reveal the robustness of the model across various parameters. For the reliability, consistency, and correctness of the model across various states, and the numerical analysis with graphical form of the statistical indices based on correlation, error histograms, transition of state, and regression analysis is presented.