Applications of artificial neural network to solve the nonlinear COVID-19 mathematical model based on the dynamics of SIQ

Abstract

The purpose of this research work is to present a numerical study through artificial neural networks (ANNs) to solve a SIQ-based COVID-19 mathematical model using the effects of lockdown. The effects of lockdown are considered to the three-dimensional mathematical model, “susceptible”, “infective” and “quarantined”, i.e. the SIQ system. ANNs and Levenberg–Marquardt backpropagation (LMB) are used to present the numerical analyses of the SIQ system-based COVID-19. Three different types of authentications, testing and training as sample data are applied to solve the SIQ system. Statistical ratios for the SIQ-based COVID-19 mathematical model are selected – 80% for training and 10% for both testing and authentication. The obtained numerical results of the SIQ mathematical system have been compared with the reference dataset, which is constructed through Adams solutions. The obtained numerical performances of the nonlinear SIQ dynamical model are testified with a reduction of the error in mean square sense in the range of 10−9 to 10−12.

Keywords

• COVID-19
• nonlinear SIQ system
• artificial neural networks
• Levenberg–Marquardt backpropagation
• reference dataset