A stochastic scale conjugate neural network procedure for the SIRC epidemic delay differential system

Abstract

In this study, a stochastic computing structure is provided for the numerical solutions of the SIRC epidemic delay differential model, i.e. SIRC-EDDM using the dynamics of the COVID-19. The design of the scale conjugate gradient (CG) neural networks (SCGNNs) is presented for the numerical treatment of SIRC-EDDM. The mathematical model is divided into susceptible $S(\rho ),$ recovered $R(\rho ),$ infected $I(\rho ),$ and cross-immune $C(\rho ),$ while the numerical performances have been provided into three different cases. The exactitude of the SCGNNs is perceived through the comparison of the accomplished and reference outcomes (Runge-Kutta scheme) and the negligible absolute error (AE) that are performed around 10⁻⁰⁶ to 10⁻⁰⁸ for each case of the SIRC-EDDM. The obtained results have been presented to reduce the mean square error (MSE) using the performances of train, validation, and test data. The neuron analysis is also performed that shows the AE by taking 14 neurons provide more accurateness as compared to 4 numbers of neurons. To check the proficiency of SCGNNs, the comprehensive studies are accessible using the error histograms (EHs) investigations, state transitions (STs) values, MSE performances, regression measures, and correlation.

Keywords:

• SIRC
• delay
• scale conjugate gradient
• numerical results
• neural networks

Disclosure statement

There is no conflict of interest among the authors.

Data availability statement

All data generated or analyzed during this study are included in this article.