Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 18 Yunlin 64002, Taiwan, R. O. C.

E-mail Address: adiqa@yuntech.edu.tw

Corresponding author.

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, and

Muhammad Asif Zahoor Raja

Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 18 Yunlin 64002, Taiwan, R. O. C.

E-mail Address: rajamaz@yuntech.edu.tw

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https://doi.org/10.1142/S0217979222501004Cited by:14 (Source: Crossref)

Abstract

This paper portrays the exploitation/exploration of artificial intelligence (AI) inspired computing to study the behavior of the multi-delay differential systems that revealed the impact of latent period and the dynamics of the a susceptible, vaccinated, exposed, infectious and recovered (SVEIR) epidemic model involving vaccination by means of the neural networks backpropagation with Levenberg–Marquardt scheme (NNs-BLMS). The reference solutions of the five classes ordinary differential equations (ODEs) model of SVEIR dynamics are calculated by applying the Adams method for variation in delay due to the time spent in preventing the infection and delay due to the duration of recovery immunity in the cured population. The designed NNs-BLMS used the created dataset arbitrarily for training, validation, as well as, testing samples to determine the estimated results of the nonlinear SVEIR epidemic model involving vaccination impact. The achieved accuracy of the designed NNs-BLMS is authenticated/proven by analyzing the fitness function based on mean square error (MSE), regression analysis, and error histogram for sundry scenarios of SVEIR epidemic system with the impact of vaccination.

Keywords:

PACS: 47.11.−j, 47.10.−A, 07.05.Mh

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