Open Access

ARTICLE

Numerical Computation of SEIR Model for the Zika Virus Spreading

by Suthep Suantai^1,2, Zulqurnain Sabir^3,4, Muhammad Asif Zahoor Raja⁵, Watcharaporn Cholamjiak^6,*

1 Data Science Research Center, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
2 Research Group in Mathematics and Applied Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
3 Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
4 Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
5 Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road,Section 3, Douliou, Yunlinspan,64002, Taiwan
6 School of Science, University of Phayao, Phayao 56000, Thailand

* Corresponding Author: Watcharaporn Cholamjiak. Email:

Computers, Materials & Continua 2023, 75(1), 2155-2170. https://doi.org/10.32604/cmc.2023.034699

Received 25 July 2022; Accepted 08 December 2022; Issue published 06 February 2023

Abstract

The purpose of this study is to present the numerical performances and interpretations of the SEIR nonlinear system based on the Zika virus spreading by using the stochastic neural networks based intelligent computing solver. The epidemic form of the nonlinear system represents the four dynamics of the patients, susceptible patients S(y), exposed patients hospitalized in hospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIR model. The computing numerical outcomes and performances of the system are examined by using the artificial neural networks (ANNs) and the scaled conjugate gradient (SCG) for the training of the networks, i.e., ANNs-SCG. The correctness of the ANNs-SCG scheme is observed by comparing the proposed and reference solutions for three cases of the SEIR model to solve the nonlinear system based on the Zika virus spreading dynamics through the knacks of ANNs-SCG procedure based on exhaustive experimentations. The outcomes of the ANNs-SCG algorithm are found consistently in good agreement with standard numerical solutions with negligible errors. Moreover, the procedure’s constancy, dependability, and exactness are perceived by using the values of state transitions, error histogram measures, correlation, and regression analysis.

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Keywords

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