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# A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

Mohammad Aslefallah1, Şuayip Yüzbaşi2, Saeid Abbasbandy1,*

1 Department of Applied Mathematics, Imam Khomeini International University, Qazvin, 34149, Iran
2 Department of Mathematics, Faculty of Science, Akdeniz University, Antalya, 07058, Turkey

* Corresponding Authors: Saeid Abbasbandy. Email: ,

(This article belongs to the Special Issue: Differential Equations and New Methods in Mathematical Biology)

Computer Modeling in Engineering & Sciences 2023, 136(2), 1687-1706. https://doi.org/10.32604/cmes.2023.025647

## Abstract

In this work, the exponential approximation is used for the numerical simulation of a nonlinear SITR model as a system of differential equations that shows the dynamics of the new coronavirus (COVID-19). The SITR mathematical model is divided into four classes using fractal parameters for COVID-19 dynamics, namely, susceptible (S), infected (I), treatment (T), and recovered (R). The main idea of the presented method is based on the matrix representations of the exponential functions and their derivatives using collocation points. To indicate the usefulness of this method, we employ it in some cases. For error analysis of the method, the residual of the solutions is reviewed. The reported examples show that the method is reasonably efficient and accurate.

## Keywords

APA Style
Aslefallah, M., Yüzbaşi, Ş., Abbasbandy, S. (2023). A numerical investigation based on exponential collocation method for nonlinear SITR model of COVID-19. Computer Modeling in Engineering & Sciences, 136(2), 1687-1706. https://doi.org/10.32604/cmes.2023.025647
Vancouver Style
Aslefallah M, Yüzbaşi Ş, Abbasbandy S. A numerical investigation based on exponential collocation method for nonlinear SITR model of COVID-19. Comput Model Eng Sci. 2023;136(2):1687-1706 https://doi.org/10.32604/cmes.2023.025647
IEEE Style
M. Aslefallah, Ş. Yüzbaşi, and S. Abbasbandy "A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19," Comput. Model. Eng. Sci., vol. 136, no. 2, pp. 1687-1706. 2023. https://doi.org/10.32604/cmes.2023.025647

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