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Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

Manal. M. Alqarni1, Arooj Nasir2,3, Dumitru Baleanu4,5,6, Ali Raza7, Tahir Nawaz Cheema8,*, Nauman Ahmed9, Muhammad Rafiq10, Umbreen Fatima11, Emad E. Mahmoud12

1 Department of Mathematics, College of Sciences, King Khalid University, Abha, 61413, Saudi Arabia
2 Baqai Medical University, Karachi, 75340, Pakistan
3 Shalamar Medical and Dental College, Lahore, 54000, Pakistan
4 Department of Mathematics, Cankaya University, Balgat, Ankara, 06530, Turkey
5 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan
6 Institute of Space Sciences, Magurele-Bucharest, 077125, Romania
7 Department of Mathematics, Govt. Maulana Zafar Ali Khan Graduate College Wazirabad, Punjab Higher Education Department (PHED), Lahore, 54000, Pakistan
8 Department of Mathematics, University of Gujrat, Gujrat, 52200, Pakistan
9 Department of Mathematics and Statistics, The University of Lahore, Lahore, 54590, Pakistan
10 Department of Mathematics, Faculty of Science and Technology, University of Central Punjab, Lahore, 54000, Pakistan
11 Department of Computer Science, University of Lahore, Lahore, 54590, Pakistan
12 Department of Mathematics and Statistics, College of Science, Taif University, P. O. Box, 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: Tahir Nawaz Cheema. Email:

Computers, Materials & Continua 2023, 74(3), 6807-6822.


Artificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The research work was designed via a particular method of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations. These differential equations are established by collecting the susceptible, the exposed, the symptomatic, super spreaders, infection with asymptomatic, hospitalized, recovery, and fatality classes. The generation of the coronavirus model’s dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, validation, and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model. The performance and effectiveness of the designed methodology are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.


Cite This Article

M. M. Alqarni, A. Nasir, D. Baleanu, A. Raza, T. N. Cheema et al., "Optimization of coronavirus pandemic model through artificial intelligence," Computers, Materials & Continua, vol. 74, no.3, pp. 6807–6822, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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