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New Trends in the Modeling of Diseases Through Computational Techniques

Nesreen Althobaiti1, Ali Raza2,*, Arooj Nasir3,4, Jan Awrejcewicz5, Muhammad Rafiq6, Nauman Ahmed7, Witold Pawłowski8, Muhammad Jawaz7, Emad E. Mahmoud1

1 Department of Mathematics and Statistics, College of Science, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
2 Department of Mathematics, Govt. Maulana Zafar Ali Khan Graduate College Wazirabad, Punjab Higher Education Department (PHED), Lahore, 54000, Pakistan
3 Baqai Medical University, Karachi, 75340, Pakistan
4 Shalamar Medical and Dental College, Lahore, 54000, Pakistan
5 Department of Automation, Biomechanics and Mechatronics, Lodz University of Technology, 1/15 Stefanowskiego St., 90-924, Lodz, Poland
6 Department of Mathematics, Faculty of Sciences and Technology, University of Central Punjab, Lahore, 54000, Pakistan
7 Department of Mathematics and Statistics, University of Lahore, Lahore, 54590, Pakistan
8 Institute of Machine Tools and Production Engineering, Lodz University of Technology, 1/15 Stefanowskiego St., 90-537, Lodz, Poland

* Corresponding Author: Ali Raza. Email: email

Computer Systems Science and Engineering 2023, 45(3), 2935-2951. https://doi.org/10.32604/csse.2023.033935

Abstract

The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance. The handling strategies of real-world problems are artificial neural networks (ANN), evolutionary computing (EC), and many more. An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually, with only 25% to 45% reported to the World Health Organization (WHO). It remains one of the top parasitic diseases with outbreak and mortality potential. In 2020, more than ninety percent of new cases reported to World Health Organization (WHO) occurred in ten countries: Brazil, China, Ethiopia, Eritrea, India, Kenya, Somalia, South Sudan, Sudan, and Yemen. The transmission of visceral leishmaniasis is studied dynamically and numerically. The study included positivity, boundedness, equilibria, reproduction number, and local stability of the model in the dynamical analysis. Some detailed methods like Runge Kutta and Euler depend on time steps and violate the physical relevance of the disease. They produce negative and unbounded results, so in disease dynamics, such developments have no biological significance; in other words, these results are meaningless. But the implicit nonstandard finite difference method does not depend on time step, positive, bounded, dynamic and consistent. All the computational techniques and their results were compared using computer simulations.

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APA Style
Althobaiti, N., Raza, A., Nasir, A., Awrejcewicz, J., Rafiq, M. et al. (2023). New trends in the modeling of diseases through computational techniques. Computer Systems Science and Engineering, 45(3), 2935-2951. https://doi.org/10.32604/csse.2023.033935
Vancouver Style
Althobaiti N, Raza A, Nasir A, Awrejcewicz J, Rafiq M, Ahmed N, et al. New trends in the modeling of diseases through computational techniques. Comput Syst Sci Eng. 2023;45(3):2935-2951 https://doi.org/10.32604/csse.2023.033935
IEEE Style
N. Althobaiti et al., "New Trends in the Modeling of Diseases Through Computational Techniques," Comput. Syst. Sci. Eng., vol. 45, no. 3, pp. 2935-2951. 2023. https://doi.org/10.32604/csse.2023.033935



cc 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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