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An Approximate Numerical Methods for Mathematical and Physical Studies for Covid-19 Models

Hammad Alotaibi, Khaled A. Gepreel, Mohamed S. Mohamed, Amr M. S. Mahdy*

Department of Mathematics, College of Science, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Amr M. S. Mahdy. Email: email

(This article belongs to this Special Issue: Advances in Computational Intelligence and its Applications)

Computer Systems Science and Engineering 2022, 42(3), 1147-1163.


The advancement in numerical models of serious resistant illnesses is a key research territory in different fields including the nature and the study of disease transmission. One of the aims of these models is to comprehend the elements of conduction of these infections. For the new strain of Covid-19 (Coronavirus), there has been no immunization to protect individuals from the virus and to forestall its spread so far. All things being equal, control procedures related to medical services, for example, social distancing or separation, isolation, and travel limitations can be adjusted to control this pandemic. This article reveals some insights into the dynamic practices of nonlinear Coronavirus models dependent on the homotopy annoyance strategy (HPM). We summon a novel sign stream chart that is utilized to depict the Coronavirus model. Through the numerical investigations, it is uncovered that social separation of the possibly tainted people who might be conveying the infection and the healthy virus-free people can diminish or interrupt the spread of the infection. The mathematical simulation results are highly concurrent with the statistical forecasts. The free balance and dependability focus for the Coronavirus model is discussed and the presence of a consistently steady arrangement is demonstrated.


Cite This Article

H. Alotaibi, K. A. Gepreel, M. S. Mohamed and A. M. S. Mahdy, "An approximate numerical methods for mathematical and physical studies for covid-19 models," Computer Systems Science and Engineering, vol. 42, no.3, pp. 1147–1163, 2022.


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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