Vol.127, No.2, 2021, pp.753-769, doi:10.32604/cmes.2021.014882
Dynamical Transmission of Coronavirus Model with Analysis and Simulation
  • Muhammad Farman1, Ali Akgül2,*, Aqeel Ahmad1, Dumitru Baleanu3,4,5, Muhammad Umer Saleem6
1 Department of Mathematics and Statistics, University of Lahore, Lahore, 54590, Pakistan
2 Faculty of Art and Science, Department of Mathematics, Siirt University, Siirt, 56100, Turkey
3 Department of Mathematics, Cankaya University, Ankara, 06530, Turkey
4 Institute of Space Sciences, Magurele, R76900, Romania
5 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan
6 Department of Mathematics, University of Education, Lahore, 54770, Pakistan
* Corresponding Author: Ali Akgül. Email:
(This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics)
Received 05 November 2020; Accepted 04 February 2021; Issue published 19 April 2021
COVID-19 acts as a serious challenge to the whole world. Epidemiological data of COVID-19 is collected through media and web sources to analyze and investigate a system of nonlinear ordinary differential equation to understand the outbreaks of this epidemic disease. We analyze the diseases free and endemic equilibrium point including stability of the model. The certain threshold value of the basic reproduction number R0 is found to observe whether population is in disease free state or endemic state. Moreover, the epidemic peak has been obtained and we expect a considerable number of cases. Finally, some numerical results are presented which show the effect of parameters estimation and different step size on our obtained solutions at the real data of some countries to check the actual behavior of the COVID-19 at different countries.
Epidemic model; COVID-19; parameter estimation; reproductive number; stability analysis
Cite This Article
Farman, M., Akgül, A., Ahmad, A., Baleanu, D., Saleem, M. U. (2021). Dynamical Transmission of Coronavirus Model with Analysis and Simulation. CMES-Computer Modeling in Engineering & Sciences, 127(2), 753–769.
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