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A Novel COVID-19 Prediction Model with Optimal Control Rates

Ashraf Ahmed1, Yousef AbuHour2,*, Ammar El-Hassan1

1 Princess Sumaya University for Technology, Amman, 11941, Jordan
2 Jordan Design and Development Bureau (JODDB), Hussein Bin Ali Street Amman, Jordan

* Corresponding Author: Yousef AbuHour. Email: email

(This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)

Intelligent Automation & Soft Computing 2022, 32(2), 979-990.


The Corona (COVID-19) epidemic has triggered interest in many fields of technology, medicine, science, and politics. Most of the mathematical research in this area focused on analyzing the dynamics of the spread of the virus. In this article, after a review of some current methodologies, a non-linear system of differential equations is developed to model the spread of COVID-19. In order to consider a wide spectrum of scenarios, we propose a susceptible-exposed-infected-quarantined-recovered (SEIQRS)-model which was analyzed to determine threshold conditions for its stability, and the number of infected cases that is an infected person will transmit on a virus to, reproduction number R0 is calculated. It is established that the disease-free state is globally asymptotically stable when the reproduction number is less than unity and unstable if its value is more than one. The model is tested against real data taken from the Ministry of Health in Jordan covering three time periods between March and September 2020 wherein two infection peaks occurred in the country. Simulations show consistency and accurate spread predictions within the optimistic range and the proposed model is distinguished by its applicability to aspects including recurrent infections, asymptomatic carriers over several timespans as well as the aforementioned waves of infection.


Cite This Article

A. Ahmed, Y. AbuHour and A. El-Hassan, "A novel covid-19 prediction model with optimal control rates," Intelligent Automation & Soft Computing, vol. 32, no.2, pp. 979–990, 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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