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Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

Rachid Zagrouba1, Muhammad Adnan Khan2,*, Atta-ur-Rahman1, Muhammad Aamer Saleem3, Muhammad Faheem Mushtaq4, Abdur Rehman5, Muhammad Farhan Khan6

1 Department of Computer Information System, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
2 Department of Computer Science, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
3 Hamdard Institute of Engineering and Technology, Hamdard University, Islamabad, 44000, Pakistan
4 Department of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan
5 School of Computer Science, NCBA&E, Lahore, 54000, Pakistan
6 Department of Forensic Sciences, University of Health Sciences, Lahore, 54000, Pakistan

* Corresponding Author: Muhammad Adnan Khan. Email: email

(This article belongs to the Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)

Computers, Materials & Continua 2021, 66(3), 2397-2407.


Novel Coronavirus-19 (COVID-19) is a newer type of coronavirus that has not been formally detected in humans. It is established that this disease often affects people of different age groups, particularly those with body disorders, blood pressure, diabetes, heart problems, or weakened immune systems. The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates. Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans. It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken. The latest global coronavirus epidemic (COVID-19) has brought new challenges to the scientific community. Artificial Intelligence (AI)-motivated methodologies may be useful in predicting the conditions, consequences, and implications of such an outbreak. These forecasts may help to monitor and prevent the spread of these outbreaks. This article proposes a predictive framework incorporating Support Vector Machines (SVM) in the forecasting of a potential outbreak of COVID-19. The findings indicate that the suggested system outperforms cutting-edge approaches. The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance. The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity. The proposed SVM system model exhibits 98.88% and 96.79% result in terms of accuracy during training and validation respectively.


Cite This Article

APA Style
Zagrouba, R., Khan, M.A., Atta-ur-Rahman, , Saleem, M.A., Mushtaq, M.F. et al. (2021). Modelling and simulation of COVID-19 outbreak prediction using supervised machine learning. Computers, Materials & Continua, 66(3), 2397-2407.
Vancouver Style
Zagrouba R, Khan MA, Atta-ur-Rahman , Saleem MA, Mushtaq MF, Rehman A, et al. Modelling and simulation of COVID-19 outbreak prediction using supervised machine learning. Comput Mater Contin. 2021;66(3):2397-2407
IEEE Style
R. Zagrouba et al., "Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning," Comput. Mater. Contin., vol. 66, no. 3, pp. 2397-2407. 2021.


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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