Vol.31, No.3, 2022, pp.1857-1872, doi:10.32604/iasc.2022.020606
OPEN ACCESS
ARTICLE
Modeling the Spread of COVID-19 by Leveraging Machine and Deep Learning Models
  • Muhammad Adnan1, Maryam Altalhi2, Ala Abdulsalam Alarood3, M.Irfan Uddin1,*
1 Institute of Computing, Kohat University of Science and Technology, KUST, Kohat, 26000, Pakistan
2 Department of Management Information System, College of Business Administration, Taif University, Taif, 21944, Saudi Arabia
3 College of Computer Science and Engineering, University of Jeddah, 21959, Jeddah, Saudi Arabia
* Corresponding Author: M.Irfan Uddin. Email:
Received 31 May 2021; Accepted 22 July 2021; Issue published 09 October 2021
Abstract
Corona Virus disease 2019 (COVID-19) has caused a worldwide pandemic of cough, fever, headache, body aches, and respiratory ailments. COVID- 19 has now become a severe disease and one of the leading causes of death globally. Modeling and prediction of COVID-19 have become inevitable as it has affected people worldwide. With the availability of a large-scale universal COVID-19 dataset, machine learning (ML) techniques and algorithms occur to be the best choice for the analysis, modeling, and forecasting of this disease. In this research study, we used one deep learning algorithm called Artificial Neural Network (ANN) and several ML algorithms such as Support Vector Machine (SVM), polynomial regression, and Bayesian ridge regression (BRR) modeling for analysis, modeling, and spread prediction of COVID-19. COVID-19 dataset, maintained and updated by JOHNS HOPKINS UNIVERSITY was used for ML models training, testing, and modeling. The cost and error generated during ANN training process was reduced using technique called back propagation which dynamically adjust the synapses weights to perform better predictions. The ANN architecture included one input layer with 441 neurons, 4 hidden layers each have 90 neurons and one output layer. ANN along with other ML algorithms were trained to model the prediction of COVID-19 spread for the next 10 days. Experimental results showed that BRR technique overall performed better prediction of COVID-19 for the next 10 days. The modeling of infectious diseases can help relevant countries to take the necessary steps and make timely decisions.
Keywords
COVID-19; modeling; prediction; deep learning; machine learning; support vector machine; Bayesian modeling
Cite This Article
Adnan, M., Altalhi, M., Alarood, A. A., Uddin, M. (2022). Modeling the Spread of COVID-19 by Leveraging Machine and Deep Learning Models. Intelligent Automation & Soft Computing, 31(3), 1857–1872.
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