Research Article Open Access

Covid-19 Global Spread Analyzer: An ML-Based Attempt

Rana Husni Al Mahmoud1, Eman Omar2, Khaled Taha3, Mahmoud Al-Sharif3 and Abdullah Aref4
  • 1 University of Jordan, Jordan
  • 2 University of the People, United States
  • 3 Social Media Lab, United Kingdom
  • 4 Princess Sumaya University for Technology, Jordan


The novel Coronavirus 2019 (COVID-19) has caused a pandemic disease over 200 countries, influencing billions of humans. In this consequence, it is very much essential to the identify factors that correlate with the spread of this virus. The detection of coronavirus spread factors open up new challenges to the research community. Artificial Intelligence (AI) driven methods can be useful to predict the parameters, risks and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. In this study, we introduce two datasets, each of which consists of 25 country-level factors and covers 137 countries summarizing different domains. COVID-19STC aims to detect the increase of the total cases, whereas COVID-19STD aimed for total death detection. For each data set, we applied three feature selection algorithms (vis. correlation coefficient, information gain and gain ratio). We also apply feature selection by the Wrapper methods using four classifiers, namely, NaiveBayes, SMO, J48 and Random Forest. The GDP, GDP Per Capital, E-Government Index and Smoking Habit factors found to be the main factors for the total cases detection with accuracy of 73% using the J48 classifier. The GDP and E-Government Index are found to be the main factors for total deaths detection with accuracy of 71% using J48 classifier.

Journal of Computer Science
Volume 16 No. 9, 2020, 1291-1305


Submitted On: 18 July 2020 Published On: 2 October 2020

How to Cite: Al Mahmoud, R. H., Omar, E., Taha, K., Al-Sharif, M. & Aref, A. (2020). Covid-19 Global Spread Analyzer: An ML-Based Attempt. Journal of Computer Science, 16(9), 1291-1305.

  • 1 Citations



  • COVID-19
  • Coronavirus Disease
  • Coronavirus
  • Machine Learning
  • Prediction
  • Datasets