This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2391) Outbreak and Pandemic Preparedness and Management (1675) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Epublishing and Open Access (80) Infectious Diseases (non-STD/STI) (1485) Machine Learning (1385) Digital Science, Open Science (70) Scientometrics, Infometrics, and Altmetrics (83)

Published on in Vol 23, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23703, first published .
A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis

A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis

A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis

Authors of this article:

Alaa Abd-Alrazaq1 Author Orcid Image ;   Jens Schneider1 Author Orcid Image ;   Borbala Mifsud2 Author Orcid Image ;   Tanvir Alam1 Author Orcid Image ;   Mowafa Househ1 Author Orcid Image ;   Mounir Hamdi1 Author Orcid Image ;   Zubair Shah1 Author Orcid Image
Article Authors Cited by (29) Tweetations (11) Metrics

Alaa Abd-Alrazaq   1 , PhD ;   Jens Schneider   1 , PhD ;   Borbala Mifsud   2 , PhD ;   Tanvir Alam   1 , PhD ;   Mowafa Househ   1 , PhD ;   Mounir Hamdi   1 , PhD ;   Zubair Shah   1 , PhD

1 Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

2 College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Corresponding Author:

  • Zubair Shah, PhD
  • Division of Information and Computing Technology, College of Science and Engineering
  • Hamad Bin Khalifa University
  • Qatar Foundation
  • P.O. Box 5825, Doha Al Luqta St, Ar-Rayyan
  • Doha, 00000
  • Qatar
  • Phone: 974 55708549
  • Email: zshah@hbku.edu.qa