Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.

Journal: PeerJ. Computer science
Published Date: Nov 23, 2020

Abstract

BACKGROUND AND OBJECTIVE: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis.

Authors

  • Haruna Chiroma
    Future Technology Research Center, National Yunlin University of Science and Technology, Yulin, Taiwan.
  • Absalom E Ezugwu
    School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa.
  • Fatsuma Jauro
    Department of Computer Science, Faculty of Science, Ahmadu Bello University, Zaria, Nigeria.
  • Mohammed A Al-Garadi
    Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.
  • Idris N Abdullahi
    Department of Medical Laboratory Science, College of Medical Sciences, Ahmadu Bello University, Zaria, Nigeria.
  • Liyana Shuib
    Department of Information System, Universiti Malaya, Kuala Lumpur, Malaysia.

Keywords

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