The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

Amir Ahmad, Sunita Garhwal, Santosh Kumar Ray, Gagan Kumar, Sharaf Jameel Malebary, Omar Mohammed Barukab

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

Original languageEnglish
Pages (from-to)2645-2653
Number of pages9
JournalArchives of Computational Methods in Engineering
Volume28
Issue number4
DOIs
Publication statusPublished - Jun 2021

ASJC Scopus subject areas

  • Computer Science Applications
  • Applied Mathematics

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