Short-term prediction of covid-19 cases using machine learning models

Md Shahriare Satu, Koushik Chandra Howlader, Mufti Mahmud, M. Shamim Kaiser, Sheikh Mohammad Shariful Islam, Julian M.W. Quinn, Salem A. Alyami, Mohammad Ali Moni*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

78 Scopus citations

Abstract

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Original languageEnglish
Article number4266
JournalApplied Sciences (Switzerland)
Volume11
Issue number9
DOIs
StatePublished - 1 May 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • COVID-19
  • Forecasting
  • Infected cases
  • Machine learning

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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