Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

Leila Ismail, Huned Materwala, Alain Hennebelle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high precision.

Original languageEnglish
Title of host publicationICCMS 2021 - Proceedings of the 13th International Conference on Computer Modeling and Simulation
PublisherAssociation for Computing Machinery
Pages231-236
Number of pages6
ISBN (Electronic)9781450389792
DOIs
Publication statusPublished - Jun 25 2021
Event13th International Conference on Computer Modeling and Simulation, ICCMS 2021 - Virtual, Online, Australia
Duration: Jun 25 2021Jun 27 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Computer Modeling and Simulation, ICCMS 2021
Country/TerritoryAustralia
CityVirtual, Online
Period6/25/216/27/21

Keywords

  • Autoregressive integrated moving average (ARIMA)
  • COVID-19
  • Coronavirus
  • Damped Trend
  • Holt's Linear Trend
  • Machine learning
  • Pandemic
  • Time series

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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