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

Leila Ismail, Huned Materwala, Alain Hennebelle
3/14/2023
cs.LGcs.AIcs.CY

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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.

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Cite this paper

@article{ismail2023forecasting,
  title  = {Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning},
  author = {Leila Ismail and Huned Materwala and Alain Hennebelle},
  year   = {2023},
  eprint = {2303.07600},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.07600v1}
}

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