@inproceedings{2e758ef998094d36965d8cd558b70f2f,
title = "Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning",
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.",
keywords = "Autoregressive integrated moving average (ARIMA), COVID-19, Coronavirus, Damped Trend, Holt's Linear Trend, Machine learning, Pandemic, Time series",
author = "Leila Ismail and Huned Materwala and Alain Hennebelle",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 13th International Conference on Computer Modeling and Simulation, ICCMS 2021 ; Conference date: 25-06-2021 Through 27-06-2021",
year = "2021",
month = jun,
day = "25",
doi = "10.1145/3474963.3475844",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "231--236",
booktitle = "ICCMS 2021 - Proceedings of the 13th International Conference on Computer Modeling and Simulation",
}