利用机器学习预测海湾合作委员会(GCC)国家COVID-19感染情况

Proceedings of the 13th International Conference on Computer Modeling and Simulation Pub Date : 2021-06-25 DOI:10.1145/3474963.3475844

L. Ismail, Huned Materwala, Alain Hennebelle

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引用次数: 2

摘要

自大约一年前首次发现COVID-19以来，全球已有超过6800万人感染。机器学习时间序列模型已被用于预测COVID-19感染。在本文中，我们使用约翰霍普金斯大学的公共COVID-19数据集开发了海湾合作委员会(GCC)国家的时间序列模型。该数据集包括2020年1月22日至2021年1月22日期间的一年累积COVID-19病例。我们根据感染数据的空间分布为所研究的国家开发了不同的模型。实验结果表明，所建立的模型具有较高的预测精度。

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

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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Proceedings of the 13th International Conference on Computer Modeling and Simulation

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