An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning

被引：6

作者：

Ahmad, Hafiz Farooq ^{[1
]}

Khaloofi, Huda ^{[1
]}

Azhar, Zahra ^{[2
]}

Algosaibi, Abdulelah ^{[1
]}

Hussain, Jamil ^{[3
]}

机构：

[1] King Faisal Univ, Dept Comp Sci, Coll Comp Sci & Informat Technol CCSIT, Al Hasa 31982, Saudi Arabia

[2] Univ Calif Santa Cruz, Dept Mol Cell & Dev Biol, Santa Cruz, CA 95064 USA

[3] Sejong Univ, Dept Data Sci, Seoul 05006, South Korea

来源：

APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 23期

关键词：

COVID-19; forecasting; artificial intelligence; epidemiological; epidemiological model; machine learning; deep learning; infectious disease modelling; SPREAD; NUMBER;

D O I：

10.3390/app112311426

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The mechanisms of data analytics and machine learning can allow for a profound conceptualization of viruses (such as pathogen transmission rate and behavior). Consequently, such models have been widely employed to provide rapid and accurate viral spread forecasts to public health officials. Nevertheless, the capability of these algorithms to predict outbreaks is not capable of long-term predictions. Thus, the development of superior models is crucial to strengthen disease prevention strategies and long-term COVID-19 forecasting accuracy. This paper provides a comparative analysis of COVID-19 forecasting models, including the Deep Learning (DL) approach and its examination of the circulation and transmission of COVID-19 in the Kingdom of Saudi Arabia (KSA), Kuwait, Bahrain, and the UAE.

引用

页数：38

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