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

Applied Sciences

Hafiz Farooq Ahmad¹  Abdulelah Algosaibi¹  Huda Khaloofi¹  Jamil Hussain²  Zahra Azhar³ 

[1] Computer Science Department, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, Al-Ahsa 31982, Saudi Arabia;Department of Data Science, Sejong University, Seoul 05006, Korea;Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA 95064, USA;

关键词: COVID-19;    COVID-19 forecasting;    artificial intelligence;    epidemiological;    epidemiological model;    machine learning;   

DOI  :  10.3390/app112311426

来源: DOAJ

【 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.

【 授权许可】

Unknown