Abstract
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.
Highlights
An atypical coronavirus, COVID-19, was classified in Wuhan, China, in December2019
The following sections provide the results of both Long Short-Term Memory (LSTM) and Bi-directional LSTM (Bi-LSTM) models, and
In the case of Bahrain, linear regression gives the best results due to linear relationship independent and dependent variables, while polynomial regression gives the best results because we found the non-linear relationship within the dependent and independent variables
Summary
COVID-19, was classified in Wuhan, China, in December2019. As of 8 September 2021, the newly identified virus had become liable for hundreds of thousands of confirmed cases and over 4,610,056 deaths globally [1]. Different mathematical epidemic surveillance models [2,3,4] have been presented related to COVID-19 diseases to forecast and predict disease occurrence patterns and trends under different situations. These patterns enable policies at the government level to stop the spread of disease and decrease the chance of infection. Choi et al [5] applied the Susceptible–Infectious–Recovered (SIR) model to forecast the SARS disease cases and deaths in Canada Their finding estimated the fast spread of the SARS disease; every five days, the infection spread was about 1.5 to 3 per infection. Peng et al [6] employed a more comprehensive mathematical model for Susceptible-Exposed-Infectious-Removed (SEIR)
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