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Abstract
Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.
Highlights
COVID-19 is a global pandemic that has been rapidly spreading worldwide [1]
This study presents a review on the applications of artificial intelligence (AI) techniques for forecasting the prevalence of the COVID-19 pandemic
The basics and the mathematical formulation of different AI approaches used in this context are presented, including nonlinear autoregressive neural network, adaptive neuro-fuzzy inference system, hybrid fractal-fuzzy approach, long short-term memory network, Bayesian neural network, variational auto-encoder and singular spectrum analysis
Summary
COVID-19 is a global pandemic that has been rapidly spreading worldwide [1]. It has affected more than 100 million people and caused about two deaths per million as of the end of January 2021. It may help to define the precautions and restrictions for citizens to reduce disease transmission. These restrictions should be carefully investigated to minimize their impact on different economic outcomes, such as poverty and unemployment [4]. Numerous forecasting approaches have been reported in the last two years to predict the spread of the COVID-19 pandemic. The outperformance of the AI models over other conventional models has been reported in several investigations [9,10,11,12,13] This motivated us to prepare this literature review to shed light on the application of AI tools to predict the spread of this pandemic. A comparative study and discussion of different published articles as well as the future prospects will be discussed
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