| Title: |
Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview. |
| Authors: |
Elsheikh, Ammar H., Saba, Amal I., Panchal, Hitesh, Shanmugan, Sengottaiyan, Alsaleh, Naser A., Ahmadein, Mahmoud |
| Source: |
Healthcare (2227-9032); Dec2021, Vol. 9 Issue 12, p1614-1614, 1p |
| Subject Terms: |
COVID-19 pandemic, ARTIFICIAL intelligence, FORECASTING, STATISTICAL accuracy, COVID-19 |
| 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. [ABSTRACT FROM AUTHOR] |
|
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| Database: |
Complementary Index |
