Identifying Techniques and Models for COVID-19 Prediction

 Technologies can predict various aspects of COVID-19, such as early prediction of cases and those ‎at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases ‎and deaths. Herein, we aimed to review the published models and techniques that predict various ‎COVID-19 outcomes and identify their role in the management of the COVID-19.‎ 

 This study was a review identifying the prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until ‎September 4th, 2021. In addition, Google Scholar was also searched.‎ 

 We have reviewed 59 studies. The authors reviewed prediction techniques in COVID-19 disease ‎management. Studies in these articles have shown that in the section medical setting, most of the subjects were ‎inpatients. In the purpose of the prediction section, mortality was also the most item. In the type of data/predict ‎section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of ‎technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section.‎ 

 Artificial intelligence and machine learning methods were found to be useful in disease control and ‎prevention. They accelerate the process of diagnosis and move toward great progress in emergency ‎circumstances like the COVID-19 pandemic.‎