Identifying Techniques and Models for COVID-19 Prediction

Ahmadreza Shamsabadi; Pegah Mirzapour; Mohammad  Heydari; Hengameh  Mojdeganlou; Amirali  Karimi; Zahra Pashaei; Kowsar Qaderi; Peyman Mirghaderi; Hadiseh Azadi Cheshmekabodi; Esmaeil Mehraeen; SeyedAhmad SeyedAlinaghi

doi:10.18502/jimc.v6i2.12230

Ahmadreza Shamsabadi Department of Health Information Technology, Esfarayen Faculty of Medical Sciences, Esfarayen, Iran
Pegah Mirzapour Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran
Mohammad Heydari Department of Health Information Technology, Khalkhal University of Medical Sciences, Khalkhal, Iran
Hengameh Mojdeganlou Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States
Amirali Karimi School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Zahra Pashaei Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran
Kowsar Qaderi Department of Midwifery, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran
Peyman Mirghaderi School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Hadiseh Azadi Cheshmekabodi School of Health Information Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
Esmaeil Mehraeen Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran
SeyedAhmad SeyedAlinaghi Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran

DOI: https://doi.org/10.18502/jimc.v6i2.12230

Keywords: COVID-19, Diagnosis, Prediction, SARS-CoV-2

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.