Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review
| Autor: | Sandi Baressi Šegota, Ivan Lorencin, Nikola Anđelić, Elitza Markova-Car, Anđela Blagojević, Daniel Štifanić, Tomislav Ćabov, Nenad Filipovic, Jelena Musulin, Tijana Šušteršič |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
spread modeling Computer science COVID-19 open-access data Health Toxicology and Mutagenesis Literature based Review 01 natural sciences Field (computer science) Evolutionary computation 010305 fluids & plasmas Machine Learning 03 medical and health sciences 0302 clinical medicine AI-based methods Artificial Intelligence 0103 physical sciences Humans 030212 general & internal medicine Pandemics Forcing (recursion theory) SARS-CoV-2 business.industry Public Health Environmental and Occupational Health COVID-19 Regression analysis Electronic literature Regression Medicine Artificial intelligence business open-access data |
| Zdroj: | International Journal of Environmental Research and Public Health, Vol 18, Iss 4287, p 4287 (2021) International Journal of Environmental Research and Public Health |
| ISSN: | 1661-7827 1660-4601 |
| Popis: | COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics. |
| Databáze: | OpenAIRE |
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