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Review

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

1
Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
2
Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
3
Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
4
Faculty of Dental Medicine, University of Rijeka, Krešimirova ul. 40, 51000 Rijeka, Croatia
5
Department of Biotechnology, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Paul Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(8), 4287; https://doi.org/10.3390/ijerph18084287
Received: 23 March 2021 / Revised: 14 April 2021 / Accepted: 16 April 2021 / Published: 18 April 2021
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. View Full-Text
Keywords: AI-based methods; COVID-19; open-access data; spread modeling AI-based methods; COVID-19; open-access data; spread modeling
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MDPI and ACS Style

Musulin, J.; Baressi Šegota, S.; Štifanić, D.; Lorencin, I.; Anđelić, N.; Šušteršič, T.; Blagojević, A.; Filipović, N.; Ćabov, T.; Markova-Car, E. Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review. Int. J. Environ. Res. Public Health 2021, 18, 4287. https://doi.org/10.3390/ijerph18084287

AMA Style

Musulin J, Baressi Šegota S, Štifanić D, Lorencin I, Anđelić N, Šušteršič T, Blagojević A, Filipović N, Ćabov T, Markova-Car E. Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review. International Journal of Environmental Research and Public Health. 2021; 18(8):4287. https://doi.org/10.3390/ijerph18084287

Chicago/Turabian Style

Musulin, Jelena, Sandi Baressi Šegota, Daniel Štifanić, Ivan Lorencin, Nikola Anđelić, Tijana Šušteršič, Anđela Blagojević, Nenad Filipović, Tomislav Ćabov, and Elitza Markova-Car. 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review" International Journal of Environmental Research and Public Health 18, no. 8: 4287. https://doi.org/10.3390/ijerph18084287

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