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

Jelena Musulin, Sandi Baressi Šegota, Daniel Štifanić, Ivan Lorencin, Nikola Anđelić, Tijana Šušteršič, Anđela Blagojević, Nenad Filipović, Tomislav Ćabov and Elitza Markova-Car
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Jelena Musulin: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Sandi Baressi Šegota: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Daniel Štifanić: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Ivan Lorencin: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Nikola Anđelić: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Tijana Šušteršič: Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
Anđela Blagojević: Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
Nenad Filipović: Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
Tomislav Ćabov: Faculty of Dental Medicine, University of Rijeka, Krešimirova ul. 40, 51000 Rijeka, Croatia
Elitza Markova-Car: Department of Biotechnology, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia

IJERPH, 2021, vol. 18, issue 8, 1-39

Abstract: 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.

Keywords: AI-based methods; COVID-19; open-access data; spread modeling (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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