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

Author

Listed:
  • Jelena Musulin

    (Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
    These authors contributed equally to this work.)

  • Sandi Baressi Šegota

    (Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
    These authors contributed equally to this work.)

  • 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
    Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia)

  • Anđela Blagojević

    (Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
    Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia)

  • Nenad Filipović

    (Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
    Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 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)

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.

Suggested Citation

  • Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:4287-:d:538379
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    Cited by:

    1. Arielle Kaim & Tuvia Gering & Amiram Moshaiov & Bruria Adini, 2021. "Deciphering the COVID-19 Health Economic Dilemma (HED): A Scoping Review," IJERPH, MDPI, vol. 18(18), pages 1-13, September.
    2. Jamal Al Qundus & Shivam Gupta & Hesham Abusaimeh & Silvio Peikert & Adrian Paschke, 2023. "Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 235-246, June.
    3. Davide Barbieri & Enrico Giuliani & Anna Del Prete & Amanda Losi & Matteo Villani & Alberto Barbieri, 2021. "How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(14), pages 1-10, July.
    4. Rocío Aznar-Gimeno & Luis M. Esteban & Gorka Labata-Lezaun & Rafael del-Hoyo-Alonso & David Abadia-Gallego & J. Ramón Paño-Pardo & M. José Esquillor-Rodrigo & Ángel Lanas & M. Trinidad Serrano, 2021. "A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms," IJERPH, MDPI, vol. 18(16), pages 1-20, August.

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