IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i8p4287-d538379.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/8/4287/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/8/4287/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muñoz-Fernández, Gustavo A. & Seoane, Jesús M. & Seoane-Sepúlveda, Juan B., 2021. "A SIR-type model describing the successive waves of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. Daniel v{S}tifani'c & Jelena Musulin & Adrijana Miov{c}evi'c & Sandi Baressi v{S}egota & Roman v{S}ubi'c & Zlatan Car, 2020. "Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory," Papers 2007.02673, arXiv.org.
    3. Arora, Parul & Kumar, Himanshu & Panigrahi, Bijaya Ketan, 2020. "Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    5. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    6. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    7. Yousefpour, Amin & Jahanshahi, Hadi & Bekiros, Stelios, 2020. "Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    8. Daniel Štifanić & Jelena Musulin & Adrijana Miočević & Sandi Baressi Šegota & Roman Šubić & Zlatan Car, 2020. "Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory," Complexity, Hindawi, vol. 2020, pages 1-12, July.
    9. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H., 2020. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    10. Sina Shaffiee Haghshenas & Behrouz Pirouz & Sami Shaffiee Haghshenas & Behzad Pirouz & Patrizia Piro & Kyoung-Sae Na & Seo-Eun Cho & Zong Woo Geem, 2020. "Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications," IJERPH, MDPI, vol. 17(10), pages 1-21, May.
    11. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    12. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    13. Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Comparison of deep learning approaches to predict COVID-19 infection," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Laith Abualigah & Mohamed Abd Elaziz, 2020. "Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
    15. Behnood, Ali & Mohammadi Golafshani, Emadaldin & Hosseini, Seyedeh Mohaddeseh, 2020. "Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    16. Israel Edem Agbehadji & Bankole Osita Awuzie & Alfred Beati Ngowi & Richard C. Millham, 2020. "Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing," IJERPH, MDPI, vol. 17(15), pages 1-16, July.
    17. Igor Gadelha Pereira & Joris Michel Guerin & Andouglas Gonçalves Silva Júnior & Gabriel Santos Garcia & Prisco Piscitelli & Alessandro Miani & Cosimo Distante & Luiz Marcos Garcia Gonçalves, 2020. "Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach," IJERPH, MDPI, vol. 17(14), pages 1-26, July.
    18. Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    19. Melin, Patricia & Monica, Julio Cesar & Sanchez, Daniela & Castillo, Oscar, 2020. "Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    20. Toulis, Panos, 2021. "Estimation of Covid-19 prevalence from serology tests: A partial identification approach," Journal of Econometrics, Elsevier, vol. 220(1), pages 193-213.
    21. Farooq, Junaid & Bazaz, Mohammad Abid, 2020. "A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    22. Yongqing Nan & Yanyan Gao, 2018. "A machine learning method to monitor China’s AIDS epidemics with data from Baidu trends," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-12, July.
    23. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    24. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    3. Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    4. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    5. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    7. Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    8. Xin Jing & Jin Seo Cho, 2023. "Forecasting the Confirmed COVID-19 Cases Using Modal Regression," Working papers 2023rwp-217, Yonsei University, Yonsei Economics Research Institute.
    9. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    10. Ahed Abugabah & Farah Shahid, 2023. "Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    11. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    12. María del Carmen Valls Martínez & Pedro Antonio Martín Cervantes, 2021. "Testing the Resilience of CSR Stocks during the COVID-19 Crisis: A Transcontinental Analysis," Mathematics, MDPI, vol. 9(5), pages 1-24, March.
    13. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Guglielmo Maria Caporale & Woo-Young Kang & Fabio Spagnolo & Nicola Spagnolo, 2022. "The COVID-19 pandemic, policy responses and stock markets in the G20," International Economics, CEPII research center, issue 172, pages 77-90.
    15. Caporale, Guglielmo Maria & Gil-Alana, Luis Alberiko & Poza, Carlos, 2022. "The COVID-19 pandemic and the degree of persistence of US stock prices and bond yields," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 118-123.
    16. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    17. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    18. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    19. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    20. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:4287-:d:538379. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.