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Pregled bibliografske jedinice broj: 1123285

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, Elitza Markova-Car
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 (2021), 8; 4287, 39 doi:10.3390/ijerph18084287 (međunarodna recenzija, pregledni rad, znanstveni)


CROSBI ID: 1123285 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Autori
Jelena Musulin, Sandi Baressi Šegota, Daniel Štifanić, Ivan Lorencin, Nikola Anđelić, Tijana Šušteršič, Anđela Blagojević, Nenad Filipović, Tomislav Ćabov, Elitza Markova-Car

Izvornik
International journal of environmental research and public health (1660-4601) 18 (2021), 8; 4287, 39

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, pregledni rad, znanstveni

Ključne riječi
AI-based methods ; COVID-19, open-access data ; spread modeling

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Temeljne medicinske znanosti, Javno zdravstvo i zdravstvena zaštita



POVEZANOST RADA


Projekti:
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( POIROT)
EK-EFRR-KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša, EK - KK.01.2.2.03) ( POIROT)
EK-KF-KK.01.1.1.01.009 - DATACROSS - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Lončarić, Sven; Ivan, Petrović, EK ) ( POIROT)
NadSve-Sveučilište u Rijeci-uniri-biomed-18-257 - Cirlkadijalni geni u planocelularnog karcinoma larinksa (Markova-Car, Elitza Petkova, NadSve ) ( POIROT)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( POIROT)

Ustanove:
Tehnički fakultet, Rijeka,
Sveučilište u Rijeci - Odjel za biotehnologiju,
Fakultet dentalne medicine, Rijeka

Citiraj ovu publikaciju

Jelena Musulin, Sandi Baressi Šegota, Daniel Štifanić, Ivan Lorencin, Nikola Anđelić, Tijana Šušteršič, Anđela Blagojević, Nenad Filipović, Tomislav Ćabov, Elitza Markova-Car
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 (2021), 8; 4287, 39 doi:10.3390/ijerph18084287 (međunarodna recenzija, pregledni rad, znanstveni)
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. International journal of environmental research and public health, 18 (8), 4287, 39 doi:10.3390/ijerph18084287.
@article{article, year = {2021}, pages = {39}, DOI = {10.3390/ijerph18084287}, chapter = {4287}, keywords = {AI-based methods, COVID-19, open-access data, spread modeling}, journal = {International journal of environmental research and public health}, doi = {10.3390/ijerph18084287}, volume = {18}, number = {8}, issn = {1660-4601}, title = {Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review}, keyword = {AI-based methods, COVID-19, open-access data, spread modeling}, chapternumber = {4287} }
@article{article, year = {2021}, pages = {39}, DOI = {10.3390/ijerph18084287}, chapter = {4287}, keywords = {AI-based methods, COVID-19, open-access data, spread modeling}, journal = {International journal of environmental research and public health}, doi = {10.3390/ijerph18084287}, volume = {18}, number = {8}, issn = {1660-4601}, title = {Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review}, keyword = {AI-based methods, COVID-19, open-access data, spread modeling}, chapternumber = {4287} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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