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Pregled bibliografske jedinice broj: 1106145

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm


Anđelić, Nikola; Baressi Šegota, Sandi; Lorencin, Ivan; Jurilj, Zdravko; Šušteršič, Tijana; Bagojević, Anđela; Protić, Alen; Ćabov, Tomislav; Filipović, Nenand; Car, Zlatan
Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm // International journal of environmental research and public health, 18 (2021), 3; 959, 26 doi:10.3390/ijerph18030959 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1106145 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Autori
Anđelić, Nikola ; Baressi Šegota, Sandi ; Lorencin, Ivan ; Jurilj, Zdravko ; Šušteršič, Tijana ; Bagojević, Anđela ; Protić, Alen ; Ćabov, Tomislav ; Filipović, Nenand ; Car, Zlatan

Izvornik
International journal of environmental research and public health (1660-4601) 18 (2021), 3; 959, 26

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial intelligence ; COVID-19 ; epidemiology curve ; genetic programming algorithm ; regression modeling

Sažetak
Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020–3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406– 0.9992, 0.9404–0.9998 and 0.9797– 0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

Izvorni jezik
Engleski

Znanstvena područja
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 (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( 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)
MINGO-ESIF-KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM – Centar kompetencija za pametne gradove) (Car, Zlatan, MINGO - Podrška razvoju centara kompetencija u okviru Operativnog programa Konkurentnost i kohezija 2014.-2020.) ( POIROT)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( POIROT)
EK-KF-KK.01.1.1.01.0009-1 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima - IJ za znanost o podatcima (Lončarić, Sven, EK - KK.01.1.1.01) ( 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)

Ustanove:
Medicinski fakultet, Rijeka,
Tehnički fakultet, Rijeka,
Klinički bolnički centar Rijeka,
Fakultet dentalne medicine, Rijeka

Profili:

Avatar Url Zlatan Car (autor)

Avatar Url Tomislav Ćabov (autor)

Avatar Url Nikola Anđelić (autor)

Avatar Url Alen Protić (autor)

Avatar Url Ivan Lorencin (autor)

Citiraj ovu publikaciju

Anđelić, Nikola; Baressi Šegota, Sandi; Lorencin, Ivan; Jurilj, Zdravko; Šušteršič, Tijana; Bagojević, Anđela; Protić, Alen; Ćabov, Tomislav; Filipović, Nenand; Car, Zlatan
Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm // International journal of environmental research and public health, 18 (2021), 3; 959, 26 doi:10.3390/ijerph18030959 (međunarodna recenzija, članak, znanstveni)
Anđelić, N., Baressi Šegota, S., Lorencin, I., Jurilj, Z., Šušteršič, T., Bagojević, A., Protić, A., Ćabov, T., Filipović, N. & Car, Z. (2021) Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm. International journal of environmental research and public health, 18 (3), 959, 26 doi:10.3390/ijerph18030959.
@article{article, year = {2021}, pages = {26}, DOI = {10.3390/ijerph18030959}, chapter = {959}, keywords = {artificial intelligence, COVID-19, epidemiology curve, genetic programming algorithm, regression modeling}, journal = {International journal of environmental research and public health}, doi = {10.3390/ijerph18030959}, volume = {18}, number = {3}, issn = {1660-4601}, title = {Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm}, keyword = {artificial intelligence, COVID-19, epidemiology curve, genetic programming algorithm, regression modeling}, chapternumber = {959} }
@article{article, year = {2021}, pages = {26}, DOI = {10.3390/ijerph18030959}, chapter = {959}, keywords = {artificial intelligence, COVID-19, epidemiology curve, genetic programming algorithm, regression modeling}, journal = {International journal of environmental research and public health}, doi = {10.3390/ijerph18030959}, volume = {18}, number = {3}, issn = {1660-4601}, title = {Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm}, keyword = {artificial intelligence, COVID-19, epidemiology curve, genetic programming algorithm, regression modeling}, chapternumber = {959} }

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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