Estimation of COVID-19 epidemic cruves using genetic programming algorithm

Anđelić, Nikola; Baressi Šegota, Sandi; Lorencin, Ivan; Mrzljak, Vedran; Car, Zlatan

doi:10.1177/1460458220976728

Pregled bibliografske jedinice broj: 1104631

Estimation of COVID-19 epidemic cruves using genetic programming algorithm

Anđelić, Nikola; Baressi Šegota, Sandi; Lorencin, Ivan; Mrzljak, Vedran; Car, Zlatan

Estimation of COVID-19 epidemic cruves using genetic programming algorithm // Health informatics journal, 27 (2021), 1; 1-40 doi:10.1177/1460458220976728 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1104631 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Estimation of COVID-19 epidemic cruves using genetic programming algorithm

Autori
Anđelić, Nikola ; Baressi Šegota, Sandi ; Lorencin, Ivan ; Mrzljak, Vedran ; Car, Zlatan

Izvornik
Health informatics journal (1460-4582) 27 (2021), 1; 1-40

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

Ključne riječi
COVID-19 ; Disease spread modeling ; Evolutionary computing ; Genetic programming ; Machine Learning

Sažetak
This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Temeljne medicinske znanosti

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) ( CroRIS)
IPA-ADRIATIC CBC-1°str./0007/0 - An Adriatic Network for Advancing Research Development and Innovation towards the Creation of new Policies for Sustainable Competiveness and Technological Capacity of SMEs (ADRIATinn) (Car, Zlatan, IPA - First call for strategical projects CCI2007CB16IPO001) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)