Pregled bibliografske jedinice broj: 1133807
Epidemiological Predictive Modelling of COVID-19 Spread
Epidemiological Predictive Modelling of COVID-19 Spread // 8th International Congress of Serbian Society of Mechanics
Kragujevac, 2021. (predavanje, međunarodna recenzija, neobjavljeni rad, znanstveni)
CROSBI ID: 1133807 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Epidemiological Predictive Modelling of COVID-19 Spread
Autori
Šušteršič, Tijana ; Blagojević, Andjela ; Cvetković, Danijela ; Cvetković, Aleksandar ; Lorencin, Ivan ; Baressi Šegota, Sandi ; Car, Zlatan ; Filipović, Nenad
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni
Skup
8th International Congress of Serbian Society of Mechanics
Mjesto i datum
Kragujevac, Srbija, 28.07.2021. - 30.07.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
COVID-19 ; disease spread modelling ; SEIRD model ; epidemiological model
Sažetak
Since the outbreak of new coronavirus COVID-19, measures for ending the global pandemic such as social distancing and contact tracing have been proposed worldwide. We propose a SEIRD model to predict the development of epidemic, which can contribute to effective planning to control it. Based on official statistical data for Belgium, we calculated the key parameters and forward them to the epidemiological model which will predict the number of infected, dead and recovered people. SEIRD model is a compartmental epidemiological model with included components - susceptible, exposed, infected (infected group is divided into three groups - mild, severe and critical), recovered, dead. Predicted (simulated) and official curves show a good match, meaning that the model is achieving promising results. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Javno zdravstvo i zdravstvena zaštita, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)
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)
Ustanove:
Tehnički fakultet, Rijeka
Profili:
Nenad Filipović
(autor)
Zlatan Car
(autor)
Sandi Baressi Šegota
(autor)
Ivan Lorencin
(autor)