Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1153642

Epidemiological predictive modeling of COVID-19 infection: development, testing, and implementation on the population of the Benelux union


Šušteršič, Tijana; Blagojević, Andjela; Cvetković, Danijela; Cvetković, Aleksandar; Lorencin, Ivan; Baressi Šegota, Sandi; Milovanović, Dragan; Baskić, Dejan; Car, Zlatan; Filipović, Nenad
Epidemiological predictive modeling of COVID-19 infection: development, testing, and implementation on the population of the Benelux union // Frontiers in Public Health, 9 (2021), 727274, 24 doi:10.3389/fpubh.2021.727274 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1153642 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Epidemiological predictive modeling of COVID-19 infection: development, testing, and implementation on the population of the Benelux union

Autori
Šušteršič, Tijana ; Blagojević, Andjela ; Cvetković, Danijela ; Cvetković, Aleksandar ; Lorencin, Ivan ; Baressi Šegota, Sandi ; Milovanović, Dragan ; Baskić, Dejan ; Car, Zlatan ; Filipović, Nenad

Izvornik
Frontiers in Public Health (2296-2565) 9 (2021); 727274, 24

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

Ključne riječi
COVID-19 ; disease spread modeling ; SEIRD model ; LSTM model ; epidemiological model

Sažetak
Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. 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, 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)

Ustanove:
Tehnički fakultet, Rijeka

Profili:

Avatar Url Sandi Baressi Šegota (autor)

Avatar Url Zlatan Car (autor)

Avatar Url Ivan Lorencin (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.frontiersin.org

Citiraj ovu publikaciju:

Šušteršič, Tijana; Blagojević, Andjela; Cvetković, Danijela; Cvetković, Aleksandar; Lorencin, Ivan; Baressi Šegota, Sandi; Milovanović, Dragan; Baskić, Dejan; Car, Zlatan; Filipović, Nenad
Epidemiological predictive modeling of COVID-19 infection: development, testing, and implementation on the population of the Benelux union // Frontiers in Public Health, 9 (2021), 727274, 24 doi:10.3389/fpubh.2021.727274 (međunarodna recenzija, članak, znanstveni)
Šušteršič, T., Blagojević, A., Cvetković, D., Cvetković, A., Lorencin, I., Baressi Šegota, S., Milovanović, D., Baskić, D., Car, Z. & Filipović, N. (2021) Epidemiological predictive modeling of COVID-19 infection: development, testing, and implementation on the population of the Benelux union. Frontiers in Public Health, 9, 727274, 24 doi:10.3389/fpubh.2021.727274.
@article{article, author = {\v{S}u\v{s}ter\v{s}i\v{c}, Tijana and Blagojevi\'{c}, Andjela and Cvetkovi\'{c}, Danijela and Cvetkovi\'{c}, Aleksandar and Lorencin, Ivan and Baressi \v{S}egota, Sandi and Milovanovi\'{c}, Dragan and Baski\'{c}, Dejan and Car, Zlatan and Filipovi\'{c}, Nenad}, year = {2021}, pages = {24}, DOI = {10.3389/fpubh.2021.727274}, chapter = {727274}, keywords = {COVID-19, disease spread modeling, SEIRD model, LSTM model, epidemiological model}, journal = {Frontiers in Public Health}, doi = {10.3389/fpubh.2021.727274}, volume = {9}, issn = {2296-2565}, title = {Epidemiological predictive modeling of COVID-19 infection: development, testing, and implementation on the population of the Benelux union}, keyword = {COVID-19, disease spread modeling, SEIRD model, LSTM model, epidemiological model}, chapternumber = {727274} }
@article{article, author = {\v{S}u\v{s}ter\v{s}i\v{c}, Tijana and Blagojevi\'{c}, Andjela and Cvetkovi\'{c}, Danijela and Cvetkovi\'{c}, Aleksandar and Lorencin, Ivan and Baressi \v{S}egota, Sandi and Milovanovi\'{c}, Dragan and Baski\'{c}, Dejan and Car, Zlatan and Filipovi\'{c}, Nenad}, year = {2021}, pages = {24}, DOI = {10.3389/fpubh.2021.727274}, chapter = {727274}, keywords = {COVID-19, disease spread modeling, SEIRD model, LSTM model, epidemiological model}, journal = {Frontiers in Public Health}, doi = {10.3389/fpubh.2021.727274}, volume = {9}, issn = {2296-2565}, title = {Epidemiological predictive modeling of COVID-19 infection: development, testing, and implementation on the population of the Benelux union}, keyword = {COVID-19, disease spread modeling, SEIRD model, LSTM model, epidemiological model}, chapternumber = {727274} }

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


Citati:





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font