Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data

The spatial epidemic dynamics of COVID-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Foll...

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Detalles Bibliográficos
Autores principales: Vitale, Vincenzina, D’Urso, Pierpaolo, De Giovanni, Livia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277433/
https://www.ncbi.nlm.nih.gov/pubmed/34277332
http://dx.doi.org/10.1016/j.spasta.2021.100529
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author Vitale, Vincenzina
D’Urso, Pierpaolo
De Giovanni, Livia
author_facet Vitale, Vincenzina
D’Urso, Pierpaolo
De Giovanni, Livia
author_sort Vitale, Vincenzina
collection PubMed
description The spatial epidemic dynamics of COVID-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by means of spatial and time lagged variables. The model could be a valid instrument to support or validate policy makers’ decisions strategies.
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spelling pubmed-82774332021-07-14 Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data Vitale, Vincenzina D’Urso, Pierpaolo De Giovanni, Livia Spat Stat Article The spatial epidemic dynamics of COVID-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by means of spatial and time lagged variables. The model could be a valid instrument to support or validate policy makers’ decisions strategies. Elsevier B.V. 2022-06 2021-07-14 /pmc/articles/PMC8277433/ /pubmed/34277332 http://dx.doi.org/10.1016/j.spasta.2021.100529 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Vitale, Vincenzina
D’Urso, Pierpaolo
De Giovanni, Livia
Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data
title Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data
title_full Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data
title_fullStr Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data
title_full_unstemmed Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data
title_short Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data
title_sort spatio-temporal object-oriented bayesian network modelling of the covid-19 italian outbreak data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277433/
https://www.ncbi.nlm.nih.gov/pubmed/34277332
http://dx.doi.org/10.1016/j.spasta.2021.100529
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