Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspa...

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Autores principales: Ferrari, Luisa, Gerardi, Giuseppe, Manzi, Giancarlo, Micheletti, Alessandra, Nicolussi, Federica, Biganzoli, Elia, Salini, Silvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296340/
https://www.ncbi.nlm.nih.gov/pubmed/34207174
http://dx.doi.org/10.3390/ijerph18126563
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author Ferrari, Luisa
Gerardi, Giuseppe
Manzi, Giancarlo
Micheletti, Alessandra
Nicolussi, Federica
Biganzoli, Elia
Salini, Silvia
author_facet Ferrari, Luisa
Gerardi, Giuseppe
Manzi, Giancarlo
Micheletti, Alessandra
Nicolussi, Federica
Biganzoli, Elia
Salini, Silvia
author_sort Ferrari, Luisa
collection PubMed
description In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.
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spelling pubmed-82963402021-07-23 Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model Ferrari, Luisa Gerardi, Giuseppe Manzi, Giancarlo Micheletti, Alessandra Nicolussi, Federica Biganzoli, Elia Salini, Silvia Int J Environ Res Public Health Article In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data. MDPI 2021-06-18 /pmc/articles/PMC8296340/ /pubmed/34207174 http://dx.doi.org/10.3390/ijerph18126563 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ferrari, Luisa
Gerardi, Giuseppe
Manzi, Giancarlo
Micheletti, Alessandra
Nicolussi, Federica
Biganzoli, Elia
Salini, Silvia
Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model
title Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model
title_full Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model
title_fullStr Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model
title_full_unstemmed Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model
title_short Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model
title_sort modeling provincial covid-19 epidemic data using an adjusted time-dependent sird model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296340/
https://www.ncbi.nlm.nih.gov/pubmed/34207174
http://dx.doi.org/10.3390/ijerph18126563
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