Endemic–epidemic models to understand COVID-19 spatio-temporal evolution

We propose an endemic–epidemic model: a negative binomial space–time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affecte...

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Detalles Bibliográficos
Autores principales: Celani, Alessandro, Giudici, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274278/
https://www.ncbi.nlm.nih.gov/pubmed/34307007
http://dx.doi.org/10.1016/j.spasta.2021.100528
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author Celani, Alessandro
Giudici, Paolo
author_facet Celani, Alessandro
Giudici, Paolo
author_sort Celani, Alessandro
collection PubMed
description We propose an endemic–epidemic model: a negative binomial space–time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterized by similar non-pharmaceutical policy interventions.
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spelling pubmed-82742782021-07-20 Endemic–epidemic models to understand COVID-19 spatio-temporal evolution Celani, Alessandro Giudici, Paolo Spat Stat Article We propose an endemic–epidemic model: a negative binomial space–time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterized by similar non-pharmaceutical policy interventions. Elsevier B.V. 2022-06 2021-07-12 /pmc/articles/PMC8274278/ /pubmed/34307007 http://dx.doi.org/10.1016/j.spasta.2021.100528 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
Celani, Alessandro
Giudici, Paolo
Endemic–epidemic models to understand COVID-19 spatio-temporal evolution
title Endemic–epidemic models to understand COVID-19 spatio-temporal evolution
title_full Endemic–epidemic models to understand COVID-19 spatio-temporal evolution
title_fullStr Endemic–epidemic models to understand COVID-19 spatio-temporal evolution
title_full_unstemmed Endemic–epidemic models to understand COVID-19 spatio-temporal evolution
title_short Endemic–epidemic models to understand COVID-19 spatio-temporal evolution
title_sort endemic–epidemic models to understand covid-19 spatio-temporal evolution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274278/
https://www.ncbi.nlm.nih.gov/pubmed/34307007
http://dx.doi.org/10.1016/j.spasta.2021.100528
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