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Spatial robust fuzzy clustering of COVID 19 time series based on B-splines
The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123527/ https://www.ncbi.nlm.nih.gov/pubmed/34026473 http://dx.doi.org/10.1016/j.spasta.2021.100518 |
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author | D’Urso, Pierpaolo De Giovanni, Livia Vitale, Vincenzina |
author_facet | D’Urso, Pierpaolo De Giovanni, Livia Vitale, Vincenzina |
author_sort | D’Urso, Pierpaolo |
collection | PubMed |
description | The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects. |
format | Online Article Text |
id | pubmed-8123527 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81235272021-05-17 Spatial robust fuzzy clustering of COVID 19 time series based on B-splines D’Urso, Pierpaolo De Giovanni, Livia Vitale, Vincenzina Spat Stat Article The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects. Published by Elsevier B.V. 2022-06 2021-05-15 /pmc/articles/PMC8123527/ /pubmed/34026473 http://dx.doi.org/10.1016/j.spasta.2021.100518 Text en © 2021 Published by Elsevier B.V. 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 D’Urso, Pierpaolo De Giovanni, Livia Vitale, Vincenzina Spatial robust fuzzy clustering of COVID 19 time series based on B-splines |
title | Spatial robust fuzzy clustering of COVID 19 time series based on B-splines |
title_full | Spatial robust fuzzy clustering of COVID 19 time series based on B-splines |
title_fullStr | Spatial robust fuzzy clustering of COVID 19 time series based on B-splines |
title_full_unstemmed | Spatial robust fuzzy clustering of COVID 19 time series based on B-splines |
title_short | Spatial robust fuzzy clustering of COVID 19 time series based on B-splines |
title_sort | spatial robust fuzzy clustering of covid 19 time series based on b-splines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123527/ https://www.ncbi.nlm.nih.gov/pubmed/34026473 http://dx.doi.org/10.1016/j.spasta.2021.100518 |
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