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COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data
Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real datas...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253246/ https://www.ncbi.nlm.nih.gov/pubmed/34249598 http://dx.doi.org/10.1007/s13721-021-00323-5 |
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author | Milano, Marianna Zucco, Chiara Cannataro, Mario |
author_facet | Milano, Marianna Zucco, Chiara Cannataro, Mario |
author_sort | Milano, Marianna |
collection | PubMed |
description | Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D. |
format | Online Article Text |
id | pubmed-8253246 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-82532462021-07-06 COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data Milano, Marianna Zucco, Chiara Cannataro, Mario Netw Model Anal Health Inform Bioinform Original Article Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D. Springer Vienna 2021-07-02 2021 /pmc/articles/PMC8253246/ /pubmed/34249598 http://dx.doi.org/10.1007/s13721-021-00323-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Milano, Marianna Zucco, Chiara Cannataro, Mario COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data |
title | COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data |
title_full | COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data |
title_fullStr | COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data |
title_full_unstemmed | COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data |
title_short | COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data |
title_sort | covid-19 community temporal visualizer: a new methodology for the network-based analysis and visualization of covid-19 data |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253246/ https://www.ncbi.nlm.nih.gov/pubmed/34249598 http://dx.doi.org/10.1007/s13721-021-00323-5 |
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