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Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic
Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598047/ https://www.ncbi.nlm.nih.gov/pubmed/37875598 http://dx.doi.org/10.1038/s41598-023-45482-9 |
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author | Fragua, Ángel Jiménez-Martín, Antonio Mateos, Alfonso |
author_facet | Fragua, Ángel Jiménez-Martín, Antonio Mateos, Alfonso |
author_sort | Fragua, Ángel |
collection | PubMed |
description | Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used. |
format | Online Article Text |
id | pubmed-10598047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105980472023-10-26 Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic Fragua, Ángel Jiménez-Martín, Antonio Mateos, Alfonso Sci Rep Article Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used. Nature Publishing Group UK 2023-10-24 /pmc/articles/PMC10598047/ /pubmed/37875598 http://dx.doi.org/10.1038/s41598-023-45482-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Fragua, Ángel Jiménez-Martín, Antonio Mateos, Alfonso Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic |
title | Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic |
title_full | Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic |
title_fullStr | Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic |
title_full_unstemmed | Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic |
title_short | Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic |
title_sort | complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598047/ https://www.ncbi.nlm.nih.gov/pubmed/37875598 http://dx.doi.org/10.1038/s41598-023-45482-9 |
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