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Big data for human security: The case of COVID-19
The COVID-19 epidemic has changed the world dramatically since societies are changing their behaviour according to the new normal, which comes along with numerous challenges and uncertainties. These uncertainties have led to instabilities in several facets of society, most notably health, economy an...
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/PMC8843337/ https://www.ncbi.nlm.nih.gov/pubmed/35186173 http://dx.doi.org/10.1016/j.jocs.2022.101574 |
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author | Cárdenas, Pedro Ivrissimtzis, Ioannis Obara, Boguslaw Kureshi, Ibad Theodoropoulos, Georgios |
author_facet | Cárdenas, Pedro Ivrissimtzis, Ioannis Obara, Boguslaw Kureshi, Ibad Theodoropoulos, Georgios |
author_sort | Cárdenas, Pedro |
collection | PubMed |
description | The COVID-19 epidemic has changed the world dramatically since societies are changing their behaviour according to the new normal, which comes along with numerous challenges and uncertainties. These uncertainties have led to instabilities in several facets of society, most notably health, economy and public order. Measures to contain the pandemic by governments have occasionally met with increasing discontent from societies and have triggered social unrest, imposing serious threats to human security. Big Data Analytics can provide a powerful force multiplier to support policy and decision makers to contain the virus while at the same time dealing with such threats to human security. This paper presents the utilisation of a big data forecasting and analytics framework and its utilisation to deal with COVID-19 triggered social unrest. The paper is an extended version of paper Cárdenas et al. (2021) presented at the 2021 International Conference on Computational Science. |
format | Online Article Text |
id | pubmed-8843337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88433372022-02-15 Big data for human security: The case of COVID-19 Cárdenas, Pedro Ivrissimtzis, Ioannis Obara, Boguslaw Kureshi, Ibad Theodoropoulos, Georgios J Comput Sci Article The COVID-19 epidemic has changed the world dramatically since societies are changing their behaviour according to the new normal, which comes along with numerous challenges and uncertainties. These uncertainties have led to instabilities in several facets of society, most notably health, economy and public order. Measures to contain the pandemic by governments have occasionally met with increasing discontent from societies and have triggered social unrest, imposing serious threats to human security. Big Data Analytics can provide a powerful force multiplier to support policy and decision makers to contain the virus while at the same time dealing with such threats to human security. This paper presents the utilisation of a big data forecasting and analytics framework and its utilisation to deal with COVID-19 triggered social unrest. The paper is an extended version of paper Cárdenas et al. (2021) presented at the 2021 International Conference on Computational Science. Published by Elsevier B.V. 2022-04 2022-02-15 /pmc/articles/PMC8843337/ /pubmed/35186173 http://dx.doi.org/10.1016/j.jocs.2022.101574 Text en © 2022 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 Cárdenas, Pedro Ivrissimtzis, Ioannis Obara, Boguslaw Kureshi, Ibad Theodoropoulos, Georgios Big data for human security: The case of COVID-19 |
title | Big data for human security: The case of COVID-19 |
title_full | Big data for human security: The case of COVID-19 |
title_fullStr | Big data for human security: The case of COVID-19 |
title_full_unstemmed | Big data for human security: The case of COVID-19 |
title_short | Big data for human security: The case of COVID-19 |
title_sort | big data for human security: the case of covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843337/ https://www.ncbi.nlm.nih.gov/pubmed/35186173 http://dx.doi.org/10.1016/j.jocs.2022.101574 |
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