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Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns
The pandemic caused by the spread of the SARS-CoV-2 virus forced governments around the world to impose lockdowns, which mostly involved restricting non-essential activities. Once the rate of infection is manageable, governments must implement strategies that reverse the negative effects of the lock...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8035617/ https://www.ncbi.nlm.nih.gov/pubmed/33867586 http://dx.doi.org/10.1007/s11135-021-01129-3 |
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author | Almulhim, Tarifa S. Barahona, Igor |
author_facet | Almulhim, Tarifa S. Barahona, Igor |
author_sort | Almulhim, Tarifa S. |
collection | PubMed |
description | The pandemic caused by the spread of the SARS-CoV-2 virus forced governments around the world to impose lockdowns, which mostly involved restricting non-essential activities. Once the rate of infection is manageable, governments must implement strategies that reverse the negative effects of the lockdowns. A decision support system based on fuzzy theory and multi-criteria decision analysis principles is proposed to investigate the importance of a set of key indicators for post-COVID-19 reopening strategies. This system yields more reliable results because it considers the hesitation and experience of decision makers. By including 16 indicators that are utilized by international organizations for comparing, ranking, or investigating countries, our results suggest that governments and policy makers should focus their efforts on reducing violence, crime and unemployment. The provided methodology illustrates the suitability of decision science tools for tackling complex and unstructured problems, such as the COVID-19 pandemic. Governments, policy makers and stakeholders might find in this work scientific-based guidelines that facilitate complex decision-making processes. |
format | Online Article Text |
id | pubmed-8035617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-80356172021-04-12 Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns Almulhim, Tarifa S. Barahona, Igor Qual Quant Article The pandemic caused by the spread of the SARS-CoV-2 virus forced governments around the world to impose lockdowns, which mostly involved restricting non-essential activities. Once the rate of infection is manageable, governments must implement strategies that reverse the negative effects of the lockdowns. A decision support system based on fuzzy theory and multi-criteria decision analysis principles is proposed to investigate the importance of a set of key indicators for post-COVID-19 reopening strategies. This system yields more reliable results because it considers the hesitation and experience of decision makers. By including 16 indicators that are utilized by international organizations for comparing, ranking, or investigating countries, our results suggest that governments and policy makers should focus their efforts on reducing violence, crime and unemployment. The provided methodology illustrates the suitability of decision science tools for tackling complex and unstructured problems, such as the COVID-19 pandemic. Governments, policy makers and stakeholders might find in this work scientific-based guidelines that facilitate complex decision-making processes. Springer Netherlands 2021-04-10 2022 /pmc/articles/PMC8035617/ /pubmed/33867586 http://dx.doi.org/10.1007/s11135-021-01129-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Almulhim, Tarifa S. Barahona, Igor Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns |
title | Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns |
title_full | Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns |
title_fullStr | Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns |
title_full_unstemmed | Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns |
title_short | Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns |
title_sort | decision support system for ranking relevant indicators for reopening strategies following covid-19 lockdowns |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8035617/ https://www.ncbi.nlm.nih.gov/pubmed/33867586 http://dx.doi.org/10.1007/s11135-021-01129-3 |
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