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Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets
COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in İstanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342988/ https://www.ncbi.nlm.nih.gov/pubmed/34357491 http://dx.doi.org/10.1007/s11356-021-15703-7 |
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author | Boyacı, Aslı Çalış Şişman, Aziz |
author_facet | Boyacı, Aslı Çalış Şişman, Aziz |
author_sort | Boyacı, Aslı Çalış |
collection | PubMed |
description | COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in İstanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals. This study focuses on the methods used for site selection for a pandemic hospital in Atakum, a district of Samsun City, Turkey. As a solution to the problem, initially, spatial analysis was performed using GIS to produce maps based on seven criteria obtained from the insight of an expert team. Analytic hierarchy process (AHP) augmented by interval-valued Pythagorean fuzzy numbers (PFNs) was then used to determine weights for the criteria. Distance to transportation network was the most important criterion influencing the selection process and the least significant one was the distance to fire stations. Based on the criteria weights, and five rules specified by the expert team, 13 suitable locations for a pandemic hospital were determined using GIS. The technique for order preference by similarity to ideal solution (TOPSIS) method was used to determine the final ranking of 13 alternative locations (A1–A13). A10 was identified as the most appropriate site and A11 as the least appropriate site for a pandemic hospital. Finally, sensitivity analysis was performed to investigate how changes in weight values of the criteria affect the ranking of the alternatives. |
format | Online Article Text |
id | pubmed-8342988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-83429882021-08-06 Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets Boyacı, Aslı Çalış Şişman, Aziz Environ Sci Pollut Res Int Research Article COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in İstanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals. This study focuses on the methods used for site selection for a pandemic hospital in Atakum, a district of Samsun City, Turkey. As a solution to the problem, initially, spatial analysis was performed using GIS to produce maps based on seven criteria obtained from the insight of an expert team. Analytic hierarchy process (AHP) augmented by interval-valued Pythagorean fuzzy numbers (PFNs) was then used to determine weights for the criteria. Distance to transportation network was the most important criterion influencing the selection process and the least significant one was the distance to fire stations. Based on the criteria weights, and five rules specified by the expert team, 13 suitable locations for a pandemic hospital were determined using GIS. The technique for order preference by similarity to ideal solution (TOPSIS) method was used to determine the final ranking of 13 alternative locations (A1–A13). A10 was identified as the most appropriate site and A11 as the least appropriate site for a pandemic hospital. Finally, sensitivity analysis was performed to investigate how changes in weight values of the criteria affect the ranking of the alternatives. Springer Berlin Heidelberg 2021-08-06 2022 /pmc/articles/PMC8342988/ /pubmed/34357491 http://dx.doi.org/10.1007/s11356-021-15703-7 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 | Research Article Boyacı, Aslı Çalış Şişman, Aziz Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets |
title | Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets |
title_full | Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets |
title_fullStr | Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets |
title_full_unstemmed | Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets |
title_short | Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets |
title_sort | pandemic hospital site selection: a gis-based mcdm approach employing pythagorean fuzzy sets |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342988/ https://www.ncbi.nlm.nih.gov/pubmed/34357491 http://dx.doi.org/10.1007/s11356-021-15703-7 |
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