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A new combination rule for Spatial Decision Support Systems for epidemiology
BACKGROUND: Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842522/ https://www.ncbi.nlm.nih.gov/pubmed/31706302 http://dx.doi.org/10.1186/s12942-019-0187-7 |
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author | de Lima, Luciana Moura Mendes de Sá, Laísa Ribeiro dos Santos Macambira, Ana Flávia Uzeda de Almeida Nogueira, Jordana de Toledo Vianna, Rodrigo Pinheiro de Moraes, Ronei Marcos |
author_facet | de Lima, Luciana Moura Mendes de Sá, Laísa Ribeiro dos Santos Macambira, Ana Flávia Uzeda de Almeida Nogueira, Jordana de Toledo Vianna, Rodrigo Pinheiro de Moraes, Ronei Marcos |
author_sort | de Lima, Luciana Moura Mendes |
collection | PubMed |
description | BACKGROUND: Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS). METHODS: Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map. RESULTS: An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: “non-priority”, “non-priority tendency”, “priority tendency” and “priority”, for the fight against diseases. CONCLUSION: The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities. |
format | Online Article Text |
id | pubmed-6842522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68425222019-11-14 A new combination rule for Spatial Decision Support Systems for epidemiology de Lima, Luciana Moura Mendes de Sá, Laísa Ribeiro dos Santos Macambira, Ana Flávia Uzeda de Almeida Nogueira, Jordana de Toledo Vianna, Rodrigo Pinheiro de Moraes, Ronei Marcos Int J Health Geogr Research BACKGROUND: Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS). METHODS: Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map. RESULTS: An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: “non-priority”, “non-priority tendency”, “priority tendency” and “priority”, for the fight against diseases. CONCLUSION: The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities. BioMed Central 2019-11-09 /pmc/articles/PMC6842522/ /pubmed/31706302 http://dx.doi.org/10.1186/s12942-019-0187-7 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research de Lima, Luciana Moura Mendes de Sá, Laísa Ribeiro dos Santos Macambira, Ana Flávia Uzeda de Almeida Nogueira, Jordana de Toledo Vianna, Rodrigo Pinheiro de Moraes, Ronei Marcos A new combination rule for Spatial Decision Support Systems for epidemiology |
title | A new combination rule for Spatial Decision Support Systems for epidemiology |
title_full | A new combination rule for Spatial Decision Support Systems for epidemiology |
title_fullStr | A new combination rule for Spatial Decision Support Systems for epidemiology |
title_full_unstemmed | A new combination rule for Spatial Decision Support Systems for epidemiology |
title_short | A new combination rule for Spatial Decision Support Systems for epidemiology |
title_sort | new combination rule for spatial decision support systems for epidemiology |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842522/ https://www.ncbi.nlm.nih.gov/pubmed/31706302 http://dx.doi.org/10.1186/s12942-019-0187-7 |
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