A new combination rule for Spatial Decision Support Systems for epidemiology
| Autor: | Rodrigo Pinheiro de Toledo Vianna, Luciana Moura Mendes de Lima, Ana Flávia Uzeda dos Santos Macambira, Laísa Ribeiro de Sá, Jordana de Almeida Nogueira, Ronei Marcos de Moraes |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Decision support system
General Computer Science Operations research Computer science Epidemiology Decision Making Spatial Decision Support Systems lcsh:Computer applications to medicine. Medical informatics Health informatics 03 medical and health sciences 0302 clinical medicine Statistical inference Humans Tuberculosis 030212 general & internal medicine Cities Set (psychology) Spatial analysis Space–time analysis 030505 public health Fuzzy rule business.industry Research Public Health Environmental and Occupational Health Multiple-criteria decision analysis General Business Management and Accounting Variable (computer science) Multiple Criteria Decision Making Geographic Information Systems lcsh:R858-859.7 0305 other medical science business Brazil |
| Zdroj: | International Journal of Health Geographics, Vol 18, Iss 1, Pp 1-10 (2019) International Journal of Health Geographics |
| DOI: | 10.1186/s12942-019-0187-7 |
| Popis: | BackgroundDecision 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).MethodsMakes 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.ResultsAn 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.ConclusionThe 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. |
| Databáze: | OpenAIRE |
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