A new combination rule for Spatial Decision Support Systems for epidemiology

Abstract

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.

Perfil epidemiológico e distribuição espacial dos casos de hanseníase na Paraíba

“…O X i (g ) , i=1, ..., n, refere-se a uma variável aleatória dada pela contagem do evento epidemiológico em um determinado período de tempo para cada geo-objeto i g ; e o M( i g ) é denominado como a população sob risco em cada geo-objeto i g . Dessa forma, a RIE para cada geo-objeto i g é dada pela Equação 1 a seguir 16 :…”

“…A RIE é interpretada como RIE ( (g i) = 0, quando não há registro de casos naquela subárea; 0 < RIE ( (g i ) < 0,5 quando a RIE naquela subárea é inferior à metade da incidência em toda a região geográfica; 0,5 ≤ RIE ( (g i) < 1,0, a RIE naquela subárea é maior ou igual à metade e inferior à incidência em toda a região geográfica; 1,0 ≤ RIE ( (g i) < 1,5, a RIE naquela subárea é maior ou igual à incidência em toda a região geográfica, mas não a excede em mais do que 50%; 1,5 ≤ RIE ( (g i) < 2,0, a RIE naquela subárea é maior ou igual a 1,5 vezes a incidência em toda a região geográfica, mas não a excede em mais do que 2 vezes; e quando RIE ( (g i) ≥ 2,0, a RIE naquela subárea é maior ou igual a 2,0 vezes a incidência em toda a região geográfica 16 .…”

Epidemiologia espacial e a detecção de aglomerados espaciais do dengue na Paraíba: uma comparação entre os métodos Scan flexível e Scan circular

“…Para analisar os mapas das estatísticas espaciais, tomamos como referência o mapa da Razão de Incidências Espacial (RIE) 18 . Para a obtenção do mapa coroplético, que descreve a incidência do dengue no estado, faz-se necessário o cálculo da RIE.…”

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

“…As stated by van Schalkwyk and McKee [24] there have been "challenges of translating knowledge into policy". In public health and disease prevention, the use of spatial models tends to increase, with the growing availability and accessibility of data on disease incidence with higher granularity, leveraged by the need for heterogeneous territorially based public health policies [25,26]. Therefore, in the current pandemic context is of the utmost importance the implementation of spatiotemporal surveillance systems that prioritize interventions in areas of higher infection risk [27] and a better incorporation of social factors into COVID-19 models can improve predictive accuracy for more tailored and effective responses [28].…”