Hierarchical Clustering with Spatial Constraints and Standardized Incidence Ratio in Tuberculosis Data

Dalila Camêlo Aguiar, Ramón Gutiérrez Sánchez and Edwirde Luiz Silva Camêlo
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Dalila Camêlo Aguiar: Department of Statistics and Operational Research, Faculty of Science, University of Granada, Avda. Fuentenueva, S/N, 18071 Granada, Spain
Ramón Gutiérrez Sánchez: Department of Statistics and Operational Research, Faculty of Science, University of Granada, Avda. Fuentenueva, S/N, 18071 Granada, Spain
Edwirde Luiz Silva Camêlo: Department of Statistics, State University of Paraíba, Rua Baraúnas, 351—Bairro Universitário, Campina Grande 58429-500, Brazil

Mathematics, 2020, vol. 8, issue 9, 1-12

Abstract: In this paper, we propose presenting a solution based on socio-epidemiological variables of tuberculosis, considering a clustering with spatial/geographical constraints; and, determine a value of alpha that increases spatial contiguity without significantly deteriorating the quality of the solution based on the variables of interest, i.e. those of the feature space. For the application of Ward’s hierarchical clustering method, two dissimilarity matrices were calculated, the first provides the dissimilarities in the feature space calculated from the socio-epidemiological variables D 0 and the second provides the dissimilarities in the calculated constraints space from the geographical distances D 1 , together with an α mixing parameter and the non-uniform weight w assigned to the calculation of the dissimilarity matrix defined by the standardized incidence ratio (SIR) of TB and that contributed significantly to the increase in clarity, both from a spatial and socio-epidemiological point of view. The method is shown to be feasible in epidemiological studies in the joint understanding of factors of different dimensions, aggregated from a spatial perspective. It is analysis tool that allows making a better understanding of the socio-epidemiological reality of the municipality.

Keywords: ward-like algorithm; spatial constraints; measure of risk; Tuberculosis; State of Paraíba, Brazil (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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