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Open Access Research

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

Andréa S Almeida1* and Guilherme L Werneck12

Author Affiliations

1 Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rua São Francisco Xavier, 524, Pavilhão João Lyra Filho, 7º andar/blocos D e E, e 6º andar/bloco E, Maracanã, CEP 20550-013 Rio de Janeiro, Brazil

2 Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, S/N - Próximo a Prefeitura Universitária da UFRJ, Ilha do Fundão - Cidade Universitária, CEP 21941-598 Rio de Janeiro, RJ, Brazil

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International Journal of Health Geographics 2014, 13:13  doi:10.1186/1476-072X-13-13

Published: 20 May 2014


Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed.

Leishmaniasis; Predictive models; Remote sensing