Int J Health Geogr - Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data.

Tópicos

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Resumo

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.

Resumo Limpo

spatial heterogen incid viscer leishmaniasi vl import aspect consid plan control action diseas object studi predict area high risk viscer leishmaniasi vl base socioeconom indic remot sens data appli classif regress tree develop valid predict model perform model assess mean sensit specif area roc curv model develop abl discrimin subset census tract ct differ probabl contain ct high risk vl occurr model present respect valid learn sampl sensit specif area roc curv consid complex network factor involv occurr vl urban area result studi show develop predict model vl might feasibl use guid intervent diseas still challeng demonstr unsatisfactori predict perform model develop

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