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• W830405569 startingPage "e0129535" @default.
• W830405569 abstract "Hansen's disease (leprosy) elimination has proven difficult in several countries, including Brazil, and there is a need for a mathematical model that can predict control program efficacy. This study applied the Approximate Bayesian Computation algorithm to fit 6 different proposed models to each of the 5 regions of Brazil, then fitted hierarchical models based on the best-fit regional models to the entire country. The best model proposed for most regions was a simple model. Posterior checks found that the model results were more similar to the observed incidence after fitting than before, and that parameters varied slightly by region. Current control programs were predicted to require additional measures to eliminate Hansen's Disease as a public health problem in Brazil." @default.
• W830405569 created "2016-06-24" @default.
• W830405569 creator A5076953122 @default.
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• W830405569 date "2015-06-24" @default.
• W830405569 modified "2023-10-17" @default.
• W830405569 title "Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program" @default.
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• W830405569 doi "https://doi.org/10.1371/journal.pone.0129535" @default.
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