01248nas a2200145   4500000000100000008004100001100001300042700001400055245014700069856005700216300001300273490000700286520079500293022001401088       2015                            d1 aSmith RL1 aGröhn YT00aUse of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program.  uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479607/  ae01295350 v103 a<p>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.</p>
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