title: Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program.
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.
authors.affiliation: Department of Pathobiology, University of Illinois College of Veterinary Medicine, Urbana, Illinois, United States of America.
authors.forename: Rebecca Lee
authors.initials: RL
authors.lastname: Smith
authors.affiliation: Department of Population Medicine and Diagnostic Science, Cornell University College of Veterinary Medicine, Ithaca, New York, United States of America.
authors.forename: Yrjö Tapio
authors.initials: YT
authors.lastname: Gröhn
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001938:Brazil; D003140:Communicable Disease Control / Q000379:methods*; D006801:Humans; D015994:Incidence; D007917:Leprostatic Agents / Q000627:therapeutic use; D007918:Leprosy / Q000453:epidemiology* / Q000382:microbiology / Q000628:therapy*; D015233:Models, Statistical; D009010:Monte Carlo Method; D009166:Mycobacterium leprae; D015397:Program Evaluation
chemical_list: D007917:Leprostatic Agents