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Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program

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  • Rebecca Lee Smith
  • Yrjö Tapio Gröhn

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.

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  • Rebecca Lee Smith & Yrjö Tapio Gröhn, 2015. "Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0129535
    DOI: 10.1371/journal.pone.0129535
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    Cited by:

    1. Rebecca Lee Smith, 2016. "Proposing a Compartmental Model for Leprosy and Parameterizing Using Regional Incidence in Brazil," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 10(8), pages 1-14, August.

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