ArticlePloS one2015
Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.Applied network science · 2025Article
- Prediction of the occurrence of leprosy reactions based on Bayesian networks.Frontiers in medicine · 2023Article
- Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit.Engineering biology · 2020Article
- Compartmental Model Diagrams as Causal Representations in Relation to DAGs.Epidemiologic methods · 2017Article
- Modeling the architecture of the regulatory system controlling methylenomycin production inJournal of biological engineering · 2017Article
- Proposing a Compartmental Model for Leprosy and Parameterizing Using Regional Incidence in Brazil.PLoS neglected tropical diseases · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.