A bayesian framework for parameter estimation in dynamical models

Resumo

Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.

Ficha do documento

Tipo

Artigo científico

Ano

2011

Instituição

Public Library Science

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Acesso aberto

DOI

10.1371/journal.pone.0019616

Identificador

oai:repositorio.fgv.br:10438/23217

Palavras-chave

Differential-equationsInfectious-diseasesSeasonal influenzaInferenceBiologyCiências sociaisBiologia - Modelos matemáticosInfluenza