A Bayesian Framework for Parameter Estimation in Dynamical Models

• Published in: PloS one Vol. 6; no. 5; p. e19616
• Main Authors: Coelho, Flávio Codeço, Codeço, Cláudia Torres, Gomes, M. Gabriela M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.05.2011; Public Library of Science (PLoS)
• Subjects: Bayes Theorem; Bayesian analysis; Belgium; Biological models (mathematics); Biology; Calibration; Disease transmission; Dynamical systems; Economic models; Epidemiology; Illnesses; Influenza; Markov analysis; Mathematical models; Methods; Models, Theoretical; Monte Carlo simulation; Netherlands; Parameter estimation; Parameter uncertainty; Portugal; Probability distribution; Random variables; Uncertainty; Uncertainty analysis; Belgium; Portugal; Netherlands; Brazil; Rio de Janeiro Brazil
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0019616

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.