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A Bayesian Framework for Parameter Estimation in Dynamical Models

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  • Flávio Codeço Coelho
  • Cláudia Torres Codeço
  • M Gabriela M Gomes

Abstract

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.

Suggested Citation

  • Flávio Codeço Coelho & Cláudia Torres Codeço & M Gabriela M Gomes, 2011. "A Bayesian Framework for Parameter Estimation in Dynamical Models," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-6, May.
  • Handle: RePEc:plo:pone00:0019616
    DOI: 10.1371/journal.pone.0019616
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    References listed on IDEAS

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    1. Luís M A Bettencourt & Ruy M Ribeiro, 2008. "Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-9, May.
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    1. Brajendra K Singh & Moses J Bockarie & Manoj Gambhir & Peter M Siba & Daniel J Tisch & James Kazura & Edwin Michael, 2013. "Sequential Modelling of the Effects of Mass Drug Treatments on Anopheline-Mediated Lymphatic Filariasis Infection in Papua New Guinea," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-16, June.
    2. Sánchez-Romero, Miguel & Di Lego, Vanessa & Fürnkranz-Prskawetz, Alexia & Queiroz, Bernardo Lanza, 2020. "How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates," ECON WPS - Working Papers in Economic Theory and Policy 05/2020, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.

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