IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0019616.html
   My bibliography  Save this article

A Bayesian Framework for Parameter Estimation in Dynamical Models

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0019616
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0019616&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0019616?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
    2. De Simone, Andrea & Piangerelli, Marco, 2020. "A Bayesian approach for monitoring epidemics in presence of undetected cases," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Kris V. Parag & Robin N. Thompson & Christl A. Donnelly, 2022. "Are epidemic growth rates more informative than reproduction numbers?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 5-15, November.
    4. Fajar, Muhammad, 2020. "Estimasi angka reproduksi Novel Coronavirus (COVID-19), Kasus Indonesia (Estimation of COVID-19 reproductive number, case of Indonesia [Estimation Of Covid-19 Reproductive Number (Case Of Indonesia," MPRA Paper 105099, University Library of Munich, Germany, revised 28 Mar 2020.
    5. Chad Cotti & Bryan Engelhardt & Joshua Foster & Erik Nesson & Paul Niekamp, 2021. "The relationship between in‐person voting and COVID‐19: Evidence from the Wisconsin primary," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 760-777, October.
    6. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    7. Victor W. Chu & Raymond K. Wong & Chi-Hung Chi & Wei Zhou & Ivan Ho, 2017. "The design of a cloud-based tracker platform based on system-of-systems service architecture," Information Systems Frontiers, Springer, vol. 19(6), pages 1283-1299, December.
    8. Kernel Prieto & M Victoria Chávez–Hernández & Jhoana P Romero–Leiton, 2022. "On mobility trends analysis of COVID–19 dissemination in Mexico City," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
    9. Schimit, P.H.T. & Monteiro, L.H.A., 2012. "On estimating the basic reproduction number in distinct stages of a contagious disease spreading," Ecological Modelling, Elsevier, vol. 240(C), pages 156-160.
    10. Anup Malani & Satej Soman & Sam Asher & Paul Novosad & Clement Imbert & Vaidehi Tandel & Anish Agarwal & Abdullah Alomar & Arnab Sarker & Devavrat Shah & Dennis Shen & Jonathan Gruber & Stuti Sachdeva, 2020. "Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown," NBER Working Papers 27532, National Bureau of Economic Research, Inc.
    11. Ida Johnsson & M. Hashem Pesaran & Cynthia Fan Yang, 2023. "Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 across U.S. States and Selected Countries," CESifo Working Paper Series 10659, CESifo.
    12. Kernel Prieto, 2022. "Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    13. Adam Brzezinski & David Van Dijcke & Valentin Kecht, 2020. "The Cost of Staying Open: Voluntary Social Distancing and Lockdowns in the US," Economics Series Working Papers 910, University of Oxford, Department of Economics.
    14. Bo Huang & Zhihui Huang & Chen Chen & Jian Lin & Tony Tam & Yingyi Hong & Sen Pei, 2022. "Social vulnerability amplifies the disparate impact of mobility on COVID-19 transmissibility across the United States," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    15. Sean Elliott & Christian Gourieroux, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Papers 2012.11542, arXiv.org.
    16. Jean-Paul Renne & Guillaume Roussellet & Gustavo Schwenkler, 2020. "Preventing COVID-19 Fatalities: State versus Federal Policies," Papers 2010.15263, arXiv.org, revised Dec 2020.
    17. Singh, Anurag & Arquam, Md, 2022. "Epidemiological modeling for COVID-19 spread in India with the effect of testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    18. Lu Tang & Yiwang Zhou & Lili Wang & Soumik Purkayastha & Leyao Zhang & Jie He & Fei Wang & Peter X.‐K. Song, 2020. "A Review of Multi‐Compartment Infectious Disease Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 462-513, August.
    19. Mostafa Adimy & Julien Molina & Laurent Pujo-Menjouet & Grégoire Ranson & Jianhong Wu, 2022. "Forecasting the Effect of Pre-Exposure Prophylaxis (PrEP) on HIV Propagation with a System of Differential–Difference Equations with Delay," Mathematics, MDPI, vol. 10(21), pages 1-24, November.
    20. Maeno, Yoshiharu, 2016. "Detecting a trend change in cross-border epidemic transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 73-81.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0019616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.