IDEAS home Printed from https://ideas.repec.org/a/taf/mpopst/v27y2020i4p232-258.html
   My bibliography  Save this article

Bayesian inference for a susceptible-exposed-infected-recovered epidemic model with data augmentation

Author

Listed:
  • Chouaib Beldjoudi
  • Tewfik Kernane
  • Hamid El Maroufy

Abstract

A Bayesian data-augmentation method allows estimating the parameters in a susceptible-exposed-infected-recovered (SEIR) epidemic model, which is formulated as a continuous-time Markov process and approximated by a diffusion process using the convergence of the master equation. The estimation was carried out with latent data points between every pair of observations simulated through the Euler-Maruyama scheme, which involves imputing the missing data in addition to the model parameters. The missing data and parameters are treated as random variables, and a Markov-chain Monte-Carlo algorithm updates the missing data and the parameter values. Numerical simulations show the effectiveness of the proposed Markov-chain Monte-Carlo algorithm.

Suggested Citation

  • Chouaib Beldjoudi & Tewfik Kernane & Hamid El Maroufy, 2020. "Bayesian inference for a susceptible-exposed-infected-recovered epidemic model with data augmentation," Mathematical Population Studies, Taylor & Francis Journals, vol. 27(4), pages 232-258, October.
    • Handle: RePEc:taf:mpopst:v:27:y:2020:i:4:p:232-258
    DOI: 10.1080/08898480.2019.1656491
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/08898480.2019.1656491
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/08898480.2019.1656491?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2022. "Knowledge-based data augmentation of small samples for oil condition prediction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

    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:taf:mpopst:v:27:y:2020:i:4:p:232-258. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GMPS20 .

    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.