IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v32y2005i2p265-280.html
   My bibliography  Save this article

Bayesian inference for epidemics with two levels of mixing

Author

Listed:
  • NIKOLAOS DEMIRIS
  • PHILIP D. O'NEILL

Abstract

. Methodology for Bayesian inference is considered for a stochastic epidemic model which permits mixing on both local and global scales. Interest focuses on estimation of the within‐ and between‐group transmission rates given data on the final outcome. The model is sufficiently complex that the likelihood of the data is numerically intractable. To overcome this difficulty, an appropriate latent variable is introduced, about which asymptotic information is known as the population size tends to infinity. This yields a method for approximate inference for the true model. The methods are applied to real data, tested with simulated data, and also applied to a simple epidemic model for which exact results are available for comparison.

Suggested Citation

  • Nikolaos Demiris & Philip D. O'Neill, 2005. "Bayesian inference for epidemics with two levels of mixing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 265-280, June.
    • Handle: RePEc:bla:scjsta:v:32:y:2005:i:2:p:265-280
    DOI: 10.1111/j.1467-9469.2005.00420.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9469.2005.00420.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9469.2005.00420.x?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
    ---><---

    Citations

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


    Cited by:

    1. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    2. James N Walker & Joshua V Ross & Andrew J Black, 2017. "Inference of epidemiological parameters from household stratified data," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.

    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:bla:scjsta:v:32:y:2005:i:2:p:265-280. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.