IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v67y2005i5p731-745.html
   My bibliography  Save this article

Bayesian inference for stochastic multitype epidemics in structured populations via random graphs

Author

Listed:
  • Nikolaos Demiris
  • Philip D. O'Neill

Abstract

Summary. The paper is concerned with new methodology for statistical inference for final outcome infectious disease data using certain structured population stochastic epidemic models. A major obstacle to inference for such models is that the likelihood is both analytically and numerically intractable. The approach that is taken here is to impute missing information in the form of a random graph that describes the potential infectious contacts between individuals. This level of imputation overcomes various constraints of existing methodologies and yields more detailed information about the spread of disease. The methods are illustrated with both real and test data.

Suggested Citation

  • Nikolaos Demiris & Philip D. O'Neill, 2005. "Bayesian inference for stochastic multitype epidemics in structured populations via random graphs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 731-745, November.
    • Handle: RePEc:bla:jorssb:v:67:y:2005:i:5:p:731-745
    DOI: 10.1111/j.1467-9868.2005.00524.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2005.00524.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2005.00524.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. Gail E. Potter & Niel Hens, 2013. "A penalized likelihood approach to estimate within-household contact networks from egocentric data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 629-648, August.
    2. 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.
    3. Christopher Avery & William Bossert & Adam Clark & Glenn Ellison & Sara Fisher Ellison, 2020. "An Economist's Guide to Epidemiology Models of Infectious Disease," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 79-104, Fall.

    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:jorssb:v:67:y:2005:i:5:p:731-745. 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: https://edirc.repec.org/data/rssssea.html .

    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.