IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v34y2014i10p1820-1829.html
   My bibliography  Save this article

Fractional Poisson—A Simple Dose‐Response Model for Human Norovirus

Author

Listed:
  • Michael J. Messner
  • Philip Berger
  • Sharon P. Nappier

Abstract

This study utilizes old and new Norovirus (NoV) human challenge data to model the dose‐response relationship for human NoV infection. The combined data set is used to update estimates from a previously published beta‐Poisson dose‐response model that includes parameters for virus aggregation and for a beta‐distribution that describes variable susceptibility among hosts. The quality of the beta‐Poisson model is examined and a simpler model is proposed. The new model (fractional Poisson) characterizes hosts as either perfectly susceptible or perfectly immune, requiring a single parameter (the fraction of perfectly susceptible hosts) in place of the two‐parameter beta‐distribution. A second parameter is included to account for virus aggregation in the same fashion as it is added to the beta‐Poisson model. Infection probability is simply the product of the probability of nonzero exposure (at least one virus or aggregate is ingested) and the fraction of susceptible hosts. The model is computationally simple and appears to be well suited to the data from the NoV human challenge studies. The model's deviance is similar to that of the beta‐Poisson, but with one parameter, rather than two. As a result, the Akaike information criterion favors the fractional Poisson over the beta‐Poisson model. At low, environmentally relevant exposure levels (

Suggested Citation

  • Michael J. Messner & Philip Berger & Sharon P. Nappier, 2014. "Fractional Poisson—A Simple Dose‐Response Model for Human Norovirus," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1820-1829, October.
    • Handle: RePEc:wly:riskan:v:34:y:2014:i:10:p:1820-1829
    DOI: 10.1111/risa.12207
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.12207
    Download Restriction: no
    File URL: https://libkey.io/10.1111/risa.12207?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. Philip J. Schmidt & Katarina D. M. Pintar & Aamir M. Fazil & Edward Topp, 2013. "Harnessing the Theoretical Foundations of the Exponential and Beta‐Poisson Dose‐Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1677-1693, September.
    Full references (including those not matched with items on IDEAS)

    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. Philip J. Schmidt & Monica B. Emelko & Mary E. Thompson, 2020. "Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit," Risk Analysis, John Wiley & Sons, vol. 40(2), pages 352-369, February.
    2. Gang Xie, 2017. "Authors’ Response to Comment on “Guidelines for Use of the Approximate Beta‐Poisson Dose–Response Model” – Previously Published Guidelines Continue to Be Ignored," Risk Analysis, John Wiley & Sons, vol. 37(2), pages 201-203, February.

    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:wly:riskan:v:34:y:2014:i:10:p:1820-1829. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    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.