IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v29y2018i8ne2538.html
   My bibliography  Save this article

A large‐scale spatio‐temporal binomial regression model for estimating seroprevalence trends

Author

Listed:
  • Stella C. Watson Self
  • Christopher S. McMahan
  • Derek A. Brown
  • Robert B. Lund
  • Jenna R. Gettings
  • Michael J. Yabsley

Abstract

This paper develops a large‐scale Bayesian spatio‐temporal binomial regression model to investigate regional trends in antibody prevalence to Borrelia burgdorferi, the causative agent of Lyme disease. Our model uses Gaussian predictive processes to estimate the spatially varying trends and a conditional autoregressive scheme to account for spatio‐temporal dependence. A novel framework, easily scalable to large spatio‐temporal data, is developed. The proposed model is used to analyze about 16 million B. burgdorferi antibody Lyme tests performed on canine samples in the conterminous United States over the sixty‐month period from January 2012 to December 2016. This analysis identifies areas of increasing canine Lyme disease risk; prevalence of infection is getting worse in endemic regions and increases are also seen in non‐endemic regions. Because Lyme disease is zoonotic, affecting both humans and dogs, the analysis also serves to pinpoint areas of increasing human risk.

Suggested Citation

  • Stella C. Watson Self & Christopher S. McMahan & Derek A. Brown & Robert B. Lund & Jenna R. Gettings & Michael J. Yabsley, 2018. "A large‐scale spatio‐temporal binomial regression model for estimating seroprevalence trends," Environmetrics, John Wiley & Sons, Ltd., vol. 29(8), December.
    • Handle: RePEc:wly:envmet:v:29:y:2018:i:8:n:e2538
    DOI: 10.1002/env.2538
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2538
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.2538?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. Marwah Soliman & Vyacheslav Lyubchich & Yulia R. Gel, 2020. "Ensemble forecasting of the Zika space‐time spread with topological data analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.

    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:envmet:v:29:y:2018:i:8:n:e2538. 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.interscience.wiley.com/jpages/1180-4009/ .

    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.