IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0182028.html
   My bibliography  Save this article

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States

Author

Listed:
  • Yan Liu
  • Stella C Watson
  • Jenna R Gettings
  • Robert B Lund
  • Shila K Nordone
  • Michael J Yabsley
  • Christopher S McMahan

Abstract

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.

Suggested Citation

  • Yan Liu & Stella C Watson & Jenna R Gettings & Robert B Lund & Shila K Nordone & Michael J Yabsley & Christopher S McMahan, 2017. "A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-18, July.
  • Handle: RePEc:plo:pone00:0182028
    DOI: 10.1371/journal.pone.0182028
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182028
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182028&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0182028?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. Peter Congdon, 2022. "A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic," IJERPH, MDPI, vol. 19(11), pages 1-17, May.
    2. Peter Congdon, 2022. "A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates," Journal of Geographical Systems, Springer, vol. 24(4), pages 583-610, October.
    3. Alexandra Sack & Elena N. Naumova & Lori Lyn Price & Guang Xu & Stephen M. Rich, 2023. "Passive Surveillance of Human-Biting Ixodes scapularis Ticks in Massachusetts from 2015–2019," IJERPH, MDPI, vol. 20(5), pages 1-11, February.
    4. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2020. "Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia," Journal of Geographical Systems, Springer, vol. 22(1), pages 105-142, January.
    5. Dilaram Acharya & Ji-Hyuk Park, 2021. "Seroepidemiologic Survey of Lyme Disease among Forestry Workers in National Park Offices in South Korea," IJERPH, MDPI, vol. 18(6), pages 1-10, March.

    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:plo:pone00:0182028. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.