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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
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    Cited by:

    1. 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.
    2. 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.
    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. 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.
    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.

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