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A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States

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

Abstract

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge.

Suggested Citation

  • Stella C Watson & Yan Liu & Robert B Lund & Jenna R Gettings & Shila K Nordone & Christopher S McMahan & Michael J Yabsley, 2017. "A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    • Handle: RePEc:plo:pone00:0174428
    DOI: 10.1371/journal.pone.0174428
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    References listed on IDEAS

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    1. J. Besag & D. Mondal, 2013. "Exact Goodness-of-Fit Tests for Markov Chains," Biometrics, The International Biometric Society, vol. 69(2), pages 488-496, June.
    2. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    3. Emily R Adrion & John Aucott & Klaus W Lemke & Jonathan P Weiner, 2015. "Health Care Costs, Utilization and Patterns of Care following Lyme Disease," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-14, February.
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    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. Berloco, Claudia & Argiento, Raffaele & Montagna, Silvia, 2023. "Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1065-1077.
    5. 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.
    6. Yuri P Springer & Pieter T J Johnson, 2018. "Large-scale health disparities associated with Lyme disease and human monocytic ehrlichiosis in the United States, 2007–2013," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
    7. 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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