title: A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Ehrlichia species in domestic dogs within the contiguous United States.
abstract: BACKGROUND
  Dogs in the United States are hosts to a diverse range of vector-borne pathogens, several of which are important zoonoses. This paper describes factors deemed to be significantly related to the prevalence of antibodies to Ehrlichia spp. in domestic dogs, including climatic conditions, geographical factors, and societal factors. These factors are used in concert with a spatio-temporal model to construct an annual seroprevalence forecast. The proposed method of forecasting and an assessment of its fidelity are described.
  
  
  METHODS
  Approximately twelve million serological test results for canine exposure to Ehrlichia spp. were used in the development of a Bayesian approach to forecast canine infection. Data used were collected on the county level across the contiguous United States from routine veterinary diagnostic tests between 2011-2015. Maps depicting the spatial baseline Ehrlichia spp. prevalence were constructed using Kriging and head-banging smoothing methods. Data were statistically analyzed to identify factors related to antibody prevalence via a Bayesian spatio-temporal conditional autoregressive (CAR) model. Finally, a forecast of future Ehrlichia seroprevalence was constructed based on the proposed model using county-level data on five predictive factors identified at a workshop hosted by the Companion Animal Parasite Council and published in 2014: annual temperature, percentage forest coverage, percentage surface water coverage, population density and median household income. Data were statistically analyzed to identify factors related to disease prevalence via a Bayesian spatio-temporal model. The fitted model and factor extrapolations were then used to forecast the regional seroprevalence for 2016.
  
  
  RESULTS
  The correlation between the observed and model-estimated county-by-county Ehrlichia seroprevalence for the five-year period 2011-2015 is 0.842, demonstrating reasonable model accuracy. The weighted correlation (acknowledging unequal sample sizes) between 2015 observed and forecasted county-by-county Ehrlichia seroprevalence is 0.970, demonstrating that Ehrlichia seroprevalence can be forecasted accurately.
  
  
  CONCLUSIONS
  The forecast presented herein can be an a priori alert to veterinarians regarding areas expected to see expansion of Ehrlichia beyond the accepted endemic range, or in some regions a dynamic change from historical average prevalence. Moreover, this forecast could potentially serve as a surveillance tool for human health and prove useful for forecasting other vector-borne diseases.
authors.affiliation: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA.
authors.forename: Robert B
authors.initials: RB
authors.lastname: Lund
authors.affiliation: Department of Molecular and Biomedical Sciences, Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA.
authors.forename: Shila K
authors.initials: SK
authors.lastname: Nordone
authors.affiliation: Department of Population Health, Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine and the Warnell School of Forestry and Natural Resources, The University of Georgia, Athens, GA, USA. myabsley@uga.edu.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Yabsley
authors.affiliation: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA. mcmaha2@clemson.edu.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: McMahan
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D004283:Dog Diseases / Q000453:epidemiology / Q000469:parasitology*; D004285:Dogs; D004536:Ehrlichia / Q000276:immunology*; D016873:Ehrlichiosis / Q000453:epidemiology / Q000469:parasitology / Q000662:veterinary*; D008954:Models, Biological; D016036:Seroepidemiologic Studies; D014481:United States
keywords: Autoregression; CAR Model; Ehrlichiosis; Head-banging; Kriging; Prevalence; Spatio-temporal modeling