title: Linking bovine tuberculosis on cattle farms to white-tailed deer and environmental variables using Bayesian hierarchical analysis.
abstract: Bovine tuberculosis is a bacterial disease caused by Mycobacterium bovis in livestock and wildlife with hosts that include Eurasian badgers (Meles meles), brushtail possum (Trichosurus vulpecula), and white-tailed deer (Odocoileus virginianus). Risk-assessment efforts in Michigan have been initiated on farms to minimize interactions of cattle with wildlife hosts but research on M. bovis on cattle farms has not investigated the spatial context of disease epidemiology. To incorporate spatially explicit data, initial likelihood of infection probabilities for cattle farms tested for M. bovis, prevalence of M. bovis in white-tailed deer, deer density, and environmental variables for each farm were modeled in a Bayesian hierarchical framework. We used geo-referenced locations of 762 cattle farms that have been tested for M. bovis, white-tailed deer prevalence, and several environmental variables that may lead to long-term survival and viability of M. bovis on farms and surrounding habitats (i.e., soil type, habitat type). Bayesian hierarchical analyses identified deer prevalence and proportion of sandy soil within our sampling grid as the most supported model. Analysis of cattle farms tested for M. bovis identified that for every 1% increase in sandy soil resulted in an increase in odds of infection by 4%. Our analysis revealed that the influence of prevalence of M. bovis in white-tailed deer was still a concern even after considerable efforts to prevent cattle interactions with white-tailed deer through on-farm mitigation and reduction in the deer population. Cattle farms test positive for M. bovis annually in our study area suggesting that the potential for an environmental source either on farms or in the surrounding landscape may contributing to new or re-infections with M. bovis. Our research provides an initial assessment of potential environmental factors that could be incorporated into additional modeling efforts as more knowledge of deer herd factors and cattle farm prevalence is documented.
authors.affiliation: U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, University Park, Pennsylvania, United States of America.
authors.forename: W David
authors.initials: WD
authors.lastname: Walter
authors.affiliation: Animal Industry Division, Michigan Department of Agriculture and Rural Development, Lansing, Michigan, United States of America.
authors.forename: Rick
authors.initials: R
authors.lastname: Smith
authors.affiliation: Animal Industry Division, Michigan Department of Agriculture and Rural Development, Lansing, Michigan, United States of America.
authors.forename: Mike
authors.initials: M
authors.lastname: Vanderklok
authors.affiliation: United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, United States of America.
authors.forename: Kurt C
authors.initials: KC
authors.lastname: VerCauteren
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D002418:Cattle Diseases / Q000453:epidemiology*; D003670:Deer / Q000382:microbiology*; D018562:Disease Transmission, Infectious / Q000662:veterinary*; D004777:Environment; D016013:Likelihood Functions; D016015:Logistic Models; D008824:Michigan / Q000453:epidemiology; D015995:Prevalence; D018570:Risk Assessment; D012987:Soil / Q000737:chemistry; D012988:Soil Microbiology; D014380:Tuberculosis, Bovine / Q000453:epidemiology*
chemical_list: D012987:Soil