Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses

dc.citation.doi10.1371/journal.pone.0140666
dc.citation.issn1932-6203
dc.citation.issue10
dc.citation.jtitlePLoS One
dc.citation.spage15
dc.citation.volume10
dc.contributor.authorBoysen, C.
dc.contributor.authorDavis, Elizabeth G.
dc.contributor.authorBeard, Laurie A.
dc.contributor.authorLubbers, Brian V.
dc.contributor.authorRaghavan, Ram K.
dc.contributor.authoreidegdavis
dc.contributor.authoreidlabeard
dc.contributor.authoreidblubbers
dc.contributor.authoreidrkraghavan
dc.date.accessioned2016-04-04T22:27:50Z
dc.date.available2016-04-04T22:27:50Z
dc.date.issued2015-10-16
dc.date.published2015
dc.descriptionCitation: Boysen, C., Davis, E. G., Beard, L. A., Lubbers, B. V., & Raghavan, R. K. (2015). Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses. Plos One, 10(10), 15. doi:10.1371/journal.pone.0140666
dc.descriptionKansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (>= 1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (>= 35 degrees C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed.
dc.identifier.urihttp://hdl.handle.net/2097/32269
dc.relation.urihttps://doi.org/10.1371/journal.pone.0140666
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSpatial Autocorrelation
dc.subjectHabitat Fragmentation
dc.subjectPigeon Fever
dc.subjectBiodiversity
dc.subjectColorado
dc.subjectDrought
dc.titleBayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses
dc.typeArticle

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