Spatial Risk Mapping of West Nile Virus in Correlation to Vegetation in Montana

carrollscholars.legacy.contextkey10998352
carrollscholars.legacy.itemurlhttps://scholars.carroll.edu/lifesci_theses/44
carrollscholars.object.degreeBachelor's
carrollscholars.object.departmentLife & Environmental Sciences
carrollscholars.object.disciplinesBiodiversity; Biology; Ecology and Evolutionary Biology; Entomology; Epidemiology; Life Sciences; Statistics and Probability
carrollscholars.object.seasonSpring
dc.contributor.advisorGrant Hokit
dc.contributor.advisorSam Alvey
dc.contributor.advisorKevin Stewart
dc.contributor.authorWreggelsworth, Amanda
dc.date.accessioned2020-04-30T10:00:22Z
dc.date.available2020-04-30T10:00:22Z
dc.date.embargo12/31/1899 0:00
dc.date.issued2013-04-01
dc.description.abstractIn the present study, spatial epidemiology was applied to the vegetation in Montana by combining NDVI data, landcover data, and a thermal layer to create a predictive model of C. tarsalis in Montana. Samples of C. tarsalis, which has been identified as the primary vector for West Nile Virus in Montana, were collected from June to August, 2013, at various sites across the state. ArcGIS and MaxENT software was used to create a model for June, July, and August. Overall, there was a modal correlation between vegetation density and the presence of C. tarsalis and a positive correlation between both the thermal and vegetation layers and the presence of C. tarsalis. The models and their corresponding data outputs provide insight into mosquito presence throughout Montana, an outcome which, in turn, may help isolate WNV hot spots.
dc.identifier.urihttps://scholars.carroll.edu/handle/20.500.12647/2787
dc.titleSpatial Risk Mapping of West Nile Virus in Correlation to Vegetation in Montana
dc.typethesis
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