Remote Sensing and Geographic Information Systems: Charting Sin Nombre Virus Infections in Deer Mice
Supporting Files
Public Domain
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2000/05/01
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Series: Emerging Infectious Diseases
File Language:
English
Details
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Journal Article:Emerging Infectious Diseases
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Personal Author:
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Description:We tested environmental data from remote sensing and geographic information system maps as indicators of Sin Nombre virus (SNV) infections in deer mouse (Peromyscus maniculatus) populations in the Walker River Basin, Nevada and California. We determined by serologic testing the presence of SNV infections in deer mice from 144 field sites. We used remote sensing and geographic information systems data to characterize the vegetation type and density, elevation, slope, and hydrologic features of each site. The data retroactively predicted infection status of deer mice with up to 80% accuracy. If models of SNV temporal dynamics can be integrated with baseline spatial models, human risk for infection may be assessed with reasonable accuracy.
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ISSN:1080-6059 (digital) ; 1080-6040 (print)
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Pubmed ID:10827114
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Pubmed Central ID:PMC2640872
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Rights:Public Domain
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Volume:6
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Issue:3
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Citation:Emerg Infect Dis. 6(3):248-258.
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Main Document Checksum:urn:sha-512:3f01d69e3209c8dfe427aaec545388ddade308cd055f7096effb424c980319b902e5f2faeb90d59ca98b956af34f650dfe468e6afa60db27074a706b63970728
Supporting Files
File Language:
English
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Emerging Infectious Diseases