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Emerg Infect Dis. 2000 May-Jun;6(3):248-58.

Remote sensing and geographic information systems: charting Sin Nombre virus infections in deer mice.

Author information

1
University of Nevada, Reno, Nevada 89557, USA. boone@scsr.nevada.edu

Abstract

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.

PMID:
10827114
PMCID:
PMC2640872
DOI:
10.3201/eid0603.000304
[Indexed for MEDLINE]
Free PMC Article

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