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

Authors
  • Boone, J D
  • McGwire, K C
  • Otteson, E W
  • DeBaca, R S
  • Kuhn, E A
  • Villard, P
  • Brussard, P F
  • St Jeor, S C
Type
Published Article
Journal
Emerging infectious diseases
Publication Date
Jan 01, 2000
Volume
6
Issue
3
Pages
248–258
Identifiers
PMID: 10827114
Source
Medline
License
Unknown

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.

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