Mapping Sin Nombre Virus Infections in Deer Mice
Author Information
Author(s): John D. Boone, Kenneth C. McGwire, Elmer W. Otteson, Robert S. DeBaca, Edward A. Kuhn, Pascal Villard, Peter F. Brussard, Stephen C. St. Jeor
Primary Institution: University of Nevada, Reno
Hypothesis
Are remote sensing and GIS data useful indicators of the spatial pattern of Sin Nombre virus infections in deer mouse populations?
Conclusion
Remote sensing and GIS data can effectively predict the spatial patterns of Sin Nombre virus infections in deer mice.
Supporting Evidence
- Remote sensing and GIS data were used to characterize environmental features at each site.
- Predictions derived from RS and GIS data could identify ecologic settings with high human exposure risk to SNV.
- Classification accuracy of infection status was estimated to be up to 80%.
Takeaway
Scientists used maps and satellite images to find out where deer mice might be sick with a virus. They found that certain areas are more likely to have sick mice.
Methodology
The study involved testing deer mice for SNV infections at 144 sites and analyzing remote sensing and GIS data to identify environmental factors associated with infection.
Potential Biases
Potential biases in capturing deer mice could affect the accuracy of infection status classification.
Limitations
The study's findings may not apply to all environments, and the classification of infection status may have uncertainties.
Participant Demographics
The study focused on deer mice (Peromyscus maniculatus) in the Walker River Basin, Nevada and California.
Statistical Information
P-Value
p<0.0001
Confidence Interval
95% confidence intervals derived from the binomial distribution.
Statistical Significance
p<0.0001
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