Remote Sensing and Geographic Information Systems: Charting Sin Nombre Virus Infections in Deer Mice

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From: Emerging Infectious Diseases(Vol. 6, Issue 3)
Publisher: U.S. National Center for Infectious Diseases
Document Type: Article
Length: 4,759 words

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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.

Remote sensing (RS) and geographic information systems (GIS) are map-based tools that can be used to study the distribution, dynamics, and environmental correlates of diseases (1,2). RS is gathering digital images of the earth's surface from airborne or satellite platforms and transforming them into maps. GIS is a data management system that organizes and displays digital map data from RS or other sources and facilitates the analysis of relationships between mapped features. Statistical relationships often exist between mapped features and diseases in natural host or human populations (1). Examples include malaria in southern Mexico and in Asia (3,4), Rift Valley fever in Kenya (5), Lyme disease in Illinois (6), African trypanosomiasis (7), and schistosomiasis in both humans (8) and livestock in the southeastern United States (9). RS and GIS may also permit assessment of human risk from pathogens such as Sin Nombre virus (SNV; family Bunyaviridae), the agent primarily associated with hantavirus pulmonary syndrome (HPS) in North America (10,11). RS and GIS are most useful if disease dynamics and distributions are clearly related to mapped environmental variables. For example, if a disease is associated with certain vegetation types or physical characteristics (elevation, average precipitation), RS and GIS could identify regions where risk is relatively high.

We examined whether RS and GIS data were useful indicators of the spatial pattern of SNV infections in populations of the primary rodent host, the deer mouse (Peromyscus maniculatus) (12-15). Our approach involved determining the infection status of rodents at 144 field sites, collecting RS and GIS data for each site, testing for statistical relationships between these data and infection, using the statistical relationships to retroactively classify infection status of rodents at these sites, and using the classifications to estimate prediction accuracy. Predictions derived from RS and GIS data could identify the ecologic settings where human exposure to SNV is most likely to occur.

SNV and its Host

Since the first recognized outbreak of liPS in the southwestern United States in 1993, approximately 240 cases have occurred, with a death rate of approximately 40% (J. Mills, pers. comm.) (16). Information about SNV host-virus-environment relationships is limited (16,17). No simple relationships have been found between host density and antibody seroprevalence (16-18), but more complex nonlinear relationships appear to exist (17). SNV infections also appear to be less frequent in relatively high- or low-elevation habitats...

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Gale Document Number: GALE|A62870284