PARENT SESSION
Poster Session 14: GIS and Remote Sensing
Tuesday, August 9, 5:00 PM - 6:30 PM, Exhibit Hall 220 A-E, Level 2, Palais des congrès de Montréal
Forecasting Risk of Hantavirus Pulmonary Syndrome Using Remote Sensing and Bioclimatology.
Loehman, Rachel*,1, Heinsch, Faith Ann1, Running, Steven1, 1 University of Montana, Missoula, MT, USA
ABSTRACT- Recent predictive models for hantavirus pulmonary syndrome (HPS) have used remotely sensed spectral reflectance data to characterize risk areas with limited success. We present an alternative method using gross primary production (GPP) and Enhanced Vegetation Indices (EVI) from the MODIS sensor to estimate the effects of biomass accumulation on population density of Peromyscus maniculatus (deer mouse), the principal reservoir species for Sin Nombre virus (SNV). The majority of diagnosed HPS cases in North America are attributed to SNV, which is transmitted to humans through inhalation of excretions and secretions from infected rodents. A logistic model framework is used to evaluate MODIS GPP and EVI and site bioclimatology as predictors of P. maniculatus density at established trapping sites across the western United States. Rodent populations were estimated using monthly minimum number alive (MNA) data from distributed trapping sites. Both local meteorological data from nearby weather stations and 1.25 degree x 1 degree gridded data from the NASA DAO were used in the regression model to determine the spatial sensitivity of the response. The use of MODIS remote sensing data to forecast HPS risk may result in a marked improvement over past reflectance-based risk area characterizations. The MODIS products provide vegetation dynamics estimates that are unique to disease models, and target the fundamental ecological processes responsible for increased rodent density and amplified disease risk.
Key words: hantavirus, disease, bioclimatology, model