Using MODIS satellite imagery to predict hantavirus risk
Version of Record online: 17 FEB 2011
© 2011 Blackwell Publishing Ltd
Global Ecology and Biogeography
Volume 20, Issue 4, pages 620–629, July 2011
How to Cite
Cao, L., Cova, T. J., Dennison, P. E. and Dearing, M. D. (2011), Using MODIS satellite imagery to predict hantavirus risk. Global Ecology and Biogeography, 20: 620–629. doi: 10.1111/j.1466-8238.2010.00630.x
- Issue online: 7 JUN 2011
- Version of Record online: 17 FEB 2011
- Deer mice;
- Sin Nombre virus;
- vegetation indices
Aims Sin Nombre virus (SNV), a strain of hantavirus, causes hantavirus pulmonary syndrome (HPS) in humans, a deadly disease with high mortality rate (> 50%). The primary virus host is the deer mouse, and greater abundance of deer mice has been shown to increase the human risk of HPS. Our aim is to identify and compare vegetation indices and associated time lags for predicting hantavirus risk using remotely sensed imagery.
Location Utah, USA.
Methods A 5-year time-series of moderate-resolution imaging spectroradiometer (MODIS) satellite imagery and corresponding field data was utilized to compare various vegetation indices that measure productivity with the goal of indirectly estimating mouse abundance and SNV prevalence. Relationships between the vegetation indices and deer mouse density, SNV prevalence and the number of infected deer mice at various time lags were examined to assess which indices and associated time lags might be valuable in predicting SNV outbreaks.
Results The results reveal varying levels of positive correlation between the vegetation indices and deer mouse density as well as the number of infected deer mice. Among the vegetation indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) produced the highest correlations with deer mouse density and the number of infected deer mice using a time lag of 1.0 to 1.3 years for May and June imagery.
Main conclusions This study demonstrates the potential for using MODIS time-series satellite imagery in estimating deer mouse abundance and predicting hantavirus risk. The 1-year time lag provides a great opportunity to apply satellite imagery to predict upcoming SNV outbreaks, allowing preventive strategies to be adopted. Analysis of different predictive indices and lags could also be valuable in identifying the time windows for data collection for practical uses in monitoring rodent abundance and subsequent disease risk to humans.