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Integrating Multi-Sensor Satellite Data for Malaria Early Warning in the Amhara Region of Ethiopia
Alemayehu
Midekisa, South Dakota State University, alemayehu.midekisa@sdstate.edu
(Presenter)
Malaria is a major health problem in most of sub-Saharan Africa, including Ethiopia where sixty-eight percent of the population (52 million people) is at risk of malaria. A malaria early warning system that accounts for both local scale spatial variation and seasonal patterns is essential in the prevention and mitigation of malaria epidemics. This approach can be enhanced by integrating environmental data from high spatial and high temporal resolution sensors. The current study aims to develop an integrated modeling approach by coupling high spatial and high temporal resolution multi-sensor NASA Earth Science data for enhanced malaria early warning. This will allow public health decision makers to more effectively respond to malaria epidemics in the Amhara region of Ethiopia. ASTER and SRTM derived parameters will be used to predict the spatial locations of mosquito breeding habitats. Time series models will be used to forecast malaria cases using remotely sensed variables from MODIS and TRMM sensors within a lead time of several months at district level. A GIS modeling approach will be used to integrate land use, physiography, and other risk factors to map potential mosquito breeding areas. The integrated model results will be overlaid with population data to estimate the population at risk of malaria. Based on initial results we developed time series models using remotely-sensed environmental variables with lags ranging from 1 to 3 months. Incorporating remote-sensed environmental data improved the fit of all the models, which were able to forecast future outbreaks with a lead time of 1 to 3 months. Presentation Type: Poster Session: Science in Support of Decision Making (Wed 10:00 AM) Associated Project(s):
Poster Location ID: 196
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