Incorporating Hydroepidemiology into the Epidemia Malaria Early Warning System

Monday, 15 December 2014: 4:40 PM
Michael C Wimberly1, Christopher L. Merkord1, Geoffrey M Henebry1 and Gabriel B Senay2, (1)South Dakota State University, Geospatial Sciences Center of Excellence, Brookings, SD, United States, (2)USGS EROS, Sioux Falls, SD, United States
Early warning of the timing and locations of malaria epidemics can facilitate the targeting of resources for prevention and emergency response. In response to this need, we are developing the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. EPIDEMIA incorporates software for capturing, processing, and integrating environmental and epidemiological data from multiple sources; data assimilation techniques that continually update models and forecasts; and a web-based interface that makes the resulting information available to public health decision makers. The system will enable forecasts that incorporate lagged responses to environmental risk factors as well as information about recent trends in malaria cases. Because the egg, larval, and pupal stages of mosquito development occur in aquatic habitats, information about the spatial and temporal distributions of stagnant water bodies is critical for modeling malaria risk. Potential sources of hydrological data include satellite-derived rainfall estimates, evapotranspiration (ET) calculated using a simplified surface energy balance model, and estimates of soil moisture and fractional water cover from passive microwave radiometry. We used partial least squares regression to analyze and visualize seasonal patterns of these variables in relation to malaria cases using data from 49 districts in the Amhara region of Ethiopia. Seasonal patterns of rainfall were strongly associated with the incidence and seasonality of malaria across the region, and model fit was improved by the addition of remotely-sensed ET and soil moisture variables. The results highlight the importance of remotely-sensed hydrological data for modeling malaria risk in this region and emphasize the value of an ensemble approach that utilizes multiple sources of information about precipitation and land surface wetness. These variables will be incorporated into the forecasting models at the core of the EPIDEMIA system, and. future model development will involve a cycle of continuous forecasting, accuracy assessment, and model refinement.