An Integrated Atmospheric and Hydrological Based Malaria Epidemic Alert System
Abstract
Malaria is a growing global threat, with increasing morbidity and mortality. In India there have been >40 epidemics in the last five years, in part due to abnormal meteorological conditions as well as the buildup of an immunologically naïve population. In most parts of India, periodic epidemics of malaria occur every five to seven years. Malaria epidemics are serious national/regional health emergencies, occurring with little or no warning where the public health system is unprepared to respond to the emerging problem. However, epidemic conditions develop over several weeks, theoretically allowing time for preventative action. The study area for the proposed research is located in Mewat, south of Delhi. It is estimated that 90% of the malaria burden is influenced by environmental factors, so that successful malaria intervention approaches must be adapted to local environmental conditions. Of particular importance are air and water temperature, relative humidity, soil moisture, and precipitation. Extreme climatic conditions prevail in Mewat, with uneven topography, 450mm average annual rainfall in 25 to 35 days, high temperature variability in different seasons, low relative humidity. Automated surface measurements are obtained for temperature, relative humidity, water temperature, precipitation and soil moisture. The Regional Atmospheric Modeling System (RAMS) is used to predict these variables over the spatial domain which are used in dynamic hydrological models to yield the parameters important to malaria transmission, including surface wetness, mean water table depth, percent surface saturation and total surface runoff. The locations of saturated surface regions associated with mosquito breeding sites near populated regions, along with water temperature, and then are used to determine larvae development and mosquito abundance. ASTER, LANDSAT and MODIS imagery are used to retrieve soil moisture, vegetation indices and land cover types. Pan-sharpened 1m spatial resolution QuickBird data has been used to identify small mosquito breeding sites with an accuracy of 90 %, as verified by ground observations. These layers of information, along with a 30m resolution Digital Elevation Model and field measurements of malaria incidence, larvae and mosquito counts, were examined in a GIS system to identify the environmental parameters effective in mosquito distribution. The Genetic Algorithm for Rule Set Production (GARP) has been applied to the region using the parameters defined above to predict regions susceptible to malaria transmission.
- Publication:
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AGU Spring Meeting Abstracts
- Pub Date:
- May 2005
- Bibcode:
- 2005AGUSM.H34A..06A
- Keywords:
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- 1640 Remote sensing;
- 1704 Atmospheric sciences;
- 1800 HYDROLOGY;
- 1866 Soil moisture;
- 2447 Modeling and forecasting