85th AMS Annual Meeting

Wednesday, 12 January 2005: 4:45 PM
An Integrated Hydrological and Atmospheric Model to predict Malaria Epidemics
Salvi Asefi, University of Alabama, Huntsville, AL; and J. Li, U. S. Nair, D. K. Ray, R. M. Welch, N. Padilla, E. Barrios, and M. E. Benedict
Poster PDF (757.4 kB)
The research focus is the integration of hydrological modeling with the Regional Atmospheric Modeling System (RAMS) along with mosquito abundance and malaria parasite transmission studied in a GIS-based system applied to the Ixcan region of Guatemala. Approximately 35% of Guatemalan population lives in malaria areas. Results of field studies by the Medical Entomology Research and Training Unit-Guatemala (MERTU) and the center for health studies at (UVG) demonstrate that malaria incidence has increased in recent years. P. falciparum now accounts for as much as 20% of malaria infections at study sites.

Malaria transmission is strongly associated with environmental conditions, which control mosquito maturity and parasite development. Of particular importance are surface temperature, relative humidity, precipitation and wind speed. The Regional Atmospheric Modeling System is used to predict these variables which, in turn, are used in dynamic hydrological models to yield the parameters important to malaria transmission include surface wetness, mean water table depth, percent surface saturation and total surface runoff. The locations of saturated surface regions associated with mosquito breeding sites nearby 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 IKONOS 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.

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