Type of Document	Master's Thesis
Author	McNally, Kelsey Lee
Author's Email Address	kmcnal1@lsu.edu
URN	etd-1111103-094734
Title	Developing Risk Assessment Maps for Schistosoma Haematobium in Kenya Based on Climate Grids and Remotely Sensed Data
Degree	Master of Science (M.S.)
Department	Veterinary Microbiology & Parasitology (Veterinary Medical Sciences)
Advisory Committee	Advisor Name Title John B Malone Committee Chair James Miller Committee Member Oscar Huh Committee Member
Keywords	AVHRR disease prediction GIS Schistosoma haematobium Bulinus globosus growing degree days
Date of Defense	2003-10-27
Availability	unrestricted
Abstract It is important to be able to predict the potential spread of water borne diseases when building dams or redirecting rivers. This study was designed to test whether the use of a growing degree day (GDD) climate model and remotely sensed data (RS) within a geographic information system (GIS), could be used to predict both the distribution and severity of Schistosoma haematobium. Growing degree days are defined as the number of degrees centigrade over the minimum temperature required for development. The base temperature and the number of GDD required to complete one generation varies for each species. A monthly climate surface grid containing the high and low temperature, rainfall, potential evapotranspiration (PET), and the ratio of rain to PET was used to calculate the total number of GDD provisional on suitable moisture content in the soil. The latitude and longitude for known snail locations were used to create a point file. A 5km buffer was made around each point. Mean values were extracted from buffer areas for Advanced Very High Resolution Radiometer (AVHRR) data on maximum land surface temperature (Tmax) and normalized difference vegetation index (NDVI). The values for Tmax ranged from 15-28 and the NDVI values were 130-157. A map query found all areas that meet both criteria and produced a model surface showing the potential distribution of the vectors for this disease. Results indicate that the GDD and AVHRR models can be used together to define both the distribution range and relative risk of S.haematobium in anticipated water development projects and for control program planning and better allocation of health resources in endemic vs. non-endemic areas.
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