Title page for ETD etd-1111103-094734


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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