Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Veterinary Medical Sciences - Pathobiological Sciences

First Advisor

John B. Malone

Second Advisor

Simon Shane


This study has identified environmental factors influencing the distribution of schistosomiasis and the prevalence of the disease in the population in the state of Bahia, Brazil. The Intergraph Modular Environmental Systems nucleus (MGE), a geographic information system (GIS), was applied to 30 municipalities in Bahia to establish a descriptive and quantitative study of the spatial and temporal dynamics of infection. The GIS was constructed by digitizing maps of soil type, vegetation, topology, and hydrologic features including temperature, rainfall, and seasonal pattern of precipitation, prevalence of schistosomiasis, and the distribution of snails, which are the intermediate hosts of the disease. The results of this study suggest that the duration of the annual dry period is the most important determinant in the prevalence of schistosomiasis in the areas selected for study. This conclusion has specific implications in the design of efficient control programs. Neither maximum rainfall nor total precipitation during three consecutive months are limiting factors, nor are the populations or distribution of snails. Atmospheric variables, including annual maximum and minimum temperature and diurnal differences in temperature, contribute to survival of the vector. The prevalence of the disease is highest in the coastal areas of the state. The population of snails is directly related to schistosomiasis prevalence rates and is influenced by soil type. The highest population were determined in areas with latossolo soil types covered by transition and coastal vegetation. Future work should address the environmental and demographic factors in areas which influence rate of infection, socioeconomic factors, housing, contact with water, level of exposure, and degree of reinfection in children and adults should be addressed. Results can be analyzed to develop an empirical model of schistosomiasis risk assessment.