Schistosomiasis is a tropical parasitic disease caused by blood-dwelling fluke worms of the genus Schistosoma. More than 200 million people worldwide, especially in sub-Saharan Africa, the Middle East and Southeast Asia, suffer from this disease. The disease is characterized by focal epidemiology and over-dispersed distribution pattern, with higher infection rates in children than in adults. Beside acute symptoms, chronic schisto-infection results in indirect morbidity such as cognitive and physical impairment in children. While some advances have been made in the control of the disease through population-based chemotherapy more efforts are required to achieve the goal of disease elimination with limited resources. World Health Organization has proposed several schistosomiasis control guidelines and programs by, and their outcomes need to be assessed. Mathematical models can be used for prediction and control analysis. Here we develop several of them that focus on specific parts and processes of schisto-infection and disease. They are (i) dynamics model of snail population with rainfall input; (ii) heterogeneous transmission for distributed human/snail population systems, and over-dispersed worm burden in host populations; (iii) the effect of chronic schisto-infection on childhood growth and development. The models were calibrated using epidemiological and demographic data from a coastal region in eastern Kenya. We used these models to simulate and predict the effect of different control strategies, to do their cost-benefit analysis and find the optimal regimens. Our models are based on nonlinear differential equations, and exploit diverse mathematical tools and techniques for analysis. For numeric simulations, calibration and symbolic algebra we used Wolfram Mathematica 7.
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