Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data


Simon Brooker Department of Infectious Disease Epidemiology, Imperial College School of Medicine, Norfolk Place, London W2 1PG, UK. Fax: +44(0)20 7262 7912; E-mail:


In this paper, remotely sensed (RS) satellite sensor environmental data, using logistic regression, are used to develop prediction maps of the probability of having infection prevalence exceeding 50%, and warranting mass treatment according to World Health Organization (WHO) guidelines. The model was developed using data from one area of coastal Tanzania and validated with independent data from different areas of the country. Receiver operating characteristic (ROC) analysis was used to evaluate the model’s predictive performance. The model allows reasonable discrimination between high and low prevalence schools, at least within those geographical areas in which they were originally developed, and performs reasonably well in other coastal areas, but performs poorly by comparison in the Great Lakes area of Tanzania. These results may be explained by reference to an ecological zone map based on RS-derived environmental data. This map suggests that areas where the model reliably predicts a high prevalence of schistosomiasis fall within the same ecological zone, which has common intermediate-host snail species responsible for transmission. By contrast, the model’s performance is poor near Lake Victoria, which is in a different ecological zone with different snail species. The ecological map can potentially define a template for those areas where existing models can be applied, and highlight areas where further data and models are required. The developed model was then used to provide estimates of the number of schoolchildren at risk of high prevalence and associated programme costs.