Skip to main content

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

Buy Article:

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.

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.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: Schistosoma haematobium; Tanzania; prediction; receiver operating characteristic analysis; remote sensing; urinary schistosomiasis

Document Type: Research Article

Publication date: 2001-12-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more