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. 2001 Dec;6(12):998-1007.
doi: 10.1046/j.1365-3156.2001.00798.x.

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

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

S Brooker et al. Trop Med Int Health. 2001 Dec.

Abstract

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.

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Figures

Figure 1
Figure 1
The spatial distribution of reported urinary schistosomiasis in Tanzania. Data are available for 166 099 children from 591 schools in Tanga Region (Partnership for Child Development 1999a), 164 schools in Kilosa district (Lengeler et al. 1991; WHO 1995b), 49 schools in Magu district (Guyatt et al. 1999) and 176 schools in Mtwara Region (Partnership for Child Development, unpublished data). ●くろまる ≥ 50% prevalence, ○しろまる < 50% prevalence.
Figure 2
Figure 2
Receiver operator characteristic (ROC) plots for schistosomiasis ecological models. (a) Tanga Region model applied to validation data in Tanga Region (n = 290); Tanga Region model applied to (b) Kilosa District (n = 164), (c) Mtwara and Tandahimba districts (n = 176) and (d) Magu District (n = 49).
Figure 3
Figure 3
Map of ecological zones in Africa based on RS-derived ecological variables. This map is based on the mean annual summaries (1982–2000 inclusive) of multitemporal RS data from the Advanced Very High Resolution Radiometer (AVHRR) processed using standard procedures, reviewed in Hay (2000), to provide, middle infrared brightness temperatures, land surface temperature (LST) and photosynthetic activity estimates [expressed as the Normalized Difference Vegetation Index (NDVI)] for the African continent. These data in combination with a digital elevation model (DEM) of Africa were used to generate 20 ‘ecological zones’ using the unsupervised classification procedures of Earth Resources Data Analysis System (ERDAS) Imagine 8.4TM software. ERDAS implements the Iterative Self-Organizing Data Analysis Technique (ISODATA), an iterative method that uses the Euclidean distance as a similarity measure to cluster data into different classes. A complete synopsis of the environmental criteria used to define and hence separate each zone is not appropriate here but the table shows the mean values of those clusters used to define the zones mentioned in the text.
Figure 4
Figure 4
Prediction models for S. haematobium transmission in Tanzania. The map shows the probability of an area having an infection prevalence > 50%.
Figure 5
Figure 5
Known distribution of endemic S. haematobium in East Africa (Taken from McCullough 1972).

References

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