Geographical information systems (GIS) are tools that have been recently tested for improving our understanding
of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model
and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression
model (R
2
= 0.39) was established, after a variable selection phase, with a set of spatial variables including the
presence or absence of
Biomphalaria glabrata
, winter enhanced vegetation index, summer minimum temperature and
percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the
state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions:
1 (R
2
= 0.97), 2 (R
2
= 0.60), 3 (R
2
= 0.63) and 4 (R
2
= 0.76). Based on these models, a schistosomiasis risk map was
built for MG. In this paper, geostatistics was also used to make inferences about the presence of
Biomphalaria spp.
The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which
can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.
