Carlos Alberto Felgueiras

Spatial distribution of Biomphalaria mollusks at São Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures.

Geostatistics is used in this work to make inferences about the presence of the species of Biomphalaria (B. glabrata, B. tenagophila and/or B. straminea), intermediate hosts of Schistosoma mansoni, at the São Francisco River Basin, in Minas Gerais, Brazil. One of these geostatistical procedures, known as indicator kriging, allows the classification of categorical data, in areas where the data are not available, using a punctual sample set. The result is a map of species and risk area definition. More than a single map of the categorical attribute, the procedure also permits the association of uncertainties of the stochastic model, which can be used to qualify the inferences. In order to validate the estimated data of the risk map, a fieldwork in five municipalities was carried out. The obtained results showed that indicator kriging is a rather robust tool since it presented a very good agreement with the field findings. The obtained risk map can be thought as an auxiliary tool to formulate proper public health strategies, and to guide other fieldwork, considering the places with higher occurrence probability of the most important snail species. Also, the risk map will enable better resource distribution and adequate policies for the mollusk control. This methodology will be applied to other river basins to generate a predictive map for Biomphalaria species distribution for the entire state of Minas Gerais.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

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.

Use of Indicator Kriging to Investigate Schistosomiasis in Minas Gerais State, Brazil

Geographic Information Systems (GISs) are composed of useful tools to map and to model the spatial distribution of events that have geographic importance as schistosomiasis. This paper is a review of the use the indicator kriging, implemented on the Georeferenced Information Processing System (SPRING) to make inferences about the prevalence of schistosomiasis and the presence of the species of Biomphalaria, intermediate hosts of Schistosoma mansoni, in areas without this information, in the Minas Gerais State, Brazil. The results were two maps. The first one was a map of Biomphalaria species, and the second was a new map of estimated prevalence of schistosomiasis. The obtained results showed that the indicator kriging can be used to better allocate resources for study and control of schistosomiasis in areas with transmission or the possibility of disease transmission.

Modelagem de fertilidade do solo por simulação estocástica com tratamento de incertezas

This work described a methodology of fertility modeling that integrates chemical properties of the soil through the Monte Carlo method. The chemical attributes are spatialized, along with their estimation uncertainties, by a geostatistical procedure of random simulation. Uncertainties of the chemical properties are propagated to the resulting fertility model allowing the generation of fertility maps conditioned to preset risk levels. The method presented here is illustrated by a case study of fertility for soy culture, at the State of Santa Catarina, Brazil, considering the following chemical properties of the soil: exchangeable aluminum, potassium and cation exchange capacity.