spatial analysis of areas likely to harbor american cutaneous leishmaniasis in seropédica, rio de janeiro state, brazil

The goal of the present study was to evaluate the spatial distribution of American cutaneous leishmaniasis (ACL) in Seropédica, Rio de Janeiro State, Brazil according to the favorability of occurrence. The Vista-SAGA Geo-environmental Analysis System (Sistema de Análise Geoambiental Vista-SAGA) was used to create a database (theme maps) and perform environmental analyses, signatures and combinations. The degrees of likelihood were obtained by evaluating soil use and plant cover; soil type and geomorphology (geo-environmental factors); and altitude and slope (topographic factors). The factors used in the analyses were selected based on results from the literature and according to the technical feasibility of their inclusion in the databases. Weights and grades were attributed to the factors and their categories, respectively, according to their relevance to the life cycle of parasites from genus Leishmania and parasite vectors. Based on the results of the environmental analyses, areas were classified and grouped to represent the spatial distribution of favorability. Areas were classified as very unfavorable, unfavorable, somewhat favorable, favorable and very favorable for ACL occurrence. The results of the signature analyses showed that the municipality included 6.81% very favorable areas, 65.14% favorable areas, 17.98% somewhat favorable areas, 2.39% unfavorable areas and 0.07% very unfavorable areas for ACL occurrence. The signature analyses of the results and theme maps were able to locate and characterize the ecosystem profiles in terms of risk of ACL occurrence, indicating that rural, peri-urban and urban areas of the municipality of Seropédica may be classified as favorable for the occurrence of this disease. The results of the present study may be used to support management decisions made by authorities responsible for ACL control and prevention in Seropédica. In addition, geoprocessing was found to be a low-cost alternative for data integration in areas where it is difficult to perform surveys.