| Summary: | Leishmaniasis is a public health problem in Brazil, and despite the control programs in place, Bahia remains one of the states with the highest incidence rates of cutaneous leishmaniasis (CL) and visceral leishmaniasis (VL). This study proposed to develop and evaluate the applicability of ecological niche models (ENM) for leishmaniasis, to determine the influence of environmental and socioeconomic factors on the incidence of these diseases at three geographic scales: national, statewide and community. Water availability, garbage collection, precipitation and temperature were the most important variables in predicting areas suitable for VL and CL in the country. The extensive geography of Brazil and the coarse scale of the data used to evaluate both socioeconomic and environmental variables revealed the need of a more refined scale to define the role of these factors in risk area identification. At the statewide scale, the models were developed for Bahia state and data on vector occurrence was added to the analysis. Three environment structural indices were evaluated in addition to the environmental variables explored in the national model. Water content of vegetation was a very strong predictor of CL and VL incidence followed by NDVI. The sand fly species found in Bahia were sensitive to variations in temperature and rainfall related variables. The occurrence of Lutzomyia longipalpis, the vector of VL in the state, was most influenced by precipitation and vegetation. The district of Monte Gordo, in Bahia, was selected for development of a community level ENM using high resolution WorldView-2 imagery. CDC light traps were used to collect sand flies for a period of three months. Sand flies were tested by polymerase chain reaction (PCR) to determine host feeding preferences and natural infection by Leishmania spp. The sand flies preferably fed on chickens and humans. No natural Leishmania spp. infections were detected. NDVI was the most influencing factor in the ENM model (99.4% contribution). Implementation of a multi-scale geospatial surveillance and risk modeling capability to monitor disease incidence and their vectors, with the addition of molecular analysis, into the actions of the control program can help reduce the impact of endemic leishmaniasis in Bahia.
|
|---|
