Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector Lutzomyia longipalpis in Sao Paulo and Bahia states, Brazil.

Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.

Predicted distribution of sand fly (Diptera: Psychodidae) species involved in the transmission of Leishmaniasis in São Paulo state, Brazil, utilizing maximum entropy ecological niche modeling.

Leishmaniasis is a public health problem worldwide. We aimed to predict ecological niche models (ENMs) for visceral (VL) and cutaneous (CL) leishmaniasis and the sand flies involved in the transmission of leishmaniasis in São Paulo, Brazil. Phlebotomine sand flies were collected between 1985 and 2015. ENMs were created for each sand fly species using Maximum Entropy Species Distribution Modeling software, and 20 climatic variables were determined. Nyssomyia intermedia (Lutz & Neiva, 1912) and Lutzomyia longipalpis (Lutz & Neiva, 1912), the primary vectors involved in CL and VL, displayed the highest suitability across the various regions, climates, and topographies. L. longipalpis was found in the border of Paraná an area currently free of VL. The variables with the greatest impact were temperature seasonality, precipitation, and altitude. Co-presence of multiple sand fly species was observed in the cuestas and coastal areas along the border of Paraná and in the western basalt areas along the border of Mato Grosso do Sul. Human CL and VL were found in 475 of 546 (86.7%) and 106 of 645 (16.4%) of municipalities, respectively. Niche overlap between N. intermedia and L. longipalpis was found with 9208 human cases of CL and 2952 cases of VL. ENMs demonstrated that each phlebotomine sand fly species has a unique geographic distribution pattern, and the occurrence of the primary vectors of CL and VL overlapped. These data can be used by public authorities to monitor the dispersion and expansion of CL and VL vectors in São Paulo state.

Environmental risk factors of leishmaniasis in Bahia State, Brazil using NASA Earth observation satellites / Fatores de risco ambientais para leishmanioses no estado da Bahia, Brasil, utilizando satélites da NASA de observação da Terra

NASA's Earth Observing Satellites (EOS) were used to calculate three vegetation indices, extract precipitation and elevation data, and then evaluate their applicability for assessing risk of visceral leishmaniasis (VL) and cutaneous leishmaniasis (CL) in Bahia State, Brazil. Regression models showed that either form of leishmaniasis can be predicted by NDVI, NDMI, NDWI data products and TRMM) precipitation data (R2= 0.370; p<0.001). Elevation was not significantly associated with the distribution of either VL or CL. In areas of high annual precipitation, CL was 3.6 times more likely to occur than VL. For vegetative moisture (NDMI), CL was 2.11 times more likely to occur than VL. Odds of CL occurrence increased to 5.5 times when vegetation (NDVI) and 13.5 times when liquid water content of vegetation canopies (NDWI) was considered. Areas at risk of CL and VL were mapped based on the selected explanatory variables. Accuracy of models were assessed using area under the receiver operating characteristic curve (AUC=0.72). We propose that statewide scale risk models based on use of EOS products will be a useful tool at 1 km2 spatial resolution to enable health workers to identify and target high risk areas to prevent transmission of leishmaniasis.(AU)

Os satélites de observação da Terra (SOT) da NASA foram usados para calcular três índices de vegetação, extrair dados de precipitação e elevação e avaliar sua aplicabilidade para identificar o risco para leishmaniose visceral (LV) e leishmaniose tegumentar (LT) no Estado da Bahia, Brasil. Modelos de regressão mostraram que ambas as formas de leishmaniose podem ser preditas pelos NDVI, NDMI, NDWI e precipitação TRMM (R2 = 0,370; p<0,001). A elevação não foi significativamente associada à distribuição de LV ou LT. Em áreas de alta precipitação anual, a LT foi 3,6 vezes mais provável de ocorrer do que a LV. Para a umidade vegetativa (NDMI), a LT apresentou 2,11 maior probabilidade de ocorrer do que a LV. As chances de ocorrência de LT aumentaram para 5,5 vezes em relação com a vegetação (NDVI) e 13,5 vezes quando o conteúdo de água líquida dos dosséis da vegetação (NDWI) foi considerado. Áreas em risco de LT e LV foram mapeadas com base nas variáveis explicativas selecionadas. A precisão dos modelos foi avaliada usando a área sob curva característica de operação do receptor (Curva COR=0,72). Propusemos que os modelos de risco em escala estadual baseados no uso de produtos SOT são uma ferramenta útil na resolução espacial de 1 km2 por permitir que profissionais de saúde identifiquem e direcionem áreas de alto risco para evitar a transmissão da leishmaniose. (AU)

Environmental risk factors of leishmaniasis in Bahia State, Brazil using NASA Earth observation satellites / Fatores de risco ambientais para leishmanioses no estado da Bahia, Brasil, utilizando satélites da NASA de observação da Terra

NASA’s Earth Observing Satellites (EOS) were used to calculate three vegetation indices, extract precipitation and elevation data, and then evaluate their applicability for assessing risk of visceral leishmaniasis (VL) and cutaneous leishmaniasis (CL) in Bahia State, Brazil. Regression models showed that either form of leishmaniasis can be predicted by NDVI, NDMI, NDWI data products and TRMM) precipitation data (R2 = 0.370; p<0.001). Elevation was not significantly associated with the distribution of either VL or CL. In areas of high annual precipitation, CL was 3.6 times more likely to occur than VL. For vegetative moisture (NDMI), CL was 2.11 times more likely to occur than VL. Odds of CL occurrence increased to 5.5 times when vegetation (NDVI) and 13.5 times when liquid water content of vegetation canopies (NDWI) was considered. Areas at risk of CL and VL were mapped based on the selected explanatory variables. Accuracy of models were assessed using area under the receiver operating characteristic curve (AUC=0.72). We propose that statewide scale risk models based on use of EOS products will be a useful tool at 1 km2 spatial resolution to enable health workers to identify and target high risk areas to prevent transmission of leishmaniasis.

Os satélites de observação da Terra (SOT) da NASA foram usados para calcular três índices de vegetação, extrair dados de precipitação e elevação e avaliar sua aplicabilidade para identificar o risco para leishmaniose visceral (LV) e leishmaniose tegumentar (LT) no Estado da Bahia, Brasil. Modelos de regressão mostraram que ambas as formas de leishmaniose podem ser preditas pelos NDVI, NDMI, NDWI e precipitação TRMM (R2 = 0,370; p<0,001). A elevação não foi significativamente associada à distribuição de LV ou LT. Em áreas de alta precipitação anual, a LT foi 3,6 vezes mais provável de ocorrer do que a LV. Para a umidade vegetativa (NDMI), a LT apresentou 2,11 maior probabilidade de ocorrer do que a LV. As chances de ocorrência de LT aumentaram para 5,5 vezes em relação com a vegetação (NDVI) e 13,5 vezes quando o conteúdo de água líquida dos dosséis da vegetação (NDWI) foi considerado. Áreas em risco de LT e LV foram mapeadas com base nas variáveis explicativas selecionadas. A precisão dos modelos foi avaliada usando a área sob curva característica de operação do receptor (Curva COR=0,72). Propusemos que os modelos de risco em escala estadual baseados no uso de produtos SOT são uma ferramenta útil na resolução espacial de 1 km2 por permitir que profissionais de saúde identifiquem e direcionem áreas de alto risco para evitar a transmissão da leishmaniose.