Moara de Santana Martins Rodgers, Maria Emilia Bavia, Bernard Eichold II, Claire Shipman, N. Owen, H. Winstanley, Margaret Gordon, M. Karapetyan, Marta Mariana Nascimento Silva, Deborah Daniela Madureira Trabuco Carneiro, Luciana Lobato Cardim, Elivelton Da Silva Fonseca, John Brooks Malone
{"title":"利用美国宇航局地球观测卫星分析巴西巴伊亚州利什曼病的环境风险因素","authors":"Moara de Santana Martins Rodgers, Maria Emilia Bavia, Bernard Eichold II, Claire Shipman, N. Owen, H. Winstanley, Margaret Gordon, M. Karapetyan, Marta Mariana Nascimento Silva, Deborah Daniela Madureira Trabuco Carneiro, Luciana Lobato Cardim, Elivelton Da Silva Fonseca, John Brooks Malone","doi":"10.53393/rial.2019.v78.35848","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":86209,"journal":{"name":"Revista do Instituto Adolfo Lutz","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Environmental risk factors of leishmaniasis in Bahia State, Brazil using NASA Earth observation satellites\",\"authors\":\"Moara de Santana Martins Rodgers, Maria Emilia Bavia, Bernard Eichold II, Claire Shipman, N. Owen, H. Winstanley, Margaret Gordon, M. Karapetyan, Marta Mariana Nascimento Silva, Deborah Daniela Madureira Trabuco Carneiro, Luciana Lobato Cardim, Elivelton Da Silva Fonseca, John Brooks Malone\",\"doi\":\"10.53393/rial.2019.v78.35848\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":86209,\"journal\":{\"name\":\"Revista do Instituto Adolfo Lutz\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revista do Instituto Adolfo Lutz\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53393/rial.2019.v78.35848\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista do Instituto Adolfo Lutz","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53393/rial.2019.v78.35848","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Environmental risk factors of leishmaniasis in Bahia State, Brazil using NASA Earth observation satellites
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.