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Background: Recently, most onchocerciasis control programs have begun to focus on elimination. Developing an effective elimination strategy relies upon accurately mapping the extent of endemic foci. In areas of Africa that suffer from a lack of infrastructure and/or political instability, developing such accurate maps has been difficult. Onchocerciasis foci are localized near breeding sites for the black fly vectors of the infection. The goal of this study was to conduct ground validation studies to evaluate the sensitivity and specificity of a remote sensing model developed to predict S. damnosum s.l. breeding sites. Methodology/Principal Findings: Remote sensing images from Togo were analyzed to identify areas containing signature characteristics of S. damnosum s.l. breeding habitat. All 30 sites with the spectral signature were found to contain S. damnosum larvae, while 0/52 other sites judged as likely to contain larvae were found to contain larvae. The model was then used to predict breeding sites in Northern Uganda. This area is hyper-endemic for onchocerciasis, but political instability had precluded mass distribution of ivermectin until 2009. Ground validation revealed that 23/25 sites with the signature contained S. damnosum larvae, while 8/10 sites examined lacking the signature were larvae free. Sites predicted to have larvae contained significantly more larvae than those that lacked the signature. Conclusions/Significance: This study suggests that a signature extracted from remote sensing images may be used to predict the location of S. damnosum s.l. breeding sites with a high degree of accuracy. This method should be of assistance in predicting communities at risk for onchocerciasis in areas of Africa where ground-based epidemiological surveys are difficult to implement. Author Summary: Onchocerciasis, or river blindness, represents a major cause of socio-economic disruption throughout much of Africa. The discovery that Mectizan (ivermectin) was effective in treating onchocerciasis, together with the decision of Merck Sharpe and Dohme to donate this drug for the treatment of this disease, revolutionized onchocerciasis control efforts. But until recently, it was thought that ivermectin alone was not sufficient to eliminate this scourge. However, recent studies have suggested that long-term ivermectin treatment can eliminate onchocerciasis in some foci in Africa. This has revolutionized the thinking of the international community, turning its attention from an emphasis on control to an emphasis on elimination. For an elimination program to be successful, it is necessary to accurately map all at-risk communities. In the case of onchocerciasis, this is commonly done by epidemiological surveys. Here we report the validation of a remote sensing method to identify areas endemic for onchocerciasis. This method is based upon predicting the sites where the black fly vector for the parasite breeds. The method was found to be very accurate at predicting black fly breeding sites in West Africa and in northwest Uganda. The method should be useful in assisting in mapping at-risk communities in areas of Africa where ground-based surveys are difficult or impossible to implement. [ABSTRACT FROM AUTHOR]

Background: Recently, most onchocerciasis control programs have begun to focus on elimination. Developing an effective elimination strategy relies upon accurately mapping the extent of endemic foci. In areas of Africa that suffer from a lack of infrastructure and/or political instability, developing such accurate maps has been difficult. Onchocerciasis foci are localized near breeding sites for the black fly vectors of the infection. The goal of this study was to conduct ground validation studies to evaluate the sensitivity and specificity of a remote sensing model developed to predict S. damnosum s.l. breeding sites. Methodology/Principal Findings: Remote sensing images from Togo were analyzed to identify areas containing signature characteristics of S. damnosum s.l. breeding habitat. All 30 sites with the spectral signature were found to contain S. damnosum larvae, while 0/52 other sites judged as likely to contain larvae were found to contain larvae. The model was then used to predict breeding sites in Northern Uganda. This area is hyper-endemic for onchocerciasis, but political instability had precluded mass distribution of ivermectin until 2009. Ground validation revealed that 23/25 sites with the signature contained S. damnosum larvae, while 8/10 sites examined lacking the signature were larvae free. Sites predicted to have larvae contained significantly more larvae than those that lacked the signature. Conclusions/Significance: This study suggests that a signature extracted from remote sensing images may be used to predict the location of S. damnosum s.l. breeding sites with a high degree of accuracy. This method should be of assistance in predicting communities at risk for onchocerciasis in areas of Africa where ground-based epidemiological surveys are difficult to implement. [ABSTRACT FROM AUTHOR]