Validation of a remote sensing model to identify Simulium damnosum s.l. breeding sites in Sub-Saharan Africa

Abstract

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.

Figure 1. Spectrally decomposed signature of the S. damnosum s.l. larval riverine habitat pixel.

The signature was extracted from 0.61 m² QuickBird satellite data, as described in the text. The figure depicts the wave band color reflectance ratio (i.e. pixel digital number) of the extracted spectral signature. Colors correspond to the bandwidths indicated in the figure.

Figure 2. Northern Uganda study site.

The map presents data collected by the Ugandan Ministry of Health on the prevalence of onchocerciasis in Uganda. The red circle indicates the location of the Achwa River basin examined in this project.

Figure 3. Ground verification of predicted S. damnosum s.l. larval habitats.

Panel A: Location of sites surveyed. Locations are presented as the Euclidian distance downstream from the start point of the survey (Lat 445861.2662, Lon 1224410.647). Red circles indicate locations predicted to be breeding sites by the model, while blue squares represent locations of other likely larval sites identified by the survey team as described in the text. Panel B: Larval counts from each survey point. Red bars represent larval counts from sites predicted by the model to represent larval habitats and blue bars (all zero) represent larval counts at the other sites surveyed.

Figure 4. Comparison of habitats predicted by the wet rock and dry rock/elevation change models.

Potential S. damnosum s.l. breeding habitats were predicted from images collected from the Sarakawa river basin using the BRR model (small black dots) or using a the model based upon the signature for wet or dry Precambrian rock and a sufficient elevation change to support fast flowing water if present (large red dots).

Figure 5. Predicted S. damnosum s.l. larval riverine sites in the Achwa River basin.

Larval habitats were predicted using the BRR model as described in the text.

Figure 6. S. damnosum s.l. aquatic habitat not predicted by the BRR model.

The photo illustrates the hanging vegetation immersed in fast flowing water characteristic of the two breeding sites in the Achwa River not predicted by the BRR model.