Onchocerciasis is a debilitating disease caused by infection with the parasite Onchocerca volvulus. The public health importance of onchocerciasis has been recognized by the international community, which has sponsored several programs whose goal (depending on the program) is to control onchocerciasis eradicate the infection entirely. All current onchocerciasis control programs rely upon mass treatment of at risk populations with ivermectin. Onchocerciasis control is a long term process, requiring several years of treatment of the at-risk population. Thus, carefully identifying the at-risk population is critically important for the efficient allocation of the limited resources available to the onchocerciasis control programs. However, onchocerciasis is endemic in some of the most inaccessible areas of the world. In such areas, it is difficult or impossible to conduct thorough ground based epidemiological surveys to precisely delineate the at-risk population. We believe that it will be possible to use remote sensing data to develop a spatially based ecological model to predict areas endemic for onchocerciasis. To accomplish this overall goal, we propose the following specific aims: 1. To use existing data collected by the former Onchocerciasis Control Program of West Africa in conjunction with remote sensing data to develop a predictive model for the breeding sites utilized by the vector for the parasite. 2. To validate the model developed using pre-existing data provided by the existing onchocerciasis control programs active throughout sub-Saharan Africa. 3. To predict the location of potential onchocerciasis endemic areas using the models developed in Specific Aims1 and 2, and to confirm the model predictions by ground-based entomological and epidemiologic al studies. 4. To measure the mean movement of the vector from the breeding sites, thereby delineating how far from a breeding site the risk of infection to the human population may extend.
|Jacob, Benjamin G; Novak, Robert J; Toe, Laurent D et al. (2013) Validation of a remote sensing model to identify Simulium damnosum s.l. breeding sites in Sub-Saharan Africa. PLoS Negl Trop Dis 7:e2342|