The widespread deployment of vector control measures, such as long-lasting insecticidal nets (LLIN), has resulted in significant declines in the global burden of malaria. Yet, these strategies alone are insufficient to interrupt transmission and sustain gains. Thus, innovations in vector control are urgently needed. Larval source management (LSM) is the modification of potential breeding habitats to prevent immature mosquitos from developing into adults, often through the application of larvicides. LSM is, however, resource intensive and thereby only recommended in select environments. New methods of efficiently locating and targeting Anopheles breeding sites must be developed to make LSM feasible in a wider variety of settings. Geographic profiling (GP) was originally developed as an analytical tool in criminology, using the locations of linked crimes to narrow the search area for likely suspects. GP has been successfully adopted to a number of biological problems, but in the case of malaria, it has only been applied to a single retrospective data set. Nevertheless, the model was able to efficiently identify mosquito breeding sites based on the location of clinical cases. My long-term career goal is to become an independent investigator with expertise in applied epidemiology and spatial analysis in order to advance our understanding of the geographic factors that influence malaria transmission and target interventions in the most effective manner. However, to achieve my career goals and scientific objectives, I need additional mentorship and training in: (i) geographic information science (GISc), (ii) entomological surveillance, and (iii) molecular epidemiology, all specifically in the context of malaria control. I will draw upon this training and leverage the resources of my mentors to achieve my scientific objective, which is to evaluate the effectiveness of a Bayesian GP model to identify Anopheles breeding sites in a low transmission, highland area of Western Uganda. My central hypothesis is that GP will facilitate the identification of Anopheles breeding sites without the need for large scale field operations. I will test this hypothesis by pursuing three specific research aims: (1) establish the accuracy and efficiency of GP to identify Anopheles breeding sites in comparison to field-based larval surveillance; (2) demonstrate the benefit of adding the spatial distribution of adult mosquito densities to the GP model; and (3) evaluate the effect of using a novel, high-throughput sequencing method to exclude imported malaria cases to improve the accuracy of the GP model. I am well positioned to achieve these aims given the vast institutional resources available to me through the University of North Carolina at Chapel Hill and the London School of Hygiene and Tropical Medicine, along with an internationally-renowned team of mentors and advisors, including Dr. Jonathan Juliano, Dr. Sarah Staedke, and Dr. Steven Le Comber, who pioneered the use of GP to epidemiological problems. The proposed research is innovative and significant, as the results will represent the first prospective use of GP to identify mosquito breeding sites, and could make LSM more cost-effective in sub-Saharan Africa.
Currently employed vector control strategies, including long-lasting insecticidal nets (LLIN) and indoor residual spraying (IRS), can substantially reduce the burden of malaria, but are generally insufficient to achieve and/or sustain malaria elimination. Larval source management (LSM), defined as the treatment of aquatic habitats that are breeding sites for mosquitos, has been a mainstay of historical malaria control programs, but this approach requires sustained field operations that are resource-intensive and currently limit its employment in sub-Saharan Africa. This study seeks to evaluate the use of geographic profiling, a novel spatial analysis tool derived from criminology, to more efficiently identify Anopheles larval habitats, thus making LSM more affordable and feasible in a wider range of settings.
