Newly developed ?gene drive? systems provide a means of introducing and spreading genes through natural populations. The potential application of gene drive system towards the control of mosquito-borne diseases, including malaria, has stimulated a great deal of debate. Evaluation of how these genetic elements will behave in real ecosystems, however, lack information. Models describing the predicted spread of gene-drive systems have been presented but are limited by a paucity of information describing certain aspects of mosquito biology, especially estimates of dispersal rates and population sizes. Furthermore, no models have been developed in the context of actual field sites. In this proposal, we aim to develop and evaluate population genomic-based estimates of dispersal rates and population size and utilize these to describe real field sites at multiple time points over a range of African habitats. We will employ state-of-the-art remote sensing technology and analytic methods to measure environmental parameters fluctuating seasonally as well as inter-annually. Biological and environmental parameter estimates obtained through these methods will be incorporated into mathematical models aimed at describing and comparing the performance of the three leading gene drive-based strategies for malaria control in Africa. Study areas were selected for evaluating (1) candidate sites for confined trials (oceanic islands-The Comoros and ecological islands-Cameroon) and (2) a large scale deployment of GMM strategies in a complex environment (Cameroon). Overall, we aim to (i) develop improved methods to measure key aspects of mosquito biology, (ii) characterize environmental factors that affect these traits, (iii) use these data to produce relevant mathematical models, and (iv) utilize these models to compare the performance of three leading GMM strategies for controlling malaria in Africa.
Malaria in Africa is not expected to be eliminated with currently-available tools, consequently novel control strategies such as the use of genetically modified mosquitoes (GMM) are being considered. As progress is being made in the development of GMM, there is an urgent need to study how these systems will behave in real ecosystems. In this context, we propose to evaluate leading GMM strategies using novel malaria vector population dynamic data and mathematical models.
