Fighting mosquito-borne diseases with mathematical models, genetic analysis and machine learning

Malaria, dengue, Zika and other mosquito-borne diseases continue to pose a major global health burden through much of the world, despite the widespread distribution of insecticide-based tools and antimalarial drugs. Consequently, there is interest in novel strategies to control these diseases, including the release of mosquitoes transfected with Wolbachia and engineered with CRISPR-based gene drive systems. The safety and efficacy of these strategies, and considerations regarding field trial design, are critically dependent upon a detailed understanding of the distribution of mosquitoes and their movement between habitat patches at both fine and broad spatial scales. In this talk, I will outline the work of my research group in using mathematical models to inform the safe implementation of CRISPR-based gene drive strategies to control mosquito populations, and the use of Bayesian model fitting techniques to characterize these systems. I will also discuss two new research directions in our group: i) using machine learning to characterize mosquito habitat distribution, and ii) using genetic sequencing and likelihood-based kinship analysis to characterize the fine-scale movement patterns of mosquitoes of relevance to control.

Speaker: John Marshall