Using predictive models to optimize Wolbachia-based strategies for vector-borne disease control

Abstract

The development of resistance to insecticides by vector arthropods, the evolution of resistance to chemotherapeutic agents by parasites and the lack of clinical cures or vaccines for many diseases has stimulated a high-profile effort to develop vector-borne disease control strategies based on release of genetically-modified mosquitoes. Because transgenic insects are likely to be less fit than their wild-type counterparts, transgenic traits must be actively driven into the population in spite of fitness costs (population replacement). Wolbachia are maternally-inherited symbionts that are associated with numerous alterations in host reproductive biology. By a variety of mechanisms, Wolbachia-infected females have a reproductive advantage relative to uninfected females, allowing infection to spread rapidly through host populations to high frequency in spite of fitness costs. In theory, Wolbachia can be exploited to drive costly transgenes into vector populations for disease control. Before conducting an actual release, it is important to be able to predict how released Wolbachia infections are expected to behave. While inferences can be made by observing the dynamics of naturally-occurring infections, there is no ideal way to empirically test the efficacy ofa Wolbachia gene driver under field conditions prior to the first actual release. Mathematical models are a powerful way to predict the outcomes of transgenic insect releases and allow one to identify knowledge gaps, identify parameters that are critical to the success of releases, conduct risk-assessment analysis and investigate worst-case scenarios, and ultimately identify the most effective, most logistically feasible control method or methods. In this chapter, I review current and historical advances in applied models of Wolbachia spread, specifically within the context of applied population replacement strategies for vector-borne disease control.