Releasing Wolbachia-infected Aedes aegypti to prevent the spread of dengue virus: A mathematical study

Infect Dis Model. 2020 Jan 7:5:142-160. doi: 10.1016/j.idm.2019.12.004. eCollection 2020.

Abstract

Wolbachia is a bacterium that is present in 60% of insects but it is not generally found in Aedes aegypti, the primary vector responsible for the transmission of dengue virus, Zika virus, and other human diseases caused by RNA viruses. Wolbachia has been shown to stop the growth of a variety of RNA viruses in Drosophila and in mosquitoes. Wolbachia-infected Ae. aegypti have both reproductive advantages and disadvantages over wild types. If Wolbachia-infected females are fertilized by either normal or infected males, the offspring are healthy and Wolbachia-positive. On the other hand, if Wolbachia-negative females are fertilized by Wolbachia-positive males, the offspring do not hatch. This phenomenon is called cytoplasmic incompatibility. Thus, Wolbachia-positive females have a reproductive advantage, and the Wolbachia is expanded in the population. On the other hand, Wolbachia-infected mosquitoes lay fewer eggs and generally have a shorter lifespan. In recent years, scientists have successfully released these Wolbachia-adapted mosquitoes into the wild in several countries and have achieved a high level of replacement with Wolbachia-positive mosquitoes. Here, we propose a minimal mathematical model to investigate the feasibility of such a release method. The model has five steady-states two of which are locally asymptotically stable. One of these stable steady-states has no Wolbachia-infected mosquitoes while for the other steady-state, all mosquitoes are infected with Wolbachia. We apply optimal control theory to find a release method that will drive the mosquito population close to the steady-state with only Wolbachia-infected mosquitoes in a two-year time period. Because some of the model parameters cannot be accurately measured or predicted, we also perform uncertainty and sensitivity analysis to quantify how variations in our model parameters affect our results.

Keywords: Bistability; Mathematical modeling; Optimal control; Sensitivity analysis; Steady-states; Uncertainty analysis.