Robust control of the invasive Asian tiger mosquito

Invasive mosquitoes can have devastating impacts on ecosystems and human health. The Asian tiger mosquito, Aedes albopictus, is the most invasive mosquito in the world having shifted its range due to increased global trade. This mosquito is an epidemiologically important vector for numerous viral pathogens, including yellow fever, Zika, dengue and Chikungunya. Mosquito breeding locations are closely associated with human habitat and daytime biting activity means that this is a serious pest for many communities.

Understanding how the environment drives seasonal patterns in adult Ae. albopictus abundance is important for developing strategies to control densities to non-harmful levels. It has been shown that spatial and environmental heterogeneity are crucial in defining population seasonality and abundance as well as life-history traits. As such, management programs to reduce the potential for disease outbreak need to account for these multiple, interacting environmental factors, but, currently, it is not clear how this should be effectively achieved.

Since there are no known vaccines or drugs against many vector-borne diseases carried by Ae. albopictus, vector control remains as the cornerstone disease prevention. Conventional methods for controlling this species (e.g. chemical insecticides, larval habitat removal), have largely failed, possibly because the dynamics of the system are poorly understood. Novel biological and genetic methods are currently being explored, but have yet to be deployed in the field. By modelling the vector and control system, improvements in existing control strategies and guidance for future control strategies will be achieved.

The aim of this project is threefold: to model the population dynamics of Ae. albopictus using delay-differential equation techniques coupled with SIR-type disease models; to use the coupled model to predict potential outbreaks of mosquito-borne diseases; and to use these models to inform development of optimal and robust disease management strategies, based upon adaptive management and adaptive control approaches. The population models will be fitted to datasets from a range of spatially and environmentally distinct locations to build robustness into our predictions, as well as to understand the significance of the environmental drivers leading to variability in population abundance and to incorporate the context of alternative management practices. The management approach will lead to strategies that mitigate against this underlying uncertainty; adaptive management learning systems can tolerate system variability, while adaptive controls perform with minimal information therefore are robust to uncertainty and disturbances.

In this project the PhD student will be provided with training in mathematical modelling, mosquito ecology, vector-borne disease epidemiology, statistical model fitting, adaptive management, optimal and robust control theory and scientific programming. An exciting aspect of this project will be to visit Prof Annapaola Rizzoli and her group at the Research and Innovation Centre, Fondazione Edmund Mach, Italy. Here, the student will gain experience on high resolution Ae. albopictus spatiotemporal data, existing statistical approaches to modelling vector dynamics as well as visiting catch sites and interacting with stakeholders in disease/vector management.

The successful candidate will have a strong background in mathematics, statistics, theoretical physics or theoretical ecology. In addition, the candidate will have demonstrated substantial knowledge of numerical analysis techniques for solving differential equations in a suitable scientific programming language (e.g. Fortran, Matlab etc). Training will be given in mosquito laboratory and field work. A demonstrated interest in population ecology, population modelling, control theory and/or vector-borne disease ecology is desirable.

This project will be based at the Centre for Ecology & Hydrology (CEH) Wallingford in Oxfordshire, UK under the supervision of Dr Steven White and will be co-supervised by Prof Stuart Townley (University of Exeter), Dr Markus Muller (University of Exeter), Dr Beth Purse (CEH) and Dr Christina Cobbold (University of Glasgow).

Applicants for a studentship must have obtained, or be about to obtain, a 2.1 degree or higher. If you have a 2.2 degree, but have also obtained a masters qualification, you are also eligible. Substantial relevant post-graduate experience may also be sufficient, please contact the supervisors for more information.

To apply please send your CV and a covering letter stating your suitability for the project to the project supervisor, Dr S. White ([Email Address Removed]).