Predicting seasonal dynamics of UK mosquito vectors across urban and rural gradients

Understanding how urban and rural gradients alter mosquito temporal abundance in a way that is likely to cause outbreaks of mosquito-borne disease (e.g. West Nile virus) is central to predicting the impact and management of diseases. Environmental drivers (such as rainfall, temperature, evaporation, competition and predation) that vary along urban to rural gradients, affect development, fecundity, survival and therefore abundance of mosquitoes. Spatial variability in environmental drivers also affects abundance and phenology of wildlife and human hosts upon which the mosquitoes feed. Whilst spatial and land use gradients have direct effects on pathogen transmission rates, perhaps the most subtle and profound impacts are on vector species phenology and interactions with susceptible hosts. The temporal pattern of female-blood feeding mosquito abundance in relation to the availability of susceptible hosts is a key determinant of whether a mosquito-borne disease can establish and persist in wildlife, humans and domestic animals.

The aim of this project is to investigate the role of urban and rural gradients in driving seasonal and spatial variability in mosquito populations and overlap with hosts. The student will develop and analyse a system of state-dependent delayed differential equations in which environmental drivers affect life-history parameters and development lags focusing on UK mosquito species such as Culex pipiens and Culiseta annulata, which are potential vectors of disease. The model will be analysed by extensive mathematical and numerical simulation techniques using a suitable programming language on high performance computing clusters. In addition, the student will validate the models against new and existing temporal datasets using a broad range of statistical techniques. Finally, the student will derive and analyse a mosquito community model to investigate how host availability shapes the likelihood of disease across urban and rural gradients controlling for competition between mosquito species, predation and abiotic drivers.

Overall, the research will demonstrate the value of predictive models that help to understand the dynamics of mosquitoes and mosquito-borne diseases.