The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

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. Further reading: Townroe S, Callaghan A. Morphological and fecundity traits of Culex mosquitoes caught in gravid traps in urban and rural Berkshire, UK. Bulletin of entomological research. 2015 Oct 1;105(05):615-20. Ewing DA, Cobbold CA, Purse BV, Nunn MA, White SM. Modelling the effect of temperature on the seasonal population dynamics of temperate mosquitoes. Journal of theoretical biology. 2016 Jul 7;400:65-79. Rosà R, Marini G, Bolzoni L, Neteler M, Metz M, Delucchi L, Chadwick EA, Balbo L, Mosca A, Giacobini M, Bertolotti L. Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy. Parasites & vectors. 2014 Jun 12;7(1):269.
Steven White
Amanda Callaghan
Bethan Purse, CEH Wallingford, beth@ceh.ac.uk; Christina Cobbold, School of Mathematics & Statistics, University of Glasgow, cc@maths.gla.ac.uk
Development of mathematical theory, Computing, Quantitative data analysis, Ecological observations / data collection
Steven White
Mathematical modelling of mosquito population dynamics. DDE model simulation using solver algorithms on a high performance computer cluster. Using statistical modelling to fit mathematical models to data.
The student will develop novel mathematical models for predicting mosquito abundance. Unlike most mechanistic models, the student will validate their models against large ecological datasets.
The project will develop ecological theory for the population dynamics of mosquito populations, in particular understand how environmental gradients shape patterns of abundance.
Management of mosquito populations is difficult to deliver, this is in part due to not fully understanding the drivers of dynamics and patterns of abundance. This project aims to reduce this uncertainty and therefore aid control strategies.
Correlative approaches are mainly used to investigate seasonal patterns of temperate mosquito abundance, thus limiting their use to the training dataset. As such, correlative approaches fail to understand the underlying mechanisms. The project will set new standards by delivering novel mechanistic models, underpinned by data, which are inherently more useful for scenario testing.
This project marries together mathematical and statistical modelling, big data, epidemiology and mosquito biology.
Community ecology, Population ecology, Ecological/Evolutionary tools, technology & methods
Training in ecological and epidemiological theory relevant for mosquito population dynamics, mathematical and simulation modelling using high performance computing clusters, statistical analysis of large datasets and experimental design for simulation experiments. There will be opportunities to interact and visit EU partners to obtain data and visit field sites.
CEH Wallingford, Universities of Reading and Glasgow. There will also be an opportunity to train with EU partners at Fondazione Edmund Mach (Prof Annapaola Rizzoli).
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