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Modelling the Effects of Climate Change on Mosquito-Borne Diseases


UK CEH

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Dr Steven White , Dr S Pawar , Dr Lauren Cator , Prof C Cobbold No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

Climate change is having a profound effect on biotic and abiotic drivers of disease spread. However, predicting how individual diseases respond to climate change is challenging and requires new predictive frameworks. For mosquito-borne diseases, where mosquito life-stages occupy different niches, drivers may affect components of the pathosystem in complex and opposing ways. For example, changes in the patterns of seasonal rainfall and temperature can affect variable life-history traits such as development, survival, fecundity and disease transmission. These traits may combine to give rise to sharp changes of species abundance and transmission dynamics, especially under climate change extremes, which impacts on our ability to predict mosquito population dynamics and the likelihood of disease outbreaks and subsequent impact of mitigation measures.

The aim of this project is to investigate the role of climate change in driving seasonal variability in mosquito populations and their diseases. The student will develop and analyse a system of state-dependent delayed differential equations in which environmental drivers affect life-history traits. The model will be analysed by extensive mathematical and numerical simulation techniques using a suitable programming language. The student will extend the mosquito model to include disease transmission to investigate how climate change will shape the likelihood of disease outbreaks in future climates. In addition, the student will validate the models against new and existing trait and spatial-temporal datasets provided by the VectorByte Research Coordination Network.

This project will provide the candidate with training in state-of-the-art mathematical modelling and simulation techniques. The student will have the opportunity to engage with the VectorByte multidisciplinary network, taking advantage of the data resources for parameterising models. Further, this studentship will receive all the training benefits provided by the Imperial College SSCP DTP.

The student will be based at the UK Centre for Ecology & Hydrology in Wallingford, Oxfordshire, supervised by Dr Steven White (UKCEH), Dr Samraat Pawar (Imperial College London, Silwood Park), Prof Christina Cobbold (University of Glasgow) and Dr Lauren Cator (Imperial College London, Silwood Park). The degree will be awarded by Imperial College London and is part of the SSCP DTP.

The successful candidate will have a strong background in mathematics, statistics, theoretical physics or quantitative ecology. In addition, the candidate will have demonstrated substantial knowledge of mathematical or population modelling, and simulation techniques for solving models in a suitable scientific programming language (e.g. R, Matlab, Julia etc). A demonstrated interest in climate change, population ecology, population modelling and/or vector-borne disease ecology is desirable. Knowledge of statistical modelling would also be beneficial.

To apply, please send your CV and cover letter to Dr Steven White ([Email Address Removed]). Informal enquiries are welcomed prior to application.

Funding Notes

Please visit the SSCP DTP website for further funding and eligibility information (https://www.imperial.ac.uk/grantham/education/science-and-solutions-for-a-changing-planet-dtp/).


References

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. https://doi.org/10.1016/j.jtbi.2016.04.008

Cator LJ, Johnson LR, Mordecai EA, El Moustaid F, Smallwood TR, LaDeau SL, Johansson MA, Hudson PJ, Boots M, Thomas MB, Power A. The role of vector trait variation in vector-borne disease dynamics. Frontiers in Ecology and Evolution. 2020;8:189. https://doi.org/10.3389/fevo.2020.00189
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