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The project integrates mosquito/disease population models with socio-economic data to predict global changes of mosquito-borne disease risk.

Climate change is having a profound effect on infectious disease dynamics via impacts on wide-ranging abiotic and biotic drivers. For mosquito-borne diseases, where mosquito life-stages occupy different niches, drivers may affect components of the pathogen system 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, the likelihood of disease outbreaks and the subsequent impact on humans.

Despite the known complexities of climate change on vector-borne diseases, existing modelling frameworks often over-simplify the system, concentrating on particular components of the host-vector-pathogen triad and neglecting human mediated processes of intervention and spread. The key tenet of this project is to take a more holistic approach by integrating state-of-the-art mosquito population models with epidemiological models and socio-economic data to predict global changes of mosquito-borne disease risk, focussing on a range of Aedes-borne infections, which may include Dengue, Chikungunya and Zika viruses. Then, using human population projections, the student will aim to assess the future impact of disease burden, thus helping to inform disease mitigation policies.

This project will focus on modelling the population dynamics of the highly invasive mosquito species Aedes albopictus and Aedes aegypti, and their human diseases such as Dengue, Chikungunya and Zika. The modelling approach will be to use systems of environmentally driven stage-structured delayed differential equation models (see References & Further Reading) to predict current and future disease risk at a global scale. The models will be fitted and validated against a broad range of datasets and overlaid with data on human populations, tourism and trade pathways, and disease interventions. The models will be driven by an ensemble of climate change models to predict future risk. Using interactive visualisations, model outputs will be disseminated through key stakeholder and community engagement activities such as international epidemiology conferences and community events held by continental Public Health organisations such as ECDC, EFSA and CDC.

Project Timeline
Year 1
Derive and analyse a detailed mosquito-borne disease model, including novel expressions for R0. Detailed parameterisation of model components from literature, including statistical fitting. Coding the model to run on the NERC supercomputer and scenario testing.
Year 2
Global predictions of current disease risk. Model validation against spatial-temporal disease outbreaks.
Year 3
Predict global disease risk under climate change scenarios using ensemble climate models.
Year 3.5
Map future human population risk using human population growth models and overlay these with the disease transmission models and tourism and trade pathways, and disease interventions.

Training & Skills
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. Python, R, Matlab etc). A demonstrated interest in population ecology, population modelling and/or vector-borne disease ecology is desirable. Knowledge of statistical modelling would also be beneficial.

The student will receive training in a number of quantitative skills as well as ecological skills including mathematical modelling and analysis, high-level scientific computing on cluster environments, statistical model fitting, handling large datasets, mosquito biology, and disease vector ecology. The student will engage in the IAPETUS DTP training activities as well as transferable skills training provided by UKCEH and University of Glasgow. The student will be registered for a Postgraduate Certificate in Environmental Methods, which will recognize the transferable skills training, and carry out training in Entrepreneurship training through a Mini-MBA. IAPETUS Funded placements for work experience with an external organisation are also possible.

References & further reading
Brass, D.P., Cobbold, C.A., Ewing, D.A., Purse, B.V., Callaghan, A., & White, S.M. (2021). Phenotypic plasticity as a cause and consequence of population dynamics. Ecology Letters.

Ewing, D.A., Purse, B.V., Cobbold, C.A., & White, S.M. (2021). A novel approach for predicting risk of vector-borne disease establishment in marginal temperate environments under climate change: West Nile virus in the UK. Journal of the Royal Society Interface.

Ewing, D.A., Cobbold, C.A., Purse, B.V., Nunn, M.A., & White, S.M. (2016). Modelling the effect of temperature on the seasonal population dynamics of temperate mosquitoes. Journal of theoretical biology.

PhD position
UK Centre for Ecology & Hydrology / University of Glasgow
Wallingford / Glasgow
United Kingdom
Closing date
January 7th, 2022
Posted on
November 10th, 2021 10:22
Last updated
November 10th, 2021 10:22