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  Statistical inference for epidemics models


   School of Mathematics

  ,  Applications accepted all year round  Funded PhD Project (UK Students Only)

About the Project

Mathematical models have been established as an important tool for capturing the features that drive the spread of a disease, predicting the progression of an epidemic and guiding the development of effective control strategies to prevent potential outbreaks. However, fitting mathematical models to data from infectious disease outbreaks poses significant challenges due to high-dimensional missing data, unobserved infections, and the difficulty in observing who infected whom. The aim of this project is to address these challenges through the development of novel Bayesian methods for partially observed epidemic models, a critical step in advancing our understanding of and response to epidemics.

The student involved in this project will receive extensive training in mathematical modelling and statistical computing, including Bayesian techniques, such as Monte Carlo methods, and their application in the field of epidemiology. They will have the opportunity for close collaboration with researchers from both the School of Mathematics and the Institute of Microbiology and Infection, gaining valuable experience in interdisciplinary work. The skills that they will develop are highly valuable in both academia and industry.

Please get in touch for more information!

Mathematics (25) Medicine (26)

Funding Notes

For UK candidates: A funded scholarship is available for an excellent UK candidate with a stipend at standard rates for 3-3.5 years.
For non-UK candidates: Strong self-funded applicants will be considered. Exceptionally strong candidates in this category may be awarded a tuition fee waiver (for up to 3 years) in competition with all other PhD applications.
The application procedure and the deadlines for scholarship applications are advertised on the University of Birmingham PhD pages.

References

[1] Touloupou, P., Finkenstädt, B., Besser, T. E., French, N. P., & Spencer, S. E. (2020). Bayesian inference for multistrain epidemics with application to Escherichia Coli O157: H7 in feedlot cattle. The Annals of Applied Statistics, 14(4), 1925-1944.
[2] Seymour, R. G., Kypraios, T., & O’Neill, P. D. (2022). Bayesian nonparametric inference for heterogeneously mixing infectious disease models. Proceedings of the National Academy of Sciences, 119(10), e2118425119.

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