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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.
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