|UK Students, EU Students, International Students
|£18,622 for 2023-24
|23rd November 2023
|22nd February 2024
School of Mathematical Sciences
A 3.5 year PhD studentship, beginning in October 2024, is available to work under the supervision of Dr Kirsty Bolton and Prof Phil O’Neill on “Investigating the role of immunity, behaviour and climate in driving seasonal influenza dynamics”.
About the project
Patterns of influenza seasonality are likely driven by a combination of seasonal patterns in human behaviour, meteorological influences on viral survival, and the evolution of cellular and antibody-mediated immune responses following infection and vaccination. In temperate regions these effects have (at least pre-SARS-CoV-2) combined to yield annual winter outbreaks. Patterns of influenza disease in tropical and sub-tropical regions are often biannual or exhibit more complex patterns. Developing influenza transmission models that can explain transmission patterns across a range of geographies may help disentangle the role of immunity, behaviour and climate to improve forecasting and the timing of vaccination campaigns against influenza.
Often seasonal influenza is modelled using ODEs that capture seasonally forced multi-strain SEIRS infection dynamics and demographic turnover, with strain coupling determined by assumptions about co-infection and cross-strain immunity. However, statistical inference is challenging for such complex mechanistic models, even in temperate regions with good surveillance data, and is not well explored for non-temperate regions.
In this project we will explore the potential to use Approximate Bayesian Computation (ABC) to identify the parameters of seasonal influenza transmission models in a variety of geographical regions, using commonly available microbiological and epidemiological surveillance data. We will first consider how details of the model structure and ABC implementation influence the posterior estimation and its computational cost, informing the utility of such modelling to guide public health policy in different settings. Extensions may include exploring the degree to which enhanced data is required to address questions such as:
This project lies at the intersection of applied mathematics and statistics. We are looking for an applicant with excellent analytic and critical thinking skills, particularly as relevant for statistical analysis, inference, and modelling of epidemic processes, with capability to interpret and communicate results. Ability to code in a scientific programming language (e.g. C/C++/python/R) is also required. Previous research experience and/or knowledge of epidemic modelling are desirable.
Funding covers a stipend at the RCUK rate (£18,622 for 2023-24) and fees at the level of a UK domestic student.
For further information about postgraduate study in mathematics, please see: https://www.nottingham.ac.uk/pgstudy/course/research/mathematics-phd, https://www.nottingham.ac.uk/mathematics/study/research/index.aspx
Applicants must be resident in the UK and have a 1st class Mathematics degree, preferably at the level of an MSc/MMATH, or international equivalent. Graduates of another quantitative discipline (e.g. Physics, Data Science, Engineering, Astronomy) may also be considered if they have a strong interest in the project.
Applications to be made via the central University of Nottingham admission process (NottinghamHub, https://www.nottingham.ac.uk/pgstudy/how-to-apply/how-to-apply.aspx).
Informal enquiries may be addressed to Dr Kirsty Bolton (Kirsty.email@example.com).
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