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Jessica Talbott

My undergraduate degree was in Mathematics at the University of Portsmouth. It had a large element of application in its structure but was nevertheless built upon a foundation of pure mathematics. We worked with bespoke computer software such as Maple to derive and simulate a physical occurrence on a computer and used Matlab, initially to solve non-trivial problems using matrices and then to produce in depth coding for iterative methods of inference. My final year project was ‘Investigating Exoplant Potential To Sustain Life,’ involved the collaboration with staff at the Clanfield observatory; research led to design a Maple user interface to simulate Exoplanet positions around a given star.

My first postgraduate degree in Mathematical Medicine and Biology at The University of Nottingham enabled me to focus on my interest in biological research using mathematics. The course placed great emphasis on the theory behind biological modelling, which allowed me to fully understand academic literature and to assist in the recreation of results. The study of algebraic derivation of components used in biological modelling helped us to explain and justify simulation output to our biologist peers. My dissertation ‘Dendritic Morphology and Its Effects on Functionality’ led to the study of key approaches in the field in order to recreate results using a combination of pure mathematics and programming.

My Masters degree in Complexity Science here at The University of gave me a firm appreciation of the importance of multidiscipline research. During the course I undertook modules in Networks, Self-Organisation and Emergence, Statistical Mechanics and Computational Methods for Complex Systems, exposing me to a wide range of modelling techniques.

As part of the research element of this course I carried out two research projects, both resulting is a collation of work in the form of a journal in the style of their respective field. For the first project; ‘Cell Crawling and Interaction’, I constructed a simple model that captured basic principles of cell movement, interaction and division. Using a software package I recreated observations recorded by my biologist colleagues in a model whereby cell motion and interaction was simulated in discretized two-dimensional space. Scaling the likelihood of events over time with independent probabilities, I was able to determine the qualitative and quantitative behaviour captured in experimental data.

For my second project I had the opportunity to work with school absenteeism data collected during the height of the H4N1 virus in 2009. The strain was known to predominately affect 5-14 year olds so it was thought building a model using this data would have predictive power for the peaking of influenza cases in the wider community, opening up the potential for similar methods to aid policy. I developed a model where the number of absent children due to illness on any given half day is assumed to be Poisson distributed with a mean given by an underlying deterministic equation. Following this stochastic method of fitting data we were able to use a simple SIR model framework to trace progression. Using detailed regression analysis I was able to identify parameters that had statistically significant roles in the model and how they correlated with other variables, in turn this allowed me to identify areas and sections of the cohort most affected by illness. The project motivated my keen interest in epidemiology and is the area of research I now wish to pursue.