I am a mathematical modeller and data scientist interested in using data-driven approaches to develop models to help understand the world. I studied Mathematics at the University of Durham, completing an MMath degree in 2014. I then completed a Masters in Advanced Mathematical Biology at the University of York. As part of my thesis project, I spent a summer at the University of Glasgow, studying the patterns of drug resistance in E-coli. across various species in Tanzania. I am currently completing a PhD in social medicine at the University of Bristol, studying the deployment of the BCG vaccine in the UK. Previously, I worked as a data scientist at Funding Circle developing predictive models and conducting ad hoc analysis.
PhD in Social Medicine, 2019
University of Bristol
Msc in Advanced Mathematical Biology, 2015
University of York
MMath in Mathematics, 2014
University of Durham
This shiny app has been developed to allow the exploration of the parameter space of compartmental infectious disease models. It is designed to be used as a teaching aid when introducing people to the concepts behind infectious disease models without requiring them to interact with the underlying code. A number of infectious disease models are included, such as a Susceptible, Exposed, Infected, Recovered model. Source code available here.
Quickly and easily import analysis ready TB burden data, from the World Health Orgnaisation (WHO), into R. The aim of the package is to speed up access to high quality TB burden data, using a simple R interface. Generic plotting functions are provided to allow for rapid graphical exploration of the WHO TB data. This package is inspired by a blog post, which looked at WHO TB incidence rates. See here for the WHO data permissions.
A shiny app reproducing the models used in the Introduction to Tuberculosis modelling course practicals, run by TB MAC at the 2017 Union conference. See the TB MAC website for course materials and further resources. The models used in this course, and reproduced in this shiny app, were based on one published by Lin et al.. The source code is available on here
FC dashboard is a simple tool for exploratory data analysis on the funding circle loanbook. The source code can be found here. This is an independant project and is in no way associated with funding circle. Since the original development of this dashboard Funding Circle has stopped publishing it’s full loanbook, this means that some functionality will no longer work as originally intended and that data will no longer be updated.
The Pebble Game is a shiny application that simulates the pebble game. This is a simple game that has been developed by BIDD at the University of Bristol, to help a general audience understand the role of vaccination in preventing the onward transmission of infectious disease. The source code can be found here.