I am a mathematical modeller interested in developing and fitting models for infectious disease, using data driven approaches. 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 Tanzanaia. I am currently completing a PhD in social medicine at the University of Bristol, studying the deployment of the BCG vaccine in the UK. I am also currently working part time as a data scientist at Funding Circle developing dashboards for predictive models.
PhD in Social Medicine, 2018
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
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.
Wed, Oct 11, 2017, 48th Union World Conference on Lung Health
Thu, Aug 3, 2017, Bristol infectious disease dynamics modelling workshop
Fri, Mar 24, 2017, Research and Applied Epidemiology Scientific Conference 2017
Wed, Mar 23, 2016, Research and Applied Epidemiology Scientific Conference 2016