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About me
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Contributed to recommendations for improving contact tracing
A software package designed to simulate sophisticated branching process models of epidemics, with various contact tracing processes attempting to prevent the spread of the virus
I was interviewed as part of a piece explaining the usefulness of lateral flow tests for non-scientific audiences
A comment piece in the lancet, regarding media focus on false negatives without discussion of failing to control transmission.
Published in Infectious Disease Modelling, 2020
Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions.
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Published in Philosophical Transactions of the Royal Society B, 2021
Early results from our contact tracing analyses from early in the SARS-CoV-2 pandemic. Published as part of a special issue containing the evidence base used to support the UK goverments response to the SARS-CoV-2 pandemic.
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Published in (under review at Nature), available on Arxiv, 2021
We apply several cutting-edge unsupervised machine learning techniques to four large and diverse datasets of SARS-CoV-2 positive cases. We explore differences in symptomatic presentation across different ages, and find key differences in the symptomatic presentation of cases at the extremes of age across all datasets.\nPaper submitted to Nature.
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Talk on contact tracing modelling, from early in the Pandemic.
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A talk on inferring the relationship between viral load and infectiousness using contact tracing data, with applications to evaluating tests for infection control.
Undergraduate course, University of Manchester, Department of Mathematics, 2020
I was a graduate teaching assistant in Random Models - a second year course in statistics/probability for undergraduates in the Department of Mathematics. My role was to assist students one-to-one in weekly problems classes, where they would attempt material from the course. Random models is a course covering basic stochastic processes, such as: random walks, branching processes and renewal equations.
Undergraduate Course, University of Manchester, Department of Mathematics, 2021
I provided assistance to undergraduates taking their first scientific programming course during exercise labs. Duties included: clarifying and explaining Python code, diagnosing bugs/errors, providing advice on how to approach solving problems, advising on levels of comments and documentation, reviewing code testing practices.