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Transmission of all infectious diseases is characterised by variability in the number of onward infections caused by different individuals. This variability can be driven by biological and behavioural factors – a combination of differences in the dynamics of viral shedding, timing of symptoms and contact patterns. Over the last year, a wealth of data has been collected describing small outbreaks, contact tracing, and large outbreaks. By the time the studentship starts, the volume of this data will have increased even further. However, there are few analyses which bring unifying insights to these datasets. This project will consist of developing ways to extract and store these diverse datasets, including a dataset containing 100,000s of contact tracing events with testing information for symptomatic contacts. Using this information, this project will develop methods to describe and characterise data and then aim to analyse the dynamics of spread and estimate key parameters, such as the generation time of new variants. 

This internship will be focused on the topic outlined above, but there are also likely to be opportunities to be involved in modelling and data analyses which are responsive to current policy demands, through this research group’s involvement in JUNIPER and membership of the UK government’s COVID-19 modelling committee, SPI-M.

We expect that this project will take about 8 weeks to complete.

SELECTION CRITERIA 

The project would suit a student with a background in quantitative studies – eg. physics, mathematics, computing                        

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