Leading on from the work of The Alan Turing Institute on the NHS COVID-19 app, the Institute is collaborating with the Royal Statistical Society (RSS) to conduct independent statistical modelling research projects directly relevant to the Joint Biosecurity Centre’s (JBC) remit, and to co-ordinate and strengthen modelling work across the DHSC Test and Trace programme. The alliance between the RSS and JBC will complement the existing expertise and insights already supporting and informing the government’s response to COVID-19.
Explaining the science
The Institute and RSS will provide deeper, independent insight and analysis of NHS Test and Trace data by setting up a new Statistical Modelling Laboratory to better forecast and model scenarios to grant the JBC even deeper understanding of how the virus is spreading across the country. Statistical modelling helps data scientists try to predict what the virus might do next, based on what is understood about it already.
This extra support for statistical modelling will be brought together with the data science and public health expertise of the JBC to support COVID-19 decision-making at local and national levels.
In order to achieve these outcomes, the collaboration will build upon existing modelling work within the Test and Trace programme to undertake two new activities:
- Establish a community of modellers across Test and Trace
- Establish a Statistical Modelling and Machine Learning Laboratory (SML)
The SML will focus on co-produced projects, initially to develop and validate statistical methodology for UK-wide fine-scale, near-real-time spatial nowcasting of COVID-19 incidence and prevalence. Each subsequent project will draw on the UK’s expertise in data science through open call.
The Turing-RSS SML will publish research focused on areas of national priority, including statistical methodologies, both as research papers and blog posts, and in the form of open source computer code, in line with a commitment to open science and transparency.