The transmission dynamics and impacts of SARS-CoV-2

This research is a collection of projects related to understanding how SARS-CoV-2 and other infectious diseases spread, and the wider implications of such spread. The focus is on developing and applying modern statistical techniques that allow us to improve our understanding of these processes, better leverage the data that we have, and respond to threats from infectious diseases in a more effective manner.

Specific objectives of current projects include: developing methods that allow us to better leverage data collected in epidemic surveys (such as REACT-1 and ONS), modelling how policy decisions may impact the probability of eliminating COVID-19 in low incidence scenarios, integrating wastewater data into epidemiological models to improve parameter estimation, reconstructing early epidemic trees to better understand how epidemics start, and developing new methods of estimating global orphanhood due to COVID-19 deaths. These projects all rely on advances in statistical methodology and computational resources.

The research is tied together both by a considerable amount of shared methodology and the context that it operates in. The majority of this methodology is centred in computational statistics (for example, sequential monte carlo methods), but also includes influences from survey statistics and information theory, for example.

Novelty has already been demonstrated in these projects. For example, our epidemic survey methods appear to be significantly faster, producing results in minutes where existing methods take hours. Time savings like this allow for better data exploration and unlock the more possibilities for real-time analysis, which is crucial when managing outbreaks of infectious disease. The novelty of this project is increased by the flexibility of the developed methods. This has unlocked the ability to estimate new epidemiological quantities from epidemic survey data, such as the implied proportion of a population that is susceptible to disease.

There is also a strong focus in ensuring the work is accessible from a policy perspective. The methods answer questions that policymakers charged with managing an infectious disease outbreak may ask. The results from these methods are typically given in terms of interpretable quantities, such as the now well-known reproduction number. This means the results are directly relevant to the kinds of decision-making scenarios that a policymaker may face.

This project falls within the EPSRC statistics and applied probability research area. It also has ties with analytical science and mathematical biology.

The primary CDT impact will be training 75 PhD graduates as the next generation of leaders in statistics and statistical machine learning. These graduates will lead in industry, government, health care, and academic research. They will bridge the gap between academia and industry, resulting in significant knowledge transfer to both established and start-up companies. Because this cohort will also learn to mentor other researchers, the CDT will ultimately address a UK-wide skills gap. The students will also be crucial in keeping the UK at the forefront of methodological research in statistics and machine learning.
After graduating, students will act as multipliers, educating others in advanced methodology throughout their career. There are a range of further impacts:
- The CDT has a large number of high calibre external partners in government, health care, industry and science. These partnerships will catalyse immediate knowledge transfer, bringing cutting edge methodology to a large number of areas. Knowledge transfer will also be achieved through internships/placements of our students with users of statistics and machine learning.
- Our Women in Mathematics and Statistics summer programme is aimed at students who could go on to apply for a PhD. This programme will inspire the next generation of statisticians and also provide excellent leadership training for the CDT students.
- The students will develop new methodology and theory in the domains of statistics and statistical machine learning. It will be relevant research, addressing the key questions behind real world problems. The research will be published in the best possible statistics journals and machine learning conferences and will be made available online. To maximize reproducibility and replicability, source code and replication files will be made available as open source software or, when relevant to an industrial collaboration, held as a patent or software copyright.