Spatio-Temporal Modelling of COVID Prevalence Scenarios.

Lead Research Organisation: Lancaster University
Department Name: Mathematics and Statistics


Since early 2020, the UK has been battling the SARS-CoV-2 (Covid19) pandemic. It has impacted the lives of every one of us, and no region of the UK has been able to avoid the virus. Ultimately, it is not an exaggeration to say the Covid 19 pandemic has rewritten much of the rulebook on successful epidemic modelling.
As the Covid 19 pandemic progresses, it is inevitable that we will experience waves and troughs of infections as the prevalence of the virus changes. In the future, the UK will likely reach an endemic state. This is after infection rates decline because of greater proportions of the population gaining resistance from both vaccine programs and surviving infections. Eventually the UK will experience low but sustained infection rates. Currently we are not equipped with the tools to provide meaningful UK wide surveillance in the aforementioned low prevalence, "Endgame", scenario.
The primary goal of the PhD project is to fill this gap in Spatio-Temporal statistical epidemiologic modelling, and to develop a meaningful method providing Covid 19 prevalence modelling across the UK as we progress into the Covid 19 Endgame. As the state of the SARS-CoV-2 pandemic is fast moving at the best of times, it is realistic to expect the project to adapt to future concerns identified by the Office of National Statistics, this project's sponsor, as the need arises.
The PhD project can be extended to address some open challenges the UK faces, such as Optimal Allocation of Randomised Survey Resources to Minimise Prevalence Uncertainty and extending current methods for Early Detection of Areas at Risk of High Prevalence Increase (hotspot detection). Altogether, the project hopes to help fill in the missing pages in successful epidemic modelling as the country at large looks to a future beyond Covid 19.

In partnership with the Office for National Statistics.

Planned Impact

This proposal will benefit (i) the UK economy and society, (ii) our industrial partners, (iii) the wider community of non-academic employers of doctoral graduates in STOR, (iv) the scientific disciplines of statistics and operational research and associated academic communities, (v) UK doctoral students in STOR, and (vi) the CDT students themselves.

Below we outline how each of these communities will realise these benefits:

(i) The UK economy will gain a competitive edge through a significant increase in the supply and diversity of doctoral STOR professionals with the skills required to undertake influential, responsible and impactful research, and who have been trained to become future leaders. Our goal is that our future alumni who enter industry assume leading roles in realising the major impact that STOR can make in achieving effective data driven decision-making. Our existing alumni are already starting to achieve this. A wider societal benefit will accrue from research contributions to EPSRC Prosperity Outcomes, e.g. to the UK being a Productive and Resilient Nation.

(ii) Our industrial partners will particularly benefit from the skills supply identified in item (i), as likely employers of STOR-i graduates. They will further benefit from teaming with a community of leading edge STOR researchers in the solution of substantive industrial challenges. Mechanisms for the latter include doctoral projects co-supervised with industry, industrial internships, engagement in research clusters and industrial problem-solving days. Our training programme will give students the skills they need to ensure that research is conducted responsibly and that outcomes are successfully communicated to beneficiaries. The value that our industrial partners place on working with STOR-i can be seen through the pledged cash support of £1.7M.

(iii) A wider benefit will accrue from the employment of STOR-i graduates, equipped as described in items (i) and (ii), across non-partner public and private sector organisations. The breadth and depth of training provided by the CDT will enable students to quickly make a difference in these organisations, using their research skills to affect significant change.

(iv) The STOR academic community will benefit from methodological advances and from the increase and diversity in the supply of STOR researchers who value, and have experience of, collaborative research. Our alumni will be leaders in 21st Century Statistics with a strong culture of, and training in, reproducible research and a focus on achieving impact with excellence. Our recruitment strategy will further benefit this community in achieving a healthier supply of high-quality doctoral candidates from diverse backgrounds. Our research internship programme gives top mathematically able individuals from across the UK an experience of STOR research and has been shown to increase applications for STOR PhD programmes across the UK.

(v) Elements of the STOR-i programme will benefit the wider community of UK doctoral students in STOR. Using financial support from our industrial partners, we will continue our National Associate Scheme. This will provide up to 50 UK STOR doctoral students with funding and access to elements of STOR-i's training programme. An annual conference will provide opportunities for learning, networking and sharing research progress to members of the scheme.

(vi) STOR-i students will benefit from a personalised programme that will support each individual in fully achieving their research leadership potential, whether in academia or industry. Students will be given the tools and opportunities to develop research and broader skills that will enable them to achieve maximum scientific impact for their work. Our current alumni provide strong evidence that these future graduates will be extremely employable.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022252/1 30/09/2019 30/03/2028
2440302 Studentship EP/S022252/1 30/09/2020 31/10/2024 Jordan Hood