We’ve all done it. We’ve ventured out to the shops, seen a group of young people gathered somewhere (without appropriate social distance) and thought – what if the virus spread amongst each other – and then back to their (what like look more vulnerable) families?
And what if that was a group of students in September?
One of the things I highlighted in the long piece on SAGE and higher education was the relative (to the US) lack of academic work on the potential impacts of the “return to campus” of students in September.
A working paper from a Professor at Swansea University has emerged aiming to fill part of that gap – and its executive summary has quite an arresting opening bullet point:
Without strong controls, the return to universities would cause a minimum of 50,000 deaths.
And that’s a “rough but optimistic calculation”.
Alan Dix is Director of the Computational Foundry at Swansea, and what he’s done here is tried to model two things raised in that SAGE paperwork – reducing the spread within campuses, and limiting spread into wider society – treating the university as a large self-quarantine unit.
Of course, in an ideal world we would have modelling carried out by epidemiologists rather than computer scientists – but as yet very little has appeared in a UK context.
To get a handle on it all Dix tries to estimate the impact of where much of the sector is at right now – unrestrained campus re-opening (ie socially distanced, but not “bubbled”) and discusses (in informal terms) these two flows of infection, both within the student body and to the wider population.
On “bubbles” – groups of students housed and taught together that in theory contain infection and help efforts to track and trace once cases are discovered – he looks at groups of 10, 100, and 1000 to see what the impacts would be.
Then for interaction with the local community, he takes a punt at using the “normal” population level of contact. That’s not ideal, but as he says:
This could be an overestimate of cross-infection (especially for self-contained out-of-town campus universities) as students are largely interacting with one another, or might be an under-estimate (especially for city or multi-campus universities) as students will need to travel a lot on public transport between halls and campuses, eat out, and maybe be more “risky” in terms of night-life, etc.
He also notes potential “locality effect” that universities and local Directors of Public Health might need to consider. His modelling has to assume perfect mixing outside campus – where everyone in the population is equally likely to infect any other person. But in reality there are strong geographic effects – he notes for example that 100,000 university students in Manchester is 20%, in Aberystwyth students are a third of the term time population, and in St Andrews students outnumber the rest of the population of the town.
There’s plenty in the paper on the models used – but to cut a long story short, Dix concludes that to be effective, bubbles have to be small (approx a dozen) and very strictly maintained – probably more strictly maintained than is possible once you think about it for more than a few minutes.
He calculates that even a bubble size of 10 would increase overall population R by 10-20%, and if bubbles “leak” into wider groups of 50-100, this leads to larger scale outbreaks.
Essentially, a Covid-19 freshers’ flu-like surge is coming – and suggests that bespoke, on-campus testing and track and trace will need to be established very rapidly, and that minimising student to non-student transmission is also critical to protect communities. How viable the former is and how realistic the latter is remains to be seen – or, indeed, modelled.
And even all of that assumes rapid spread amongst students is OK, and doesn’t take into account spread from students to university staff.