More or Less: will 50,000 people really die if the universities reopen?

Last Wednesday morning I had mail from a colleague to say that my paper on student bubble modelling had just been mentioned on Radio 4 ‘More or Less’ [BBC1].    This was because UCU (the University and Colleges Union) had tweeted the headline figure of 50,000 deaths from my paper “Impact of a small number of large bubbles on Covid-19 transmission within universities” [Dx1] after it had been reviewed by Jim Dickinson on Wonkhe [DW].  The issue is continuing to run: on Friday a SAGE report [SAGE] was published also highlighting the need for vigilance around University reopening and a Today interview with Dame Anne Johnson this morning [BBC2], who warned of “a ‘critical moment’ in the coronavirus pandemic, as students prepare to return to universities.

I’m very happy that these issues are being discussed widely; that is the most important thing.   Unfortunately I was never contacted by the programme before transmission, so I am writing this to fill in details and correct misunderstandings.

I should first note that the 50,000 figure was a conditional one:

without strong controls, the return to universities would cause a minimum of 50,000 deaths

The SAGE report [SAGE] avoids putting any sort of estimate on the impact.  I can understand why! Like climate change one of the clear lessons of the Covid crisis is how difficult it is to frame arguments involving  uncertainty and ranges of outcomes in ways that allow meaningful discussion but also avoid ‘Swiss cheese’ counter-arguments that seek the one set of options that all together might give rise to a wildly unlikely outcome.  Elsewhere I’ve written about some of the psychological reasons and human biases that make it hard to think clearly about such issues [Dx2].

The figure of 50,000 deaths at first appears sensationalist, but in fact the reason I used this as a headline figure was precisely because it was on the lower end of many scenarios where attempts to control spread between students fail.  This was explicitly a ‘best case worst case’ estimate: that is worst case for containment within campus and best case for everything else – emphasising the need for action to ensure that the former does not happen.

Do I really believe this figure?  Well in reality, of course, if there are major campus outbreaks local lockdowns or campus quarantine would come into place before the full level of community infection took hold.  If this reaction is fast enough this would limit wider community impact, although we would never know how much as many of the knock-on infections would be untraceable to the original cause. It is conditional – we can do things ahead of time to prevent it, or later to ameliorate the worst impacts.

However, it is a robust figure in terms of order of magnitude.  In a different blog I used minimal figures for small university outbreaks (5% of students) combined with lower end winter population R and this still gives to tens of thousands of knock-on community infections for every university [Dx3].

More or less?

Returning to “More or Less”, Dr Kit Yates, who was interviewed for the programme, quite rightly examined the assumptions behind the figure, exactly what I would would do myself.  However, I would imagine he had to do so quite quickly and so in the interview there was confusion between (i) the particular scenario that gives rise the the 50,000 figure and the general assumptions of the paper as a whole and (ii) the sensitivity of the figure to the particular values of various parameters in the scenario.

The last of these, the sensitivity, is most critical: some parameters make little difference to the eventual result and others make a huge difference.  Dr Yates suggested that some of the values (each of which have low sensitivity) could be on the high side but also one (the most sensitive) that is low.   If you adjust for all of these factors the community deaths figure ends up near 100,000 (see below).  As I noted, the 50,000 figure was towards the lower end of potential scenarios.

The modelling in my paper deliberately uses a wide range of values for various parameters reflecting uncertainty and the need to avoid reliance on particular assumptions about these.  It also uses three different modelling approaches, one mathematical and two computational in order to increase reliability.  That is, the aim is to minimise the sensitivity to particular assumptions by basing results on overall patterns in a variety of potential scenarios and modelling techniques.

The detailed models need some mathematical knowledge, but the calculations behind the 50,000 figure are straightforward:

Total mortality = number of students infected
                  x  knock-on growth factor due to general population R
                  x  general population mortality

So if you wish it is easy to plug in different estimates for each of these values and see for yourself how this impacts the final figure.  To calculate the ‘knock-on growth factor due to general population R’, see “More than R – how we underestimate the impact of Covid-19 infection” [Dx4], which explains the formula (R/(1-R)) and how it comes about.

The programme discussed several assumptions in the above calculation:

  1. Rate of growth within campus: R=3 and 3.5 days inter-infection period. –  These are not assumptions of the modelling paper as a whole, which only assumes rapid spread within student bubbles and no direct spread between bubbles.  However, these are the values used in the scenario that gives rise to the 50,000 figure, because they seemed the best accepted estimate at the time.  However, the calculations only depend on these being high enough to cause widespread outbreak across the student population.  Using more conservative figures of (student) R=2 and 5-6 day inter-infection period, which I believe Dr Yates would be happy with, still means all susceptible students get infected before the end of a term  The recent SAGE report [SAGE] describes models that have peak infection in November, consonant with these values. (see also addendum 2)
  2. Proportion of students infected. –  Again this is not an assumption but instead a consequence of the overall modelling in the paper.  My own initial expectation was that student outbreaks would limit at 60-70%, the herd immunity level, but it was only as the models ran that it became apparent that cross infections out to the wider population and then back ‘reseeded’ student growth because of clumpy social relationships.  However, this is only apparent at a more detailed reading, so it was not unreasonable for More or Less to think that this figure should be smaller.  Indeed in the later blog about the issue [Dx3] I use a very conservative 5% figure for student infections, but with a realistic winter population R and get a similar overall total.
  3. General population mortality rate of 1%. – In early days data for this ranged between 1% and 5% in different countries depending, it was believed, on the resilience of their health service and other factors. I chose the lowest figure.  However, recently there has been some discussion about whether the mortality figure is falling [MOH,LP,BPG].  Explanations include temporary effects (younger demographics of infections, summer conditions) and some that could be long term (better treatment, better testing, viral mutation).  This is still very speculative with suggestions this could now be closer to 07% or (very, very speculative) even around 0.5%.  Note too that in my calculations this is about the general population, not the student body itself where mortality is assumed to be negligible.
  4. General population R=0.7. – This is a very low figure as if the rest of society is in full lockdown and only the universities open. It is the ‘best case’ part of the ‘best case worst case’ scenario. The Academy of Medical Science report “Coronavirus: preparing for challenges this winter” in July [AMS] suggests winter figures of R=1.2 (low) 1.5 (mid) and 1.8 (high). In the modelling, which was done before this report, I used a range of R values between 0.7 and 3; that is including the current best estimates.  The modelling suggested that the worst effects in terms of excess deaths due to universities occurred for R in the low ‘ones’ that is precisely the expected winter figures.

In summary, let’s look at how the above affects the 50,000 figure:

  • 1.  Rate of growth within campus – The calculation is not sensitive to this and hence not affected at all.
  • 2 and 3.  Proportion of students infected and general population mortality rate – These have a linear effect on the final calculation (some sensitivity).  If we take a reduction of 0.7 for each (using the very speculative rather than the very, very speculative figure for reduced mortality), this halves the estimated impact.
  • 4. General population R. This an exponential factor and hence the final result is very sensitive to this. It was unreasonably low, but reasonable figures tend to lead to frighteningly high impacts.  So let’s still use a very conservative figure of 0.9 (light lockdown), which multiplies the total by just under 4 (9/2.3).

The overall result of this is 100,000 rather than 50,000 deaths.

In the end you can play with the figures, and, unless you pull all of the estimates to their lowest credible figure, you will get results that are in the same range or a lot higher.

If you are the sort of person who bets on an accumulator at the Grand National, then maybe you are happy to assume everything will be the best possible outcome.

Personally, I am not a betting man.

 

Addendum 1: Key factors in assessing modelling assumptions and sensitivity

More or Less was absolutely right to question assumptions, but this is just one of a number of issues that are all critical to consider when assessing mathematical or computational modelling:

  • assumptions – values, processes, etc, implicitly or explicitly taken as given
  • sensitivity – how reliant a particular result is on the values used to create it
  • scenarios – particular sets of values that give rise to a result
  • purpose – what you are trying to achieve

I’ve mentioned the first three of these in the discussion above. However, understanding the purpose of a model is also critical particularly when so many factors are uncertain.  Sometimes a prediction has to be very accurate, for example the time when a Mars exploration rocket ‘missed’ because of a very small error in calculations.

For the work described here my own purpose was: (i) to assess how effective student bubbles need to be, a comparative judgement and (ii) to assess whether it matters or not, that is an order of magnitude judgement.    The 50K figure was part of (ii).  If this figure had been in the 10s or 100s even it could be seen to be fairly minor compared with the overall Covid picture, but 10,000, 50,000 or 100,000 are all bad enough to be worth worrying about.  For this purpose fine details are not important, but being broadly robust is.

 

Addendum 2:  Early Covid growth in the UK

The scenario used to calculate the 50K figure used the precise values of R=3 and a 3.5 day inter-infection period, which means that cases can increase by 10 times each week..  As noted the results are not sensitive to these figures and much smaller values still lead the the same overall answer.

The main reason for using this scenario is that it felt relatively conservative to assume that students post lockdown might have rates similar to overall population before awareness of Covid precautions – they would be more careful in terms of their overall hygiene, but would also have the higher risk social situations associated with being a student.

I was a little surprised therefore that, on ‘More or Less’, Kit Yates suggested that this was an unreasonably high figure because the week-on-week growth had never been more than 5 times.  I did wonder whether I had misremembered the 10x figure, from the early days of the crisis unfolding in February and March.

In fact, having rechecked the figures, they are as I remember.  I’ll refer to the data and graphs on the Wikipedia page for UK Covid data.  These use the official UK government data, but are visualised better than on Gov.UK.

UK Cases:  https://en.wikipedia.org/wiki/COVID-19_pandemic_in_the_United_Kingdom#New_cases_by_week_reported

I’m focusing on the early days of both sets of data.  Note that both new cases and deaths ‘lag’ behind actual infections, hence the peaks after lockdown had been imposed. New cases at that point typically meant people showing serious enough symptoms to be admitted to hospital, so lags from infection by say a week or more. Deaths lag by around 2-3 weeks (indeed not included after 28 days to avoid over-counting).

The two data sets are quite similar during the first month or so of the crisis as at that point testing was only being done for very severe cases that were being identified as potential Covid. So, Iet’s just look at the death figures (most reliable) in detail for the first few weeks until the lockdown kicks in and the numbers peek.

week deaths growth (rounded)
29 Feb — 6 March 1
7–13 March 8 x8
14–20 March 181 x22
21–27 March 978 x5
28 March — 3 April 3346 x3.5
4–10 April 6295 x2

Note how there is an initial very fast growth, followed by pre-lockdown slowing as people became aware of the virus and started to take additional voluntary precautions, and then peeking due to lockdown.  The numbers for initial fast phase are small, but this pattern reflects the early stages in Wuhan with initial, doubling approximately every two days before the public became aware of the virus, followed by slow down to around 3 day doubling followed by lockdown.

Indeed in the early stages of the pandemic it was common to see country-vs-country graphs of early growth with straight lines for 2 and 3 day doubling drawn on log-log axes. Countries varied on where they started on this graph, but typically lay between the two lines.  The UK effectively started at the higher end and rapidly dropped to the lower one, before more dramatic reduction post-lockdown.

It may be that Kit recalled the x5 figure (3 day doubling) is it was the figure once the case numbers became larger and hence more reliable.  However, there is also an additional reason, which I think might be why early growth was often underestimated.  In some of the first countries infected outside China their initial growth rate was closer to the 3 day doubling line. However this was before community infection and when cases were driven by international travellers from China.  These early international growths reflected post-public-precautions, but pre-lockdown growth rates in China, not community transmission within the relevant countries.

This last point is educated guesswork, and the only reason I am aware of it is because early on a colleague asked me to look at data as he thought China might be underreporting cases due to the drop in growth rate there.  The international figures were the way it was possible to confirm the overall growth figures in China were reasonably accurate.

References

[AMS] Preparing for a challenging winter 2020-21. The Academy of Medical Sciences. 14th July 2020. https://acmedsci.ac.uk/policy/policy-projects/coronavirus-preparing-for-challenges-this-winter

[BBC1] Schools and coronavirus, test and trace, maths and reality. More or Less, BBC Radio 4. 2nd September 2020.  https://www.bbc.co.uk/programmes/m000m5j9

[BBC2] Coronavirus: ‘Critical moment’ as students return to university.  BBC News.  5 September 2020.  https://www.bbc.co.uk/news/uk-54040421

[BPG] Are we underestimating seroprevalence of SARS-CoV-2? Burgess Stephen, Ponsford Mark J, Gill Dipender. BMJ 2020; 370 :m3364  https://www.bmj.com/content/370/bmj.m3364

[DW] Could higher education ruin the UK’s Christmas?  Jim Dickinson on Wonkhe.  4 Sept 2020.  https://wonkhe.com/blogs/could-higher-education-ruin-the-uks-christmas/

[Dx1] Working paper: Covid-19 – Impact of a small number of large bubbles on University return. Working Paper, Alan Dix. created 10 July 2020. arXiv:2008.08147 stable version at arXiv |additional information

[Dx2] Why pandemics and climate change are hard to understand, and can we help?  Alan Dix. North Lab Talks, 22nd April 2020 and Why It Matters, 30 April 2020.  http://alandix.com/academic/talks/Covid-April-2020/

[Dx3] Covid-19, the impact of university return.  Alan Dix. 9th August 2020. https://alandix.com/blog/2020/08/09/covid-19-the-impact-of-university-return/

[Dx4] More than R – how we underestimate the impact of Covid-19 infection. Alan Dix.  2nd August 2020. https://alandix.com/blog/2020/08/02/more-than-r-how-we-underestimate-the-impact-of-covid-19-infection/

[LP] Why are US coronavirus deaths going down as covid-19 cases soar? Michael Le Page. New Scientist.  14 July 2020. https://www.newscientist.com/article/2248813-why-are-us-coronavirus-deaths-going-down-as-covid-19-cases-soar/

[MOH] Declining death rate from COVID-19 in hospitals in England
Mahon J, Oke J, Heneghan C.. The Centre for Evidence-Based Medicine. June 24, 2020. https://www.cebm.net/covid-19/declining-death-rate-from-covid-19-in-hospitals-in-england/

[SAGEPrinciples for managing SARS-CoV-2 transmission associated with higher education, 3 September 2020.  Task and Finish Group on Higher Education/Further Education. Scientific Advisory Group for Emergencies. 4 September 2020. https://www.gov.uk/government/publications/principles-for-managing-sars-cov-2-transmission-associated-with-higher-education-3-september-2020

 

How much does herd immunity help?

I was asked in a recent email about the potential contribution of (partial) herd immunity to controlling Covid-19.  This seemed a question that many may be asking, so here is the original question and my reply (expanded slightly).

We know that the virus burns itself out if R remains < 1.

There are 2 processes that reduce R, both operating simultaneously:

1) Containment which limits the spread of the virus.

2) Inoculation due to infection which builds herd immunity.

Why do we never hear of the second process, even though we know that both processes act together? What would your estimate be of the relative contribution of each process to reduction of R at the current state of the pandemic in Wales?

One of the UK government’s early options was (2) developing herd immunity1.  That is you let the disease play out until enough people have had it.
For Covid the natural (raw) R number is about 3 without additional voluntary or mandated measures (depends on lots of factors).   However, over time as people build immunity, some of those 3 people who would have been infected already have been.  Once about 2/3 of the community are immune the effective R number drops below 1.  That corresponds to a herd immunity level (in the UK) of about 60-70% of the population having been infected.  Of course, we do not yet know how long this immunity will last, but let’s be optimistic and assume it does.
The reason this policy was (happily) dropped in the UK was the realisation that this would need about 40 million people to catch the virus, with about 4% of these needing intensive care.  That is many, many times the normal ICU capacity, leading to (on the optimistic side) around half a million deaths, but if the health service broke under the strain many times that number!
In Spain (with one of the larger per capita outbreaks) they ran an extensive antibody testing study (that is randomly testing a large number of people whether or not they had had any clear symptoms), and found only about 5% of people showed signs of having had the virus overall, with Madrid closer to 10%.  In the UK estimates are of a similar average level (but without as good data), rising to maybe as high as 17% in London.
Nationally these figures (~5%) do make it slightly easier to control, but this is far below the reduction needed for relatively unrestricted living (as possible in New Zealand, which chose a near eradication strategy)   In London the higher level may help a little more (if it proves to offer long-term protection).  However, it is still well away from the levels needed for normal day-to-day life without still being very careful (masks, social distancing, limited social gatherings), however it does offer just a little ‘headroom’ for flexibility.  In Wales the average level is not far from the UK average, albeit higher in the hardest hit areas, so again well away from anything that would make a substantial difference.
So, as you see it is not that (2) is ignored, but, until we have an artificial vaccine to boost immunity levels, relying on herd immunity is a very high risk or high cost strategy.  Even as part of a mixed strategy, it is a fairly small effect as yet.
In the UK and Wales, to obtain even partial herd immunity we would need an outbreak ten times as large as we saw in the Spring, not a scenario I would like to contemplate 🙁
This said there are two caveats that could make things (a little) easier going forward:
1)  The figures above are largely averages, so there could be sub-communities that do get to a higher level.  By definition, the communities that have been hardest hit are those with factors (crowded accommodation, high-risk jobs, etc.) that amplify spread, so it could be that these sub-groups, whilst not getting to full herd-immunity levels, do see closer to population spread rates in future hence contributing to a lower average spread rate across society as a whole.  We would still be a long way from herd immunity, but slower spread makes test, track and trace easier, reduces local demand on health service, etc.
2)  The (relatively) low rates of spread in Africa have led to speculation (still very tentative) that there may be some levels of natural immunity from those exposed to high levels of similar viruses in the past.  However, this is still very speculative and does not seem to accord with experience from other areas of the world (e.g. Brazilian favelas), so it looks as though this is at most part of a more complex picture.
I wouldn’t hold my breath for (1) or (2), but it may be that as things develop we do see different strategies in different parts of the world depending on local conditions of housing, climate, social relationships, etc.

Update

Having written the above, I’ve just heard about the following that came out end of last week in BMJ, which suggests that there could be a significant number of mild cases that are not
detected on standard blood test as having been infected.
Burgess StephenPonsford Mark JGill DipenderAre we underestimating seroprevalence of SARS-CoV-2? https://www.bmj.com/content/370/bmj.m3364
  1. I should say the UK government now say that herd immunity was never part of their planning, but for a while they kept using the term! Here’s a BBC article about the way herd immunity influenced early UK decisions, a Guardian report that summarises some of the government documents that reveal this strategy, and a Politco article that reports on the Chief Scientific Adviser Patrick Vallance ‘s statement that he never really meant this was part of government planning.  His actual words on 12th March were “Our aim is not to stop everyone getting it, you can’t do that. And it’s not desirable, because you want to get some immunity in the population. We need to have immunity to protect ourselves from this in the future.”  Feel free to decide for yourself what ‘desirable‘ might have meant.[back]

Covid-19, the impact of university return

For many reasons, it is important for universities to re-open in the autumn, but it is also clear that this is a high-risk endeavour: bringing around 2% of the UK population together in close proximity for 10 to 12 weeks and then re-dispersing them at Christmas.

When I first estimated the actual size of the impact I was, to be honest, shocked; it was a turning point for me. With an academic hat on I can play with the numbers as an intellectual exercise, but we are talking about many, many thousands of lives at risk, the vast majority outside the university itself, with the communities around universities most at risk.

I have tried to think of easy, gentle and diplomatic ways of expressing this, but there are none; we seem in danger of creating killing zones around our places of learning.

At the very best, outbreaks will be detected early, and instead of massive deaths we will see substantial lockdowns in many university cities across the UK with the corresponding social and economic costs, which will create schisms between ‘town and gown’ that may poison civic relationships for years to come.

In the early months of the year many of us in the university sector watched with horror as we watched the Covid-19 numbers rising and could see where this would end. The eventual first ‘wave’ and its devastating death toll did not need sophisticated modelling to predict; in the intervening months it has played out precisely as expected. At that point the political will was clearly set and time was short; there was little we could do but shake our heads in despair and feel the pain of seeing our predictions become reality as the numbers grew, each number a person, each person a community.

Across the sector, many are worried about the implications of the return of students and staff in the autumn, but structurally the nature of the HE sector in the UK makes it near impossible even for individual universities to take sufficient steps to mitigate it, let alone individual academics.

Doing the sums

For some time, universities across the UK have been preparing for the re-opening, working out ways to reduce the risk. There has been a mathematical modelling working group trying to assess the impact of various measures, as well as much activity at individual institutions.  It appears too that SAGE has highlighted that universities pose a potential risk [SN], but this seems to have gone cold and universities are coping as best they can with apparently no national plan. Universities UK have issued guidance to universities on what to do as they emerge from lockdown [UUKa], but it does not include an estimate of the scale of the problem.

As I said, the turning point for me came when I realised just how bad this could be. As with the early national growth pattern, it does not require complex mathematics to assess, within rough ranges, the potential impact; and even the most conservative estimates are terrifying.

We know from freshers’ flu that infections spread quickly amongst the student community.  The social life is precisely why many students relocate to distant cities.  Without strong measures to control student infections it is clear that Covid-19 will spread rapidly on campuses, leading to thousands of cases in each university. Students themselves are at low (though not zero) risk of dying or having serious complications from Covid-19, but if there is even small ‘leakage’ into the surrounding community (via university staff, transport systems, stay-at-home students or night life), then the impact is catastrophic.

For a mid-sized university of 20,000 students, let’s say only 1 in 20 become infected during the term; that is around 1,000 student cases. As a very conservative estimate, let’s assume just one community infection for every 10 infected students. If city bars are open this figure will almost certainly be much higher, but we’ll take a very low estimate. In this case, we are looking at 100 initial community cases.

Now 100 additional cases is already potentially enough to cause a handful of deaths, but we have got used to trading off social benefits against health costs; for any activity there is always a level of risk that we are prepared to accept.

However, the one bit of mathematics you do need to know is the way that a relatively small R number still leads to a substantial number of cases. For example, an R of 0.9 means for every initial infection the total number of infections is actually 10 times higher (in general 1/(1-R), see [Dx1]).  When R is greater than 1 the effect is worse still, with the impact only limited when some additional societal measure kicks in, such as a vaccine or local lockdown.

A relatively conservative estimate for R in the autumn is 1.5 [AMS]. For R =1.5, those initial 100 community cases magnify to over 10,000 within 5 weeks and more than 600,000 within 10 weeks. Even with the most optimistic winter rate of 1.2, those 100 initial community infections will give rise to 20,000 cases by the end of a term.

That is for a single university.

With a mortality rate of 1% and the most optimistic figures, this means that each university will cause hundreds of deaths.  In other words, the universities in the UK will collectively create as many infections as the entire first wave.  At even slightly less optimistic figures, the impact is even more devastating.

Why return at all?

Given the potential dangers, why are universities returning at all in the autumn instead of continuing with fully online provision?

In many areas of life there is a trade-off to be made between, on the one hand, the immediate Covid-19 health impacts and, on the other, a variety of issues: social, educational, economic, and also longer term and indirect mental and physical health implications. This is no less true when we consider the re-opening of universities.

Social implications: We know that the lockdown has caused a significant increase in mental health problems amongst young people, for a variety of reasons: the social isolation itself, pressures on families, general anxiety about the disease, and of course worries about future education and jobs. Some of the arguments are similar to those for schools except that universities do not provide a ‘child minding’ role. Crucially, for both schools and universities, we know that online education is least effective for those who are already most economically deprived, not least because of continued poor access to digital technology. We risk creating a missed generation and deepening existing fractures in civil society.

Furthermore, the critical role of university research has been evident during the Covid crisis, from the development of new treatments to practical use of infrastructure for rapid production of PPE. Ongoing, the initial wave has emphasised the need for more medical training.  Of course, both education and research will also be critical for ‘post-Covid’ recovery.

Economic situation: Across the UK, universities generate £95 billion in gross output and support nearly a million jobs (2014–2015 data, [UUKb]).  Looking at Wales in particular, the HE sector “employs 17,300 full-time members of staff and spending by students and visitors supports an estimated 50,000 jobs across Wales”. At the same time the sector is particularly vulnerable to the effects of Covid-19 [HoC]. Universities across the UK were already financially straitened due to a combination of demographics and Brexit, leading to significant cost-cutting including job cuts [BBCa].  Covid-19 has intensified this; a Wales Fiscal Analysis briefing paper in May [WFA] suggests that Welsh universities may see a shortfall due to Covid-19 of between £100m and £140m. More recent estimates suggest that this may be understating the problem, if anything. Cardiff University alone is warning of a £168m fall in income [WO] and Sir Deian Hopkin, former Vice Chancellor of London South Bank and advisor to the Welsh Assembly, talks of a “perfect storm” in the university system [BBCb].

Government support has been minimal. The rules for Covid-19 furlough meant that universities were only able to take minimal advantage of the scheme. There has been some support in terms of general advice, reducing bureaucratic overheads and rescheduling payments to help university cashflow, but this has largely been within existing budgets, not new funding. The Welsh government has announced an FE/HE £50m support package with £27m targeting universities [WG], but this is small compared with predicted losses.

Universities across the UK have already cut casual teaching (the increase in zero-hour contracts has been a concern in HE for some years) and many have introduced voluntary severance schemes.  At the same time the competition over UK students has intensified in a bid to make up for reduced international numbers. Yet one of the principal ways to attract students is to maximise the amount of in-person teaching.

What is being done

To some extent, as in so many areas, coronavirus has exposed the structural weaknesses that have been developing in the university sector for the past 30 years. Universities have been forced to compete constantly and are measured in terms of student experience above educational impact. Society as a whole has been bombarded with messages that focus on individual success and safety rather than communal goals, and most current students have grown up in this context. This focus has been very evident in the majority of Covid-19 information and reporting [Dx2].

Everything we do is set against this backdrop, which both fundamentally limits what universities are able to do individually, and at the same time makes them responsible.  This is not to say that universities are not sharing good practice, both in top down efforts such as through Universities UK and direct contacts between senior management, and from the bottom up via person-to-person contacts and through subject-specific organisations such as CPHC.

Typically, universities are planning to retain some level of in-person teaching for small tutorials while completely or largely moving large-class activities such as lectures to online delivery, some live, some recorded. This will help to remove some student–student contact during teaching. Furthermore, many universities have discussed ways in which students could be formed into bubbles. At a large scale that could involve having rooms or buildings dedicated to a particular subject/year group for a day.  At a finer scale it has been suggested that students could be grouped into social/study bubbles of around ten or a dozen who are housed together in student accommodation and are also grouped for study purposes.

My own modelling of student bubbles [Dx3] suggests that while reducing the level of transmission, the impact is rapidly eroded if the bubbles are at all porous.  For example, if the small bubbles break and transmission hits whole year groups (80–200 students), the impact on outside communities becomes unacceptable. For students on campus the temptation to break these bubbles will be intense, both at an individual level and through bars and similar venues.  For those living at home, the complexities are even greater, and crucially they are a primary vector into the local community.

Combined with, or instead of, social/study bubbles some universities are looking at track and trace. Some are developing their own solutions both in terms of apps and regular testing programmes, but more will use normal health systems.  In Wales, for example, Public Health Wales regard university staff as a priority group for Covid-19 testing, although this is reactive (symptoms-based) rather than proactive (regular testing).

Dr Hans Kluge, the Europe regional director for the World Health Organization and others have warned that global surges across the world, including in Europe, are being driven by infections amongst younger people [BBCc].  He highlights the need to engage young people more in the science, a call that is reflected in a recent survey by the British Science Association which found that nine out of ten young people felt ignored by scientists and politicians [BSA].

As of 27th July, the UK Department for Education were “working to” two scenarios “Effective containment and testing” (reduce growth on campuses and reactive local lockdowns) and “On and off restrictions” (delaying all in-person teaching until January) [DfE].  Jim Dickinson has collated and analysed current advice and work at various government and advisory bodies including the DfE report above and SAGE, but so far there seems to be no public quantification of the risk [JD].

What can we do?

I think it is fair to say that the vast majority of high-level advice from national governments and pan-University bodies, and most individual university thinking, has been driven by safety concerns for students and staff rather than the potentially far more serious implications for society at large.

As with so many aspects of this crisis, the first step is to recognise there is a problem.

Within universitiesacknowledge that the risk level will be far higher than in society at large because the case load will be far higher. How much higher will depend on mitigating measures, but whereas general population levels by the start of term may be as low as 1 in 5,000, the rate amongst students will be an order of magnitude higher, comparable with general levels during the peak of the ‘first wave’. This means that advice, particularly for at risk groups, which is targeted at national levels, needs to be re-thought within the university context. This means that advice that is targeted at national levels, particularly for at risk groups, needs to be re-thought within the university context.  Individual vulnerable students are already worried [BBCd]. Chinese and Asian students seem more aware of the personal dangers and it is noticeable that both within the UK and in the US the universities with the greatest number of international students are more risk averse. University staff (academics, cleaners, security) will include more at risk individuals than the student body. It is hard to quantify, but the risk level will considerably higher than, say, a restaurant or pub, though of course lower than for front line medical staff. Even if it is ‘safe’ for vulnerable groups to come out of shielding in general society, it may not be safe in the context of the university. This will be difficult to manage: even if the university does not force vulnerable staff to return, the long-term culture of vocational commitment may make some people take unacceptable risks.

Outside the universities, local councils, national governments and communities need to be aware of the increased risks when the universities reopen, just as seaside towns have braced themselves for tourist surges post-lockdown. While SAGE has noted that universities may be an ‘amplifier’, the extent does not appear (at least publicly) to have been quantified.  In Aberdeen recently a cluster around a small number of pubs has caused the whole city to return to lockdown, and it is hard to imagine that we won’t see similar incidents around universities. This may lead to hard decisions, as has been discussed, between opening schools or pubs [BBCe] – city centre bars may well need to be re-thought. Universities benefit communities substantially both economically and educationally. For individual universities alone the costs of, say, weekly testing of students and staff would be prohibitive, but when seen in terms of regional or national health protection these may well be worthwhile. Although this is a ‘for example’ it could well be critical given the likelihood of large numbers of asymptomatic student cases.

Educate students – this is of course what we do as universities!  Covid-19 will be a live topic for every student, but they may well have many of the misconceptions that permeate popular discourse.  Can we help them become more aware of the aspects that connect to their own disciplines and hence to become ambassadors of good practice amongst their peers? Within maths and computing we can look at models and data analysis, which could be used in other scientific areas where these are taught.  Medicine is obvious and design and engineering students might have examples around PPE or ventilators. In architecture we can think about flows within buildings, ventilation, and design for hygiene (e.g. places to wash your hands in public spaces that aren’t inside a toilet!). In literature, there is pandemic fiction from Journal of the Plague Year to La Peste, and in economics we have examples of externalities (and if you leave externalities until a specialised final year option, rethink a 21st century economics syllabus!).

Time to act

On March 16, I posted on Facebook, “One week left to save the UK – and WE CAN DO IT.” Fortunately, we have more time now to ensure a safe university year but we need to act immediately to use that time effectively. We can do it.

References

[AMS] The Academy of Medical Sciences. Preparing for a challenging winter 2020-21. 14th July 2020. https://acmedsci.ac.uk/policy/policy-projects/coronavirus-preparing-for-challenges-this-winter

[BBCa] Cardiff University to cut 380 posts after £20m deficit. BBC News. 12th Feb 2019.  https://www.bbc.co.uk/news/uk-wales-47205659

[BBCb] Coronavirus: Universities’ ‘perfect storm’ threatens future.  Tomos Lewis  BBC News. 7 August 2020.  https://www.bbc.co.uk/news/uk-wales-53682774

[BBCc] WHO warns of rising cases among young in Europe. Lauren Turner, BBc New live reporting, 10:05am 29th July 2020. https://www.bbc.co.uk/news/live/world-53577222?pinned_post_locator=urn:asset:59cae0e7-5d3d-4e35-94ec-1895273ed016

[BBCd] Coronavirus: University life may ‘pose further risk’ to young shielders
Bethany Dawson. BBC News. 6th August 2020. https://www.bbc.co.uk/news/disability-53552077

[BBCe]  Coronavirus: Pubs ‘may need to shut’ to allow schools to reopen. BBC News. 1st August 2020.  https://www.bbc.co.uk/news/uk-53621613

[BG]  Colleges reverse course on reopening as pandemic continues.  Deirdre Fernandes, Boston Globe, updated 2nd August 2020.  https://www.bostonglobe.com/2020/08/02/metro/pandemic-continues-some-colleges-reverse-course-reopening/

[BSA] New survey results: Almost 9 in 10 young people feel scientists and politicians are leaving them out of the COVID-19 conversation. British Science Association. (undated) accessed 7/8/2020.  https://www.britishscienceassociation.org/news/new-survey-results-almost-9-in-10-young-people-feel-scientists-and-politicians-are-leaving-them-out-of-the-covid-19-conversation

[DfE] DfE: Introduction to higher education settings in England, 1 July 2020 Paper by the Department for Education (DfE) for the Scientific Advisory Group for Emergencies (SAGE). Original published 24th July 2020 (updated 27th July 2020).  https://www.gov.uk/government/publications/dfe-introduction-to-higher-education-settings-in-england-1-july-2020

[Dx1]  More than R – how we underestimate the impact of Covid-19 infection. . Dix (blog).  2nd August 2020  https://alandix.com/blog/2020/08/02/more-than-r-how-we-underestimate-the-impact-of-covid-19-infection/

[Dx2] Why pandemics and climate change are hard to understand, and can we help? A. Dix. North Lab Talks, 22nd April 2020 and Why It Matters, 30 April 2020 http://alandix.com/academic/talks/Covid-April-2020/

[Dx3] Covid-19 – Impact of a small number of large bubbles on University return. Working Paper. A. Dix. July 2020.  http://alandix.com/academic/papers/Covid-bubbles-July-2020/

[HEFCW] COVID-19 impact on higher education providers: funding, regulation and reporting implications.  HEFCW Circular, 4th May 2020 https://www.hefcw.ac.uk/documents/publications/circulars/circulars_2020/W20%2011HE%20COVID-19%20impact%20on%20higher%20education%20providers.pdf

[HoC]  The Welsh economy and Covid-19: Interim Report. House of Commons Welsh Affairs Committee. 16th July 2020. https://committees.parliament.uk/publications/1972/documents/19146/default/

[JD]  Universities get some SAGE advice on reopening campuses. Jim Dickinson, WonkHE, 25th July 2020.  https://wonkhe.com/blogs/universities-get-some-sage-advice-on-reopening-campuses/

[SN]  Coronavirus: University students could be ‘amplifiers’ for spreading COVID-19 around UK – SAGE. Alix Culbertson. Sky News. 24th July 2020. https://news.sky.com/story/coronavirus-university-students-could-be-amplifiers-for-spreading-covid-19-around-uk-sage-12035744

[UUKa] Principles and considerations: emerging from lockdown.   Universities UK, June 2020. https://www.universitiesuk.ac.uk/policy-and-analysis/reports/Pages/principles-considerations-emerging-lockdown-uk-universities-june-2020.aspx

[UUKb] https://www.universitiesuk.ac.uk/policy-and-analysis/reports/Pages/economic-impact-universities-2014-15.aspx

[WFA] Covid-19 and the Higher Education Sector in Wales (Briefing Paper). Cian Siôn, Wales Fiscal Analysis, Cardiff University.  14th May 2020.  https://www.cardiff.ac.uk/__data/assets/pdf_file/0010/2394361/Covid_FINAL.pdf

[WG]  Over £50 million to support Welsh universities, colleges and students.    Welsh Government press release.  22nd July 2020.  https://gov.wales/over-50-million-support-welsh-universities-colleges-and-students

[WO] Cardiff University warns of possible job cuts as it faces £168m fall in income. Abbie Wightwick, Wales Online. 10th June 2020.  https://www.walesonline.co.uk/news/education/cardiff-university-job-losses-coronavirus-18393947

 

 

 

 

 

 

Is Corbynism dead? The data says not.

The December 12th election saw the most disasterous Labour defeat in nearly a century and the collapse of the ‘red wall’ of Labour heartlands in the North-East. Jeremy Corbyn and John McDonnell are standing down in the New Year, and the vultures are gathering to pick the meagre bones of Corbyn’s political body.

Many Labour canvasers reported that on the doors the problem was four parts Corbyn for every one part Brexit. The message is clear, Corbyn was toxic on the doorstep and Labour needs a change.

But the numbers tell a different story.

Labour’s vote share fell dramatically from the surprise successes of 2017, when Corbyn’s campaign charisma unexpectedly set back Teresa May’s ambition to win the sort of majority that Boris Johnson has today.

But if you look before that to 2015,, the picture is less clear

.Comparing the recent election with 2015, the Labour share of the vote in 2019 is actually higher than its vote share in 2015. Yes, Labour is still performing better under the avowedly socialist Jeremy Corbyn than it did under ‘centrist’ Ed Miliband.

The difference between David Cameron’s small majority and Boris Johnson’s landslide is predominantly about the collapse of the UKIP/Brexit vote, with the hard Leavers exchanging Farage for Johnson. In 2017, Labour took a soft Brexit position, which, while annoying many Corbyn supporters at the time, seemed to hold onto many of the Leave voters who last week voted Conservative in Labour heartlands

Increasing vote share since 2015 is remarkable in the face of long-term excoriating press attacks against Jeremy Corbyn personally and a Conservative Facebook ad campaign that fact checkers rated as 88% false, not forgetting persistent undermining by sections of the Labour party itself.

More crucial is who voted for Labour and Conservative. It has always been the case that voters drift right as they age, often favouring economic security over youthful idealism. However this has dramatically shifted in the last few years. Conservative support in younger age groups has crashed utterly and it is now predominantly a party of the old. The Tory Party has effectively mortgaged its future for current electoral success.

This is evident in the demographics of voting on Dec 12th collected by Lord Ashcroft’s post-vote poll. Labour has a vast lead over Conservative in voters under 45, whereas Conservative vote share, which is over 60% in the over 65’s, shrinks to less than 20% in the under 25s.

These under 45s have lived entirely under the neoliberal individualism that started with Thatcher and adopted in large part by New Labour and Tory governments since. They have seen it, and rejected it. A generation is growing who are looking beyond themselves, recognising the disastrous impacts of past policies of all governments on the environment and humanity, and believing in the power of society to transform, not just their own lives, but those of the whole nation and world.

As Labour chooses its new leader, it should ponder whether it wants to revert to the old policies and combat the Tories for the votes of the old, or embrace the spirit of hope and change that has galvanised the youth of the country.

This post is also published in Medium.

Rich tea biscuits, sugar lumps and Bournville chocolate

I forgot.

How could I forget?

Memory is a fickle thing, not metal storage shelves, or neat filing drawers, but like the tide throwing up flotsam of your past and then withdrawing, just traces in the sand.

We had been sorting boxes long in storage, and I had made my way through plastic crates full of old screws, hinges, locks without keys, and half window-latches. Some I had collected myself over the years, some I’d inherited from Fiona’s grandpa, and some were my dad’s, accreted through a life as builder, carpenter and maintainer of the old Victorian terrace where I was born. All were coated with that dusty brown patina of age, not the rich iridescent rust of wet, but the dull discolouration that rubs off on your hands and leaves small scatterings on the bottom of tins.

There had been one tin, full of such scatterings, and it had gone into the metal recycling box, amongst others.

I had discarded the brown Tupperware box in which I’d kept my own collection of reusable screws as a boy, a few ‘liberated’ from secondary school desks when it seemed fun to see how many screws you could remove from the lids whilst still leaving them, at least apparently, intact.

What would dad have thought? Maybe some would have been the same desks he had repaired when I had still been in infant school.   A few times a year he would be in our school, repairing desks and chairs – in those days all wood. It is likely he also visited the high school where I eventually ended up, wondering how the lids got loose as a previous generation of school children had a short-lived craze of minor vandalism. How many of the scored and inked images and slogans on the desks where I later sat had been there when he had touched them.

The touch of an object, the feel of it under your fingers, bringing back the past. Only it didn’t, the tin was cast thoughtlessly amongst the decaying ironmongery, detritus of a save-it-just-in-case mentality inherited from those who had seen one or two world wars.

Only after, I remembered.

The tin was long and thin, perhaps ten inches long and two and a half or three wide; square in cross-section; I always assumed it was designed for cream crackers. The lid was large-chequered white and red, with an embossed pattern highlighted in long faded gold, but I only half remember, the way you do with things so intimate, so normal, they are merely the background.

Is it always the way that the things that are closest, most dear, are most easily forgotten?

I took no photograph.

It is gone.

I always say, as a tease, that the smell of meths is the smell of childhood; it reminds me of my dad. And it is true.

He was no drinker, certainly not when I knew him, who knows in his youth. In the sideboard cupboard there was a bottle of cherry brandy that I never recall being opened. Did mum and dad sometimes have a small glass after Jacqui and I had gone to bed? I only ever recall very occasional glasses of sherry at Christmas, and maybe that was only mum.

The smell of meths was surgical spirit; twice a day, regular as the clock that was also wound daily, he would inject insulin. Small bottles with round rubber tops, the needle reused, none of today’s disposable needles, or discreet pens, but his trousers wound down and the needle pressed into his thigh, the skin and needle cleaned with cotton wool soaked in the clear spirit. I wonder how many times he reused the needle; I guess until it was too blunt to break the skin.

When a little older, I recall going together to Cardiff Infirmary, I assume for a check-up – the dull post-war institutional painted corridors, and that smell of hospital … soap and disinfectant, and in those days I’m sure also a touch of meths. I do not know whether it was just once or many times, and why I recall it being just the two of us – maybe it was when Jacqui had started school and I had not, or perhaps Jacqui had gone with mum somewhere, or maybe just that soliloquy of childhood that sees everything through one’s own eyes, forgetting that others were there too.

But in my earliest memories, not the hospital, just the smell, the needle and, in every drawer, handbag, and car shelf, sugar lumps and gold wrapped bundles of Bournville chocolate.

When we went out for the day, or drove away on holiday, mid-morning and mid-afternoon we would always stop for a cup of tea and a bite to eat. Then as now, injected insulin was only half the cure; he had to be careful to eat regularly.

In the summer, on a fine day, we would park the car. Dad would take out a Camping Gaz stove from its blue metal box and the kettle would boil while Jacqui and I played in the sun or sat in the back of the car with a sticky-back-plastic-covered plank as a table.

At other times there were cafés, some with Formica topped tables and counters, others oak panelled – always in those days waitress service. Toasted tea-cakes are still a comfort food.

If we stopped for lunch then we would often have soup with crusty rolls. I’m sure they were in the local bakers too, but I always associate those rolls with days out and restaurants. Jacqui and I would pull out the moist white bread from the middle with our fingers, making mouse houses from the hollow crusts, and then, of course, finish with the crusty parts themselves, still the best portion of any loaf.

Neither dad nor mum took sugar in their tea, but on the table there would always be a bowl full of sugar: sometimes naked white lumps piled high, tempting for a small child, and maybe Jacqui and I would be allowed one each to suck.   Sometimes they came in little paper packets, two bundled together – standard dose for a cup of tea – and, if they did, dad would take a few and add them to his collection, for emergencies, if he felt low on sugar, or if for some reason we were late eating.

Once, I recall dad getting angry and shouting at home, a thing rare enough that I remember it. After a while he and mum realised that he had not eaten, and his temper dissolved as his sugar level rose.

The diabetes was managed, part of the background, one of those things so intimate, so common they are not thought about, but never entirely forgotten.

Once dad broke his toe whilst moving a table in the church schoolroom. His foot was in plaster for weeks, but the worry was always that gangrene would set in.

Only later, after dad had gone, I discovered that one of his brothers had died in the 1920s, still in the early days of insulin treatment when they were trying to understand the correct dosages. The insulin prolonged his brother’s life, but also, in the end, killed him.

I always assume dad’s diabetes was late-onset, otherwise he would never have lived until Jacqui and I were born. Late enough that insulin was better understood. Perhaps it had come at a time of stress, in the 1940s when he divorced his first wife, or when his second wife died.

Nowadays, whenever I have a blood test myself, I always ask about the sugar levels.

And the tin?

At home, cups of tea were as much a ritual, dad’s cup bigger than mum’s, but always a cup and saucer; mugs for tea were still many years off. Jacqui and I learnt to drink tea from dad’s saucer. He would pour a little tea on the saucer, blow it and let us sip the cool liquid. It was not just for us, but a trick he sometimes used himself to cool his tea rapidly – a habit from his work as a carpenter to drink quickly in short tea breaks.

With the tea there were no custard creams or bourbons, no chocolate biscuits, nothing iced topped nor anything too sweet, but instead rich tea fingers, thin oval-shaped biscuits with crimped edges. Dad would have two, resting on his saucer and then dunked in the tea until they were soft and warm.

They came in two kinds, one in blue and white packets and slightly lighter in colour, similar in taste to the thicker, round rich-tea biscuits that are more common today; the others in clear packets, with a darker colour and a subtly richer, more savoury, almost nutty taste.

I don’t remember now whether we regularly ate them too as small children or whether they were a grown-up thing. I do recall Jacobs Club biscuits as a treat, always the orange ones. Later as an older child, when I had my own tea, I was always torn between the soft melting texture of dunked finger biscuits, or nibbling them, first around the edge, removing just a few millimetres of the neat crimping, before starting at one end – then, with rodent-like reciprocating teeth, reducing them to sawdust-like powder in my mouth.

The rich tea fingers lived in a tin, and the tin on the sideboard, always.

Pantoum in Eindhoven

While sorting some old files I came across a small pack of notes, stapled together.  They were clearly written in a bar (the beer glasses are a give away!), and the notes mention Eindhoven.

I then remembered.  On one of my visits to Eindhoven, either teaching USI students at TU/e, or for the Desire conference,  I was sitting with someone at a bar, I think waiting for others to join us and I was describing the pantoum, a Malayan poetry form I had originally read about in “The Making of a Poem: A Norton Anthology of Poetic Form“.  This is a lovely book I was given for birthday or Christmas some years ago, that describes many poetical forms, some common such as the sonnet, others, like the pantoum, that I had never heard of before.

In a pantoum the second and fourth lines of the first stanza become the first and third lines of the second stanza, and then so on for the rest of the poem, like voices calling from verse to verse.  My favourite example was “The Method” by J. D. McClatchy, which stretches the idea of the repeated lines, modifying them slightly to be almost the same, but not quite: perhaps modified, punctuated differently or simply sounding similar but completely different words. For example, the second line of the first stanza is “Seem to pee more often, eat“, which becomes “Sympathy, more often than not” as the first line of the second stanza.

By way of demonstration I tried to write a pantoum on the spot.  There were paper slips on the table, to allow you to write your order to take to the bar, and these became manuscript paper. The lines are very short, which is only fair as I was writing a five line poem on the fly, but I had also clearly forgotten the proper rules (I just rechecked now) as I have six-line stanzas, instead of four-line quatrains.  However, I did manage to get the last stanza to cycle round and use the unrepeated (1st, 3rd and 5th) lines of the first stanza, not bad for a two minute demo 🙂

The Pantoum of the Guinness in Eindhoven

in the bar
on the street
of old Eindhoven
we sat drinking
Guinness in glasses
dark and deep

on the street
on the pavement
we sat drinking
of the visions
dark and deep
all around us

on the pavement
they told us
of the visions
they saw here
all around us
hidden souls

they told us
as children
they saw here
lonely spirits
hidden souls
are drawn to

as children
in the bar
lonely spirits
of old Eindhoven
are drawn to
Guinness in glasses

Everything feels easy

Today looked like a good Tiree Ultra day, with 40 mile an hour winds (the odd gust at 50) and occasional shafts of sunshine between driving rain!

So buoyed by knowledge from three weeks ago that I could do it, I took my first run since the ultra.

My left leg is still feeling a little gammy, but with a 40 mph wind at my back I fair sailed along – until I turned round.  Progress on the return leg was … well suffice say I could have walked faster.

I have always avoided running in the rain, but after the ultra I knew I could do it and it wasn’t so bad.  I also had a new rain poof that I’d got for the ultra – good equipment really does help.

There is something liberating about that “it can’t be worse than …” feeling.

When I did the first Tiree Ultramarathon in 2014, it was a year after I’d walked around Wales.  If I got a pain whilst walking there was always the fear that it would be worse the next day, or that it would be the thing that stopped me entirely.

Just over 2/3 of the way round the 2014 ultra I began to get some pain in my right leg.  I’d pulled the Achilles tendon on that ankle a few years before, and so I was a little worried that it would go again.  But I thought, “only 10 miles to go, and it’s just one day. I don’t have to run again tomorrow and the next day; so what if I’m hobbling for a few weeks.

After walking 1000 miles day on day, a single day and mere 35 miles was suddenly less daunting.

Now, knowing I could endure a whole day running with horizontal rain stinging my cheeks, well what of a couple of miles in heavy drizzle and 50 mile an hour winds …

After Tiree Ultra 2017, everything feels easy.

Tiree Ultra 2017 – what a difference a week makes

Last Sunday I completed my third Tiree Ultamarathon … and definitely the wettest, windiest and boggiest!

However, this Sunday what a difference …

The ultra circuit flows the coast of Tiree taking in almost all of the beaches, but also includes some road sections as well as off-road grass sward and boggy moor. There is relatively little height gain, but Will Wright tries to organise the route to ‘make the most’ of the hills there are.

Previous years have been wonderful weather, light breeze and some sun, enough to be pleasant, but not enough to cause heat problems. However, the fates had been saving their fury, and this year the heavens opened and Odysseus let the western winds loose gathering water from the warm Atlantic and flinging it at us in horizontal sheets.

It was the first time I had every run in the rain so was, well maybe not a baptism of fire, but certainly a dramatic introduction., I had recently bought a waterproof for running in, but it sill had its label on as each wet August day, I thought “well maybe run on a brighter day”. Although everyone says that the right equipment helps, I sort of only half believed it – however, I was amazed at how even driving rain was not a problem.

I only run the Tiree ultra in September and sometimes the Tiree half marathon in May. I always mean to keep on running between, but then I forget, or I am too busy – so many excuses. So, in previous years I haven’t got round to any running until a month before the ultra doubling my distance each week – far from the recommended 10-15% a week increase! To be honest I’ve been very lucky to have not injured myself.

This year I decided to break my habit and be well prepared, so started a whole two months in advance. I wondered if this had been wise as I felt I’d peaked at the end of July and seemed to be going downhill ever since.  However, this year I am definitely hobbling less afterwards and I managed to run every inch of road and beach, with just a few walking sections over bog. This said, when the wind gusted mid to high thirty miles an hour in my face, I would almost certainly have walked faster than I ran.  Indeed on Gott Bay as I ran (very slowly) into the wind another runner was power walking just behind me sheltering in my lee.

One thing I noticed while running was a subtle change in psychology.  After about 10 miles, as pain and exhaustion kicked in, I was aware of myself occasionally wondering if there was any way I could bow out without losing too much face, and then not that many miles later I caught myself thinking “next year I’ll ….” – at that point I knew I was OK!  However, the exhaustion must still have been in my face at mile 17 as the marshal as we came off the beach at Balephetrish said, “you look as if you could do with a hug”.

This year two off-island friends, Albrecht and Alun, also came to Tiree for the Ultra, which was wonderful.  Being a good host I of course let them finish ahead of me by an hour or so 😉

Albrecht has already booked for next year, but not sure if Alun is convinced!

However, the weather can only be better.

 

 

running on the verge

Tiree Fitness Facebook – Photo by Alan Millar

In a week and half’s time I’ll be joining about 250 others on the Tiree Ultramarathon, running around the edge of Tiree, which is itself on the Atlantic edge of Scotland.   Some of this will be on beach and moor, but some along single track roads, where you often have to step onto the grassy verge as cars go by.

Running on the verge has its own challenges which I’m sure are shared by many rural areas as well as Tiree.  For those coming to the Tiree Ultra or running (or cycling) in rural areas, here’s my short guide to the hazards of the verge.

on narrow roads do stop – Some roads do have space for a car to pass a runner or cyclist, but it can be close especially if you are a little tired and ‘wandering’ a little as you run.  So usually best to stop … and you get a moments breather 😉

beware the ragged tarmac edge – It is tempting to just squeeze to the left and keep going, but the tarmac often peters out, this is worst of you are cycling as the wheel can slip off the road and get trapped in the furrow between tarmac ad grass (cyclists have ended up in hospital!), but you can also trip when running … and you don’t want to fall into the path of the car that is passing.

Tiree Fitness Facebook photos – verges are not the only road hazard

running on the verge – I know many will ignore this, but just don’t.  They seem wide, tamer than running on full off-road terrain, and well within the capabilities of a off-road bike.  However there are often drainage channels hidden by the long grass – these can be a foot or more deep and can be invisible.  Even when there isn’t a deep drainage channel running parallel to the road, there are often smaller drainage channels running outwards from the road; these are typically only a few inches deep, but just designed to trip you up.  The one possible exception is where someone has mown the verge outside their house, but even then be careful of the cross-channels as they often aren’t obvious even on mown grass.

stepping onto the verge – At the risk of sounding like your granny, still take care!  I have stepped off the road and, even looking down at the ground as I did so, my foot has disappeared into a channel and I’ve almost sprained my ankle … and that was standing still not running.  On the bike be even more careful, you stop, put your outer foot into what you believe to be grass and … on a bike there is little you can do apart from topple full head over heels … and, yes, I know because I have done it.

standing on the verge – Will it never stop!  Yep, even standing has it’s dangers.  On Tiree it is normal to wave to those passing, friend and stranger alike.  However, if you are a little tired twisting round can put you off balance.  Don’t feel embarrassed to put a hand on a fence post to keep you sure footed, better than stumbling back into the path of that nicely waving driver.

stepping off the verge – Do take a peek back down the road before stepping back onto the tarmac.  Tiree is windy and when the wind is coming from in front it is hard to hear cars from behind, as a car passes you it is easy to just step back, but often there is a second car driving in convoy, especially when the road has had a lot of obstacles (such as runners), so that cars catch up with one another.

Tiree Fitness Facebook photos

… and then if you survive the verges

… there is just Dun Mor to climb …

why is the wind always against you? part 2 – side wind

In the first part of this two-part post, we saw that cycling into the wind takes far more additional effort than a tail wind saves.

However, Will Wright‘s original question, “why does it feel as if the wind is always against you?” was not just about head winds, but the feeling that when cycling around Tiree, while the angle of the wind is likely to be in all sorts of directions, it feels as though it is against you more than with you.

Is he right?

So in this post I’ll look at side winds, and in particular start with wind dead to the side, at 90 degrees to the road.

Clearly, a strong side wind will need some compensation, perhaps leaning slightly into the wind to balance, and on Tiree with gusty winds this may well cause the odd wobble.  However, I’ll take best case scenario and assume completely constant wind with no gusts.

There is a joke about the engineer, who, when asked a question about giraffes, begins, “let’s first assume a spherical giraffe”.  I’m not gong to make Will + bike spherical, but will assume that the air drag is similar in all directions.

Now my guess is that given the way Will is bent low over his handle-bars, he may well actually have a larger side-area to the wind than from in front.  Also I have no idea about the complex ways the moving spokes behave as the wind blows through them, although I am aware that a well-designed turbine absorbs a fair proportion of the wind, so would not be surprised if the wheels added a lot of side-drag too.

If the drag for a side wind is indeed bigger than to the front, then the following calculations will be worse; so effectively working with a perfectly cylindrical Will is going to be a best case!

To make calculations easy I’ll have the cyclist going at 20 miles an hour, with a 20 mph side wind also.

When you have two speeds at right angles, you can essentially ‘add them up’ as if they were sides of a triangle.  The resultant wind feels as if it is at 45 degrees, and approximately 30 mph (to be exact it is 20 x √2, so just over 28mph).

Recalling the squaring rule, the force is proportional to 30 squared, that is 900 units of force acting at 45 degrees.

In the same way as we add up the wind and bike speeds to get the apparent wind at 45 degrees, we can break this 900 unit force at 45 degree into a side force and a forward drag. Using the sides of the triangle rule, we get a side force and forward drag of around 600 units each.

For the side force I’ll just assume you lean into (and hope that you don’t fall off if the wind gusts!); so let’s just focus on the forward force against you.

If there were no side wind the force from the air drag would be due to the 20 mph bike speed alone, so would be (squaring rule again) 400 units.  The side wind has increased the force against you by 50%.  Remembering that more than three quarters of the energy you put into cycling is overcoming air drag, that is around 30% additional effort overall.

Turned into head speed, this is equivalent to the additional drag of cycling into a direct head wind of about 4 mph (I made a few approximations, the exact figure is 3.78 mph).

This feels utterly counterintuitive, that a pure side wind causes additional forward drag!  It perhaps feels even more counterintuitive if I tell you that in fact the wind needs to be about 10 degrees behind you, before it actually helps.

There are two ways to understand this.

The first is plain physics/maths.

For very small objects (around a 100th of a millimetre) the air drag is directly proportional to the speed (linear).  At this scale, when you redivide the force into its components ahead and to the side, they are exactly the same as if you look at the force for the side-wind and cycle speed independently.  So if you are a cyclist the size of an amoeba, side winds don’t feel like head winds … but then that is probably the least of your worries.

For ordinary sized objects, the squaring rule (quadratic drag) means that after you have combined the forces, squared them and then separated them out again, you get more than you started with!

The second way to look at it, which is not the full story, but not so far from what happens, is to consider the air just in front of you as you cycle.

You’ll know that cyclists often try to ride in each other’s slipstream to reduce drag, sometimes called ‘drafting’.

The lead cyclist is effectively dragging the air behind, and this helps the next cyclist, and that cyclist helps the one after.  In a race formation, this reduces the energy needed by the following riders by around a third.

In addition you also create a small area in front where the air is moving faster, almost like a little bubble of speed.  This is one of the reasons why even the lead cyclist gains from the followers, albeit much less (one site estimates 5%).  Now imagine adding the side wind; that lovely bubble of air is forever being blown away meaning you constantly have to speed up a new bubble of air in front.

I did the above calculations for an exact side wind at 90 degrees to make the sums easier. However, you can work out precisely how much additional force the wind causes for any wind direction, and hence how much additional power you need when cycling.

Here is a graph showing that additional power needed, ranging for a pure head wind on the right, to a pure tail wind on the left (all for 20 mph wind).  For the latter the additional force is negative – the wind is helping you. However, you can see that the breakeven point is abut 10 degrees behind a pure side wind (the green dashed line).  Also evident (depressingly) is that the area to the left – where the wind is making things worse, is a lot more than the area to the right, where it is helping.

… and if you aren’t depressed enough already, most of my assumptions were ‘best case’.  The bike almost certainly has more side drag than head drag; you will need to cycle slightly into a wind to avoid being blown across the road; and, as noted in the previous post, you will cycle more slowly into a head wind so spend more time with it.

So in answer to the question …

why does it feel as if the wind is always against you?

… because most of the time it is!