As an alumnus, I again had the opportunity today (with 3000 other people in over 70 countries) to attend the second Cambridge Conversations webinar, this time featuring Professor Sir David Spiegelhalter of Churchill College, Chair of the Winton Centre for Risk and Evidence Communication in the University of Cambridge, and Professor Mike Hulme, Professor of Human Geography and Fellow of Pembroke College, and a specialist in Climate Change.
The discussion, ‘COVID-19 behind the numbers – statistics, models and decision-making’, was moderated by Dr Alexandra Freeman, Executive Director at the Winton Centre for Risk and Evidence Communication.
The video of the 45 minute session will be available, and I will share it here in due course (it’s on a closed group at the moment, but will be on the Cambridge YouTube channel here in a few days, where the first Cambridge Conversation on Covid-19, from April, is currently available).
Of most interest to me, since I was interested in the modelling of the pandemic outbreak, was the first part of the scene-setting, by Professor Sir David Spiegelhalter, one of the world’s foremost biostatisticians, who unpicked the numbers surrounding COVID-19. He has been reported widely and recently regarding the interpretation of Covid-19 data.
He explored the reporting of cases and deaths; explained the bases on which predictions have been made; examined comparisons with the ‘normal’ risks faced by people; and investigated whether many deaths from COVID-19 could have been expected and have simply been brought forward.
He was joined by Professor Mike Hulme, whose expertise is in climate change, with particular interest in the role of model-based knowledge in strategic and policy decision-making relative to political and cultural values: a question of similar importance to COVID-19 as it is to Climate Change policies, his own area of study.
The first set of slides, by David Spiegelhalter on the modelling aspects and the numbers coming out of the pandemic, are here:
The second part of the scene-setting, by Dr Mike Hulme, was more about how model-based knowledge is used in decision-making and public communication around Covid-19, and the differences in wider public perceptions across countries and cultures.
Much of this part of the discussion was about the difference between the broad basis for decision making vs. the more narrow basis for any particular expert advice; and that decision makers need to take into account a far wider set of parameters than just one expert model, involving cultural, ethical and many other factors. This means that methods, conclusions and decisions don’t necessarily carry over from one country to another.
Questions and answers
There was a Q&A session after the scene-setting, moderated by Dr Alexandra Freeman, and, amazingly, a submitted question from “Brian originally of Trinity College” was chosen to be asked! My question was about how to understand and model the mutual feedback between periodic lockdown adjustments and the growth rate of the virus, but it wasn’t answered very well, if at all, combined as it was with someone else’s (reasonable) question on what data we need to take forward to help us with the pandemic, which wasn’t answered properly either.
I had the impression that Mike Hulme, in particular, was more concerned with getting his own message across, and that actually several other questions didn’t get a good answer either. Speigelhalter, for his part, is well aware of his own fame/notoriety, and was quite amusing about it, but possibly at the expense of listening to the questions and answering them.
Both of them thought some of the other questions (e.g. one about “which modellers around the word are the best?”) had sought to draw out views about a “beauty contest” of people working in the field, which they (rightly) said wasn’t helpful, as initiatives and models in different countries, contexts, cultures were all partial, dealing with their own priorities. Hulme used the phrase “when science runs hot” a few times, in the context of all the work going on when the data was unreliable, causing its own issues.
Spiegelhalter had been (in his opinion mis-) quoted both by Boris Johnson AND the new leader of the opposition, Keir Starmer, regarding recent statements he had made about the difficulty of comparing data from different countries and cultures concerning Covid-19.
But as a statistician, he will be well aware of the phrase “lies, damn lies and statistics”, so I don’t have much sympathy for his ruefulness about having created issues for himself by being outspoken about such matters. His statements are delivered in quite an authoritative tone, and any nuances, I should think, in his public pronouncements might, not be noticed.
I recommend watching the YouTube video of the presentations when available on the Cambridge YouTube channel here next week, particularly (from my own perspective) Spiegelhalter’s, which drew some good distinctions about how to read the data in this crisis, and how to think about the Coronavirus issues in different parts of the population.
He had a very good point about Population Fatality Rate (PFR) vs. Infection Fatality Rate (IFR), (the difference between the chance of catching AND dying from Covid-19 (PFR) vs. the chance of dying from it once you already have it (IFR)) and how these are conflated by the media (and others) when considering the differential effect of Covid-19 on different parts of the population. One is an overall probability, and the other is a conditional probability, and the inferences are quite different, as he exemplified and explained.
There were some quite startling and clear learnings in reading the data about the relative susceptibilities of young vs. old and men vs. women, and the importance of a more complete measure of death rates, as shown by charts of the overall Excess Deaths in the population, contrasted with narrower ways of measuring the impact of the pandemic, highlighting the wider issues we face.
Both presenters wanted the whole impact of Coronavirus to be considered, not just the specific deaths from Covid-19 itself – things such as the increase in deaths from other causes, owing to the tendency of people not to want to attend hospitals; mental health; the bringing forward of deaths that might have happened in the next influenza season, if not now; and a number of other impacts.
Spiegelhalter found the lack of testing in the UK difficult to comprehend, and felt that addressing testing going forward was on the critical path to any way out of the crisis (my words).
The other main message, from Hulme, was that Governmental decision-making should be broadly based, and not driven by any particular modelling group. He didn’t reference the phrase “science-led”, as has been used so often by Government and others dealing with Coronavirus, but I imagine that he thinks that the word “science” in that phrase should be much more broadly defined (again, my interpretation of his theme).
Watch the Cambridge YouTube channel here next week and decide for yourself! At present the first Cambridge Conversation, “responding to the medical challenges of COVID-19” is available there, and summarised in my post Cambridge Conversations and model refinement.