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Tuesday, Jun 28, 2022

Can you capture the complex reality of the pandemic with numbers? Well, we tried… | David Spiegelhalter and Anthony Masters

Can you capture the complex reality of the pandemic with numbers? Well, we tried… | David Spiegelhalter and Anthony Masters

Throughout 2021, two leading lights of the Royal Statistical Society Covid-19 Task Force drew on data for a weekly Observer column, and found themselves in the middle of Covid culture wars

Individual experiences and suffering are, of course, at the heart of the pandemic. But one way to understand what has happened is through putting those experiences together – and statistics are those personal stories writ large. And this pandemic has brought unprecedented demand to explain all the numbers that have been flying around.

This has not been without its problems and we’ve had to learn some hard lessons, such as the journalistic skill of brevity. Since January 2021, we’ve been writing a weekly column in this paper about Covid numbers, covering everything from infections to deaths, vaccines to mental health, masks to lockdowns.

It can be frustrating not being able to show all the graphs, but the same holds for radio and TV interviews and More or Less on Radio 4 has shown how much can be done to explain statistics without visual aids. Mathematical formulae and technical jargon need to be avoided, but we have also had to be sparing with numbers, which is harder when they are so precious to us.

We had to agree our purpose, as a particular challenge is to fend off the voracious media appetite for blame, speculation and controversy, naturally fed by the broad spectrum of opinion among experts. One camp has supported viral suppression and even elimination, while others have expressed scepticism about the measures taken; it’s become a cliche that their extreme followers can be identified by the phrase “I’ve done my own research”.

In the theatre of political combat, numbers are often weapons to beat opponents, but they should really be used to raise the quality of the debate. So we see ourselves as part of a group who have attempted to keep away from policy disputes and some of our most trusted sources are skilled individuals doing analyses in their spare time, who then share their insights on Twitter – to inform rather than persuade.

Unfortunately, this pandemic has been rife with false claims and misinformation, particularly about vaccines. One approach for dealing with this, supported by empirical evidence, is the idea of “inoculation” – pre-empting misinformation and telling people about the incorrect interpretation before they catch it in the wild.


We tried this strategy back in June 2021 when Public Health England first published data showing that, among older people who had recently died with Covid-19, most had been vaccinated. We wrote an article pointing out that this did not mean the vaccine was ineffective – just that it was imperfect – and that the great majority of people had been vaccinated: in essence, a small proportion of a large number can be bigger than a larger proportion of a small number. Another useful analogy is with seatbelts: most people who die in car accidents are wearing seatbelts, but this does not mean that seatbelts are not effective – it’s just that nearly everyone wears one and they are not perfect.

The response to our “pre-bunking” was not encouraging. The Twitter link to our article included only its title, Why most people who now die with Covid in England have had a vaccination, and not the subhead, Don’t think of this as a bad sign, it’s exactly what’s expected from an effective but imperfect jab. As such, it was mistakenly interpreted as an anti-vaccination article (or worse) and circulated online. This, in turn, led to critical comments suggesting that we had encouraged vaccine scepticism and even an extraordinary tweet saying we (and the paper’s editors) were “genocidal” and should be “hunted down and destroyed”. We made light of this, saying this seemed a bit harsh, but we had had worse referees’ reports. That account was subsequently removed from Twitter.

Among the repeatable insults, DS has been called an “idiot”, a “shameless liar” and “Nazi collaborator”. Of course, this is mild compared to what others have received (although “fossil” did hurt).

It got more complicated in September 2021 when ITV’s political editor, Robert Peston, tweeted (and later deleted) “Infections higher among double vaxxed for those 40 to 79 than for non-vaxxed”. Surprisingly, he was not referring to the absolute case numbers, which could be explained using the reasoning above. Instead, this referred to the case rates per 100,000 people reported by Public Health England. That seems strange, but can be explained by PHE’s use of National Immunisation Management System (NIMS) for population figures, which uses GP lists and so tends to overcount due to many and defunct registrations. Using Office for National Statistics (ONS) population estimates, themselves uncertain, leads to the expected lower case-rates in vaccinated than unvaccinated groups.

This UK data, apparently showing higher case rates in the vaccinated than the unvaccinated, was later used by Brazil’s President Bolsonaro to support his bizarre claim that vaccines cause Aids. Complaints by the watchdog Office for Statistics Regulation have had little impact and PHE’s successor, the UK Health Security Agency, stubbornly refuses to change its publication (even though unvaccinated rates are in a lighter font), so that these misleading numbers continue to feed vaccine misinformation.

These stories might give a poor impression of how the media has handled Covid-19 numbers. But, on balance, our impression is that a valiant attempt has been made by most outlets to explain their complexities. All, of course, impossible without the extraordinary behind-the-scenes work of those who produce the Covid dashboard, the ONS Infection Survey, and the other sources that it is too easy to take for granted.

We started working together through the Royal Statistical Society, since statisticians care deeply about the use of numerical evidence in public debate. Data does not speak for itself – it needs people to speak honestly and carefully on its behalf. A misunderstood statistic could have terrible consequences – in health, wealth and justice. By contrast, better statistics should lead to better debate and, we hope, better decisions.

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