Dramatic Covid models are increasingly greeted with cynicism
Glancing over the latest panic-stricken headlines about Covid, it seems the end of 2021 has brought a whole new meaning to the phrase, the Nightmare Before Christmas. Just yesterday one Guardian headline read:
At this point, the general public could be forgiven for growing cynicism about SAGE’s supposed predictions. After all, when it comes to overshooting the mark, the advisory body has form. As recently as October, SAGE was reported to have predicted 7000 hospital admissions a day, a scenario which didn’t come close to materialising; in fact, admissions barely topped 1000.
So what purpose do these models serve? Why are they always so gloomy? And why does there always seem to be little acknowledgement of how badly wrong they have been in the past? An illuminating Twitter exchange between Fraser Nelson, editor of The Spectator and Graham Medley, chairman of the SAGE modelling committee, SPI-M, may hold some of the answers. In the exchange, Professor Medley explained that the models produced by SAGE were “not predictions”; rather than models produced for a broad range of eventualities, their remit was far more limited. Policymakers discuss ‘with modellers what they need to inform their policy’ and models are created on the back of such discussions. Therefore, models are produced to ‘support a decision’ made by policymakers, rather than on the likelihood or plausibility of an event.
Typically, models are produced for worst case scenarios, which require decisions to be made and policy changes undertaken. For more promising outcomes that do not require a decision (or restrictions), models may well never be produced. As Prof Medley states: ‘decision-makers are generally only interested in situations where decisions have to be made’.
That modellers are producing only what is requested of them by policy officials should perhaps be no surprise. But this does raise questions about the drive towards negativity within government — how are sensible, objective decisions supposed to be made when the scenarios modelled are all ones in which intervention is needed — and therefore where outcomes are bad? If policymakers only ever request ‘pessimistic’ models, it is difficult to imagine how balanced decision-making can be.
There is another issue too. The models SPI-M produce do not exist in a vacuum. While Prof Medley may, rightfully, claim that the models created by SPI-M aren’t predictions or warnings, they are not treated as such by the media. Time and time again, the modelling produced by SPI-M has been utilised for alarmist headlines with dire warnings, which never materialise. Such coverage is leaving the public jaded and it is damaging our ability to respond sensibly to Covid.
All of this also ignores the harms created by restrictions, harms which have never been modelled. We still haven’t properly accounted for the damage caused by restrictions, and it may take years before we do so. Perhaps if as much attention was given to the damage caused by lockdowns as to improbable fatalistic scenarios about Covid, the Government and the public might have a better grasp on reality. The NHS would be better able to cope with the actual number of cases, and the UK, once again, wouldn’t appear to be on track for yet another lockdown.
Amy Jones is an anonymous medical doctor with a background in philosophy and bioethics. You can find her on Twitter at @skepticalzebra.