Pyser Testing

Not So SAGE After All: A Review of the Latest Models

12 May 2021  /  Updated 13 May 2021

by Glen Bishop

The latest release of papers from SAGE, modelling the relaxation of restrictions, tells us more about the psychology and authoritarian tendencies of the power drunk cabal of scientists in SAGE than they do about the future trajectory of the pandemic.

The overly pessimistic assumptions remain in the papers, such as Imperial’s assumption that two doses of the AstraZeneca vaccine have only an 80% efficacy against death from coronavirus. Numerous studies have found the protection to be 90% or even 95%. This assumption alone, even if all else were perfect, could be causing a two to four-fold over estimation in deaths of those vaccinated. Despite this, the central modelling predictions from the three teams for deaths between now and June 2022 (under the current roadmap) are as follows: 7,500 (Warwick), 9,000 (Imperial) and 11,200 (LSHTM). As data savvy readers will know, these are far below the cumulative deaths in a bad flu season – for instance, the flu season of 2017-2018 cost 22,087 lives, according to PHE.

A rational group of scientists would advise that risks are now within the normal accepted range and thus the end of restrictions is nigh and normal life will return. Unsurprisingly, that is not what these three modelling teams have done. Their models have failed to deliver the pessimism and danger craved by scientists clinging on to power, but a new obsession is taking over – the danger of variants. Imperial elaborates: “preventing the importation of variants of concerns (VOC) with moderate to high immune escape properties will be critical as these could lead to future waves orders of magnitude larger than the ones experienced so far.”

Previous Imperial models have made only passing reference to new variants and never tried to model them, yet Imperial’s latest paper, which shows (even with their modelling) the risk from covid to now be incredibly low, is half filled with predictions of theoretical super variants. The most pessimistic of the predictions entails an imaginary ‘high escape’ variant, which, if we stick to the current roadmap, would lead to a peak of over 4,500 deaths per day and a total of 225,000 deaths this summer. To put this into perspective, it would mean a death rate this summer of 3,300 per million, that is double the death rate in Florida since the pandemic began of 1,669 per million despite Florida being near fully open for the last eight months. It’s a higher total than anywhere in the world since the pandemic began. This is void of reality, but even if it weren’t, what is the proposal? Lockdown for another year until a vaccine for this new variant can be distributed, by which time even more variants will have appeared? One might as well include in the modelling a super infectious variant of Ebola or a new improved laboratory leak from our friends in the Wuhan Institute of Virology.

Zero covid is never going to be reached around the world, thus the potential for variants is never going to abate. Just like with influenza or the other human coronaviruses. This is scaremongering in order to retain control. Scientists who cannot be rational and proportionate should have no place advising governments, let alone dictating policy.

Imperial’s cult-like dogma continues with this statement: “Whilst the impact of Test Trace Isolate (TTI), mask wearing, hand hygiene, and Covid security on R is difficult to quantify, it will be vital to emphasise the importance of normalising and ensuring adherence to all measures even after ‘full lifting’ is achieved.” Imperial is openly admitting here to advocating permanent restrictions on our daily lives, the benefits of which are speculative, not evidence-based and so are “difficult to quantify”. Imperial makes clear with the quotation marks that talk of “full lifting” is an empty promise. It seems Imperial’s team is desperate to keep enough restrictions and measures in place to keep its skin in the game. If a complete return to normal occurs and no resurgence follows, it would be both obvious that Imperial was wrong and that its Covid modelling team is no longer needed. All the power, influence and financial opportunities that come with being a high-profile team of epidemiologists advising governments during a global pandemic would be lost.

This recent batch of papers also correct several assumptions which were previously distorting the output of earlier models but which should never have been made for elementary biological reasons. All the papers now include seasonality. Imperial, which I criticised for ignoring seasonality in my first article for Lockdown Sceptics, now assumes a 20% increase in transmissibility during February compared to September.

Even more damning is how the models previously assumed that a vaccinated person who became infected contributed just as much to transmission as an unvaccinated person who became infected. Clearly, a vaccinated person will have antibodies and an immune response primed to fight the virus, meaning that if infection does take hold it will be fought off more quickly, thereby reducing the total period of time a person is infectious and their peak infectiousness during that period. This is basic biology, but it took a new study quantifying the effect before the teams were prompted to take this into account. This seems naive at best. Ironically, despite the latest data, the LSHTM team still chooses to ignore it and has continued to assume that vaccinated people will contribute as much to transmission as the unvaccinated. Hence, LSHTM’s comparatively elevated estimate of deaths (see below).

This update is the main excuse given for previous overestimates by the teams and for downgrading their apocalyptic projections. It is a recurring theme that uncertainties which have a large effect on epidemic trajectory and dynamics are completely ignored if there isn’t concrete data to quantify them. It highlights how projections, even if the models are perfect, are reliant on far too many uncertainties, which compound together to produce spurious results – far too uncertain to be of any use when it comes to policy advice. At present, science is not advanced enough to accurately project pandemics in the way SPI-M has been attempting to do.

As I have stated before, real world data must take precedence over constantly failing speculative modelling and the American states such as Texas and Mississippi which released all restrictions over two months ago provide us with just that. Cases and deaths have continued to fall after their real “full lifting” and there is no scientific reason why the effect of ending all the restrictions in the UK would produce a different result. We have similar vaccination levels and rates of prior infection; what the UK lacks is competent politicians to cut through the hysteria, ditch the doom-mongers and respond rationally.

Glen Bishop is a maths student at Nottingham University in his second year.