Bewildered? Mistrustful? You should be. Here’s Peter Koenig, writing yesterday for Information Clearing House:
On March 26, in a peer-reviewed article in the highly reputed New England Journal of Medicine (NEJM), Dr. Anthony Fauci, Director of NIAID (National Institute of Allergy and Infectious Diseases), likened COVID19 to a stronger than usual flu:
“If one assumes that the number of asymptomatic or minimally symptomatic cases is several times as high as the number of reported cases, the case fatality rate may be considerably less than 1%. This suggests that the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza (which has a case fatality rate of approximately 0.1%) or a pandemic influenza (similar to those in 1957 and 1968) rather than a disease similar to SARS or MERS, which have had case fatality rates of 9 to 10% and 36%, respectively.“
This assessment in the New England Journal of Medicine has not prevented Dr. Fauci from saying the opposite, when interviewed by the mainstream media:
Koenig’s piece links only to the NEJM article, not that Health and Science quote, but the latter does check out.
Was Dr Fauci induced to get with the big boys and girls on this?
For those fed up of soundbytes, cherry-picked figures and “my scientist knows more than yours” Facebook know-it-alls, here’s a thoughtful piece by Dr John Lee, a recently retired professor of pathology and former NHS consultant pathologist.
In a witty 1865 worder in the Spectator yesterday under the header, Where is the vigorous debate about our response to Covid? , Dr Lee writes that:
… one particular approach to modelling the Covid-19 epidemic – that of Imperial College – is holding court in the UK. The actions we are taking were based on these modelling results. Barely a day goes by without a politician saying they will be ‘led by the science’. But what we are seeing is not ‘science’ in action. Science involves matching theories with evidence and testing a theory with attempts to falsify it,1 so it can be refined to better match reality. A theory from a group of scientists is just that: a theory. Believing the opinion of that group without critical verification is just that: belief.
The modelling results may be close to the truth, or very far from it. The idea of science is that you can test the data and the assumptions, and find out.
… input data in the run-up to lockdown was extremely poor. For example, it’s highly likely a large majority of Covid-19 cases have not even been detected – and most of those that were identified were in hospitals, hence the most severe. Because of this, the WHO initially suggested a case fatality rate (CFR) of 3.4%, which would have been genuinely awful. But as new evidence comes in the predictions of the models change accordingly. A paper from Imperial on 10 February suggested CFR of 0.9 per cent, a more recent one on 30 March 0.66 per cent (both based on Chinese figures, the reliability of which many doubt). Recent data from a German town suggest a CFR of 0.37 per cent, having found an actual infection rate in the town of about 15 per cent.
From poor quality data2 Dr Lee proceeds to ask about:
… the assumptions of the models? These are many and complex, including, among other things, ideas about virulence, infection rates and population susceptibility, all of which are supported only weakly if at all by directly measured evidence. But to give an example from left field (the sort of thing that destroys predictions): what do the models say about transmission between humans and animals? Apparently a tiger in a zoo has caught Covid-19. But could our cats therefore be susceptible to the disease and could they spread it between us? If true, would that make a difference to the validity of the model? Of course it could. Did the model predict or discuss this? Of course it didn’t.
More surprisingly, the Imperial College paper of 30 March states that ‘Our methods assume that changes in the reproductive number – a measure of transmission – are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour’ (my emphasis). That is to say: in this study, if the virus transmission slows it is ‘assumed’ that this is due to the lockdown and not (for example) that it would have slowed down any way. But this is a key point, one absolutely vital to understanding our whole situation? I may be missing something, but if you are presenting a paper trying to ascertain if the lockdown works, isn’t it a bit of a push to start with an assumption that lockdown works?
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- Dr Lee refers here to an important epistemological principle: that of falsifiability. All knowledge is provisional, and the best we can say of any theory is that it has not yet been proved wrong. We hold scientific knowledge in high regard because its claims – if x plus y then z – take risks. They can in principle be proved wrong, and in practice a competitive scientific community will indeed try to do just that.
- One of Professor Bhakdi’s questions to Chancellor Merkel – preceded by him saying he had no desire to “downplay the severity” of covid-19 – was whether Germany was following other European nations in relaxing criteria for writing death certificates. He wanted to know if the causal chain (three or four links) required by law – patient died of X caused by Y caused by Z – was being abandoned. Since the letter was ignored by the media I can’t say whether he had an answer, let alone what it was. But the UK’s Coronavirus Act 2020 does lower the bar. A GP may attribute to CV-19 the death in a care home of a person s/he hasn’t even seen if that’s what s/he suspects. This cannot but exacerbate the problem of very poor data.