Article 6

The Commonsense Approach.

In the Daily Telegraph of 29/4/20 Allison Pearson took the commonsense approach to assessing the significance of the death toll registered for Covid-19 compared with those of more familiar diseases. Thus, she reported that cancer kills 165,000 people in the UK every year while nearly 170,000 people die of heart disease with 42,000 of those being classified as premature;  that tragically those deaths are about to go through the roof because fear of corona virus has deterred thousands from going to hospital; and that it may well be that “excess deaths” will eventually exceed those attributed to the virus. Again, she reported that just 140 people under the age of 40 had died in this pandemic, while recalling that one gets absolutely no sense of these comparisons from the alarmist reportage of Covid-19 deaths.  Furthermore, she reports that while the daily press-briefings continuously emphasise the numbers of new cases and deaths since the previous day, little to no attention is given to the economic impact of the lockdown in contrast to the significance of her foregoing comparisons of death rates.

As to ‘the science’ which underlies the current government assessment of this pandemic and justifies its lockdown response, the relative absence of media criticism prior to that of Matt Ridley and David Davis is surprising. However, their Daily Telegraph article of 10/5/20 simply asks ‘was lockdown based on crude guesswork?’ Nonetheless, while they answer in the affirmative, they would be more convincing had they properly compared non-science (guesswork) with science. Before proceeding, I will again describe the unique nature of science, which I first published in my third book in 2010 and which continues to be available from Amazon on the basis of print-on-demand. In this book, entitled The Rational Trinity: Imagination, Belief and Knowledge, I describe Science as cause-effect knowledge and its method of acquiring this knowledge as being to imagine the cause of an effect; to treat this belief as a hypothesis for evaluation of its compliance or non-compliance with reality by design of an experimental apparatus which enables the cause to be applied and the effect to be observed in isolation from all other possible causes. If the effect is observed, the cause is identified. If it is not observed, then the scientist must design an experimental apparatus which will enable a second cause to be thus reality-evaluated. When the scientist has thus identified the true cause, his experimental apparatus will enable him to vary its magnitude and to observe and measure the changed magnitude of the effect, from which couplings he produces a mathematical equation which enables quantified effects to be calculated from measured causes ever after, this knowledge having been acquired for all subsequent time.

As to mathematics, it can be concluded that equations are transformable to other equations by their rearrangement; but that such rearrangements produce no new knowledge, they merely make it possible to apply existing knowledge content in other ways such as to calculate effect from cause, all of the sequential equations in such mathematical analysis being restatements of the initial equation, this being the meaning of the repeated equals sign in all such transformations.  As to mathematical modelling the knowledge mathematically inserted into the model is the knowledge which comes out. The only advantage of such modelling is the ability to apply computing-power to the intervening mathematical analysis.  It is possible, however, to use computers to test the validity of a hypothesis by producing outcomes for comparison with reality. Thus, meteorologists interested in weather forecasting can test the effects in the atmosphere of measurable parameters such as temperature and pressure by modelling their effects in respect of their ability to predict the onset of such large scale effects as the trade winds or the monsoon in any given year; to observe the extent of the observed agreement in reality; and thus to evaluate the effectiveness of their current understanding of how such temperature/pressure measurements might relate to their ability to forecast weather more generally and more accurately.

It is clear from Ridley and Davis that the models used by ‘the science’ respecting Covid-19 predicted mortalities no better than guesses. Thus, in comparison with my foregoing analytical comparison, we see that instead of using the actual mortalities to evaluate their theories as to infection transmission, Ferguson sought to predict the actual mortalities. In confirmation of his failure to do so, Ridley and Davis report that Ferguson’s track record is that his modelling in 2001 led to the culling of 6 million livestock and was criticised by epidemiologists as severely flawed, while later in the 2000s he predicted 136,000 deaths from mad cow disease, 200million from bird flu and 65,000 from swine flu when the final death tolls in each case were in the hundreds.  Again, they report that application of the Ferguson model to Sweden’s Covid-19 strategy predicted 40,000 deaths by 1 May – 15 times too high; and that according to Edinburgh University which ran the Ferguson model, the same inputs gave different results on different machines, and even on the same machine with different central processors. 

          9/6/20