This post follows on from the overview of the basis of calling COVID-19 a a pandemic. Soon after lockdowns were imposed, based on the Imperial College (IPC) model, as discussed in previous posts, another team of scientists put forward a very different model that suggested the infection rate had already started to fall before lockdowns were initiated, making the catastrophic impact of shutting down economies completely unnecessary. This has been proven to be correct, with the ghastly mortality rates predicted by the IPC being orders of magnitude lower, and many analysts putting forward critiques reveal the flaws of model that show how flawed it is.

On 21 May 2020, Freddie Sayers of Unherd spoke to Professor Sunetra Gupta, professor of Theoretical Epidemiology at the University of Oxford, (and c0-author of the Barrington declaration), and also the head of the team that released a very different model about a week after the Ferguson model, “which speculated that as much as 50% of the population in the UK may already have been infected and the true Infection Fatality Rate could be as low as 0.1%.” In their view, the virus had likely already passed through the population and was on the wane. Gupta also affirmed what other experts have been saying; when comparing different lockdown scenarios in various countries the pattern of illness had been very similar, i.e. lockdowns had not changed the course of the disease.

Lockdowns have not “flattened the curve”

Since the hard lockdowns were implemented,(via video presentations on his YouTube channel, Peerless Reads, Mather has been analysing the changes in prevalence rates for many, many countries, based on the data submitted by states to the WHO, including for South Africa. Mather notes that while some countries, particularly in the EU, had experienced the highest death rates in 2020 – and even if these higher death rates have been accurate – still the meme that lockdowns have flattened the curve is not evidenced by the epidemiological representation, and he demonstrates this by comparing various countries’ data. Here is one of Mather’s shorter video presentations. PANDA also have constantly updated data on the global mortality rates, for countries and regions, on their website, where it is quite clear that there is no correlation between mortality rates and lockdown stringency.

The flawed model that formed the basis of the lockdown measures

The Imperial College paper exaggerated the danger of the situation 150 times greater than the reality.

Andrew Mathers

A key problem Mather identifies with the Ferguson model is that while it was seeded exponentially, which does model how epidemics develop initially, rising steeply in the early stage, the problem was that the “epidemic” continued to be modelled exponentially, when it would not continue to develop in such a way, which is a prime reason for the massive inflation in the modelled prediction of the death rates. He also notes that the model implies that 100% of people will get infected, which is a second reason why the modelled number of deaths became inflated. His and Gupta’s findings accord with that of Panda’s, as mentioned earlier.

During the course of his analysis, he also came to note an intrinsic flaw in these kind of epidemiological models – they don’t accurately account for the time it takes to recover, which further skews the picture towards an exaggerated death rate.

Shockingly, this is history repeating – there are devastating precedents where the projected outcomes of viral outbreaks cause the imposition of measures that result in untold suffering on a mass scale, where the predictions are later found to be exponentially exaggerated and the measures unwarranted. An excellent two-part analysis, by Vanessa Beeley on UK Column, compares the impact of lockdown versus no lockdown and deconstructs the UK government’s response to the alleged pandemic. This article also looks back at past decades, to lay bare the wake suffering caused by projections of the 2001 foot and mouth disease “epidemic”, which led to the forced culling of millions of animals, with generations-old businesses going under and many suicides. Researchers have since found that the models, generated by a team also led by Neil Ferguson, formed the evidentiary basis for these actions. Neil Ferguson also headed modelling teams that have presented the epidemiological projects for “bird flu” and “swine flu”, which were also found to be exaggerated.

Redoing IPC’s model confirms falling infection rate before first lockdown

A recent article in The Spectator in the UK, on 14 April 2021, “COVID and the lockdown effect: a look at the evidence,” by Professor Simon Wood, gives an overview of a paper he had published in Biometrics on 30 March 2021, which outlines how he redid the ICP model, but with some specific changes. Since these results are so important in light of the model that locked the world down, I quote Wood extensively.

“In particular, the Imperial team’s model assumed that they knew when and how R [R is the basic reproduction number – the average number of people one person with an infectious disease will likely infect in the future] was changing. They only needed the data to tell them by how much it changed.”

Simon Wood

“I repeated Imperial’s analysis, but with one important difference: the data were used to also determine when and how R changed. The Imperial model then gives a very different result. It suggests that R was already below 1 before lockdown. If that is the case then, rather than surging, new infections were already in decline.

“In the same paper I also used a different approach, bypassing the Imperial model altogether, to directly estimate the daily number of new fatal infections from the data on daily deaths and fatal disease duration. This direct approach also strongly suggests that infections were in substantial decline before lockdown, and that R was already below one.”

“And then there is Sweden: the western European country that tried a different approach, and did not lockdown. My analysis finds that its daily infection rate started declining only a day or two after the UK, as the following plot shows [see article and paper for plot].

“The Imperial study does not disagree that Swedish infections declined without lockdown. In fact, to accommodate this anomaly their model treats the final March intervention in Sweden (shutting colleges and upper years secondary schools) as if it was lockdown. As many others have pointed out, that’s a strange way to model the set of data that most directly suggests that lockdown might not have been essential.

“Imperial’s Report 41 — the paper published last December that concluded that ‘only national lockdown brought the [UK] reproduction number below 1 consistently’. It fits an impressively detailed model with hundreds of equations to data on Covid-19 test results, hospital and care home deaths, hospital and ICU occupancy and hospital admissions. As with the previous Imperial study, the key question is when, how and by how much the R number had changed — this time over most of 2020.

“Again as with the previous study, Report 41 assumes that the ‘when and how’ parts are essentially known, and the data need only be used to tell us the size of the R changes. The conclusion reached is that lockdowns are essential to bringing R below 1 and that deaths in the first wave could have been reduced from 36,700 to 15,700 had lockdown been called on 16 rather than 23 March.”

 “Two other points from Imperial’s Report 41 were troubling:
 The model structure forced the average time from infection to infection to be quite a bit longer than the times reported in the literature.
The model-fitting appeared to be set up in a way that attributed unusually low weight to the actual data, relative to assumptions built into the model." 

“With professor Ernst Wit of Università della Svizzera Italiana in Switzerland, I repeated the Report 41 analysis as reported in a pre-print (a not yet peer-reviewed study) on medRxiv. The Report 41 assumptions around the first lockdown are even more restrictive than in Imperial’s earlier study, and we again replaced them with an approach that allows the data to tell us when and how R changed, as well as by how much. Because far more data are involved this time, the scope for our own assumptions to bias our results is lessened, but we nonetheless took an approach designed to minimise such problems.”

Through this process, Wood and Wit found that: “Imperial’s model was using key input measures that were shorter than the times given in the published papers cited as sources,” which included the incubation period, from infection to symptoms, estimated to be 5.5 or 5.8 but was 4.6 in the IPC model; and the time from showing symptoms to hospitalisation was reduced from a mean period of six days to four. “These two changes subtract three days from the model time between Covid infection and hospitalisation, compared to the values given in the cited literature. This is not a small issue if so much is to be made about every day mattering.”

“We tried to correct each of these four issues. The resulting model and analysis are very far from perfect, but we think that the results can give a somewhat more accurate picture of what the data imply than the original.”

Wood and Wit provide a graph of the different model that emerged, providing a modelling of “infections, by region and in total, around the time of first lockdown. Again the results imply that infections were in retreat before lockdown was called.

“So the most reasonable interpretation of the publicly available data seems to be that R was less than 1, and infections in decline, before each of the three full lockdowns to date. Measures short of full lockdown, and perhaps people’s own behavioural response to rising deaths, appear more likely to have been responsible for turning the tide of infection.

Wood also notes that in a paper published in March in the Journal of Clinical Epidemiology, Vincent Chin, John Ioannidis, Martin Tanner and Sally Cripps also use, “the same Imperial model together with a second model, also originally produced by the Imperial group. By fitting these alternative models to the data, they show that, if anything, the data imply that lockdown had little or no benefit. At a minimum, this means that the Imperial results are not robust.”

The next post looks more closely at mortality rates.