Five reasons people stopped listening to Covid-19 modellers: PANDA’s Krige, Hudson

Flawed Covid-19 models have fed into government policy, with devastating consequences for the economy. Since the beginning of the lockdown, PANDA — a group of actuaries, lawyers and economists — has been providing empirical data parallel to the information given by government-appointed agencies on what the impact of Covid-19 is on the country. PANDA has argued that the true cost of the lockdown, which is on level 2 and has lasted for over six months, was unnecessary and drastic in its implementation, affecting the livelihoods of millions and resulting in excess deaths. The founder of PANDA, Nick Hudson, has been vocal in sharing his evidence-based insight and, in this article, makes a strong claim against the misrepresentation of information which was presented to government in Covid-19 models. – Bernice Maune. 

Covid-19 models: Why the public isn’t listening to modellers anymore 

By Shayne Krige and Nick Hudson

PANDA has been inundated with requests for our model and with notes from members of the public thanking us for being “the one accurate source of information”. Why does the public not take epidemiological experts seriously anymore? 

It is not that we are generally disrespectful of authority, nor is it that we have been brainwashed into believing that Covid-19 is not the disaster it was made out to be. We don’t listen to these experts for the same reason we don’t obey the bizarre lockdown regulations anymore.

Epidemiological modellers have made themselves irrelevant by producing work that failed to align with reality and they remain unrepentant in the face of overwhelming evidence of their failings. They made themselves irrelevant by choosing to support the hand that feeds them and allowing their work to be used to justify violations of our constitutional rights through illegal lockdowns that destroy lives and livelihoods on a daily basis.

Earlier this week, we learned that top American Covid-19 experts made a basic error by confusing “Infection Fatality Rate” (IFR) with “Case Fatality Rate” (CFR) resulting in the overblown Covid-19 predictions that prompted crippling lockdowns across the United States.

This is not the first example of incompetence from the Covid-19 experts. Imperial College, who have a history of exaggerating disease outbreaks, produced a model that was described as “totally unreliable” and “a buggy mess that looks more like a bowl of angel hair pasta than a finely tuned piece of programming.”

Read also: Covid-19 vaccine expert Prof Madhi on trial delay, township immunity – and why lockdown is a bad idea

Although it is not even clear when they were using that model and when they weren’t, they confidently predicted over half a million deaths for the UK (42,000 actual deaths to date well after the peak) and over two million deaths in the United States (195,000 actual deaths). The World Health Organisation (WHO) repeatedly blurred the distinction between IFR and CFR and was publicly rebuked by Sweden and other nations for misrepresenting their data. 

The South African epidemiological modelling team also produced figures of between 89,000 and 351,000 deaths and a requirement for between 236,000 and 944,000 hospital beds. How useful were these predictions? Anyone who’s ever hosted a dinner party knows that it’s very difficult to plan accurately when you’re being told that either 23 or 94 guests will be coming.

Our experts seemingly chose to present to government only the worst numbers that could be reasonably justified. When presenting the 351,000 number to government, it seems they failed to mention that, using WHO assumptions on infection, their model also estimated 19,800 deaths and a requirement for 70,200 beds.

We have yet to find someone who would have supported a six-month lockdown, the loss of millions of jobs and the loss of thousands of lives to suicide, depression and poverty, had they known that the death toll would be about the same as a bad flu season and amount to around 4% of the total deaths for 2020.

PANDA brought an application forcing the modellers to release the initial model and when it was released, we were told that it was never intended as a tool for policymaking but only for “situational awareness”. Strange then that Stellenbosch University never saw fit to respond to press reports citing that model as the reason Cabinet was motivated to implement lockdown. Without pomp or ceremony, the modelling team “internally withdrew” the initial model and joined a consortium, SACMC, that produced the “Epi Model”, forcing PANDA to make another application for the new model.

The Epi Model, produced by a consortium of Universities under government’s National Institutes for Communicable Diseases (NICD), predicted 10,000 deaths by the end of June (the actual figure was 2,657), 30,000 by the end of July (the actual figure was 7,812) and 48,000 deaths by the end of September. They stand by that last figure, which would mean roughly 3,800 deaths a day for the rest of the month – we are currently on 118 deaths a day and descending.

For those of us whose livelihoods are impacted by market forces, those of us who get ignored or fired if we are incompetent, it seems crazy that government still takes these people seriously. Or do they? Are these models simply a convenient justification for the theft of emergency cash? It is important to understand the reasons for this situation so that it doesn’t happen again.

Reason 1 – The role of uncertainty in lockdown

The theory of science – that hypotheses develop into scientific theory – has been abandoned by many epidemiologists. To wit, Hippocrates “First do no harm” principle has been discarded. In fact what Hippocrates said was, “The physician must be able to tell the antecedents, know the present, and foretell the future – must mediate these things, and have two special objects in view with regard to disease, namely, to do good or to do no harm.” In 2019, the WHO recommended against lockdowns  – saying the evidence of their effectiveness was limited. 

SACMC recently claimed, “[W]e have made it clear that our projections are subject to great uncertainty.” In response to the criticism that government made no attempt to model the cost of lockdown, SACMC responded, “[W]hat is the meaning of a finding that the cost of the lockdown outweighs that of an uncontrolled pandemic by a factor of x if the effect of the lockdown on the economy is unclear; the duration of this effect has to be fully assumed, and the effect of an uncontrolled epidemic on the economy is also unknown?”

To be clear, the logic being deployed here is that whilst it makes sense to model the impact of Covid-19 even though there is “great uncertainty” about all aspects of the virus, it is pointless to model the impact of lockdown given the uncertainty of economic modelling.

This from a team that does not even claim any qualification in economics. We now know, including because Neil Ferguson of the Imperial College has told us so, countries that imposed no lockdown performed precisely as well as those that did. China’s lockdown did not save Wuhan and emulating China was a foolish strategy. Lockdown is China’s default response. As it has everywhere else in the world, in Wuhan the virus simply did what the virus does until herd immunity developed.

The South African epidemiological modelling team also produced figures of between 89,000 and 351,000 deaths and a requirement for between 236,000 and 944,000 hospital beds. How useful were these predictions?

PANDA, which does have relevant experience in-house, showed that there is far less uncertainty in modelling lockdown than the epidemiologists assumed. Every day, insurance companies work with models that predict the impact of poverty on life expectancy.

Every day, economists calculate the number of people that will be impoverished as a result of government economic policies. And those calculations are subjected to real-world, market verification. Those who get these calculations wrong go out of business.

What we need to learn from this process is that when faced with uncertainty, government should not take draconian steps. 

Reason 2 – Lack of qualifications

Modelling Covid-19 is not just about modelling the course of the virus. It is about understanding the economic impact of lockdown, it requires data mining and computer coding skills, it requires knowledge of actuarial science. Modelling teams must be integrated, multi-disciplinary teams. The SACMC acknowledges this requirement but government as a whole failed to properly identify the skills needed and assemble teams that were properly qualified to model Covid-19’s impact.

On the other hand, those of us who criticised the models have, for months, been told to, “stay in our lane,” and let the experts do their work. The implication being that all epidemiologists were aligned. They were not. Many disagreed with the SACMC, WHO and Imperial College. Moreover, experts in other fields have been proven to be more correct than the experts, notably because, for many relevant analyses, epidemiological skills are less relevant than data analysis skills, statistics and economics. 

People respect science because it has proven itself to have better predictive power than other predictive techniques. They have lost respect for these particular experts because their predictions have failed.

Reason 3 – Transparency in lockdown 

Emergency situations should require a higher, not a lower, level of scrutiny of decision-making and the advice that supports it. Only recently were minutes of the deliberations of the advisory council on Covid-19 released. We still do not have a clear answer on what science government relied upon to take the decision to lock down, other than the initial 351,000 deaths model. 

There is a cosy relationship between government and academia that creates conflicts of interest and exposes academics to the risk of becoming accessories to corruption. Most of the major South African universities are represented on the SACMC and they signed non-disclosure agreements with government.

One academic from the University of Cape Town, who was employed by SAMRC to assist with excess death calculations, propagated the false claim that the SAMRC found Covid-19 had led to three times the official death tally whilst proudly announcing that he was also involved in modelling Covid deaths for the NICD.

Another UCT academic threatened legal action against the press unless they de-platformed critics of his work. Universities had to be forced through legal processes to publicise their work so that it could be peer-reviewed. And SAMRC, the custodian of excess death data, has yet to correct those who misrepresent their data.

The WHO estimates that a 6-month disruption of antiretroviral therapy in South Africa could lead to more than 500,000 additional deaths from AIDS-related illness in 2021.

Public scrutiny of scientific models and transparency of data should be a minimum for any model that policymakers rely upon.

Reason 4 – A paradigm problem

Epidemiologists are wont to exaggerate the impact of virus outbreaks. Being mostly government employees and academics, epidemiologists are, by and large, immune to market correction.

They think it a bigger sin to underestimate deaths and required healthcare resources than it is to overestimate them and therefore care more about not being wrong than being accurate. They also believe that big numbers are required to scare governments and citizens into action. However, they failed to concede that lockdown changes the paradigm, given the known harms that result.

The exaggeration does not end with the outbreak. After the Swine flu outbreak in 2009/2010, the WHO took the official figures of 18,000 proven deaths and applied a model that turned these into 151,700 – 575,400 deaths prompting a finding by the European Parliament that the WHO had deliberately exaggerated the severity of the Swine flu outbreak. We also see excess death and second wave scaremongering across the globe.

Reason 5 – Protecting reputations

The scientific process involves robust public debate of ideas and public retractions of explanations that are falsified. For many academics and government employees admitting mistakes in the public eye is uncomfortable.

Instead, what we see the world over is some academics clinging to positions they took early on, attempting to explain away their errors by arguing that deaths are being missed, that deaths are still coming or that the level of uncertainty was so high that no one could possibly have done a better job.

Last week the SACMC made the false claim that Covid-19 was only eight months old and that little could be known of something so new. Covid-19 is, in fact, SARS-COV2, substantially similar to SARS-COV1 and by the time South Africa had its first death, on 27 March 2020, there was a significant amount known about SARS-COV2, including that lockdowns were not flattening curves or producing any other benefits upon which they were premised.

Earlier this week, SACMC claimed, without any scientific support, that 80% of the excess deaths that have occurred during lockdown are Covid deaths. To be clear, SACMC is suggesting that over 20,000 people died in this country from Covid-19 without being diagnosed, despite the fact that South Africa has conducted millions of tests.

SAMRC themselves do not claim that 80% of excess deaths are Covid deaths, but the unsubstantiated claim by SACMC just happened to be about the number of deaths missing from the most optimistic estimate the Epi Model ever produced (40,000 deaths).

A survey recently revealed that 57% of people who needed hospital care in South Africa were apprehensive about visiting a clinic or hospital during the hard lockdown and that 13.2% of individuals who need medication for chronic diseases found that medication to be inaccessible.

Read also: Covid-19 rules kept the sick away and now they’ve disappeared – SA doctor

There has been a 59% drop in TB tests and a 33% increase in new TB diagnoses over lockdown. By July, 1,090 TB patients and 10,950 HIV patients in one province alone had not collected their medication on schedule since the start of the lockdown. TB accounted for 124,000 deaths in South Africa in 2016 and even a mild decrease in quality of care would result in a large number of excess deaths.

The WHO estimates that a 6-month disruption of antiretroviral therapy in South Africa could lead to more than 500,000 additional deaths from AIDS-related illness in 2021. Terminally ill cancer patients and car accident victims have been listed as Covid deaths.

There has been a surge in suicides with nearly 2,000 recorded over lockdown so far indicating high levels of anxiety, depression and other mental illnesses in a society which are known to cause poor health. Cancer diagnoses and treatments are down and both Discovery and Momentum medical schemes have shown significant decreases in members accessing hospitals.

Hospitals are reporting increases in malnutrition cases, in amputations, emergency appendectomies and heart attacks. The UK reported 6,000 excess deaths that have been confirmed not to be Covid. In short, all indicators suggest that lockdown has led to a dramatic increase in deaths and it seems unrealistic to say that these represent only 20% of the increase we have seen so far. Any statement that large numbers of Covid deaths have been missed should be treated with scepticism particularly if it comes from a group that has modelled significantly more deaths than the verified deaths.

The main purpose of modelling Covid-19 was to prepare the healthcare system by predicting healthcare resources. All over the world, epidemiologists got this wrong. Our own Hospital of Hope, like the Javits Convention Centre and the Comfort Navy hospital ship in New York were underutilised.

SACMC overstated the number of Covid-19 beds that would be required at peak by 18 times and ICU beds that would be required at peak by 15 times. They can dig for deaths in the excess deaths pile to make their fatality predictions more right, but they cannot find hospital patients who never turned up. 

The truth of the matter is that there is no evidence that the problems with the Epi Model are explained by a massive failure of our healthcare system to correctly identify Covid deaths. Nor is there evidence that South Africa has performed worse than any other country (which would be a consequence of excess deaths being Covid deaths). Instead of admitting that they were wrong, government (including the SACMC) seeks to protect the legacy of its response to Covid-19. 

We have a voraciously corrupt government that had first got caught with its hands in the cookie jar when Minister Dlamini-Zuma was in charge of a healthcare response to another epidemic—HIV/AIDS. The scientists and academics working for the NICD, SACMC and SAMRC are being paid with taxpayer money and have received full pay throughout lockdown.

Each time they insist that their models are correct and government spends money on their recommendation, they are accountable to us. The day for holding them responsible will come. In the interim, the public that has been treated with such disdain is right to move on and listen to others.

For more on Covid-19 models:

Dr Jo Barnes: Why epidemiologists like me reject “just a bad flu” assessment of Covid-19

PANDA: SA govt Covid-19 model continues to ‘grossly overestimate’ deaths

Covid-19 epidemiologists: Fortune-tellers or witches? PANDA reviews the evidence

Ted Black: Government handling of Covid-19 has imposed unnecessary poverty on millions

Who will answer for Covid-19’s dark science? Brian Pottinger busts 10 myths. MUST READ!

(Visited 8,509 times, 372 visits today)