Governments and Covid-19 – sniper fire beats a machine gun

In this rationally argued treatise, the author says that the reaction of governments to Covid-19 has been to respond with panic and a one-size-fits-all, over-the-top ‘solution’ that has done far more harm than good. Instead of drilling down on the special cause of cyclical events as the data emerges and examining excess death history, the ‘experts’ have responded to the common cause, thus creating huge collateral damage to the financial and physical wellbeing of us all. It’s written by a smart and successful entrepreneur who himself – being in the more vulnerable upper age bracket – narrowly escaped death from the virus. Among the sledgehammer-like responses he’s so critical of was heavy lockdown, blanket vaccination, and mask-wearing. As events and emerging data are increasingly showing, these did little to mitigate viral spread. The data on age-related deaths strongly backs his thesis. – Chris Bateman

Covid-19 … a deafening pandemic

By Ted Black*

In early May 2020, in a piece on how the virus of mismeasurement[1] leads to mismanagement, I linked it to the world’s strange, panicked reaction to the Covid-19 virus, noting that UK numbers told us “people above age 65 never account for less than 85% of all deaths. The number at last count was 87%. That raises a question. Why couldn’t the rest of the population have gone to work? The worst that could happen is you die – but, based on evidence so far, probably won’t.”

Being among those in life’s departure lounge, it was tempting the fates. Six months later, this nasty virus struck my wife, thankfully not too badly, then me. With pneumonia, a burst lung and stressed heart, Death called me to the boarding gate.

However, highly competent doctors and warm, caring medical and nursing staff in Plett Mediclinic bumped me off for a later flight. We both caught the latest version of it a year later but it was no worse than a bad cold or mild flu. So, thankfully we didn’t croak.

Even at my age you have a more than 99% chance of survival if you don’t eat and drink too much of the wrong stuff, or have a life-threatening illness. But if it hasn’t been as deadly as the fear mongers claimed it would be, the virus sure has hit the world with deafening, surround sound.

On that topic, Noise – A Flaw in Human Judgement is Nobel prize winner Daniel Kahneman’s latest co-authored book. After his 50-year focus on bias and its effect on decisions, now it’s ‘Noise’. He calls it unwanted variability caused by people and systems.

In law, judges’ rulings differ on similar cases. Medics differ when they diagnose and treat illness. Decisions on issues in common made by government, business leaders and ‘the science’ all vary. He calls that system noise.

It interferes with the flow of information and makes it tough for anyone bombarded by countless opinions and few, if any facts, to decide how to act in a way where gain exceeds costs. It’s especially hard to do in a crisis. You don’t have much time to think. Kahneman says only a statistical approach helps. The trouble is we shy away from what we think is complex maths.

Yet, we can do it with a method described in my 2020 article. It cuts through noise and leads to more effective decisions. Almost a hundred years ago, statistician Dr Walter Shewhart devised it for production settings. It’s his Statistical Process Control Chart (SPC Chart), one of the most important discoveries in management.

Without grasping its true purpose, most managers view it as one of a bunch of tools used in process improvement programmes like Six Sigma and Lean. With most big, top-down change efforts, CEOs hire consulting firms to jab them into their employees thinking that quick-fix training and brainwashing with the latest fad will improve a company’s health. Often, little or no meaningful change follows. That’s because the ‘patient’ has a culture and value system that rejects them.

The charts aren’t just about production control, meeting product specs or targets. Much more than that, when used correctly they promote a way of thinking[2] that lifts productivity. To work, the thinking must start at the top and after that, among operating people, the ‘money makers’. They do the value-adding work along the stream of activities from supplier through to customers who are the purpose of a business. If more of them keep buying from you, profit and growth follow.

It’s all about growing people not controlling them. The aim is to get brains at all levels to focus on the system and keep redesigning it to enable people to work more effectively together to make resources productive. It’s the first of two prime management tasks. The other is strategy, but productivity drives it.

Most managers manage by numbers and ratios but as Shewhart said, they’re only a part of the original data. They make plans and decide what to do with elusive, opaque, often biased and fudged, random numbers. Random variation within the system generates them. To understand what’s going on needs careful, orderly analysis.

Doing a simple, layman’s test of his method with data from the UK’s Office for National Statistics, I started to plot charts in March 2020 and updated them every week since. The number is a physical one and harder for bean counters to fudge. It measures the one issue authorities and social scientists have used to control us: our visceral fear of death.

The table below shows them measured monthly in thousands in England and Wales since 2015.

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Total
2015	70	47	44	41	48	38	44	36	36	48	40	48	540
2016	47	44	43	55	40	37	46	37	44	39	42	52	526
2017	52	48	53	39	40	46	37	37	46	40	41	54	533
2018	56	50	59	45	39	46	37	44	37	39	50	39	541
2019	47	46	53	40	49	39	36	45	38	40	53	42	528
2020	63	44	44	79	67	39	45	37	35	52	49	49	603
2021	73	59	43	47	38	36	38	41	42	55	47	58	586

Displayed this way, what do they tell you? Nothing much if you use the binary approach that compares one number with another. You can do it by the hour, day, week, month, quarter and year but it’s history. It won’t help you predict what’s likely to happen. As the late Dr Myron Tribus of MIT said, “It’s like steering your car by watching the rear-view mirror.”

Instead, let’s first go to weekly numbers and view them through a run chart. The central line (CL) of 10,200 deaths is the average from 2015 to 2019 before Covid-19 struck in 2020.

The wag who posted an image on the internet in 2020 showing a gloomy President Ramaphosa saying, “Covid-19 comes in waves, that’s why we’re closing the beaches,” must have seen a similar graph!

It shows how random numbers are. How big weekly variation is. How predictable the steep, seasonal spikes are as flu and lung disease arrive like clockwork every winter. Then, just like an ocean wave set, deaths hover around or below 10,200 in warmer months before the next surge. Looking at the spikes, doesn’t it make you wonder how much good annual flu jabs do? Maybe they help. Who knows?

Badly weakened by years of cost-cutting and throttled by the dead hand of an ever-growing management bureaucracy, the UK’s NHS hospitals, even before this pandemic, were often under severe strain, coping with the big numbers of patients when winter flu and colds struck.

In late winter and early spring of 2020 when Covid-19 first hit, the wave climbed into what politicians and bureaucrats, pharma-funded ‘scientists’, academics, and panic-fuelling mainstream media, called a “deadly tsunami”. To alarm and keep us fearful, they used daily numbers that show much bigger variation. Yet, even though both waves spiked higher than previous years, it was for only a few weeks. The NHS, even though stressed, never used the emergency Nightingale hospitals formed to help it cope.

Next, we go from weekly data to what most top management teams see – the monthly numbers – and look at them through a Shewhart control chart. Using a given formula, you calculate a central line with average moving range data and use it to set upper and lower limits based on three standard deviations (3 Sigma).

The dotted lines are the upper, or lower natural process limits (UNPL and LNPL). These spell out the range in the data. Coupled to your experience, they help you predict how a process or system, is likely to behave.

The chart reveals what a table of monthly and annual numbers hides. Until you see it, you can’t understand what variation is. Except for four data points and one close to the upper limit in January 2020, deaths fall between 62,900 and 25,600 a month at an average of 44,400.

Most points fit within the two natural limits and have done since 2010. They are ‘in control’. It means you can predict confidently within limits where they will be next month, even next year. Any change is due to ‘common causes’ of variation in the system. They make ‘noise’ that shouldn’t surprise or alarm you.

However, if a data point falls outside the limits, there’s a ‘special cause’ of it. It’s a signal that something different happened. What was it? It’s deeply important to explore it. In the world of work, because most of us don’t see variation, we view all changes in a result as having a special cause. A knee-jerk reaction follows. This can cause more randomness and things get worse, not better.

That’s why it’s vital to tell the difference between the two types of cause before deciding what to do next. When you use only two bits of data – a binary approach – you steer with a rear-view mirror. Put the data into a Shewhart chart and you can look at the road ahead.

Management evangelist Tom Peters punted the idea of “managing by walking around” (MBWA). It’s an enticing, simple notion for a boss: let’s go and see what’s happening out there. However, as Myron Tribus said, if he doesn’t understand variation, workers know what a menace he can be when he reacts to his sample of one, biased opinion.

If it’s a special cause and you can remove it, the system will be more predictable. On the other hand, if it’s a common cause and you overreact to it, you tamper and cause more problems. Finally, if things are predictable but with too much variation, then look for root causes and redesign the system to remove them.

Going back to the chart, the first special cause of unpredictability in January 2015 is unknown; Covid-19 caused the other three. These three points account for 30,500 deaths above the upper limit even though total deaths with Covid-19 were 153,780 to end 2021. The alarming excess deaths numbers given us by ‘the science’ are meaningless. They are binary numbers based on an average.

Looking into the special cause, the next chart shows Covid-19 death curves, or spikes, in England and Wales for those over and under 50 years of age. Bear in mind, this is a fudged number. We don’t know how many people died from or with it.

Lockdown started in late March 2020. The wave crested four weeks later in April; 98% of deaths were among the over 50s and 90% of those were among the over 65s … less than 20% of the population.

Within a month, the data confirmed knowledge gained from the cruise ship Diamond Princess quarantined in Japan during February 2020. This was a perfect test of how the virus would most likely behave in the wider population. Boris Johnson and his team of ‘expert’ advisers must have known that only 14 out of 2,666 (0,52%) elderly passengers of median age 69 died. None of the 1,045 crew did. Their median age was 36.

Instead of using this knowledge, they locked down the dynamic members of the population. These are the young and the money makers who innovate and do the work that builds the future and generates the cash to support the ‘money takers’, and you well know who they are. Authorities then pilloried and censored anyone who challenged ‘the science’ of their approach. They still are.

Then, as winter approached, the second surge began. A sharp climb followed the start of vaccinating on 8 December 2020. The peak came end January with only 13% of the population jabbed. Again, after the steep four-week climb it plunged just as quickly. By end February with only 30% of jabs done, the total death rate was back to normal seasonal levels.

By end 2021, 4,002 deaths were among those under 50. Only 340 people below the age of 30 died; 0,22% of 153,780 total Covid-19 deaths. As for those below 20, there were 86 deaths. Yet, governments backed by big pharma-funded experts, still want to jab and mask them.

The next chart sends a signal and raises an interesting question. It shows deaths among those below age 45.

The stable and predictable monthly average from 2015 to 2019 was 1.550 deaths. In 2020, despite Covid-19 and no vaccinations, the average stayed the same. In 2021, however, it rose by 8,4% from 1,550 to 1,683. Covid-19 deaths rose from 1,452 to 2,550 – an increase of 76%. You may think that explains it. It doesn’t. Total deaths rose faster than Covid-19 ones. Why? Could it be side effects of the strategy? Vaccines? We need the ‘science’ to explain what’s going on.

The last graph, also derived from the UK’s Office for National Statistics puts the pandemic’s effect into context. It compares two trends since 1942: deaths and population growth.

We live longer and even with Covid-19’s impact in 2020, deaths per 100,000 of the population were still lower than in 2008. They should be lower still at the end of 2021. To me, it seems like a blip in a downward trend.

So, what few key insights can we gain from this brief analysis? It seems clear from these charts that futile goals like Zero Covid, lockdowns to flatten the curve, superstitious masking, and vaccines that only seem to treat symptoms, have made no discernible impact on a virus that behaves as they always do.

They arrive in winter, nail the elderly, retreat in warmer months, mutate and return less harmful than before to keep their hosts alive. In time, a more virulent one comes along. It’s all part of evolution and natural selection on Planet Earth which works on balanced input: output ratios. But we keep unbalancing it.

Winston Churchill said, never waste a good crisis. By that, he meant using it to pull people together to achieve amazing results. Instead, this one has been perniciously divisive and destructive. That’s because of devastating, authoritarian mismanagement, not the behaviour of the virus or the world’s citizens.

It confirms in a spectacular but disastrous way, Peter Drucker’s observation that governments do not make resources productive, only entrepreneurs and effective managers do. Few nations haven’t violated the basic principle of concentration and focus of resources; in this case, onto humane care and treatment of the elderly. Instead, they’ve ‘tampered’ on a massive scale. The effects will be with us for a long time to come.

It’s typical of authoritarians to find scapegoats for their failures. They blame the virus for the huge collateral damage that has put the world into such a parlous, unbalanced situation. ‘Flatten the curve’ was the stated aim of lockdowns. Because viruses attack in short-lived, big spikes, do you hit them a sledgehammer? If you miss, what then? You cause a heck of a lot of damage around them.

We now have spiralling debt, rising inflation and unemployment; jobs, livelihoods, businesses destroyed; children’s education and future seriously damaged; the poor getting poorer; rich getting richer; growing backlogs of people needing medical care. Many more will die prematurely because of that. You can keep adding to the list, not least the effects of a vaccine that isn’t one. So much for the Hippocratic oath: Do no harm.

As the American philosopher, Dr Daniel Dennett says, “The purpose of our brains is to answer the question: what am I to do next? – to predict the future. Those who wish to lead should not do what the sea squirt does: the juvenile sea squirt wanders through the sea looking for a rock or hunk of coral to cling to and make its home for life. For this task it has a rudimentary nervous system. When it finds its spot and takes root, it doesn’t need its brain anymore, so it eats it!”

We’ve seen and heard many authoritarian and ideology-driven politicians, tenured academics, pharma-funded ‘scientists’ and ‘obedient’ citizens behave like that.

Thank goodness for the truckers … maybe the worm is finally turning and saying, “Enough of this bullshit!”

Ted Black is a mentor and coach, he uses the ROAM financial model and a 100-Day Action Project method to pinpoint and convert fuzzy problems and opportunities into high-precision, team-driven projects. Their aim is personal growth; to jack up learning fast, and to measure with tangible results. They are management on a small scale – the rule is less talk, more action. Black has written and co-authored several books that include “Who Moved My Share Price?” published by Jonathan Ball.