COVID-19. Part 3 — What’s with the high fatality ratio?

There were two indicators that formed the basis for ‘locking down’ and other drastic actions: spread and severity.

In Parts 1 and 2, we saw that the knee-jerk strategy for monitoring the virus’s spread was so flawed that those monitoring it had no idea when, where, or even whether it was spreading.  In this instalment, we’ll take a look at attempts to estimate its severity.  But first, let me tell you a story.

The speckled-duck story

Jones has a duck farm with two varieties of ducks.  To keep things simple, we’ll call them white and speckled.  The white ducks are all white; the speckled ducks are also white but they have speckles to varying degrees.  The two varieties are difficult to distinguish until you actually pick them up and take a close look.

One morning Jones felt that something was killing the speckled variety.  His careful tally of the 100-odd duck carcasses collected each month showed that, on average, half of them (50) were speckled.  Of course, this would be expected if speckled ducks made up half of his flock, but Jones was quite certain that only around 10% of his flock looked speckled.  He decided to check.

In order to check, he had to count them.  But there was a problem.  There were roughly 10,000 ducks on the farm — all in one large pen — and the variety of each duck couldn’t be distinguished without catching and examining it.  How would he do this?  

There were three ways.  The first was to catch each and every duck and tally how many were speckled.  This is what’s known as taking a census.  Jones had neither the time nor the inclination for such a tedious task.  

The second was to catch a smaller number of ducks — say 100 (which amounts to 100th of the total flock) — and count how many of these were speckled.  Then, provided he’d randomly selected the ducks (and not simply targeted the birds with darker or lighter shading) he could multiply the count by 100 to get an estimate of the total number of speckled birds in the flock: a survey.  But Jones knew nothing about surveying, so he didn’t choose this option.

The third was to simply catch as many as possible in the time that he had available that morning — about two hours — examine them for speckles, and tally the results.  However many speckled ducks he could find during that time would then become his best estimate of the total number of speckled ducks on the farm.  

Obviously the last option isn’t methodologically sound (no one should ever make an important estimate in this way), but it’s included here because it’s the one that appealed to Jones, and it’s the one that he adopted.  After morning tea, he caught and counted furiously and didn’t stop until lunch.  But Jones worked smart.  He didn’t attempt to catch and examine all the ducks; that would take him days.  He’d worked with ducks for many years, and reckoned he could pick those most likely to be speckled by their slightly darker shading — a kind of off-white.  Using this duck-sense, he would knock the job over quickly.

At any rate, by lunchtime Jones had a grand tally of 1000 speckled ducks and, from that moment, declared that there were 1000 speckled ducks on the farm.

Now it was time for some calculations.  The normal rate of loss of farm ducks in Jones’s location was 1% per month.  In other words, provided conditions were normal, duck farmers expected 1% of their flock to die each month due to old age, attack, etc.  That’s one death per hundred ducks, or 10 per 1000.  But Jones was losing 50 of his 1000 speckled ducks each month — which was 5% of his total speckled ducks!

Jones immediately called in an expert, who alerted the authorities, who in turn placed his farm in quarantine amid fears of a mystery illness.  Production ground to a halt, workers were laid off, and Jones started reaching into his savings to pay for what seemed to be never-ending and ever-conflicting advice.  

Finally, after nearly two months and exhaustive forensic efforts, it was decided that the speckled ducks did in fact have a mystery illness and needed to be destroyed.  Furthermore, the destruction had to be carried out by the authorities at a cost of one dollar per duck.  Reluctantly, Jones gave the go-ahead and drove to the bank to withdraw the last $1000 from his savings account to pay for destruction of every speckled duck.

Alas, on his return from the bank, he was handed a bill for $5000!  

It turned out that the authorities carefully examined each and every duck and found that 5000 were speckled.

The moral of the story?  It’s important to count your ducks carefully, especially when the farm depends on it.  Jones had been careful in calculating how many of the dead ducks were speckled.  That was easy.  The problem arose because he didn’t make the same effort in calculating the speckled proportion of the rest of the flock.

Of 5000 speckled ducks, 50 of them dying each month represented a 1% per month death rate — completely normal.

There had been no problem to begin with!


When governments world wide declared states of emergency, closed businesses, and ordered people to stay apart or at home, they did so due to a perception that people with the virus were dying at a high rate.  The primary indicator behind this perception was the fatality ratio.

A virus’s fatality ratio is simply the proportion who died, of those infected with it.  It is commonly expressed as a percentage, and calculated — as it was in the duck example above — via a fraction, with the number of deaths on the top and the total number infected on the bottom.

In order to understand what went wrong with the fatality ratio for COVID-19, we need to look at how these numbers were arrived at.

First, the bottom number was, as we highlighted in Parts 1 and 2, a  simple count of those who had so far tested positive for infection with the virus.  The top number was the number of deaths in those who had tested positive either before or after they died.  The ratios calculated in this way were published in relation to the world as a whole; to various countries; and to communities within countries.

For example, if a community counted 100 deaths and a total of 10,000 infected people, the fatality ratio was 1%.

On the surface, a fatality ratio calculates the average chance of dying if infected.  But the calculation’s legitimacy rests on a very important condition: that the effort to examine the living for evidence of the virus was equal, proportionally, to the effort to examine the dead.  

That rings a bell, doesn’t it?  Jones carefully examined the dead ducks for speckles, but his troubles occurred because he neglected to carefully examine the living ducks.  

Unequal testing effort

Italy attracted attention early on due to what appeared to be a high death toll, so other parts of the world looked to it for an estimate of the fatality ratio.  Here’s how it was calculated.

By March 20, as Australia was phasing in its shelter-in-place measures, Italy had recorded just over 4000 COVID-19 deaths.  These formed the top number of the fatality ratio.  At that time, the country had tested just over 200,000 individuals for the virus.  The simple count of positive results from these tests (47,000) formed the bottom number.  

A quick calculation yields a fatality ratio of more than 8 per cent.  This is a very large fatality ratio, and certainly ample justification for drastic action.  But let’s check for duck-farm blunders.  Remember, Jones carefully examined all the dead ducks but made a very poor effort to examine the living ducks.  He didn’t even conduct a survey!  That’s what got him into trouble.  

What did they do in Italy?

By March 20, the authorities had tested around 200,000 people for the virus, as mentioned.  As the calculation below shows, this represents roughly 0.33% of the Italian population. 

The virus-positive results from this miniscule effort formed the bottom number of the fraction (47,000).  To put that effort into perspective, consider Jones selecting 33 ducks from his 10,000 flock, counting how many of them were speckled (16 or 17 if they were chosen randomly; 33 if he really did have an eye for the speckles), and using that for his total count of speckled ducks on the farm.  As you can imagine, no matter how hard he’d tried to choose the birds most likely to be speckled, such a plan would be pure folly.  Yet medical authorities around the world obtained their infection figures in exactly this way.

The next question we need to ask is whether this proportion (0.33%) of the entire population was comparable with the proportion of deaths examined (tested) for the virus.  Unfortunately the proportion of deaths tested isn’t published, but we can still answer our question.

Under normal circumstances, an average of around 12,000 people die each week in Italy.  Therefore, over the four weeks that it took to accumulate the 4000 COVID-19 deaths, we would expect 48,000 deaths to have occurred.  Let’s assume for a moment that the official assumption is correct, and that the 4000 COVID-19 deaths were additional to the expected deaths from other causes, giving a total of 52,000 deaths from all causes.

We’d like to know how many of these 52,000 were or had been tested for COVID-19.  As already mentioned, we can’t determine that.  What we do know, though, is that 4000 of them were declared COVID-19 deaths.  So, at a bare minimum, those 4000 were tested.  We can, and probably should, surmise that many more than this were tested — possibly all, or nearly all — but we do know for certain that at least 4000 were tested.  Taking 4000 as the minimum, then, we can confidently say:

Comparing the testing rate of the dead (at least 7.69%) with that in the general population (0.33%), we can see that the effort to locate infected people proceeded in the dead with at least 23 times the intensity that it did in the living.

This is understandable: Italy had a policy of testing all hospital admissions, and those in the process of dying are commonly admitted to hospital.

Mind you, Jones too had good reasons for focussing his tallying efforts on the dead ducks.  Unfortunately however it meant that he failed to properly tally the living ones, and that’s what led to his problems.

Italy was not the only country for which a fatality ratio was estimated, of course.  There were many of them — and all calculated their fatality ratios as we did above.  At the start of March, the World Health Organisation (WHO) announced that the global average fatality rate was 3.4 per cent.  They contrasted it with seasonal flu, which they said “generally kills far fewer than 1% of those infected”.

Such fatality ratios were quoted frequently in the media, drumming up intense alarm and seeming to justify the drastic actions that we were about to take.  The ratios varied considerably between communities, and even within communities over time; but, until recently, all of them arose from this flawed methodology.

Which fatality ratio?

Some will think that I’m conflating two different fatality ratios.  This section is particularly for them.

There are two types of fatality ratio frequently discussed: the Case Fatality Ratio (CFR) and the Infection Fatality Ratio (IFR).  The CFR is the number of deaths divided by the number of confirmed cases, and the IFR is the number of deaths divided by the number infected. 

Note that they share the same numerator (top number): the number of deaths.  Their difference lies solely in their denominators (bottom number).  One uses confirmed cases, and the other uses infections.  Of most illnesses, a confirmed case is defined as an infection accompanied by a specific set of clinical manifestations.  That’s not the case with COVID-19.  The WHO defines a confirmed case of COVID-19 as a lab-confirmed infection irrespective of clinical manifestations.  That means that, in the case of COVID-19, the denominators for the CFR and the IFR are practically identical — one is a lab-confirmed infection; the other is an infection.  Hence, for all practical purposes, the two rates are the same; the only difference being ascertainment.  Put simply, any difference reported between the CFR and the IFR for COVID-19 is nothing more than a measure of how well we ascertained infection — again, regardless of clinical manifestations.

Having read Parts 1 and 2, you may guess that case ascertainment for COVID-19 was poor.  Indeed, the results of recent studies, which I will discuss in the next instalment, suggest that it was woeful.  

At any rate, the question on everyone’s lips is: what’s the danger if I get infected?  It’s not: what’s the danger if I get infected, and someone notices that I’m infected, and I happen to be tested at the right time, and that test happens to turn up positive?

How to count properly

There are ways to accurately estimate the true number of infected people, even when it’s impractical to count them all.  In the duck-farm story, Jones should have chosen option two — taken a random sample of the ducks, counted the speckled ones amongst them, and extrapolated that proportion to the entire farm.

A similar approach can be taken with infections in a population.  By conducting a serological survey (serosurvey for short), we can estimate how many individuals have been infected.  Serosurveys use a test that’s different from the COVID-19 test we’ve been using to ascertain cases: rather than test people’s mucus for presence of the virus, it tests their blood for antibodies to the virus, indicating whether they have been exposed to it at some point.  Testing a representative sample of the population provides a gauge of the number in that population who have been infected.

It has been quite some time since I published Part 2 in this series, and I promised that I’d follow it with a post about deaths.  I’ve been awaiting the results of these serosurveys.  They have started trickling in, but, as you may have foreseen, they have met with considerable resistance.  I can understand why, as they correct the flaw in the “method” described above and thereby demand a very uncomfortable rewrite of our recent history.

In the next instalment, we’ll take a look at serosurveys.  These represent our first real attempt to estimate a relevant denominator and thence a legitimate fatality ratio.

Later, we will explore all-cause mortality, which refers to all deaths in a population.  This measure follows a surprisingly consistent pattern throughout the year.  It deviates from that pattern during crises such as war, famine, economic depression, and epidemic illness.  All-cause mortality is great in that it can tell us exactly how many have died, free from the various problems of undercounting, sampling bias, and distinguishing who died from what.  And, although it can’t tell us what caused any deviations, it can help us to identify possible causes, by examining the deviations’ timing and extent.

COVID-19. Part 2 — Is it (or was it) spreading?

Part 2 in a series covering the COVID-19 issue. Please read Part 1 first.

It was the declaration that COVID-19 was spreading in epidemic fashion that justified the unprecedented restrictions we’re now living with. Was that declaration based on evidence?

When the media and, later, health experts all around the world, noted that the number of people testing positive was increasing substantially each day, they announced that the disease was spreading uncontrollably. But in Part 1 we discovered that they were overlooking something vital: that the number not testing positive was growing in parallel.

This is a basic mistake in science. Very basic. Let’s use another example to illustrate. Say we want to assess the extent of left-handedness in our city. We start by counting five left-handed people in our own street. A couple of days later we count 50 in our neighbourhood. We then recruit some helpers and count 500 in a whole suburb. The following week we ask the team to spend the next three weeks covering the whole city. Let’s say they count 5000.

Would that mean that the extent of left-handedness in our city increased dramatically from five to 5000 in the space of one month?

This naturally raises, then, another question: whether the available evidence supports the thesis that COVID-19 is or has been spreading.

To answer that question definitively, we need to know whether there has been an increase in COVID-19 incidence: cases per capita. This can be determined only by comparing random, representative samples of the population over time.

Unfortunately, on COVID-19, we don’t have such data. It is possible, though, to test the thesis here and there.

Daily testing figures from many countries have been published, and, as mentioned in Part 1, some of these include day-by-day totals for both the positive and negative test results. Although these samples are neither random nor representative, being fresh daily surveys of a defined subset of the population makes them worth looking at; and the eligibility criteria for this subset has remained fairly constant throughout. So plotting the ratio of positives to negatives for each day should let us gauge the trend. If the disease is spreading, the positive-to-negative ratio in a region should rise; and if the disease is spreading in epidemic fashion, the ratio should rise exponentially.

So let’s take a look at that. Starting again with South Korea, Figure 5 reminds us of the simple case count (i.e. the positives only) that was the basis for the pandemic declaration. (The data used to plot the following three graphs can be compiled from official South Korea media releases.)  It looks convincing at first, doesn’t it.

Fig 5. COVID-19 positive test results (daily and cumulative), South Korea

Remember, it was these numbers, reproduced for each country in turn, that prompted media calls for urgent action. (I’ve highlighted the steepest part of the rise in red; I’ll explain why shortly.)

Let’s now plot the daily ratio of positives to negatives. In a disease increasing in typical epidemic fashion, we should see this ratio increase markedly as the days pass. Let’s first examine the entire period that Figure 5 covered, before focusing on a particular part of it.

Fig 6. Daily COVID-19 positive to negative ratio, South Korea

I’ve included a computer-generated ‘line of best fit’ in these plots to help us visualise the trend. As mentioned above, in an epidemic we should see an increase; in fact, it should be an exponential increase. But here we see the opposite: a decrease.

Hold that thought.

Now let’s check the most dramatic segment of the period: the part marked in red in Figure 5. Clearly this represents the steepest increase in cases. If that represents a spread in the disease, we should at least see an increase here in our ratio of positives to negatives (Figure 7).

Fig 7. Daily COVID-19 positive to negative ratio, South Korea

But, as you can see, we see no such increase. Even during this — apparently, South Korea’s worst — period, there was no increase. Significantly, too, the South Korean Government imposed no lockdowns, curfews, or other severe social restrictions on the population. Was this because South Korean health experts read the signs correctly? They focussed on extensive testing, quarantining only those who tested positive but not otherwise introducing physical distancing.

Let’s now take a look at the U.S.A., where a lot seems to have happened since I published Part 1. Again, we’ll start with a reminder, in Figure 8, of the image that prompted first the media and, later, health experts to call for severe social restrictions to curb the ‘pandemic’. (Data for the following two graphs is available from the COVID Tracking Project.)

Fig 8. COVID-19 positive test results (daily and cumulative), U.S.A.

Now let’s jump straight to the daily positive-to-negative ratio over the same period, in Figure 9.

Fig 9. Daily COVID-19 positive to negative ratio, U.S.A.

This time, I’ve coloured the dots blue up to March 16 and red thereafter. I did this because the U.S.A. went into virtual lockdown after the 16th, giving us an opportunity to compare before and after and a line of best fit for each of the two periods.

The first thing to note is that the ratio decreased in the first period, just as it did in South Korea — and has increased since.

The blue half was the period during which the country was accused of dragging its heels: no social restrictions. President Trump was accused of not taking the threat seriously, and he came under intense international pressure to do something. So he did. It is only since then that the U.S.A. has been under ever-increasing lockdown, and only since then that test results suggest any evidence of COVID-19 spread.

Are you seeing a pattern yet? South Korea:- no lockdowns; COVID-19 incidence decreasing, even during the apparently worst period. The U.S.A.: no lockdowns, and incidence decreasing; then lockdowns and incidence increasing

I mentioned in Part 1 that the published data for Australia were scanty. One state, however, has published enough data to enable a similar analysis: New South Wales. Figure 10 shows the ratio of positives to negatives in that state from early March. I have again split the data into before (blue) and since (red) restrictions were put into place.

Fig 10. Daily COVID-19 positive to negative ratio, New South Wales

In Australia, restrictions date back to March 13, when the Prime Minister announced that outdoor gatherings were to be limited to 500 and indoor gatherings to 100. From that day, offices began instructing staff to work from home, and university students began switching to online classes. People were advised to stay 1.5 metres apart. On March 23, pubs, clubs, gyms, cinemas, and places of worship were closed by order, and restaurants and cafes were restricted to take-away trading. Gatherings were further restricted to 10, before being limited on March 27 to just two.

Again we see a downward trend in the ratio of positives to negatives prior to the restrictions, and an upward trend following them.

Since writing Part 1, some readers have urged me to present Italy’s data. Case numbers can be found via the official Italian source, or an English translation from Wikipedia, but, unfortunately, neither provides a day-by-day count of ‘negatives’. Although it’s tempting to calculate these by subtracting the ‘positives’ from the total number tested, I’ve resisted doing that so far, because it’s actually not the correct way to do things. Due to the time it takes to process tests, the positives reported on any day represent outcomes of tests that were likely carried out one, two, or even more days beforehand.

Fig 11. Daily positive as a fraction of total daily COVID-19 tests, Italy

The data in Figure 11, showing the number of positives reported on each day as a fraction of the total tests reported on that day, will therefore be somewhat inaccurate due to such date slippages. Nevertheless, the numbers suggest a modest increase throughout most of the month of March, followed by a decrease at the tail end. Italy was under severe restrictions for the entire period graphed. The country’s lockdowns began on February 27 in several northern regions, and increased as the days went on, culminating in a strict nationwide lockdown on March 11. For two weeks following this strict lockdown, a very high proportion (around one-quarter) of tests returned positive results.

Back to the point

I present all of this not in order to argue that the restrictions were counterproductive, although they may have been, but rather to demonstrate that the justification for imposing them did not exist. Data for both Australia and the U.S.A. showed no indication that we were facing an epidemic that required unusual intervention, let alone the restrictions we now face; and South Korea, one of the most densely populated countries in the world, demonstrates that such measures were not needed there.

Of course, there may be other explanations for what we have observed here:

  • there may be further data that the various governments have not published (although the possibility that they had published some of the data but left out the bits that provided the justification for their actions is so bizarre as to be unlikely);
  • in the cases of Australia, the U.S.A., and Italy, the increases observed after restrictions would have been much worse had restrictions not been imposed (although South Korea suggests quite the converse);
  • the data were of poor quality and shouldn’t be used for this (which, though valid, would raise the questions of what data they did base their decisions on and why they did not share them).

In relation to the last point, it’s worth noting that the tested population is not necessarily representative of the wider population (as those tested have had to meet certain criteria) and will change as the eligibility criteria for testing become more inclusive. To my knowledge, the criteria have been stable over the period examined in this post, at least in Australia. At the time of writing, however, news is emerging that the requirement to have had overseas travel or contact with a known case is about to be relaxed.

Hindsight is always good; I realise that. Nevertheless, the decision to impose social and other restrictions, the likes of which we’ve not seen before and hopefully will never see again, was evidently not justified even at the time it was made. Hindsight merely rubs that in.

One final point: even if we were facing a substantial threat, what is the evidence that lockdowns and other restrictions are the answer? Writing about the harms of exaggerated information in relation to COVID-19, John Ioannidis, one of the most cited medical researchers in the world, wrote that “A systematic review on measures to prevent the spread of respiratory viruses found insufficient evidence for entry port screening and social distancing in reducing epidemic spreading.”

Of course, we have not yet addressed the deaths ascribed to COVID-19. And I’m afraid that that topic will have to await the next instalment.

COVID-19. Part 1 — What could be worse?


The data and conclusions below are all factually correct. Although they are extremely alarming, I believe the new illness discussed herein offers us a greater understanding of our current predicament. But please read to the end of this article before buying more toilet paper!

What could be worse than COVID-19?

We’re in the grip of a pandemic. Most of the world is in lockdown, to some degree. Businesses are closing, social support structures are disintegrating, and human interaction is systemically breaking down. In some parts of the world residents are not allowed to leave their homes. Many pundits are forecasting widespread economic collapse.

The justification for all this is that we have a new disease on our hands that is spreading uncontrollably, threatening to invade every corner of the world unless it’s arrested. Every day we hear that the cases are soaring, along with headlines such as “Medical services at breaking point”, or “Virus out of control”. 

Right? What could be worse?

Well, there is something, and it’s actually much worse: a ‘new’ illness on the radar. It actually emerged at the same time as COVID-19 and has risen at the same rate as COVID-19 over exactly the same period of time. But for some reason we’ve heard nothing about it. To top things off, it has exactly the same symptom profile as COVID-19. In fact, the two are clinically indistinguishable!

Coincidence? Maybe. It’s been given the mysterious name “NC-19”. I’ll explain why later.

Getting hold of good data on NC-19 is difficult. At the time of writing, a search on “NC-19” turns up nothing; several mainstream media corporations have, however, been slowly uncovering it, including reporters from The Atlantic (U.S.A.) and The Guardian Australia. I will discuss what they’ve found shortly and lead you to where you can find the data. But first I want to show you something breathtaking from the country considered to have collected the best data on both COVID-19 and NC-19.

Figure 1 is a graph of South Korea’s NC-19 cases, both the cumulative count and the daily ‘new’ cases.

Fig 1. NC-19 cumulative and daily case count, South Korea

For comparison, Figure 2 shows South Korea’s COVID-19 cases over the same period.

Fig 2. COVID-19 cumulative and daily case count, South Korea

Note the similarity. Now look closer at the vertical axes… and hold on to your seat! There have been more cases of NC-19. Quite a few more. Almost 40 times more, in fact.

What’s going on? Why have we not been told about this before?

Let me repeat: The two illnesses emerged at the same point in time, and they spread at the same rate, over the same period. As well as this, NC-19’s symptoms mirror those of COVID-19 exactly.

Oh, there’s one more thing. All cases of NC-19 have been lab confirmed.

The only difference is that the cause of NC-19 has not been found. Of course, those familiar with lab confirmation will ask “how can it be lab confirmed if the cause isn’t known?” Good question. I’ll explain later, but, for now, please just accept that the cases have been lab confirmed, because they have.

So what’s going on?

If you feel like you’ve missed something, you’re not alone. Why have the media been silent on NC-19? Not a peep from anyone. Could it be that the disease is only in South Korea? The answer to that is an emphatic “No”. This is happening all over the world. It’s just that South Korea has published a fairly full set of data on it.

Clearly, whatever we have to fear from COVID-19, we face in a far bigger sense with NC-19. But we hear nothing about it. For some reason, the entire world is focusing on one disease and ignoring another that’s much larger.

As mentioned earlier, reporters from The Atlantic have managed to collate a limited set of data on cases of both (COVID-19 and NC-19) in the USA for the month of March 2020. Again the results are mind-blowing (see Figures 3 and 4).

Fig 3. NC-19 cumulative and daily case count, U.S.A.

Fig 4. COVID-19 cumulative and daily case count, U.S.A.

The story is similar to South Korea’s except that this time NC-19 is only around five times the problem that COVID-19 is. Why might that be? An explanation may be found in the fact that the U.S.A. has come under heavy criticism for its lack of testing. 

Note that the numbers on the graphs are multiples of 1000. That means that, despite the lack of testing, as of this writing the U.S.A. will likely have confirmed more than half a million cases of NC-19. That’s about the same as the entire worldwide tally of COVID-19.

In my home country of Australia, reporters from The Guardian Australia have collated all publicly available data on NC-19. Unfortunately they’re too scanty to graph day by day, but the latest case numbers they’ve cumulated (up to the time of this writing) are in the table below, with their COVID-19 comparisons.


These data come only from three states; but, as you can see, Australia appears to have roughly 50 times more NC-19 than COVID-19 cases.

Again, why have we heard nothing of all this? Could it be that the situation is simply too dire? The world is almost at breaking point with COVID-19. How much more could we handle?


This is clearly a distressing situation. For many it will be the first time hearing of NC-19. It’s tempting to hope that there’s something wrong with the data. There’s not, although while the media stay silent such hope will continue. Once the story breaks, it’ll be different.

But did you see what I did there? Probably not. Those who understand the situation are likely laughing their heads off, because they know exactly what I did.

I just did to you exactly what the media has done to you.

I used factual data. (Yes, it’s completely factual. You can check it for yourself using the links below.) I presented it graphically and with no tricks. But I made you think it was something big, when the truth is that it means nothing. That’s exactly what the media did to you with COVID-19.

By the way, I was the one who gave it the name NC-19. It stands for “NOT COVID-19”. That’s the only part that was made up. 

So what is NC-19 exactly? The simple answer is that it’s all the people tested for COVID-19 who turned up a negative result. All are identical to COVID-19 cases in every respect except the test result.


  • the two diseases emerged at the same time;
  • all were lab confirmed (positive if they were COVID-19 and negative if they were NC-19);
  • all were sick with exactly the same symptom profile (having had to satisfy the same criteria).

I promised that you could confirm the data for yourself. Just remember, I gave the disease the name — so don’t expect to see “NC-19” listed at any of the following links. Just look for the test results: positive means COVID-19, and negative means NC-19. Here are the sources:

  • South Korea – you’ll need to go through all the media releases one by one;
  • USA – all collated for you;
  • One example of criticism of the USA for poor testing and record-keeping;
  • Australia – collated but, as mentioned, scanty.

There’s one thing left to explain. Why did the numbers rise at the same rate? COVID-19 is spreading throughout the country because it’s a ‘new’ virus, right? And that’s what all the panic is about, right? NC-19 isn’t a ‘new’ virus, so why is it doing the same thing?

And that’s the whole reason for this article. Read on for the important take-home message!

You can’t count on just counting.

When something is new — or even not new but just something we didn’t know was there before (perhaps because we couldn’t see it) — and we start noticing it for the first time, obviously how many times we find it depends on how much effort we put in.

Does that make sense?

With COVID-19, we’ve been increasing the number of people we test each day. For example, Australia had tested fewer than 200 people by the end of January (according to official reports). By the end of February, we’d tested well over 2000; by mid-March, more than 20,000; and now, as we approach the end of March, close to 200,000. That’s an exponential rise in testing.

Can you see what’s happened? The more tests we conducted, the more results we got – both positive and negative. That’s why we see the startling graphs above. It does NOT mean either of the diseases is increasing. It only means that testing is increasing. Because our testing rose exponentially, our results — both positive and negative — also rose exponentially. That’s no surprise: of course they did!

But the media have turned that simple observation into headlines such as “Cases rising exponentially”. The correct headline would have read “Testing rising exponentially”.

This covers just one aspect of the conundrum. It’s a very important one, as it has laid the foundation for this evolving apparent emergency. But there is much more to cover. What, for instance, about the deaths? What about Italy? For now, although it may be easy to assume that the more severe later symptoms (including death) must be commoner in those who have tested positive than in those who tested negative, no evidence supporting that has been published. In fact, there’s no suggestion that those who have tested negative have even been followed up — implying that the test could simply be turning away many who are actually ill.

In the coming days, I’ll write more about COVID-19, to try to shed new light on what’s going on. Specifically, I’ll cover the following topics (which in this article will become active hyperlinks as they are  published):

  • Is it spreading? (The entire justification for the current restrictions!)
  • Is it dangerous? (What about Italy, etc.?)
  • Is it real? (What does the test even signify?)

I will also discuss the role that the media have played in all of this.
In the interim, talk to your friends. Explain to them what has happened to cause a perceived rise in cases. With all of us doing our little bit to apply critical thinking to what we’ve learnt of the situation, perhaps we will find a way to restore it in our communities.

Read Part 2 — Is it (or was it) spreading?

Vaccination destroys herd immunity?

The theory of herd immunity goes something like this:

1. Vaccines produce antibodies, which are like guards that stay resident, ready to catch a particular villain if it tries to enter. This is called immunity;

2. Mass vaccination creates mass immunity, or herd immunity;

3. When the herd is immune, the disease has nowhere to go; it’s refused entry, everywhere; so it doesn’t hang around;

4. This herd immunity protects those who, for whatever reason, can’t be vaccinated.

Sounds like a good idea, doesn’t it? I don’t subscribe to it myself. But if you’re one of the majority that do, prepare to be disappointed by the study I’m about to discuss.

First, a bit of background. Intravenous immunoglogulin (IVIG) is manfactured from blood donations. It contains antibodies and is used to treat patients with immunodeficiency disorders – that is, those who can’t produce enough antibodies on their own.

But a problem has emerged recently in that the levels of measles antibody in donated blood have been declining over the years, and are now too low to reliably meet the requirements (at least those in the USA) for IVIG.

There’s been speculation within the industry that the decline is due to vaccination. So a peer-reviewed study published just 13 days ago set out to address that question.

In a nutshell, the researchers pooled the donated blood into lots based on donor birth year. They then analysed the mean levels of measles antibody separately for those who, based on their birth year, would have received:

a) no vaccine;
b) a single dose of vaccine (either killed or live vaccine); or
c) two doses of live vaccine.

According to the findings, the unvaccinated cohort were brimming with immunity. They had three times the level of antibodies as the single dose group, and eight times that of the double dose group!

The reason there’s been a slow overall decline is that, as the unvaccinated population ages, it is gradually replaced by a doubly-vaccinated population that has almost no antibody.

Put simply, mass vaccination, rather than creating herd immunity, is destroying it.

So, those who believe in the concept of herd immunity now have something to grapple with. Especially if they believe it can be achieved via vaccination.

Good luck!