How Deadly is Covid-19?

By Dr Juergen Ude | June 16th 2021
The world’s self-destruction over Covid-19 has been justified on the basis that SARS-COV-02 is so deadly that we have no choice but to resort to the drastic actions we are used to. But more and more people now question the science, the motivation, the numbers, and the overriding objective of saving lives, without regard to the need for freedom, without regard to the possibility that we are causing deaths, without regard to perspective and without regard to the pain caused for so many. Mental health cannot be cured with money.

We now have considerably more data than we had beginning of 2020, making it appropriate to re-analyze available data to determine whether the virus is truly so deadly that it justifies the self-destruction. This is unfortunately not a straight-forward process due to the way the virus has been managed by health authorities.

We will start by discussing five different statistics used to measure deadliness and then examine registered deaths for selected countries.

Reported Deaths

These are reported daily throughout the world.

It is not possible to make an all-embracing guaranteed statement that covers every death classification definition in the world. Our general perception is that globally reported deaths are rarely based on autopsies, or any other evidence, other than a positive test result coinciding with death. Reported deaths, by admission from most countries are dying WITH Covid based on a positive test taken even a month ago. Reported deaths are not dying FROM Covid. Some doctors in Australia and overseas have confirmed that patients they know did not die from Covid 19 were classified as Covid deaths because they had a positive Covid test. Members of the public are also confirming this.

"Our friends mothers had terminal illness and were rung up and told they had died of covid .. they both said we know she didn’t , this was during Victoria’s big covid death count."

This practice is no different than defining a pimple death as a death where a patient had a pimple within the last 30 days. Pimples will suddenly be seen as deadly. In India, we know that many deaths were classified as Covid deaths not based on laboratory tests but simply because of having flu like symptoms.

The conclusion is that reported deaths are biased towards the death being caused by Covid-19 when another cause may have resulted in the death. This is called ascertainment bias. According to the BBC ascertainment bias during other pandemics has inflated true deaths by a factor of up to 10 times.

For these reasons reported deaths alone have no credibility as a measure of deadliness because we do not know how many deaths were truly due to Covid. It may be a lot, or a much smaller number. To make a firm statement either way is unscientific and twisting reality which is 'insane'. Yet that is what we are doing. We treating numbers as facts.

Cases (also see the related CFR below)

Governments and health advisers use cases to make lockdown and other containment decisions because they believe high case numbers equate to high number of deaths. For example, on the 10th of June it was reported by the Daily Mail (Covid Australia: Scott Morrison warns borders will remain closed even when most people vaccinated) that the Australian Prime Minister said if everyone vaccinated not to expect opening of borders because in the UK, even with 77% of adults vaccinated people, there are currently 4000 cases per day. He does not want 4000 cases a day in Australia because he assumes that cases mean deaths. Assumptions are not the way to manage pandemics.

Case reporting is misleading because -

  • Cases depend on test numbers. All things equal, the higher the test numbers the higher the cases.
  • Cases depend on sampling bias. When there are out breaks testing is encouraged in areas of the out break biasing the overall severity of the pandemic.
  • Cases are based on mainly on indirect tests such as the PCR test which was never designed for this purpose and whose sensitivity can be biased with cycle settings. Cycles above 25 are considered to result in excessive false alarms. Reports are now surfacing that cycles are set at 45 which if true is comitting medical fraud because positive tests are likely to be false positives. Peoples lives have been destroyed for nothing if the reports are true.
  • Cases do not reflect deadliness. Deadliness changes especially with variants.

As with reported deaths, to have blind faith in case numbers, without any regard to the reliability of the numbers and what the numbers mean is twisting reality in our minds.

Crude Mortality Rate

This is the probability of any person in the population dying of the disease. This measure does not tell you how deadly the virus is. It depends on how many people have been infected and died from the infection. If the infection rate is low deaths will be low and hence the CMR low. For island countries such as Australia and New Zealand who could easily lock borders the CMR is low relative to other countries, but this does not mean the virus is less deadly. Once the virus spreads the CMR will increase.

This does not make the CMR meaningless. It is a very useful measure. It tells us the impact on the overall population in a period. The impact will depend on number of infections, containment actions, the health status in a country, treatment competency, the health system and the reliability of the reported death statistic. Countries that report ‘died from’ instead of ‘died with’, based on autopsy will have a lower mortality rate.

The percent CMR is simply calculated as 100*Total Deaths / Population Size.

Based on 2020:

GLOBALLY UK USA Germany Sweden South Korea Japan Singapore Global Road Accidents Global Total Deaths
0.025% 0.1% 0.1% 0.04% .086% 0.0017% 0.00263% .004% 0.016% .76%
UK 0.1%
USA 0.1%
Germany 0.04%
Sweden .086%
South Korea 0.0017%
Japan 0.00263%
Singapore .004%
Global Road Accidents 0.016%
Global Total Deaths .76%

These figures show that the impact of the virus was different from country to country. It does not mean that the virus was deadlier in some countries than others. The Asian countries may have been less biased in reporting deaths, treatment may have been better, and infections in the population less. Until this is investigated, not by academia, but experts who know what goes on in the real world we will never know.

Interestingly, without drawing conclusions infections and deaths were less in countries without drastic lockdown. This relationship was found in Australia. Again, without jumping to conclusions Victoria/Au had the highest cases and highest deaths and highest number of lockdowns and other drastic responses.

If we make an adjustment for ascertainment bias by a factor of ten, as has been the case with previous pandemics then the CMR becomes:

GLOBALLY UK USA Germany Sweden South Korea Japan Singapore Global Road Accidents Global Total Deaths
0.0025% 0.01% 0.01% 0.004% .0086% 0.00017% 0.000263% .0004% 0.016% .76%
GLOBALLY 0.0025%
UK 0.01%
USA 0.01%
Germany 0.004%
Sweden .0086%
South Korea 0.00017%
Japan 0.00263%
Singapore .0004%
Global Road Accidents 0.0016%
Global Total Deaths .76%

The unfortunate reality is that we simply do not know by how much the figures were inflated due to ascertainment bias. We also do not know how many deaths were not reported, though for these countries this is expected (not proven) to be lower than for countries such as India and the Philippines.

All we can say is that with the uncertainty it is wrong to convince the trusting public that the virus is so deadly that it justifies the pain caused by following the responses recommended by Health Advisers.

Case Fatality Ratio

This is a very crude and dubious measure often used by the media. It is calculated as follows.

CFR= Total deaths divided by Total Cases.

Because of the delay between cases and deaths the estimate can underestimate the true CFR if taken early in the pandemic. It is an unreliable indicator of the deadliness of the virus because -

  • Cases are dependent on test numbers which makes use of case percentages meaningless. All things equal, as test numbers increase during a pandemic, cases increase which lowers the CFR.
  • Sampling is biased (testing those with symptoms only) which inflates the CFR. Initially those with major symptoms were tested due to lack of testing equipment hence case fatalities were high at the beginning of the pandemic.
  • The ratio does not consider deaths caused by human factors, such as bad treatment, lack of treatment, and panic by patients and doctors, especially at the beginning of a pandemic with its uncertainties. This inflates the CFR causing more panic.
  • The ratio is inflated due to the ‘pimple death’ bias.
  • Do not consider demographic factors.

If the CFR is low for most age groups why cripple a country instead of focusing on those who are frail and old with comorbidities.

The Australian Prime Minister’s assumption is based on an assumed case fatality rate of 2% which would result in 80 deaths a day. Currently the UK with the alleged deadly Delta variant is averaging 6100 cases per day over the period 4th to 11th June 2021 with only 8 deaths per day. Based on this data the current UK Case Fatality Ratio is 0.1% which is at alleged seasonal flu levels.

Based on 2020:

GLOBALLY UK USA Germany Sweden South Korea Japan Singapore
2.1% 2.9% 1.75% 1.9% 2% 1.4% 0.01% 0.05%
UK 2.9%
USA 1.75%
Germany 1.9%
Sweden 2%
South Korea 1.4%
Japan 0.01%
Singapore 0.05%

These figures show a large variation in the CFR amongst countries. This variation demonstrates that the CFR is an unreliable measure of deadliness. The listed european countries show that the risk of death is high if you are infected. For 2020 in the UK 3 people per 100 cases died and yet in Japan only 1 in 10000. This shows that there is factor involved that experts should have tried identifying instead of recommending draconian mitigation action. One possibility could have been less ascertainment bias by Asian countries, another possibility could be panicking European and American countries. Intuitively panic can result in more deaths in some of the following ways -

  • Over-treatment by panicking doctors. We know from US volunteer groups that helped Italian doctors that doctors did panic.
  • Hospital overwhelming by a fearful public
  • Reluctance to check into a hospital for fear of catching the virus.

Some experts may jump to conclusions noting the UK had the Kent and Indian variants which some experts claim to be more deadly. Our analysis has found no evidence of this. What is of interest is that end of June 2020 the CFR for the UK was 14%, i.e., almost one in 10 patients who tested positively died according to this statistic. If the CFR were a true measure of deadliness many more people will have died in the UK.

If we assume an ascertainment bias of a factor of 10 as has been shown to be the case in past flu pandemics, we will obtain these statistics:

GLOBALLY UK USA Germany Sweden South Korea Japan Singapore
.21% .29% .175% .19% .2% .14% .001% .005%
UK .29%
USA .175%
Germany .19%
Sweden .2%
South Korea .14%
Japan .001%
Singapore .005%

These are flu rates. Of course, these are based on assumptions and assumptions are not facts. The point is to raise the possibility because ascertainment bias has been high in the past.

Now consider CFR for the avian flu (H5N1) reported to the WHO during 2003 and 2018. The case deaths were 55% (fifty five percent), far greater than COVID. One in 2 people who contracted this flu died. Why was there no concern? Perhaps we did not panic because China did not set the precdence of locking down entire cities? Perhaps we were confident that virus was not contageous.

In summary the CFR is affected by too many factors to determine the deadliness of Covid.

Infection Fatality Rate

This is the risk of dying if you have the virus which is the statistic of real importance.

The IFR is calculated using this formula. IFR=100* total deaths/ Number of infected people

Unfortunately, there are three problems with this statistic.

  • We do not know the total number of infections.
  • We do not know the real number of deaths because of the ascertainment bias.
  • Deaths depend on several factors including comorbidities, age, hospital capacity, medical competence etc. Without taking these into account our responses are likely to be be unnecessarily destructive and nothing more than a shot gun approach.

We do not know the total infections because not everyone will get tested. Not everyone fears the virus. The number of cases detected through testing are probably only a fraction of the number effected.

How can we estimate the percent of infected people at a time? Sampling. But we do not sample. One can argue that tests are a form of sampling but unfortunately testing is not random. Only a certain group of people who are probably more frightened than others will submit themselves for testing. Testing is also focused on ‘hotspot’ areas. Another problem is that many countries, including Australia report test numbers and not number of people tested. Sometimes people are tested up to ten times until a positive result is obtained. The UK test numbers as at 18th June 2021 is 3 times its population size.

To obtain some insight, into the number infections in a country, we will use Japan who report people tested. This still poses a problem because some people repeat test frequently. Hence, we can only use a limited time horizon during which time repeat testing with the next sniffle is likely to be low.

During the period of 1st November 2020 to 31st of January an estimate of the prevalence based on people tested is 7%. Thus, the number of case equivalents is 126,000,000*7/100= 8,820,000. The number of reported deaths during this period is 3977. The IFR is therefore 100*3977/8820000=0.045%

That figure is likely to be lower because it does not include asymptomatic cases and ascertainment bias cannot be assumed to be zero for Japan. There is also the possibility that some people repeat tested during the period.

According to data from Japan the IFR is low, but the estimate can only provide an insight.

The US CDR has provided some estimates which are higher, but these are based on models which we cannot trust.

Registered Deaths

Total registered deaths can be inspected to see if there has been a change in expected deaths. Some call these excess deaths. Unfortunately, excess deaths do not really measure deadliness. Measuring deadlines would assume that the excess deaths are due to the virus. Such assumptions are dangerous and can ruin lives and probably have ruined lives. It may not be the virus that caused the excess deaths. Other factors may be responsible such as –

  • Overwhelming of hospitals due to mismanagement of panic.
  • Reluctance to seek treatment for a life-threatening ailment for fear of catching the virus due to mismanagement of panic.
  • Over treatment by panicking Doctors. Ventilators and ECMO can kill healthy people.
  • Nursing homes placing elderly on morphine with a confirmed positive case. There is considerable anecdotal evidence in Victoria that this happens.
  • Fear induced immune system breakdown by patients having been told they have the deadly corona virus.
  • More suicides

Pandemic experts calculate excess deaths with a simple formula as follows:

% Excess Death = 100 * (Current deaths – average deaths of 5** previous years for same period) / average deaths of 5 previous years for same period

** Some experts use different values e.g., 3.

This method is unprofessional because it can inflate excess deaths because the method is affected by random variation from year to year. This is shown below for Wales + England.

BIS.Net Analyst - Curve Fitting Analysis used in Covid-19 analysis - Registered Deaths
Registered Deaths for 2020 to June 2021 - Wales
BIS.Net Analyst - Curve Fitting Analysis used in Covid-19 analysis - Excess Deaths
Excess Deaths for 2020 to June 2021 - England

Academically calculated excess deaths often exaggerate the situation. Hence Control Charts as used in Industry are the better solution to determine if during the Covid period there were more than normal registered deaths.

The Control Chart for the UK shows that the deaths are certainly higher than the norm but not statistically excessive by real-world standards. Some countries even had higher values during the flu pandemic.

BIS.Net Analyst - Hybrid SPC used in Covid-19 analysis - Total Registered Deaths in England and Wales from 2020 to June 2021
Total Registered Deaths for 2020 to June 2021 - England and Wales

The difference between the year 2020 and the average of the last ten years was calculated as 26824 deaths.

A report by the UK Government SAGE report estimated that hospital Chaos has led to 46000 avoidable deaths by ‘end of next month’. The report was dated 17th of December 2020 and hence we can assume this number applies to 2020. This was for the UK (which includes Northern Ireland and Scotland).

This supports the possibility that we killed many people due to mismanaging panic.

For an insight into the deadliness of the Virus by age group using data for England and Wales there was no statistical change in the covid period for all age groups below 50. This does not mean that some people below 50 did not die with it. Less road deaths may have compensated. But we cannot be obsessed with covid. If over all it did not affect normal deaths, then we need tp take it down a notch or two.

This begs the question. Why are those below 50 asked to vaccinate. There is already evidence that it does not stop infection and spreading infection. The argument now used is that it stops the person from dying. Are we sure? Then why not focus on the risk groups and stop wasting vaccines on people that do not benefit and give it to countries short in supply?


The science applied during the pandemic is so appalling that we cannot determine reliably how deadly the virus is. This has resulted in academic estimates which have grossly inflated the risk of dying which is not supported by the numbers.

As a matter of opinion not even the deaths encountered in countries such as the USA justify the destruction of lives. There will be those saying, had we not had such drastic actions we would have had far more deaths. That needs to be proven.

Unfortunately, most countries were not like Sweden who initially did not resort to drastic containment actions, but there are sufficient other examples that show that without draconian lockdowns we would not have had the deaths alleged we would have. South Korea is one example. Arkansas, Iowa, Nebraska, North Dakota, South Dakota, Utah, and Wyoming in the USA did not issue orders during March and April as did other US states and their excess deaths were no different than those of states that did.

There is a very strong possibility that we have killed people many if not most people through mismanagement of fear and panic and liberal use of ventilators.

No statement of fact is made but we must now be mature enough to face up to the possibility that we made terrible mistakes. We cannot go on living the way we are on the basis of data that has no credibility. The mess needs to be investigated using real world experts from outside a pure academic background.

The mistakes we made are -

  • We relied on academic advice and did not listen to proven real-world experts
  • We did not factor in the human factor that can cause death.
  • We were obsessed with the singular objective of saving lives. Living was ignored. What animal would choose captivity over freedom to extend its life expectancy.
  • We let fear stop rational thinking.
  • We believed everything we saw and read. We did not even consider that many photos were staged to dramatize.
  • We used propaganda to brainwash people which is moraly wrong. These peopel trust the authorities but were mislead with false information.
  • We used a shot gun approach without a targeted approach to various risk groups.
  • We used old strategies relying on quarantine.
  • We did not use out-of-the box thinking.
  • We censored real experts.
  • We assumed that mainstream thinking had the solution. Throughout history it is the minority that changed the world, not the majority.
  • ……


Dr Juergen Ude has a certificate in applied chemistry, a degree in applied science majoring in statistics and operations research as top student, a masters in economics with high distinctions in every subject, and a PhD in computer modelling and algorithms. He has lectured at Monash University on subjects of data analysis, computer modelling, and quality & reliability.

Prior to founding his own company (Qtech International Pty Ltd), Dr Ude worked as a statistician and operations researcher for 18 years in management roles having saved employers millions of dollars through his AI and ML algorithms. Through Qtech International, Dr Ude has developed data analysis solutions in over 40 countries for leading corporations such as Alcoa, Black and Decker, Coca-Cola Amatil, US Vision and many more. Additionally he has developed campaign analysis software for politicians.

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