“It is the right and duty of every citizen to see what the scientists have said and to analyse it for themselves.”Lord Sumption, former Justice of The Supreme Court
Statistics and charts can be tricky and they are often used to push a certain point-of-view. However, with some critical thinking and basic school-level mathematics, we are all capable of understanding what the data means. We do not need to be scientists, mathematicians or subject-matter experts to come to sensible and informed conclusions.
Let’s strip away the frightening news headlines and look at what the evidence shows us.
Table of contents
- Five key facts
- Harmless to most people
- Comparison with flu
- Flawed evidence for lock-down
- Social distancing
- Government intervention
- Understand the numbers
- Suggest evidence
- Next steps
Five key facts
According to Dr. Scott W. Atlas, we now have enough data to be sure of five key facts about COVID-19:
- The overwhelming majority of people do not have any significant risk of dying from COVID-19.
- Protecting older, at-risk people eliminates hospital overcrowding.
- Vital population immunity is prevented by total isolation policies, prolonging the problem.
- People are dying because other medical care is not getting done due to hypothetical projections.
- We have a clearly defined population at risk who can be protected with targeted measures.
Dr. Scott Atlas is the David and Joan Traitel Senior Fellow at Stanford University’s Hoover Institution and the former chief of neuroradiology at Stanford University Medical Center.
Harmless to most people
Public Health England, on their 21 March 2020 update for High consequence infectious diseases (HCID), stated, “As of 19 March 2020, COVID-19 is no longer considered to be a high consequence infectious disease in the UK.” If COVID-19 is not a high consequence infectious disease, why was the UK thrown into uncharted territory without debate?
Nevertheless, despite spending weeks on lock-down; after destroying our economy and businesses; after causing mass unemployment; after inflicting unimaginable suffering to the entire nation, the UK Government again admitted that coronavirus is completely harmless to most people.
On the 11 May 2020 Downing Street Press Briefing, the UK’s Chief Medical Officer, Professor Chris Whitty, confirmed:
“Most people will not get this virus at all. Of those who get symptoms, the vast majority will have a mild or moderate disease. The great majority of people, even in the highest risk groups, will not die.”
Infection fatality rate
Multiple studies have found that COVID-19 has an infection fatality rate of between 0.02% and 0.8%.
The infection fatality rate (IFR) is an estimated death rate in all those who have been infected with a disease. This includes those who have been found to be infected (called ‘cases’), as well as those who were undetected, either because they have not been tested or are not showing symptoms.
The case fatality rate, on the other hand, is the measure of deaths among diagnosed cases. The mortality rate is the number of deaths in a specific population, such as the population of the United Kingdom, over a period of time.
The IFR tends to give a better overall mortality for non-experts and therefore is increasingly being used by policy-makers.
Comparison with flu
During the start of the COVID-19 panic, we constantly heard the media and politicians saying that comparing COVID-19 with flu is irresponsible. Apparently, COVID-19 is so deadly and spreads so quickly that governments needed to impose unprecedented control over society in order to fight it.
In contrast, this is what epidemiologists, microbiologists and researchers have been telling us:
- We should tackle COVID-19 just as we currently do with seasonal influenza. Sick people should stay at home, the vulnerable should be protected but the healthy should get on with their lives as normal.
- COVID-19 is deadly for vulnerable people but anyone at risk from COVID-19 is also at serious risk from flu. Even the common cold may have a death rate as high as 6% in risk groups.
- In some countries COVID-19 may have a lower mortality rate than flu.
- Cases for COVID-19 are not increasing exponentially. Instead, we are seeing an ‘explosion in testing‘ for COVID-19.
- The high death rates in some countries are due counterproductive treatment methods, such as intubation and the use of steroids.
- Some level of social distancing is helpful but a general lock-down and house arrest of the entire population will prove to be a disaster.
Here is what no-one, even officially approved mainstream sources, appears to dispute: in at least 80% cases, the virus produces either no symptoms or a mild cold-like illness.
Flawed evidence for lock-down
Countries have taken different approaches and there is no evidence that lockdowns have reduced COVID-19 deaths. Inaccurate testing and inconsistent reporting methods is a big problem. We also have no evidence that our NHS would have faced any greater strain when compared to previous flu seasons. In fact, the medical systems of most countries are stretched every flu season.
In the UK, we are told that a lock-down is needed to ‘flatten the curve’. This was based on unfounded assumptions and flawed Imperial College models that predicted half a million deaths from COVID-19. The report that lead to the lock-down was retracted days after release, with the number scaled down first to 20,000 and then to just over 6,000. Professor Neil Ferguson, the author of the report, has a track record of failed predictions.
Nevertheless, the lockdown was still enforced. Our entire county has been shut down, millions have been made unemployed and businesses are ruined due to flawed models and bad advice.
Non-lockdown vs Lockdown
The graph below, from UK Column news, compares the deaths and cases, between non-lockdown (left) and lockdown (right) countries. A similar chart compares non-lockdown (left) and lockdown (right) states in the US.
We can see that in general, areas that have implemented lockdown have higher death rates.
Note: These charts are outdated. The death numbers have changed and Mexico moved into lockdown after the chart was compiled (see visitor comment 16 May 2020). They have been left to illustrate the differences between lockdown and non-lockdown areas around April. However, we will update the charts when new versions are available.
A report by J.P. Morgan Quantitative and Derivatives Strategy, released around 20 May 2020, supports the argument that lockdowns did not help stop the spread of the virus. The J.P. Morgan figure 1 chart shows that many US states saw a lower rate of transmission after lockdowns were ended. J.P. Morgan figure 2 shows that most countries saw their infection rates fall after lockdown.
Deaths caused by the lockdown
Alistair Haimes, a data professional who writes for The Critic, posted the chart below showing weekly deaths at home registered with the Office for National Statistics. The majority of these do not have COVID-19 mentioned in the death certificate. It shows a disproportionate number of deaths at home over the 5-year average, indicating that these are likely to have been caused by the lockdown.
Similarly, the InProportion2 project released the following chart showing the proportion of weekly deaths due to COVID-19 in the context of total deaths during the 1999/2000 flu season. The blue bars show the 2020 lockdown deaths not attributed to COVID-19. Interestingly, the fewer people died during the April 2020 peak than in January 2000.
Buggy modelling code
Code reviews of the Imperial College modelling software written by Professor Ferguson began to appear in early May 2020. The comments from professional programmers were scathing. They found it badly written, full of bugs and produced random results. One reviewer went on to say:
“This Ferguson Model is such a joke it is either an outright fraud, or it is the most inept piece of programming I may have ever seen in my life.”Martin Armstrong, 8 May 2020
There is only one reasonable conclusion we can draw from both the lockdown’s results and the code reviews: the British Government’s decision to lock down the country was based on bogus numbers.
Social distancing is a term that has recently gained popularity as the main way to defeat COVID-19. It includes measures ranging from spaced queues at the supermarket to complete lockdown of cities and countries. In this context, we do not equate social distancing with the historically proven practice of protecting vulnerable people from flu-like viruses by isolating them from risk.
What is the scientific rationale for COVID-19 style social distancing?
No evidence for social distancing
According to Professor Joel Hay, PhD Health Economist at University of Southern California, there is no scientific proof that social distancing prevents the spread of coronavirus. He advocates that we should “do what we always do”: isolate the frail and sick but don’t isolate the young and healthy. According to Professor Hay, herd immunity is how we’ve solved the problem in the past. Social distancing is destroying millions of lives and killing more people than it saves.
Two metre rule
The social distancing rules adopted by different countries is arbitrary and has no scientific basis. Different countries use different rules ranging from the WHO‘s recommended three feet (just under one metre) in Sweden and Asutria, 1.5 metres in Germany and The Netherlands, to two metres in the UK and US.
Robert Dingwall from the New and Emerging Respiratory Virus Threats Advisory Group (Nervtag) said Britain’s two meter rule was ‘conjured up out of nowhere’ because the government didn’t trust the public to keep 1 metre apart. UK Column, on their 18 May 2020 programme, reported that the two meter distance was Public Health England guideance only for hospital settings. It has no basis for the general the public environment.
Many people can now be seen in public wearing face masks. From 15 June 2020, face coverings became mandatory on public transportation. What is the evidence for their use? Currently there seems to be no strong medical consensus on whether face masks actually help.
Physics professor Denis Rancourt published a paper on the science relevant to face masks. After reviewing randomised controlled trial studies with measured outcomes over the last decade, he found that none showed statistically significant advantage to wearing a mask.
Nevertheless some proponents make the claim that masks are valuable for preventing asymptomatic carriers from spreading COVID-19. However, WHO in fact state that healthy people only need to wear a mask when taking care of a person with COVID-19.
Further, there seems to be growing evidence that wearing masks at best gives a false sense of security and at worst end up making healthy people more susceptible to getting sick. Dr. Jenny Harries, the UK’s deputy chief medical officer, warned that masks could increase risk of infection:
“For the average member of the public walking down a street, it is not a good idea…In fact, you can actually trap the virus in the mask and start breathing it in. Because of these behavioural issues, people can adversely put themselves at more risk than less.”
Even the HM Government’s guidance for restaurants, pubs, bars and takeaway services, published 23 June 2020, states:
[T]he role of PPE in providing additional protection is extremely limited…face covering may be marginally beneficial as a precautionary measure. The evidence suggests that wearing a face covering does not protect you…evidence of the benefit of using a face covering to protect others is weak.
The UK government’s approach to relaxing the lockdown is based on the reproductive value, sometimes referred to as the ‘R value,’ ‘R number‘ or ‘R rate.’ This is a measure of how a disease can spread through a population. The value itself is the average number of people who can catch the disease from a single infected person. For example, an R value of 3 means one person can pass on a virus to three others. If R is below 1, a disease cannot spread enough to sustain an outbreak.
In this video, Sunetra Gupta, Professor of Theoretical Epidemiology at the University of Oxford, explains that we don’t have enough information for the R value for COVID-19.
According to Dr. John Lee, the R value is not a strong enough number to bear the burden of any Government policy. ‘R‘ is an artificial figure calculated using mathematical models which, as he says, “have repeatedly been found to reach wrong-headed conclusions.”
Despite these concerns from experts, the UK Government is using the R value as the measure of whether COVID-19 is under control. We are told that the R value will be used to justify easing lock-down or enforcing local lock-downs throughout the country. However, there is a huge discrepancy with this approach. By the government’s own data, the R value had already dropped below the important threshold of 1 before lock-down was declared. Lock-down therefore did absolutely nothing to reduce the R value.
A lot has been made about testing for COVID-19 but it is far from straightforward. The death totals can sound shocking when taken out of context so it is important to compare them with the numbers the UK normally faces over a long period of time. Furthermore, as American statistician Nate Silver writes, numbers are meaningless unless you understand how they’re counted.
Types of tests
There are currently two types of tests being used for COVID-19:
- PCR Testing is a lab test used to detect if the virus is currently present in the patient. Sampling requires full personal protective equipment and results can have a long turnaround time. It is also very error prone if not processed properly. Although PCR testing is currently being used to confirm a COVID-19 infection, it seems the inventor would have warned against PCR testing to detect a virus.
- Antibody Testing, or serological testing, checks if a person has antibodies to the virus. It is intended to detect if the subject has been exposed to the virus in the past. Since COVID-19 is a newly identified disease, manufacturing for COVID-19 tests need to be developed. There is a shortage of test kits and those currently in the market are unreliable.
The shortcomings of both types of tests means that mass testing is impractical. As a result, many of the novel coronavirus victims we see reported in the statistics are ‘assumed positive’. The hospitals may not have tested the patient for COVID-19 but assumed the death was caused by the virus. The assumption is based on whether a patient presents with COVID-19 symptoms. This is problematic because COVID-19 symptoms are very similar to the flu.
This needs repeating: it is almost certainly the case that the majority of deaths in the statistics were not tested for COVID-19. This makes the statistics you see on the news worse than meaningless. In makes them misleading.
Dr. Wolfgang Wodarg, an epidemiologist and lung disease specialist, explains coronavirus testing in an inverview above. We are also told of the exponential rise in cases but according to Felix Scholkmann, a biophysicist at University Hospital Zurich, test-positive cases are not increasing exponentially in the US, France, Germany and Switzerland.
Furthermore, the current tests used by most countries have both high false positive and false negative rates. Some are so bad that samples taken from a goat and a pawpaw fruit showed COVID-19 positive.
So, what do we know about COVID-19 testing?
- The numbers are flawed because current testing methods have an unacceptably high error rate.
- Many people counted as COVID-19 victims actually died of something else. (Reports from Italy back this up.)
- The numbers resulting from these tests should not be used to drive public policy.
Some commentators have said that earlier government intervention, such as closing down boarders or locking down sooner, would have stopped COVID-19 in its tracks before it could have become a serious problem.
In contrast, experts in various fields, including Professor Dolores Cahill, Professor Isaac Ben-Israel and Professor Yoram Lass, all say that there is nothing you can do to stop the spread of coronvirus type diseases; they’ll spread and peak regardless of measures taken.
Immunologist Professor Cahill and mathematician Professor Ben-Israel both come up with similar lifespans. Coronavirus epidemics last between 4 to 10 weeks in each location as it circles the globe. As of early June 2020, actual data from different countries with COVID-19 outbreaks have borne this out.
According to Professor Lass, former Israeli Health Ministry chief, “A government cannot stop a virus. What stops a virus is natural immunity. It’s impossible to stop a virus by government decree.”
Official mortality rate statistics are publicly available and anyone can download the data for analysis. All the numbers so far show that COVID-19 is far from being a ‘once in a century’ plague. Though the numbers in news reports give the impression of an unprecedented death toll, we can see they are not at all extraordinary when placed in a historical perspective.
Novelist Hector Drummond decided to look at the annual death figures for England and Wales from the Office for National Statistics. After graphing the numbers all the way back to the turn of the twentieth century, he found that the numbers for 2020 (shown on the right) cannot even be considered a major spike over the course of the century. Analysis from other sources below support this view.
EuroMOMO is an agency that monitors deaths across Europe. Data provided by EuroMOMO is used by governmental agencies including Public Health England. The chart below compares death levels in Week 2 of 2017 with Week 14 of 2020. The darker blue means more deaths.
Week 14 and 15 was roughly the peak of the deaths throughout Europe in 2020. How different is the 2017 chart from 2020? If governments did not shut down society in 2017, why are they doing so now?
By week 17, excess mortality in most of the surveyed countries have dropped to moderate or below. The UK, which is holding to its lockdown policy, remains high.
Office for National Statistics
We have made contact with the inProportion2 project which collects and analyses statistics from the Office for National Statistics, National Health Service, Public Health England and EuroMOMO. Please head over there for a more in-depth analysis and comparisons of the numbers.
COVID-19 in proportion
The charts below provide a simple way of viewing COVID-19 deaths in proportion to other deaths.
The InProportion2 chart below compares cumulative totals of COVID-19 deaths with totals for 2000 and 2018. As of late June 2020, we see there is not a huge difference between them. We did not shut down the world economy in previous years yet the entire globe is now at a standstill for COVID-19. Why?
COVID-19 is shown compared to the 1968/69 Asian Flu below.
Understand the numbers
Keep these observations in mind the next time you hear about the rapidly rising death toll on the nightly news:
- Dr. John Lee, a professor of pathology and former NHS consultant pathologist, explains how to interpret the numbers.
- Nate Silver, an American statistician, tells us why coronavirus case counts are meaningless.
- Steve Goodman, a professor of epidemiology at Stanford University, says that confirmed coronavirus cases is an ‘almost meaningless’ metric.
The information in this section may take a bit of patience but you’ll be rewarded with the ability to better understand charts.
If you would like to post some evidence or information that you think would be helpful for other readers, please use the comment form below. The suggestions are moderated and anything off topic may be moved to our discussions page.
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By now you will hopefully be reassured that COVID-19 is not a the doomsday virus we hear about in the news. What next?