COVID-19 SCIENCE

Modelling shows mandatory face masks during Victoria's Covid-19 second wave helped 'save thousands'.

By Dr Juergen Ude | April 16th 2021
The following is in response to the article published by ABC News on April 14th 2021 which captioned: "Modelling shows mandatory face masks during Victoria's COVID-19 second wave helped 'save thousands'"

We disagree with this statement as reported by the ABC. But, we must emphasize that our disagreement is not with the ABC, who are merely reporting, but the scientists who made these conclusions.

Models cannot be used to make statements of fact. Models are always wrong which has been accepted by the science community. It is not possible to define life with mathematical equations and algorithms. Models are heavily DEPENDENT ON ASSUMPTIONS which rarely, if ever, apply.

Of course, models are not useless if they can be tweaked. That is why weather forecasting through models are useful because the model has been tweaked over the years. But this pandemic is one off. Australia has never had a pandemic of this nature and whichever model is used they have not sufficiently been tweaked with real-world data.

The model used to determine that face masks saved 'thousands of lives' is a compartmental model. Effectively the population is assigned to compartments with labels, such as S for susceptible, I for infectious and R recovered. Here is the first source of error - 'the number of infectious'. The model has been calibrated on case notifications which is highly flawed science because cases are dependent on test numbers. The cases reported from testing are nowhere near the cases in Victoria’s population hence the model must be erroneous.

Transition rates are needed for a full specification of the model. The transition rate is ASSUMED to follow a certain function. We cannot make such assumptions outside the academic world. The reproduction number is the expected number of new infections from a single infection. This too is based on academic assumptions which does not take variability into account. The modelling is too simplistic for the real world.

The report MENTIONS NOTHING on HOW THE DEATHS SAVINGS WERE CALCULATED. If models were used, they will have been based on deaths with Covid, not from Covid, which is nonsensical.

The issue of real world versus academic thinking is a common problem. Academic thinking takes place in the imagination of the scholar. Real world thinking takes place in the real world.

Examining performance of past models

Let’s apply real world thinking and examine the performance of models instead of worrying about details of the actual model used which we have not seen. The mathematical cleverness is irrelevant. Proven performance is. There is no proof that this model did save thousands, but what about other models?

For the first wave in Victoria this was one headline:

“Theoretical modelling shows some 36,000 people would have died from coronavirus in Victoria if physical-distancing restrictions were not put into place “

Sweden, almost twice the size of Victoria, had about 6000 deaths during the period where there was virtually no physical distancing. Sweden is in a part of the world where deaths are much higher than in Australia’s region. If we make an adjustment for Sweden’s size, Victoria would have had 3,600 deaths in one year due to the coronavirus. NOWHERE NEAR 36000.

The report by the ABC states:

“If you can imagine a situation where you didn't mandate masks and… you continued on with life as previous, I think we would have had months of very, very high rates and thousands of deaths.”

3,600 deaths (as mentioned above) for almost a whole year is not the same as many months of thousands of deaths.

Another modelling prediction for Victoria was:

“The modelling shows 650 people could have died each day at the state's coronavirus peak without physical-distancing measures”

  • Sweden WITHOUT LOCKDOWN averaged 44 deaths per day during the equivalent peak period. When adjusted for Victoria’s size 20 deaths per day, nowhere near 650 people per day.
  • Japan WITHOUT LOCKDOWN during a peak death period averaged 17 deaths per day. When adjusted for Victoria’s size 1 death per day, nowhere near 650 people per day.
  • South Korea which has controlled its first wave without major physical distancing, but contact tracing, has during its peak death period at that time averaged around 7 deaths per day. When adjusted for Victoria’s size less than 1 death per day.

There many other examples showing that models have been completely wrong. To therefore use models to justify actions which have instead destroyed lives is morally wrong. Models used in this pandemic and many others have no credibility based on actual performance. Even scientists acknowledge models are always wrong. If something is wrong how can they be used as a basis of decisions that will destroy lives?

Victoria’s models have been almost deceitful, though not by intent, just incompetence. One model used to justify lockdown 3 saved 36,000 lives was based on an incorrect assumption that the cases followed an exponential curve. Read the article here Referring to the Change Analysis chart below, Victoria's cases followed a linear relationship prior and after lockdown 3.

BIS.Net Analyst Change Analysis used in Covid-19 analysis
Change Analysis: Victoria's cases follows a segmented linear relationship prior and during stage 3 lockdown.

Another model based on moving averages was ‘deceitful’(not necessarily by intent) trying to prove lockdown 4 worked because moving averages lag, which resulted in a drop of cases at the expected time, whereas the cases were already coming down when lockdown 4 started. Our conclusions show that we did not need lockdown 4 as shown in the following article: Did Victoria, Australia really need lockdown stage 4?

Without offence meant, and due apologies if offence is taken, academic thinking must stay in an academic environment to sensitize students to the real world. But real-world thinking must be applied by real world experienced scientists or we will destroy ourselves as we have done.

(One has to ask would the academic thinking experts from their financially secure academic positions have advised governments to lock down the population based on their imagined academic scenarios if it meant they would their jobs too?)

Let us now use real world thinking driven by data

According to the ABC report:

“Prior to July 23rd, we saw that the effective reproduction number was still significantly above one, so that means that the epidemic will be going up".

Why bother to complicate things with clever sounding indexes when the same information and more meaningful information can be obtained just by looking at the charts which showed cases were going up. All we are doing is bamboozling the public and media.

"As the face covering policy came through, there was this real step change to the proportion of people using face coverings and that brought the effective reproduction number down below one."

How can we conclude that the change in proportion of people using face coverings bought down the effective reproduction number? It is not scientific and incompetent to assume association means causality. Unfortunatly concluding causality from association alone is a modern trend of academic thinking which has no place in the real world.

According to our analysis it took around 14 days for Victorian cases to drop after use of face masks. We would expect 7 days. How do we know that this drop was due to facemasks? Perhaps it is but there is no real world evidence. Academic thinking has jumped to conclusions. No real world scientist would make such a statement. We have studied data for every country and for many countries there were drops in cases at a time when there was little done. The common cold comes down by 'itself', as does the seasonal flu. (What goes up comes down). To conclude that face masks are the cause just because sometime after face masks were used there was a drop is highly biased, especially considering that the change occurred two weeks later.

Inconsistently the Victorian government went out of its way to prove that the drop was due to lockdown 4. Now we are saying it is mostly due to face masks. We need to make up our minds and become realistic and stop making statements of facts when they cannot be proven. We need to be consistent.

We have analysed data for 50 countries using face masks and there was no evidence it worked. Why would Victoria work? Are we so conceited to think we are the only state or country to get lockdown and face masks right and others are incapable of wearing face masks?

BIS.Net Analyst Change Analysis used in Covid-19 analysis

"In Australia, in New Zealand we saw what was happening with the rest of the world — where they hoped and waited it hasn't worked so well".

In other words the rest of the world was foolish but Australia the clever one. Effectively what is said is that Autrlia's actions were right. That is easy to say by academics who have paid jobs. What about the front-liners of the economy who carry the population and have had to suffer unbearable stress? Over what? Deaths that are based on dying with covid, not from and many caused by our reactions. Sadly those who recommend such decisions cannot see the damage they have caused or are encouraging. After all they are not affected. They think they are heros.

The reason Australia/Victoria managed to squash the curve is very simple. We are an isolated island. We also locked down after the peak in cases (refer to the Change Analysis chart below). Other’s would have achieved the same, if they were lucky enough to be isolated from the rest of the world. Yet, we seem to take pride in isolating our people just so that we are the envy of the world. What about the price?

BIS.Net Analyst Change Analysis used in Covid-19 analysis
Change Analysis: Australia locked down after the peak. The expected change would have been realized 7 days later.

The study said Victoria's second wave was "virtually unique in that the pattern of a substantial number of cases — escalating daily in the community — was reversed following the policy changes".

There is no evidence that the policy changes reversed the increase in number of cases. Cases reversed 25 to 30 days after Lockdown 3 started. If Lockdown 3 were to have any effect, we would have noticed something after 7 days. Cases were already dropping when lockdown 4 was implemented hence did not need lockdown 4 as shown below.

BIS.Net Analyst Change Analysis used in Covid-19 analysis
Change Analysis: Victorian cases were already coming down at time of stage 4 lockdown. Expected change would have been realised 7 days later.

Lets get real. We are scientists, but old school. We are not biased. Common sense dictates that lockdown and face masks must make a difference in theory and in the academic mind. But reality is not in the academic mind. We can only go by data and the data shows no evidence. Of course this does not mean if everyone diligently uses face masks as instructed, washes hands, replaces soiled face masks, and don’t let other family matters use them this would not make a difference. But we are human beings.

We cannot deny that with lockdown and face masks we have hardly had common colds and the flu. Lockdowns and facemasks must affect spreading of viruses. But here is the problem. Why were there so many covid cases instead? Is it that the covid cases are our cold out breaks from yester year? We never tested as extensively before so how can scientists be so confident that what we are experiencing now is due to a novel virus. How can a scientist conclude a virus is new just because they never found it before. We never looked as extensively. Is it what we are experiencing just another common cold virus and are/were the high deaths caused by mismanagement, incompetence and over treatment due to our belief that the virus is so deadly because of academic models?

Instead of bamboozling the public with science which no one can disprove we should spend our efforts looking at how we may have caused deaths, not the virus. More and more reports are now blaming ourselves for deaths. There are people saying that nursing homes have caused deaths by administrating morphine in patients just because they had the virus, even without symptoms.

After almost one year of analysis, monitoring all the mind changes and theories we conclude that the academic experts really do not understand the real world. The real world is not a test tube and certainly far more complicated than in the imagination of the academic. Has anyone ever questioned that by spreading the curve we have bought time for the virus to mutate? Has anyone questioned the possibility that by rebreathing the air because of face mask use viruses will mutate faster? Each time we breath out the virus mutates.

When we talk about academic experts we are taking about experts thinking with an academic mind. This does not include university professors who have proven real world experience. There are many, and many have spoken out only to be called outrageous. Based on the incompetent science used through the pandemic we can only conclude that governments were driven by academic thinkers unable to understand the real world and that a singular objective of saving lives was all they are capable of. Real world experts would make protecting lives, livelihoods and living the objective. They would not use models that are always wrong but common sense.

Finally, further (without apology) digressing from face masks, in 2019 the risk of dying across the board was 8.4 per 1000 Swedes who did little for most of 2020. In 2020 the risk of dying across the board was 9.2 per 1000, so the increase in deaths was due to covid 19 the risk of dying of covid across the board is an extra 0.8 people per 1000 in a year. How many of these deaths were caused by hospital mismanagement, incompetence, bad practices in age care facilities. Even if none how can we justify the human cost for just and extra 0.8 per 1000 deaths? DID MODELS TAKE THE HUMAN COST INTO ACCOUNT?

ABOUT THE AUTHOR

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.