Addressing COVID19 gaps between data, models and decision brings us back to hierarchical PID control, folks!

I’d like to comment about the way how careful we should be when we use data (even if it’s accurate at the source and at its processing steps!), when we build models to extract some dependencies between elements of the data, and ultimately when we make decisions.

Long time ago (approx. 40 years), when my father took over as head of control engineering department in St. Petersburg electrical engineering institute (LETI) from the previous head, Professor Alexander Vavilov, at their school they were excited by exploring the idea of evolutionary synthesis of control systems. One crucial part of this study was the development of theory of structural synthesis, where models of the system at each level of granularity had to be adequate to the criteria of optimal control. (By the way, graphs were essential in those models)

The basic idea was that depending on the level of granularity (or hierarchy) considered by the modeller, the system can have completely different criteria of correctness and/or optimality, hence certain aspects that are significant at small scale may not be important at a larger scale.
A bit like the criteria of control in the national level is not the same as criteria for control at the municipal level, and not the same at the level of local community, and not the same at the level of family units and individual households.
So, because of these differences and clashes of interest between different levels there is a lot of anxiety and misunderstanding in societies.

So, what the relationship with COVID-19?

Well the relationship is direct.

Let’s take the data on Mortality 2017 from the UK National Statistics: https://www.ons.gov.uk/visualisations/dvc509/chart1/index.html

This data shows that the number of deaths across the country in one year is significant – hundreds of thousands – not far from 1M. The relative number of deaths, that we witness now as a result of COVID-19 even if it will hit 10K-20K would be quite small though.

So, we clearly have different perspectives here, one is national (spatial) that stretches across the whole year (temporal), while the other could be local (e.g. an area of population in London) and taken during these 2-3 weeks of March-April. The relative increase in the number of deaths at the national scale is a small bump on the curve. I.e., integrating the number of deaths, caused by respiratory problems thanks to COVID-19, at the national scale will not give much effect to the game of the totals.

However, if we look spatiotemporally at the small scale we may see a significant rise in terms of differential and even proportional response. So, if we are particularly sensitive to these two aspects, differential and proportional, we may actually decide to react with a powerful action.

What we are facing here is exactly what I started with in my blog. We are facing with the different levels of granularity (or hierarchy, whatever we call it). Consider the coarse granularity. From this point of view our Mother Nature in us may say, well, why bother, the integral response (let’s denote it by letter I) is very small, and we look at time intervals of decades, so there is no need for any great change in decision-making. The problems of environmental nature are much more serious.

But let’s go down to the level of individuals, especially those living in the most affected areas of COVID-19. Again, our Mother Nature in us would tell us, that the rise in deaths due to coronavirus is an alarm, it may trigger a disaster, we may lose the loved ones, lose a job and income. What’s happening here? It’s actually that at the lower granularity level, the criteria for decision-making are based on differential and proportional responses (let’s denote them D and P). So, in mathematical terms at different levels of granularity we apply different coefficients, or what engineers call gains, to these aspects P, I and D, and form our decisions according to those gains or criteria of importance.

So, ultimately it is vital that the data we use, and the models which characterise this data in time and in space, where we calculate partial or full derivatives and integrate in space and time, or proportionalise in space and time, must be adequate to the criteria of significance we apply, and lead to corresponding decision-making at the appropriate level.

No doubt, the nations that are harmoniously hierarchical and fractally uniform, may have less problems in matching criteria of optimality with the P, I and D responses brought be the models from the actual data.

Yet, again we face that PID-control seems to rule the world we live in!

COVID-19 – Why China Did What it Did

From the horse’s mouth. Received this morning from a Chinese  source who is a top class engineering expert.

Very revealing!

Some of the actions of the Chinese government, which seemed counter-intuitive at the time, became quite clear from this explanation.

  1. How the hell did they decide to close up Wuhan when the official death figure was only 30 something? 
    Remember that the city is a uniquely important communications hub with air, rail and river transport crossing in multiple directions (in a war they’d probably prioritize bombing the place). The time was just before the Spring Festival before the annual spring travel crush started. Closing Wuhan spoils the SF(CNY) for a huge number of people, hurts the feelings of even more and damages the economy significantly. The modelling teams were assembled much earlier than this date and this action was significantly model-driven. The models tested different actions and the actual sequence was chosen as the least bad one. Closing Wuhan on its own looked stupid to some degree, but not as the first of the sequence of actions that followed:
  2. What about the rest of the country then?
    The rest of the country was allowed to continue through the first phase of the spring travel rush, which decanted probably 1/3 of the population from large cities onto the countryside, then the entire country was closed down preventing their return. This prevented the appearance of another Wuhan, with which the government would have no way of dealing.
  3. Volunteering albeit under peer pressure is a key
    As it happened, they were able to assemble large teams of medics from elsewhere in the country (the so-called volunteers – if you were a party member not volunteering was not an option, and non-members esp. low ranking nurses had incentives such as conversion from contract worker to full-time permanent worker) to descend on Wuhan and its province Hubei en masse. This depletion of medical strengths elsewhere proved sustainable because another flareup never happened. The President did not formally thank the people of Wuhan on behalf of the nation for nothing. When the people of China hear western media portray this as an apology for government errors they find this play quite difficult to imagine/understand. The hard/cold decision was to contain the spread locally from the first and therefore those local people had to suffer more hardships without volunteering. The least the nation could do is to appreciate this.
  4. Fangcang – makeshift hospitals are effective
    The establishment of the fangcang (makeshift hospitals using stadiums and exhibition centres) seemed strange, given that you were assembling ‘suspected cases’ all in one single space. The models predicted success which was borne out by reality. This has to do with how you want to deal with suspected cases and confirmed cases with light symptoms. It was determined that these people are better assembled together under professional care and control than remain at home to self-isolate with family. Fangcang-induced infections turned out to be negligible, almost zero. With beds a few metres from each other and everyone breathing the same air how was this possible? The answers are in the obligatory wearing of masks, on-hand medical and professional help and admin and enforced discipline, and almost continuous cleaning of the environment. These put together turned out to be vastly preferable, so far as the numbers are concerned, to home isolation where people do it any amateur manner they like/can.
  5. Testing methods with replication are crucial (real engineers can appreciate the use of time redundancy and diversity)
    The testing method adopted has practically 100% accuracy in the lab, close enough to 100% to be dependable for a tested population where the infection rate is only 1%, but in the field negative results were not trust-worthy (positives are completely fine). This was also put into the models and the resulting standard changes converted a large number of suspects to confirmed in a single day (all such converted cases had negative test results, but did not pass a CT scan test). The scientists read the UK’s confident reporting of how many tested with a large proportion of negatives with fascination, and speculate that the UK may have a more reliable testing procedure. This testing situation also inspired the fangcang approach as well as the very tight lockdown measures taken across the country. You don’t get cleared just because you had a negative. You need 2-3 negatives in a row without symptoms. In other words, treat everyone as a suspect case and everyone with symptoms as a confirmed case and design your control measures based on this assumption. The CCP is able to do this, other countries maybe not.
  6. Modelling approaches, also diverse and competing, are a must.
    The modelling gravitated towards two competing camps, by design of the government organizers. One is called the maths model and the other the medicine model. The first is led by system theorists and the second, epidemiologists. The commonly seen model of first order differential equation with an R0 factor is nowhere to be seen in either groups of models actually consulted by the decision makers – they are much more sophisticated than that. The maths model consistently returned more accurate predictions with worst case on death numbers error below 7% at all stages – this is the only hard number my friend was willing to disclose. All published models, either from within or without China which have appeared have been comparatively checked with the decision models and found to be inferior, usually by a lot.
  7. Future of the models?
    There is very little chance of seeing these decision models published, not any time soon. My friend’s words: “We should not publish when there is an atmosphere in which such a publication might result in extra-science interpretations and uses” and such an atmosphere will linger for a long time, by the looks of it. I read the CCP propaganda as well as the stuff coming out of our government and can see this stuff buried deep for long. However the modellers continue to work on data from the wider world now and the government continues to listen to them. One difference between China and much of the rest of the world is that the scientists cannot just tell the government the science says this and that without providing evidence, as the members of the government can understand scientific evidence at an academic level. And they organize multiple teams to work against each other to form a peer-review like environment from the start.
  8. Protection of medics is a key factor
    The most important issue, highlighted by the models and tested in real life, is the protection of the medics. Initially the disaster was when Wuhan people crowded general-purpose hospitals where the medics were not protected. When the external teams went to Wuhan+Hubei they were well prepared and formed special-purpose facilities which had a far greater success rate with next to zero infection of medics. Although this is intuitive, the actual numerical differences made in the deaths was unintuitively large.
  9. Ventilators is a last resort when it’s 20% survival chance left.
    One of the little-publicized facts is that the starting and ending procedures of ventilator use on a patient (putting them on/off the machine) represents the standing-out worst point for medic infections. This has caused a reluctance in China of using ventilators and the threshold for their use is set quite high, leading to ventilated patients having only a 20% rate of survival – if you are not already dying you are not ventilated. So they are a bit fascinated by the current western thing about seeing ventilators as some sort of almighty saviour, esp. given the current suboptimal PPE state for medics in an environment of retired medics (presumably not young) re-joining service.
  10. Masks, hand washing – NOT to be neglected 
    On how to protect ourselves, my friend emphasizes mask wearing and hand washing – diligent mask wearing and hand washing mimics the fangcang regime to some degree. Contrary to common belief, the wearing of even three-ply surgery masks protects not only the environment from the wearer but also the wearer from the environment, and N95 masks are indeed better. He became a bit rhetorical and urged us to disregard imagined stigmatization to prioritize life, both our own and that of those who may stigmatize us.

A 12 Day battle with COVID-19 of my colleague – in mid 40s and fit.

My close colleague Professor Patrick Degenaar

https://www.ncl.ac.uk/engineering/staff/profile/patrickdegenaar.html

has just sent his report. With his permission I am pasting it here.

“I’ve now basically recovered from what I believe (it’s impossible to get a test) to have been a COVID19 infection.

Just so you know what you have to look forward to in the future, I kept a brief symptoms diary:

Day 1:   Very slight ache in joints

Day 2:   Asymptomatic

Day 3:   Tired, lethargic, dizzy, and out of breath

Day 4:   Reduced symptoms compared to day 3. Started to assume it was getting better.

Day 5:   Morning felt almost fine. Then afternoon: Very tired, very out of breath, heart palpitations, Mild temperature = 37.5C

Day 6:   Reduced symptoms compared to day 5, but still very tired and dizzy. New symptom: a chest pain – like a claw embedded in the chest.

Day 7:   Similar to day 6, but also developed an occasional dry cough

Day 8:   Much worse – extremely tired, very out of breath. Climbing the stairs felt like climbing Everest. Feeling like very bad high-altitude sickness. A feeling of nausea (just like bad high-altitude sickness)

Day 9:   Similar to day 8

Day 10: Starting to get better similar to day 5

Day 11: Starting to feel much better. Can ascend stairs without getting out of breath. But still tired and dizzy.

Day 12: almost OK, but still need periodic Siestas

Stay safe!”

Can Socialism be built a la carte in just a few weeks from Capitalism?

The current fight of the human race against the deadly coronavirus shows the obvious inability of a capitalist, free-market system to handle it.

Nations with more centralised economy and command-control mechanisms already in place are better equipped with tools to respond and act.

Many businesses in all industries close to our daily life are at standstill, and sadly may never recover from this plight or it might take a long time if things get back to normal. It is obvious that prolonging capitalism and its functionalities, and not rapidly changing the course to socialism would lead to great human losses and disasters. The nation will suffer at all levels of its structure enormously if the crisis extends for months.

What then to do? How to re-act?

It is worth looking at the history of societies and nations which underwent economic and political cataclysms and see what was done there and at what cost, and what perhaps could have been done differently.

Take Soviet Union after the October revolution for example. A switch to socialism was very painful, it was not done smartly and systematically but as a result of a bloody and brutal overthrow of the previous system, but there were certain moments when a clever action of the leadership helped to mitigate the tragedy. For example, switching to the so called “Military Communism” was essential during that plight. One thing should be clear is that the leaders should be smart enough and steer the nation quickly towards socialist realities.

Instead of trying to pay a significant salary replacement to workers who are now effectively unemployed (the bureaucracy of this process will not be sustainable in these speedy dire straights of the pandemic), we need to face the reality and give people the absolute basics. People should be given some comfort of hope in material sense – guaranteed food, shelter, moral and medical support. If someone rents a place to live and has no cash to pay the rent now, the government should, perhaps in a very crude and direct way, issue a decree that the owners shall NOT demand rent from people who lost their job. Small elements of “temporary expropriation” (I am not calling to the disownership of the property!) are needed. The fate of the nation is at stake. And the nation is its people.

Clearly, a government that was brought up on the principles of free economy, conservatism and capitalist values, would have enormous problems to simply turn the switch from capitalism to socialism. But what can we do? We have to live with the government that was elected by the people. And it happens to be conservative. So be it. Thus, we can only hope in some remains of common sense in this government and we can only try to impact on their policies to be more decisive. They should realise that the country urgently needs to switch to some forms of socialism and more direct rule.

The answer to why women are more robust to COVID-19 than men may lie in the dynamics of women’s gene pool

Today, people are asking why women are less affected by COVID-19 and have significantly lower death rate than men (in Italy, for example: more than 60% of infected are males and more than 70% of death cases are of male).

While there are hypotheses that this is caused by various societal and life style factors and norms, such as ‘because more men are smokers’ etc., I would like to examine potential genetic causes of that.

Men carry both X and Y chromosomes. Women carry only X chromosomes.

As I wrote a couple of years ago on my blog about the differences of dynamics between X and Y chromosomes (see links to my two articles below), I made a hypothesis that women’s chromosome pool is significantly more dynamic and mutable than men’s. The Y part of men’s genes don’t mutate. They carry Y-DNA through generations unchanged. Thus women naturally bring greater adaptability and robustness to environmental conditions than men. Contrary to that men bring certain long-term elements and inertiality, which is also important for stable societies.

Importantly, perhaps, I also showed an analogy between the combined process of gene evolution in humans and other species, thanks to the presence of both males and females) and PID (Proportional-Integral-Differential) control that is proven to be the most successful type of control in engineering systems.

So, the nature’s own PID control (where the role of P and D is greater than that of I for the purposes of quick response to effects such as viruses) makes sure that only a relatively smaller number of males compared to the number of females are needed to maintain the human kind.

So, as usual, Mother Nature and genetics are the winners in this almost game-theoretic scenario of our battle against coronavirus.

Potential rise of interest in STEM subjects in society

I predict that during and following this period of COVID pandemics, we will witness a significant rise in of interest and some kind of renaissance of mathematics and other STEM subjects. You might ask, why?

Well, let’s look back into history. The development of many mathematical ideas and forms such as mathematical series like geometric series, Fibonacci series, theory of probability etc. were the result of people observing various processes in time or frequency domains during those epidemics like plague, cholera and so on, that took place in the past centuries.

Now, you can see how many smart people are doing home schooling and teach their kids to look at the geometric series and exponential and power laws of the proliferation of virus. A 7-8 year ol kid can have a good grasp of the series based models because he or she could witness its manifestation (sadly, but) in vivo.

So, being an academic in Engineering and curious in anything natural, I hope there will be more students doing Maths, Sciences and Engineering after that ….

Is there any effect of weather on the spread of Covid-19?

Weather reports:

https://www.timeanddate.com/weather/china/wuhan/historic?month=12&year=2019

Average pressure in Wuhan in December 2019 was 1026 mbar, with some days going as high as 1040 mbar. Wind was very low too – 1-5mph. Dry.

https://www.timeanddate.com/weather/italy/milan/historic?month=1&year=2020

Average pressure in Milan in January 2020 was 1027 mbar, with some days as high as 1045 mbar. Wind was very low – 1-3 mph, mostly dry

For comparison

https://www.timeanddate.com/weather/uk/newcastle-upon-tyne/historic?month=2&year=2020

Average pressure in Newcastle upon Tyne in February 2020 was 999 mbar, Wind was typically very strong – more than 20mph, lot of rain.

Molecular and cellular transmission:

What is the relative permittivity of air for odours and viruses? How does it depend on the weather?

Have you every walked behind a person having a lot of perfume? On a windy and rainy day, with low pressure you’d hardly feel any smell. But on a dry, sunny day, with high pressure, the scent of perfume stays so long that you can feel it even if the lady is 100-200 meters ahead of you, or even long past.

What is smell? What is its nature? In science it is explained via special types of molecules, called odorants.

http://resources.schoolscience.co.uk/ICI/16plus/smells/smellsch2pg1.html

With Covid-19, we have been told that we should keep the distance of 2m in social distancing. Is it enough? In what weather?

The Covid-19 cells are very small. Apparently the size of 100 nanometers. So we are talking about something like 1000 molecules. On a high pressure, dry and non-windy day, they can stay in the air probably for quite a while.

The other factor of good and dry weather is that people are much more out and about, and naturally socialise more. So, the weather and social proximity are correlated too.

Extra point. On a low pressure day our body naturally extract more fluid, mucus etc. This is actually good to help not letting virus into your body. On the contrary on a high pressure sunny day we are naturally keeping everything inside and actively breath oxygen rich air. Especially if we exercise outdoors. Perhaps, virus likes that we help it with extra oxygen and give its way into our lungs when we exercise. So is active exercising is good during those days and in a social company of potentially viral people. I am not sure.

We are often mistaken that by doing something normally good we can win. Unfortunately, there is no universal win. What’s good for your body under normal conditions may be bad under these viral conditions. Good old saying, you can’t win, man, can you!?

My hypothesis is that a good weather is really a ‘good’ promoter for viral transmission.

The Heaviside Prize

Last weekend I twitted on the following exciting challenge:

The Heaviside Prize:

https://youtube.com/watch?v=mr9-Nu5HvWM&feature=youtu.be…

$5000 for someone who will explain the physical reality (without using maths!) of the electric current when a digital step propagates in USB-like transmission line. Students, engineers, academics, tackle this challenge!!!

On Hall Effect from the Energy Current viewpoint

People often talk about Hall effect as being an artefact of the interaction of the flow of electrons (aka electric current) in a metal plate with an external magnetic field, which causes (as an effect of Lorentz force) a potential difference created between the longitidal sides of the plate.

See for example, this site for the description of the Hall Effect:

https://electricalfundablog.com/hall-effect-principle-history-theory-explanation-mathematical-expressions-applications/

I have had a look at the Hall effect and tried to scribble some rough picture in terms of Energy Current.

The story requires 3 dimensions x-y-z, and it is clear that the superposition of energy currents caused by the battery (where we don’t see the H aspect), and magnet (where we don’t see E) aspect, ends up in the cumulative energy current moving in the direction xy, while H is pointed in yz and E in xz, so no wonder that its projection on z is non-zero (this is the potential difference that is measured between plates 2).

The same effect is when the boat is moving from the wind, part of which is tangential, or plane lifts up despite the initial equilibrium between its weight and the counteraction of the ground.

The trick is to create a superposition and turn the direction of the field by ‘blowing’ perpendicular energy current.

All these effects like Hall’s are just special cases of contrapuntal effects on the field from superposition of different energy currents.

As usual Occam’s razor wins!

Origins of sine waves

There is a lot of resistance amongst engineers and scientists to believing that the physics of electromagnetic signals and dynamic processes involving such signals is NOT based on sine waves. There is so much in engineering that has been and is still being invested into the mathematics and tools supporting harmonics based analytics and design activities of thousands of engineers. Equally, thousands of academics are teaching hundreds of thousands of electrical engineering students all this on and on.

But let’s think what we can learn from nature that communicates signals from one point in space to another point and does it in a most frugal and at the same time prodigal way.

Imagine that we need to send a signalling event from point A to point B in space. This clearly involves some sort of motion. What kinds of motions we know. Well, at least two. One directional – that would be, for our example, moving along a straight line between A and B. Another type of motion we know rotational. This latter one should go if there is a point in the circle or sphere around which the notion can revolve. So, if the signal event involves the change in say electric field E and magnetic field H, we know that their vector product ExH forms Poynting vector, which shows the direction of the signal propagation. This propagation can only happen at speed of light in the medium (as there has been no evidence of otherwise!). If the direction of the vectors E and H is not changing in time at point A, our signal, defined by the Poynting vector ExH, will travel from point A to point B directly, albeit, this direction may not be straight line as it may be determined by the surrounding environment’s properties of epsilon and mu. So, for example if that environment is a transmission line (TL) formed by two metallic plates, then the Poynting vector will travel in the direction defined by the TL.

Now, if E and H elements of the vector are steps, then clearly these steps will form pulses, both in time and space. Now if for example these components of the vector are themselves in the rotational motion, and this rotation of E and H happens with a particular frequency omega, then the phase of the rotation omega*time will form an instantaneous value of the angle whose sine and cosine will determine the value of the potential produced, say, by the E component, between the plates of the TL. Unfold this rotation of vector E in space in the direction of the propagation of the Poyning vector, and you get a spiral – which is a superposition of the rotational and directional motions. The projection of this spiral on the normal plane along the Poynting vector’s direction will give us a sine-wave for E, likewise a cosine-wave for H. Hence rotational shift of 90 degrees will manifest itself as a longitudal phase shift of 90 degrees.

So, to summarise, the sine-wave is a product of a combined effect of a rotational motion and longitudal motion. But what’s important is that this sine-wave has a clear point of the start and end, and it’s not being there as a fundamental element. It is a mathematical trace of the superposition of fundamental elements of motion.

Another possible source of sine-waves is a successive oscillation of step-wise process in the longitudal direction, between two points. This is something that we have already discussed in earlier blogs, where we talked about the physical processes of propagation of energy current in transmission lines. Indeed if we connect a TL which has an open circuit end with the TL which is short circuited end, we have the effect of C and L combined, and the process of reflection of the energy current in this system, will form a series of steps which can be approximated by a sine-wave. Again, the sin-wave is a mathematical product of more primitive physical process.

In my recent email exchange with a group of people arguing with Ivor Catt about the primal nature of sine-waves, I wrote:

“Mechanics tells us there are two basic motion types. Direct and rotation around a point. If we assume that ExH can follow both, the latter for example, under the influence of another force (gravity?), can help us the express the effect of “corkscrew propagation”, and hence spatio temporal sinewaves. Actually a corkscrew or spiral is a good explanation of an AC shaped energy current around a wire. Light can propagate from its source in myriads of corkscrews by the way! ”

“By the way a corkscrew-like propagation of signal (hence, information) via a medium with natural resistance or friction (usual epsilon and mu properties) would be most effective, again from mechanical analogy point of view. Nature, again, following Occam’s razor principle, would do it the same way we penetrate into a cork or a wall, and that’s what nature would always do if it needs to pass information from one point to another! Energy current screws up everything! Ha-ha!”

I also recommend to someone who wishes to visualise these corkscrew processes to have a look at these wonderful videos on youtube:

https://www.youtube.com/watch?v=WCxXPTtQFm4

https://www.youtube.com/watch?v=0jHsq36_NTU