Herd Immunity Ratio
As an intellectual exercise
let’s think of an imaginary state, “State A.” Our fictional State A is
devastated that 100 of its citizens are infected with Covid-19. For this
exercise, we accept that these 100 citizens are representative of State A‘s
demography, classes, ethnicities and so on. Apparently, State A’s nightmare is
just the beginning because out of its 100 Covid-19 carriers, not one survives
the next three weeks.
Let’s now imagine another case,
we will call “State B.” State B is similar to state A in terms of its size,
population, geography, climate, culture, ethnicity, nutrition, etc. In State B
100 citizens also tested positive for Covid-19. Following the experience of
State A, State B braces itself for the possibility that all its infected
citizens may perish but then for reasons that are not yet clear to us, no one
in state B dies. And if this is not different enough, hardly any of the 100
develop any symptoms.
The crude difference between
State A and B may tell us something about the herd immunity in States A and B.
It is easy to detect that the ratio created by the number of fatalities (F)
divided by the number of those infected (I) is an indication of the level of
immunity or ‘herd immunity’ in a given region or a state.
State A: F/I = 100/100=1
State B: F/I = 0/100=0
State B: F/I = 0/100=0
State A’s immunity ratio equals
1. This means that anyone who contracts the virus in State A will likely die.
In state B, on the other hand, one is likely to survive the virus. In fact,
they may, without knowing it, have already survived.
But let us now consider some
more realistic cases. In “State C,” again, a state similar to A and B, out of
100 who tested Covid-19 positive, 10 people died within the next few weeks.
State C: F/I=10/100=0.1
The herd immunity ratio in
State C is 0.1. In terms of herd immunity, State C is far better off than State
A as a virally infected subject may benefit from a 0.9 chance to survive. But
State C’s situation is not as good as in State B where no one is expected to
die as the F/I ratio in State B is O. We can see that the smaller the F/I ratio
is, the greater is the herd immunity in a given state or a region.
But let us look at another
realistic case. In “State D” out of 100 patients only 1 died within a few
weeks.
State D: F/I=1/100=0.01.
This means that in State D the
herd immunity is close to perfect. Someone who contracts the Covid-19 virus has
only a remote chance that he will lose his life. In other words, the survival
rate is 0.99
State C and D are not
completely imaginary cases. The F/I ratio in State C is a good representation
of the numbers we saw in Northern Italy, NYC, Spain, UK and other vulnerable
regions that have suffered heavily in the last few weeks. The ratio in State D
is very similar to South Korea and Israel. Though many people are identified
with Covid-19 in these two states alone, very few have died.
Such a methodical search for
herd immunity ratio may help to identify the survival rate in different states,
regions and cities. It may help us to determine policy; to decide who, what and
how to lockdown or maybe not to lockdown at all. It can also help to locate the
origin and the spreaders of the disease as we have a good reason to believe
that the regions with the most immunity to a given viral infection have likely
experienced the disease in the past and have developed some form of resistance.
In reality, this model is
problematic for many reasons and can hardly be applied. As things stand (in
reality), we are comparing data that was collected under different
circumstances and using various procedures designed with completely different
strategies and philosophies. Both Israel and South Korea, for instance,
conducted testing on mass scale and hence, identified many more carriers. More
crucially both Israel and S. Korea made a huge effort to identify super
spreaders and applied strict isolation measures to those spreaders and those
who were infected by them. Britain, USA and Italy on the other hand conducted
limited testing and have generally tested those who developed symptoms or were
suspected of being infected.
But there is a far greater
problem with the above herd immunity ratio model. It assumes that we know what
we are dealing with i.e., an infectious viral situation, while the evidence may
point otherwise.
The Radioactive Clock
It has become clear that the health crisis we are facing isn’t consistent
with anything we are familiar with. Those who predicted a colossally genocidal
plague weren’t necessarily stupid or duplicitous. They assumed that they knew
the root cause of the current crisis. They applied recognized models and
algorithms associated with viral pandemics. They ended up eating their words,
not because their models were wrong but because they applied their models to
the wrong event. While no one can deny the alarming exponential growth of the
disease, it is the unusual ‘premature’ curve-flattening point and then the
rapid decline of infections which no one explained. In fact, some still prefer
to deny it.
Many of us remember that our so-called ‘experts’ initially tended to
accuse China of ‘hiding the real figures’ as
no one could believe that the virus, all of a sudden, pretty much ran out of
steam. Some also claimed that Iran was faking its figures to
make its regime look better. Then came South Korea and the scientific community
started to admit that despite its initial rapid exponential growth, for an
unexplained reason, the ‘virus’ seems to run out of energy in an unpredictable
fashion: the curve straightens out almost abruptly and starts to drop soon
after, almost literally disappearing to the point where even a country as
enormous as China passes days without diagnosing a single new Covid-19 carrier.
When Italy experienced its
Corona carnage, every health ‘expert’ predicted that when the ‘virus’ slipped
out of the rich Lombardy region and made it to the poor south, we would see
real genocide. That didn’t happen.
We have also started to notice
that lockdowns have not necessarily saved the situation and that adopting
relatively light ‘lockdown’ measures doesn’t translate into a total disaster as
Sweden has managed to prove. The ‘virus,’ appears to stop spreading according
to its own terms rather than the terms we impose upon it.
Thinking about the anomalies to do with the virus in analytical
mathematical terms, as opposed to seeing the virus in biological or medical
terms, has made me believe that a paradigm shift may be inevitable. We seem to
have been applying the wrong kind of science to a phenomenon that is not really
clear to us.
This may explain what led a British ‘scientist’ to reach a ludicrous and
farfetched estimate that Britain could be heading towards an astronomic death
figure of 510.000. Following the same flawed algorithm, Anthony Fauci advised
the American president that America could see two million dead.
Both scientists were wrong by a factor of 25-40 times. Such a mistake in
scientific prediction should be unforgivable considering the damage it
inflicted on the world’s economy and its future. One might say that the good
news is that our governments are finally listening to scientists, the tragedy,
however, is that they are listening to the most idiotic scientists around.
Looking at the tsunami of raw data regarding worldwide spread of Covid
19 reveals a lot, perhaps more than we are willing to admit at this stage. The
numbers, the shape of the Corona growth curve and the manner in which it
flattens and declines suggests to me that something different may be at play.
It seems as if the disease is shaped by an autonomous internal clock that
determines its time frame and that it is not impeded by any form of organic
resistance such as antibodies or herd immunity. The curve’s rise toward that
flattening instant is indeed characterized by consistent and exponential
growth. But then, in a seemingly arbitrary manner, the disaster stops its
increase and the numbers of those infected by Covid-19 starts to drop.
Looking for such a pattern that
produces an exponential growth that comes to a sudden end calls to attention
the concepts of radioactivity in general and of the half-life in particular.
Each radioactive isotope has
its own decay pattern. The rate at which a radioactive isotope decays is measured in
‘half-life.’ The term half-life is defined as the time it takes for one-half of
the atoms of a radioactive material to disintegrate. Radioactive decay is the
disintegration of an unstable atom with an accompanying emission of radiation.
The change from an unstable atom to a completely stable atom may require
several disintegration steps and radiation will be given off at each step.
Half-life is a measurement of
time (set by the radioactive isotope) that involves a repeated release of
radiation. Each time radiation is released the radioactive isotope is splitting
in half, this repeats until it either reaches stability or maybe becomes
ineffective. If you bear the half-life dynamic in mind you can see how one
person can ‘infect’ or shall I say, radiate an entire stadium a few times over
during a two hour football match. All it takes is a radioisotope with a
half-life cycle of a few seconds.
Once the atom reaches a stable
configuration, no more radiation is given off. For this reason, radioactive
sources become weaker with time, as more and more unstable atoms become stable
atoms, less radiation is produced and eventually the material will become
non-radioactive. I wonder whether this could provide an explanation for the
abrupt curve flattening that is associated with Covid-19
What may be possible is that
Covid 19 is not the root cause of the current disease, it may instead be a
by-product of a radioactive interaction. I am not in any position to
substantiate this theory. Instead, I offer an alternative way of thinking about
the problem that may shed light on the situation. If Covid-19 is a by-product
of radiation, then the sudden decrease in radioactivity due to the nature of
half-life reactions can explain why the virus loses its growth energy when it
seems as if it has become unstoppable.
If this theory has any merit, then we are misdiagnosing the Corona crisis, misapplying the science and implementing the wrong strategies. It may also indicate that herd immunity won’t work, as we are not dealing with a viral infection but instead becoming ourselves, a source of radiation.
If this theory has any merit, then we are misdiagnosing the Corona crisis, misapplying the science and implementing the wrong strategies. It may also indicate that herd immunity won’t work, as we are not dealing with a viral infection but instead becoming ourselves, a source of radiation.
This theory may help explain
why Israel and South Korea (State D) were so successful in combating the
crisis. It wasn’t the lockdown that saved these countries. It was their
aggressive search for and quarantine of super spreaders and those who were
potentially radiated by them. Consciously or not, rather than stopping the
virus they isolated the catalysts that were leading to the creation of the
virus.
Our world is in a grave crisis and
could benefit from thinkers who are slightly more creative, sophisticated and
responsible than the characters who currently occupy the World Health
Organisation, the CDC and London’s Imperial College. But more than anything
else, I reiterate once again: we need to escalate our response to the Corona
crisis into a criminal investigation so we can figure out every possible error
or malevolent act that led humanity into the current grim situation.
https://www.unz.com/gatzmon/corona-crisis-a-viral-episode-or-a-half-time-nightmare/
