COVID-19 DEATHS US. P-Modeling Pt B. The Great Vaccine Debate
Welcome Hyperspace Travers,
Current Total Stands at 789,870
Please Play Pt A, first.
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If H:1 holds true upon good data input: of TOTAL US COVID DEATHS.
H1 is proposition of development of argument towards slope velocity and of model projection of such.
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Slope can A. stay on the current path at 75 degree slope. By the end of January almost 1 million will have died.
Slope can B. stay elevated at 65 degrees.
Slope C. stays on cycled compression at a modest 35 degrees.
Severity of slope is directly correlated with velocity of infection per X density of population.
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Arguments against H:1 are as follows:
Z. Argument given. You can manipulate death totals at various levels of the collection process ie.state, county, etc.. Thus, your data is manipulated, leading to incorrect analysis.
- Sure, but variance between cycles of infection per X density of population shows velocity (V) depicted by slope against a compressed plane. See that gray line stemming from the start of the data. That is an approximation of the mean in linear terms, showing a variety of compression/expansion cycles in the data provided.
The variance velocity is naturally immune to manipulation of an input/output average consensus of a metric death.
When X density of infections follows rolling cycles, the problem becomes if the velocity of the variance increases quickly in any one segment of time (Y).
The initial peak of infection can be said to be 90 degrees from baseline 0 to the first junction of amplification.
If X density of population affected by Z virus has steep velocity variance in any one segment of time Y, then we see a social consequence from it. ie.overwhelmed hospitals, shortages of supplies *in a worldwide supply chain crisis cough*, staff shortages, medical burnout, etc. You get the idea. This leads to increased reporting of death totals due to covid-19. Regardless, we simply care about social consequence. Meaning people you love start getting sick, it usually doesn't mean as much until it hits home. Then it strikes a nerve of importance.
If we can come to a consensus of populations of America, its roughly 330 million.
If we roughly estimate that 200 million are vaccinated with first, second or third boosted levels of the vaccines out currently. Let's say hypothetically that 20% of that 200 million have breakthrough potential. + 40 million.
We now have 130 million of 330 total population that are unvaccinated. So, put them in a pool for risk of infection and dying.
Plus, we have 20% (compounding variables of what ifs') of 200 million with a breakthrough potential for infection despite still being vaccinated. Furthermore, even if we said oh 10% of the covid-19 death is bullshit inflated manipulated politics. That actually strengthens the argument of immediate concern to increased variance velocity in total deaths because you have further increased the potential pool those who can be infected, but in a shorter time-span. The 20% who are vaccinated but still could be classified as vaccine covid-19 'breakthrough" includes everyone with a severe disease, elderly folk, and the young children whose immune systems are not strong enough yet to adapt to changing spike protein based viruses.
That's now 160 (+/- 10 m) million in the probability pool who have a potential of dying due to strain of a nasty virus.
Out of that 160 million, a variance velocity increase at the levels stated above can easily take out another couple hundred thousand people in a short Y timeframe.
EASILY.
I probably made some mistake in my calculations, but I think you get the picture.. maybe?
There is logic to this madness.
Unless shit is shut down immediately. We once again risk increased variance velocity due to a wide range of ignorance that I can not even begin to address here.
But who knows if we learned.
This is just really going to suck either way.
Probably not.
But probably..
Thanks for Pondering the Unknown with Me,
Glitch420.
_______________________________________________________________
Current Total Stands at 789,870
Please Play Pt A, first.
\
If H:1 holds true upon good data input: of TOTAL US COVID DEATHS.
H1 is proposition of development of argument towards slope velocity and of model projection of such.
_________________________________________________
Slope can A. stay on the current path at 75 degree slope. By the end of January almost 1 million will have died.
Slope can B. stay elevated at 65 degrees.
Slope C. stays on cycled compression at a modest 35 degrees.
Severity of slope is directly correlated with velocity of infection per X density of population.
__________________________________________________________________________
Arguments against H:1 are as follows:
Z. Argument given. You can manipulate death totals at various levels of the collection process ie.state, county, etc.. Thus, your data is manipulated, leading to incorrect analysis.
- Sure, but variance between cycles of infection per X density of population shows velocity (V) depicted by slope against a compressed plane. See that gray line stemming from the start of the data. That is an approximation of the mean in linear terms, showing a variety of compression/expansion cycles in the data provided.
The variance velocity is naturally immune to manipulation of an input/output average consensus of a metric death.
When X density of infections follows rolling cycles, the problem becomes if the velocity of the variance increases quickly in any one segment of time (Y).
The initial peak of infection can be said to be 90 degrees from baseline 0 to the first junction of amplification.
If X density of population affected by Z virus has steep velocity variance in any one segment of time Y, then we see a social consequence from it. ie.overwhelmed hospitals, shortages of supplies *in a worldwide supply chain crisis cough*, staff shortages, medical burnout, etc. You get the idea. This leads to increased reporting of death totals due to covid-19. Regardless, we simply care about social consequence. Meaning people you love start getting sick, it usually doesn't mean as much until it hits home. Then it strikes a nerve of importance.
If we can come to a consensus of populations of America, its roughly 330 million.
If we roughly estimate that 200 million are vaccinated with first, second or third boosted levels of the vaccines out currently. Let's say hypothetically that 20% of that 200 million have breakthrough potential. + 40 million.
We now have 130 million of 330 total population that are unvaccinated. So, put them in a pool for risk of infection and dying.
Plus, we have 20% (compounding variables of what ifs') of 200 million with a breakthrough potential for infection despite still being vaccinated. Furthermore, even if we said oh 10% of the covid-19 death is bullshit inflated manipulated politics. That actually strengthens the argument of immediate concern to increased variance velocity in total deaths because you have further increased the potential pool those who can be infected, but in a shorter time-span. The 20% who are vaccinated but still could be classified as vaccine covid-19 'breakthrough" includes everyone with a severe disease, elderly folk, and the young children whose immune systems are not strong enough yet to adapt to changing spike protein based viruses.
That's now 160 (+/- 10 m) million in the probability pool who have a potential of dying due to strain of a nasty virus.
Out of that 160 million, a variance velocity increase at the levels stated above can easily take out another couple hundred thousand people in a short Y timeframe.
EASILY.
I probably made some mistake in my calculations, but I think you get the picture.. maybe?
There is logic to this madness.
Unless shit is shut down immediately. We once again risk increased variance velocity due to a wide range of ignorance that I can not even begin to address here.
But who knows if we learned.
This is just really going to suck either way.
Probably not.
But probably..
Thanks for Pondering the Unknown with Me,
Glitch420.
_______________________________________________________________
Trade active:
Trade active:
Comment:
following 65 degree slope with excellent accuracy.
Trade active:
incredible accuracy.
Comment:
Comment:
Trade active:
Trade active:
doubt..
sure.
deny what your eyes experience.
Are you listening yet?
sure.
deny what your eyes experience.
Are you listening yet?
Trade active:
Comment:
it's like a point play by play of the future. But better.. NO COMMERCIALS.
Comment:
Like I am getting worried here. Please deviate for my own sanity.
Trade active:
Comment:
so yea... still following
Trade active:
Trade active:
Yep.
845,437
The next data point is at 855,200.
10k+ more dead by Tuesday keeps us on track with surreal accuracy.
Notice how the fractal is playing out. Notice how the velocity of deaths is what really matters.
Notice how the compressed fractal is "flattening the curve".
THAT IS WHAT IS MEANT BY "FLATTENING THE CURVE". It is a continuous dynamic. Not a static one.
The spread of the virus is rampant.
It is killing people at rate steady rate, a rate that outpaces all other reasons of death.
It's really about perspective..
330 million Americans. 845,437 (+/-2% error) that have died since Feb 2020 in America.
Even accounting for "fluff" in the data the velocity is on pace with the previous first wave. Each fractal you see was taken from different points in the overall data. This is crucial for understanding the structure. Clearly, in a structural sense, movements are not extreme or outlier. They are steady. This is important in determining truth from falsification.
If one was to manipulate the death pool data, we would see an erratic curve mismatch from any previous curve. Why?
Because the manipulators would have to add the SAME variance residual to velocity at every timeframe along the trend. Do you have ANY idea how incredibly hard that would be?
It is more likely that the count is suppressed, then added onto!
I know that blows ones mind to think about but my deduction comes straight from what you see above.
The fact that the trend is following with such accuracy should help imply natural growth based upon actual normalized functions of input:outputs.
But yea. Word salad as most say..
I say.. ITS SCIENCE.
845,437
The next data point is at 855,200.
10k+ more dead by Tuesday keeps us on track with surreal accuracy.
Notice how the fractal is playing out. Notice how the velocity of deaths is what really matters.
Notice how the compressed fractal is "flattening the curve".
THAT IS WHAT IS MEANT BY "FLATTENING THE CURVE". It is a continuous dynamic. Not a static one.
The spread of the virus is rampant.
It is killing people at rate steady rate, a rate that outpaces all other reasons of death.
It's really about perspective..
330 million Americans. 845,437 (+/-2% error) that have died since Feb 2020 in America.
Even accounting for "fluff" in the data the velocity is on pace with the previous first wave. Each fractal you see was taken from different points in the overall data. This is crucial for understanding the structure. Clearly, in a structural sense, movements are not extreme or outlier. They are steady. This is important in determining truth from falsification.
If one was to manipulate the death pool data, we would see an erratic curve mismatch from any previous curve. Why?
Because the manipulators would have to add the SAME variance residual to velocity at every timeframe along the trend. Do you have ANY idea how incredibly hard that would be?
It is more likely that the count is suppressed, then added onto!
I know that blows ones mind to think about but my deduction comes straight from what you see above.
The fact that the trend is following with such accuracy should help imply natural growth based upon actual normalized functions of input:outputs.
But yea. Word salad as most say..
I say.. ITS SCIENCE.
Comment:
Comment:
Very High probability of increased death output per vol population.
Highest velocity variance seen since data started.
Highest velocity variance seen since data started.
