1/ I actually don't like working with VAERS. One would need a data-prep tool, which CDC has, but me not (out of scope to develop). Still, I took a look.
The issues with lot IDs and VEARS deaths/AE are:
-Age confounded (average administered dates).
-Different batch sizes.
2/ We immediately see the high age of most deaths and related batches. We do have confounding to resolve. Deaths, both C19 and vaxx, is an elderly business and includes a lot of comorbidities in both cases. The young don't die in relevant numbers from Covid not vaxx.
3/ One thing that looks strange are the low death batches / weeks in many cases for the very old groups. It should follow an exponential life table to my view. There should be no points in the red circled area (high age, low deaths).
4/ Those lots here look strange. Almost like if they were placebo. But even then, we should expect to see the age confounding (so died "with" vaxx). But maybe the lot size was small. Without batch sizes...very difficult to say much.
5/ Filtering to the first 30 weeks, we get a quite decent exponential trend, like expected in the case of age confounding (died โwithโ or died from vaxx due to higher risk when old and fragile).
6/ The death age relation follows an exponential trend. It would be clearer if normalizing by doses per age bin. We also see Moderna having same count similar as Biontech, despite a 20% lower number of doses.
This is likely a true signal: the 3x higher dose in Moderna.
7/ To do this better we need to merge in:
Doses by lot, manufacturer and age (lso sex).
Categories: pre-existing conditions.
Population sizes.
As this is not straightforward, I dislike to work with VAERS. It's still much better than having nothing (like in EMA, PEI, Lareb).๐
โข โข โข
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Imagine claiming the trial was correct, deploying it to 95% in NZ/AUT, and thenโboom!โthe incidence explodes instead of the virus being eliminated which should already happen at ~70% rate, and was calculated mathematically to happen based on that very promise. False. Study โก๏ธ๐ฎ
Moreover, mortality rises instead of falling. Who are these people still lying about its mortality effectiveness? Itโs a failure, and rightfully, Pfizer's stock is plummeting. Keep grieving; wonโt help. We want the money back. Those who wanted it can still buy it with own money.
They think that they will get out of this? Desperation. Or did he just admit that everybody (including the CEO Fauci CDCโฆ) were involved in deceptive advertising claims? I doubt that it is going to have a better outcome. Keep digging the hole ๐
1/ Important. ERA5 is a weather model, not a measurement. This summer field tests revealed: rural areas suffer heat bias due to urban heat pollution, making models/interpolations heat biased.
Here a demo that ERA5 is wrong on the tested location.
2/ This implies that all temperature aggregations in climate aggregations incorporate the heat bias prevalent in rural areas. This outcome is hardly surprising given that the majority of weather stations are situated in urban or airport environments.
2/Context: When aiming to determine the Age-Standardized Mortality Rate (ASMR) rather than Life Expectancy (LE), we employ a straightforward relationship:
ASMR = 90 - LE
(valid for ESP2013 population)
However, for those who find it more relevant, we can maintain the LE-CO2
3/ It's important to mention that money is an abstraction of promised future work (energy future). This is why the US dollar is linked to oil; US have grasped this concept.
Rather than $ inflation adjustments, you can express your wealth / income as tons CO2 (or MWh) instead.
1/ Thanks to the Simpsonโs paradox (alle age vaxx rates + all age excess) + spurious correlation (ecological fallacy), the Professor is resurfacing the manipulative fallacy from 2021.
Letโs demonstrate on pre-vaxx year 2020.
@MartinKulldorff
2/ Just to highlight further: the vaccination rate in the age group 65+ where 99% of mortality comes from, is equivalent in almost all European countries and higher than 90%.
3/ Heโs furthermore using the ecological fallacy, which we can use to make a time machine (called spurious correlation) and have the vaccine given 2021 working in 2019 or earlier.