In order to Calculate VE, we need to calculate Risk Ratio (RR).
CDC, in their epidemiological handbook, gives this definition/example for Varicella:
Then we can calculate the VE as follows:
I've rebuilt above example in Google Sheets:
- VE for this varicella example/outbreak is at 73%.
- Not statistically significant at 90%, but close.
Now we should be able to do the same for Covid19 right?
I'm using the CDC MWWR Case Study of the Massachussets/Barnstable County outbreak, as that had quite a lot of data.
Unfortunately they do not disclose the # of negative tests per group - so we can try to estimate them.
So we know that about 60k people were in the county.
CDC also discloses the % of vaccinated/unvaccinated.
So we can calculate the absolute size of the two groups.
Now, the question is, what's the proportion of people in each group that got tested? We don't know.. let's use 10%
Using 10%, gives us the total # of tests, and we can finally calculate VE.
VE is calculated the same way as before. So what's the result? --> -26%
So if the proportion of people tested is equal, the VE is negative! WOW
So to get to a CDC defined minimum VE of 50%, the amount of people tested in the vaccinated group would have had to be at least 2.5x times (10% vs 4%) higher.
Is that realistic?
So unfortunately CDC does not know or publish the # of tests by vaccine group.
What do you think?
Please let me know your thoughts in the comments?
A list of statistical tricks, that can be used to calculate an illusion of vaccine efficacy with a placebo alone.
For this exercise, I have used a sine wave to simulate weekly deaths:
... and a logistic growth function to simulate placebo vaccination from 0 to 75% of the population.
By the green/red dots, we can see no difference/effect, as no statistical tricks are applied yet.
Trick 1: Unknown Vaccination Status --> Unvaccinated.
If 50% of Unknown vaccination status is treated as unvaccinated, almost 3x higher mortality rates appear for unvaccinated. This is entirely an illusion.
🔥 All-cause mortality by vaccination status from the Netherlands shows likely no vaccine efficacy, possible harm!
Deaths per 100k population by vaccination status shows an initial spike for the vaccinated during the vaccination rollout, and consistently higher mortality levels.
The initial peak may be related to confounding as more elderly/frail were prioritized, to reporting artifact (Fenton et al.), or vaccine harm.
Only focusing on the mid 2021 data, where the lines move in tandem, we still see a diverging of rates after the late 2021 winter peak.
Here adjusted for the levels during extremely low COVID-19 prevalence in Summer of 2021, we can possibly see, no efficacy and a drop for unvaccinated and slight increase for vaccinated, possibly even indicating negative efficacy?
💥💥💥 The latest official New Zealand FOIA data of All-Cause Mortality by COVID-19 vaccination status & age, shows that the vaccinated are the driver of all-cause excess mortality!
Clearly, unvaccinated deaths did not account for any major spikes in excess mortality!
I have analyzed the official NZ data which was published due to a FOIA, and initially analyzed by @sco0psmcgoo.
Here split by age group & vaccination status!
0-20 and 100+ may be incomplete, but those are also rather small numbers.
Plotted against the official total monthly all-cause deaths from , shows a very close match of this dataset. stats.govt.nz
They give it all away in the package insert. Flu vaccines are a scam. By definition, they cannot work!
Moreover, they did not even bother to test for potential of these vaccines to cause cancer or mutations.
Yet, they use the say 14 day trick to claim efficacy!
Ontop of the viral strains, which contain multiple chemicals and biological substances, such as cow blood, this flu vaccine also contains mercury (THIMEROSAL)!