@denisrancourt I believe the efficacy to be 0. It is trivially easy to pull off paper efficacy of >90%. Heres how done.

1. Have no one deliberately exposed to the virus
2. Only test those that report symptoms
3. Give a placebo (saline) shot with no side effects
..
@denisrancourt 4. Fail to run a test group with no shot
5. Fail to run a test group with non-pharma intervention such as exercise, diet and vitamin D
6. Fail to run test group with on related immune boost
7. Partipants knew which group they were in based on the side effects.
....
@denisrancourt 8. Many participants openly admitted in public online forums to getting antibody tests to determine which group they were in
9. Most likely everyone wanted the trial to succeed.
10. Knowing which group you were in, you could just not get tested if in the vaccine group
....
@denisrancourt 11. There would have been enormous pressure to not get tested if in the vaccine group. No one would want to be the reason billions of people did not get a vaccine.
12. Prevalence was so low that every one positive test in the placebo group reduced efficacy by more than 1%
...
@denisrancourt 13. 99.2% of the placebo group did not get covid
14. 99.95% did not get a severe case of covid
15. Prevalence of the disease was much higher during the trial than 0.8%.
16. Vaccine might only be optimized to pass a PCR test and not actually do anything about the disease
....
@denisrancourt 17. They were given two shots to boost immune. Each person got two times to determine which group they were in.

I'd love to see independent attempts at replication, or better yet, deliberate exposure. IFR is 0 for healthy under 25.

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More from @TedPetrou

3 Jan
I gave masks the benefit of the doubt in the spring/summer of 2020. I have since changed my mind and see no scenario where it makes sense to wear them in public.

Even if they provided 100% protection, I believe masks to be harmful. An explanation below 👇👇👇
Let's say masks provide 100% protection to both the wearer and others (perhaps non-masked individuals) in close proximity. This is perhaps the worst scenario.

The immune system requires a consistent influx of both microbes and viruses to remain trained.
Halting the exposure to microbes and viruses is extremely harmful in the long run and leaves you exposed to enormous risk in the future.

Without training, you will become susceptible to even weaker microbes that your immune system would have normally handled with ease.
Read 19 tweets
2 Jan
In 2019, Sweden recorded its lowest ever mortality rates for those under 65.

In 2020, women under 65 will record their best ever mortality rates and men their second best ever.

Explanation 👇🏻👇🏻👇🏻
Total deaths for the under 65 population through early dec (men/women)

2019 - 9400 (5700/3700)
2020 - 9700 (6100/3600)

This is not taking into account pop. increase of ~0.5%.

statistikdatabasen.scb.se/pxweb/en/ssd/S…
2019 had the lowest ever mortality rates for all age groups in history and dropped ~5% (huge for one year) from an already record low 2018.
Read 7 tweets
15 Dec 20
Sweden's two-year mortality rate in 2019-2020 is one of the lowest in the last 10 years.

I even overestimated 2020 deaths by 2-3%.

Share this to everyone you know who tries to point at Sweden as an example of covid policy failure.

Their strategy has been a huge success.
Data is directly from Statistics Sweden - scb.se/en
Overall deaths have grown slightly but not in proportion to the population, so mortality rates have decreased.
Read 14 tweets
4 Dec 20
.@CDCgov - Why is your upper bound threshold for deaths less in 2019 than in 2020? This is extremely fishy to me.

It's overall 2.3% less for the same periods in 2019 vs 2020 or ~63k. Image
For those who want an explanation. The CDC uses this chart to estimate excess deaths, but the upper bound threshold in 2020 is 2.3% lower than it was for 2019. US population is growing.

cdc.gov/nchs/nvss/vsrr… Image
The threshold grew 2.4% from 2018 to 2019 and about the same from 2017 to 2018.

This totaled threshold in millions for the first 47 weeks is:
2018: 2.67
2019: 2.74
2020: 2.67
Read 5 tweets
2 Mar 20
Explanation of matplotlib "inches" - a tutorial thread

Inches is a relative term. You must know the figure dpi (dots per inch) and your screen's dpi to make sense of it. Default inline dpi is 72. Below figure is 5 x 2 "figure inches" or 360 x 144 pixels.
Using a screen ruler program (Onde Rulers) it actually measures as 2.2 x .98 "screen inches" on my screen and 324 x 144 pixels
Why not 360 x 144 pixels? Because figures are displayed in the notebook with setting bbox_inches set to 'tight' which trims some of the figure.
Read 9 tweets
22 May 19
There are many types of scikit-learn Estimators. The most common are:
Regressors
Classifiers
Clusterers
Transformers
Meta-estimators

Read their exact definition in the glossary scikit-learn.org/stable/glossar…

They all follow the same three-step process - import, instantiate, fit
Here are examples of each one. They all look the same

# Regressor
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X, y)
# Classifier
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X, y)
Read 6 tweets

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