1/
In 🇩🇪 werden wir jetzt wieder monatelang diskutieren, warum es zu einem Abflachen der #Covid Fälle noch vor dem #Lockdown2 kam und ob dieser notwendig war.
Fakt ist: vom 1. bis 30.10. lag exponentielles Wachstum vor. Anschließend kam es zu einem Abflachen der Kurve.
2/
Grund hierfür kann sein, dass bereits ab dem 15.10. die Maßnahmen in Deutschland gemäß Government Response Stringency Index verschärft wurden laut @OurWorldInData. Und ~2 Wochen später kam es zu einem Abflachen der #CovidFälle. Am 22.10. kam es zu einer erneuten Verschärfung.
3/
Die Maßnahmen Kritiker werden hingegen argumentieren, dass der #Lockdown2 unnötig war, da die Fallzahlen Wachstum schon vor dem Beginn am 2.11 abgeschwächt wurde.
Stichwort: logistisches Wachstum (Sigmoid Funktion)
So könnte ein Sigmoid ungefähr aussehen für die 2. Welle
4/
Brauchte es also den #LockdownLight ab 2.11. oder hätten die verschärften Maßnahmen ab 15.10. ausgereicht für #FlattenTheCurve?
Ehrlich gesagt: keine Ahnung.

Auf diese und andere Fragen gibt es auch keine einfachen Antworten, da gesellschaftliches Verhalten sehr komplex ist.

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More from @roberto_blanko

1 Nov
#SayNo2Q Thread

1/
In 🇩🇪 kennen die meisten die QAnon Bewegung seit den #Querdenken Demos. Und es fällt erst mal schwer diese Horrorgeschichten über satanische Pädophile die Kinder quälen und töten, um Adrenochrom zu farmen ernstzunehmen.
Doch die Bewegung ist älter und größer
2/
„Q“ tauchte erstmal im Oktober 2017 auf dem Imageboard 4chan auf. Nach kurzer Zeit wechselte Q auf die Plattform 8chan.
Ein Mann wird immer mehr mit Q in Verbindung gebracht: Jim Watkins, ein Schweinefarmer der jahrelang auf den Philippinen lebte.
dailydot.com/debug/who-is-q…
3/
Seit 2015 gehört Watkins das von Fredrick Bannon kreierte Imageboard 8chan (heute 8kun). 8chan war ein Zufluchtsort für bei 4chan gebannte User, die dort eine Heimat mit minimaler Kontrolle fanden…Ein Nährboden für Radikalisierung im Netz.
Read 14 tweets
14 Oct
#Covid19 Excess mortality: Thread

1/
Of the 32 countries analyzed, 17 showed no (real) increase in Exc. mortality. For the remaining 15 countries are shown:
- Exc. deaths over time (simple approach)
- Deaths 2016-2020
- Cumulated Exc. deaths 2020

All data on weekly basis
2/
AUSTRIA
➡️ Moderate (=not unprecedented) increase in Mortality due to #Covid

Cumulated Excess deaths 2020: 1983 = 215 per Million population ImageImageImage
3/
BELGIUM
➡️ Dramatic (=unprecedented) increase in Mortality due to #Covid

Cumulated Excess deaths 2020: 7713 = 666 per Million population ImageImageImage
Read 18 tweets
29 Sep
#Covid19 myth #4: Thread

1/
In nearly every argument about the necessity of a lockdown the phrase "...but Sweden" is used all around the globe.

So the question is:
Is Sweden 🇸🇪 really a role model for saving the economy by refusing to lockdown?

Spoiler alert: NO
2/
With Eurostat's data for the GDP development in Q2/20 and the Cumulated deaths until the end of Q2 and the respective Government response Index within Q2/20 from Ourworldindata, I analyzed the given data.

Data file with sources can be found as a reply to this tweet
3/
It's worth noting that also Sweden implemented measures like banning public gatherings with more than 50 people and banning visits of homes designed for older people.
So the argument that Sweden didn't implement measures at all, is just false:
folkhalsomyndigheten.se/the-public-hea…
Read 8 tweets
28 Sep
#Covid19: Excess mortality Europe
Thread

(1) From Eurostat's weekly deaths data (preliminary for 19&20) P-Score is calculated to allow comparison.

P-score: percentage diff. of weekly deaths compared to average of previous 5 years.

Countries listed in no particular order
(2) SPAIN
Data available from 2000 until 2020 ➡️ P-Score from 2005 until 2020 can be displayed.

Top 3 weeks:
#1 CW14/20
#2 CW13/20
#3 CW15/20

#Covid's spike in Excess mortality in 2020 is unprecedented
(3) BELGIUM
Data available from 2000 until 2020 ➡️ P-Score from 2005 until 2020 can be displayed.

Top 3 weeks:
#1 CW15/20
#2 CW14/20
#3 CW16/20

#Covid's spike in Excess mortality in 2020 is unprecedented
Read 8 tweets
16 Sep
#Covid antiscientists Ep. 1: @FatEmperor
Thread

(1)
I have never seen such an obvious and dangerous display of anti-science like in this video.

His method is basically cherrypicking data mixed with unproven assumptions like herd immunity and dry tinder:
(2)
It really is amazing how he diminishes all data and information which contradicts his hypothesis.

To put all his jibberish into perspective, here is an excellent thread by Dr. Pimenta debunking this nonsense:
(3)
What fascinates me the most is that the foundation of his dry tinder theory is that the time period preceeding #Covid had a "VERY LOW DEATH RATE".

Funny thing is: That's completely wrong Image
Read 6 tweets
15 Sep
#Covid19 myths Ep. 2: Thread

(1)
Self-proclaimed "experts" are spreading this type of chart where I added lovely remarks.
From this chart you can calculate e.g. monthly Excess Deaths. Here for Sweden.

But does it allow you to calculate the Excess Deaths correctly?
Spoiler: NO Image
(2)
In general, Excess Deaths are the difference between the observed deaths to an average death count in a defined time period.

So, you need 2 values:
#1: The observed value. This value is correct on the chart
#2 The average or baseline. This value is wrong
(3)
Authorities and real experts would calculate the monthly average of deaths by averaging the value of the respective month of the previous 3 years.
But in this chart a rolling average of the previous 36 months is used.

To illustrate this difference here is an example:
Read 15 tweets

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