Most 🌍 comparisons for C19 go wrong as they don't considering demographics.

For countries I've managed to src detailed death data for, here's total d/1m numbers.

Sure,🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁷󠁬󠁳󠁿 look similar to 🇧🇷🇵🇪, but just look at the differences <60, 🇵🇪 4x larger.

So what does this mean?

1/9

Basically, fewer >80, means they've had⏫spread, and ⏫younger deaths, but👀equal.

Here ranking⏫2⏬by age are:
▶️20 countries
▶️NY city
▶️The World
That I've 👀at so far.

50% marks the median age, e.g.
🇮🇹 47
🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁷󠁬󠁳󠁿41
🌍30
🇳🇬18

So expecting similar deaths overall is silly.

2/9
So how have deaths actually played out?

Here are the props. by age to late Dec for places with detailed data.

Looks like NYC, 🇧🇷 & 🇵🇪 have seen far more in the young.

Now these all have younger pops. so is spread the same? Are diff just down to demographics?

3/9
No, many factors, the biggest age, and the likelihood of death in each age band.

🌍serology studies have sampled a similar risk in each 10yr band, with this going up 3 fold with each band.

Here is a plot of what🌍avgs are.

So what else do we need to worry about?

4/9
Factors like:
▶️healthcare: beds nos, better care, etc
▶️comorbidity rates: e.g. obesity, OECD 4x India, but only effects 15%
▶️lifestyle impacts: carehomes VS elderly@home
etc

But, many cancel out.
e.g. 1st🌍better healthcare, but fatter.

What about a lack of treatment?

5/9
Worst case, 🇬🇧 HFR is 15%, with 50% needing O2, so 3x die without hosps.

But, 🇧🇷🇮🇳etc data is hosp data, they don't have near realtime all-cause like 1st🌍.

So🌍the IFRs of deaths per band likely far more similar.

We'll see for NYC vs 🇧🇷 likely, 🇵🇪 not so sure.
🤏of🧂

6/9
So, keeping that in mind, let's est:
▶️Pop. IFR for equal spread (IFRp)
▶️The wgted IFR of deaths, where avail (IFRd)

Range is:
🇮🇹0.9%
🇳🇬0.09%
Just 1/10 the pop. capacity!

FYI, a⏫dIFR/pIFR means they've had relatively⏫older deaths.
e.g. 🇨🇭🇩🇰, caveat most have⏬deaths.

7/9
Further:
▶️% of possible deaths
▶️% of spread

100% susceptibility unlikely. Sure, IgA/Tcell resistance possible, but, regardless:
▶️implied spread varies hugely
▶️NYC's alone suggests 🇬🇧 more spread likely
▶️If 🇵🇪, not healthcare diffs, likely only one near true HIT.

8/9
Finally, here's how each age fared rel. to🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁷󠁬󠁳󠁿
▶️Per 2/9 1/5 >80, means 🇧🇷🇵🇪 much⏫per band
▶️NYC worse, likely⏫density & slower LD speed
▶️NYC&🇧🇷 so similar!? Favellas as dense? Same work ethic?
▶️🇺🇦similar to🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁷󠁬󠁳󠁿, protected old better
▶️🇪🇺 more LD, bad at CHs
▶️🇰🇷 best

9/9
Summary, all correlated, not causal.

But, all comparisons must remove big factors for diff🥇
e.g. demographics.

Once you do, assumption flaws become evident.
e.g. 🇵🇪 either
▶️worse healthcare outcomes
▶️or, LD was not effective

Reality, spread⏫in 2nd/3rd🌍than deaths imply.

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It is a series of Sanskrit shlokas recited by Jambavant to Hanuman to remind Him of his true potential.

1. धीवर प्रसार शौर्य भरा: The brave persevering one, your bravery is taking you forward.


2. उतसारा स्थिरा घम्भीरा: The one who is leaping higher and higher, who is firm and stable and seriously determined.

3. ुग्रामा असामा शौर्या भावा: He is strong, and without an equal in the ability/mentality to fight

4. रौद्रमा नवा भीतिर्मा: His anger will cause new fears in his foes.

5.विजिटरीपुरु धीरधारा, कलोथरा शिखरा कठोरा: This is a complex expression seen only in Indic language poetry. The poet is stating that Shivudu is experiencing the intensity of climbing a tough peak, and likening

it to the feeling in a hard battle, when you see your enemy defeated, and blood flowing like a rivulet. This is classical Veera rasa.

6.कुलकु थारथिलीथा गम्भीरा, जाया विराट वीरा: His rough body itself is like a sharp weapon (because he is determined to win). Hail this complete

hero of the world.

7.विलयगागनथाला भिकारा, गरज्जद्धरा गारा: The hero is destructive in the air/sky as well (because he can leap at an enemy from a great height). He can defeat the enemy (simply) with his fearsome roar of war.
The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the *hundreds of millions*. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.


This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/


A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/

https://t.co/uAORIJ5BYX 4/

https://t.co/yOGZ0pLfHG 5/