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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Rig Ved 1.36.7

To do a Namaskaar or bow before someone means that you are humble or without pride and ego. This means that we politely bow before you since you are better than me. Pranipaat(प्राणीपात) also means the same that we respect you without any vanity.

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Surrendering False pride is Namaskaar. Even in devotion or bhakti we say the same thing. We want to convey to Ishwar that we have nothing to offer but we leave all our pride and offer you ourselves without any pride in our body. You destroy all our evil karma.

2/9

We bow before you so that you assimilate us and make us that capable. Destruction of our evils and surrender is Namaskaar. Therefore we pray same thing before and after any big rituals.

3/9

तं घे॑मि॒त्था न॑म॒स्विन॒ उप॑ स्व॒राज॑मासते ।
होत्रा॑भिर॒ग्निं मनु॑षः॒ समिं॑धते तिति॒र्वांसो॒ अति॒ स्रिधः॑॥

Translation :

नमस्विनः - To bow.

स्वराजम् - Self illuminating.

तम् - His.

घ ईम् - Yours.

इत्था - This way.

उप - Upaasana.

आसते - To do.

स्त्रिधः - For enemies.

4/9

अति तितिर्वांसः - To defeat fast.

मनुषः - Yajman.

होत्राभिः - In seven numbers.

अग्निम् - Agnidev.

समिन्धते - Illuminated on all sides.

Explanation : Yajmans bow(do Namaskaar) before self illuminating Agnidev by making the offerings of Havi.

5/9