This week, the daily bucket chart also highlights weekday public holidays that affect footfalls:
Eid Jul 21
Janmashtami Aug 30
Ganesh Chaturthi Sep 10
Navratri Oct 7-15
Lesser/regional holidays have been skipped only due to lack of pan-India data impact.
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The vaccine availability chart shows anomalous behavior this month - rather than a seesaw between supply and use, it has risen until 10th.
Oct started with 50m doses, supply rose to 85m, while consumption was 42.5m -> 77.5m doses supplied, or likely ~265m for the month.
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In other words - supply on track, but a drop in daily numbers as the week-long holiday hit.
CoWin data shows the Govt tried - open vaccination centers rose from 65k on Tue to 90k Thu-Sat, and 50K+ on Sun.
This tactic does not appear to have worked well, however.
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This suggests opening many centers doesn’t ensure enough people show up during an extended holiday - a vaccination drive is needed.
States that have been doing regular drives continue to do so, e.g. UP here (image credit: covid19india.org) is due for its next.
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Perhaps a better tactic during an extended holiday is more closely spaced mass vaccination drives, since supply is no constraint. This also reduces the pressure on HCWs who otherwise man the greater number of centers each day despite fewer footfalls.
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Now to address the question, has vaccination reached close to saturation, causing this slowdown ?
This graph shows how much the 1st dose coverage of eligible pop has risen each wk for the last 8 wks.
Hint: The white space on top is what is left to do of 1st doses.
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Similarly, the same data for second doses. The gap is large because Covishield (85% of total) has a 12-16 week interval and a lot of 1st doses happen in that time.
The west primarily uses a vaccine with 21 day interval so fully vaxed numbers will track closer.
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This chart shows how far each state is from the 2.0 mark signifying both doses done there, i.e total doses / 2*eligible pop.
Again there is a lot of white space on top, i.e. quite a bit of extra
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By tracking weekly data in this manner, the goal is to see if first dose incremental number compress beyond some threshold , e.g. 75-80%, or whether they keep going the same at least during normal weeks. Correspondingly, 2nd dose increments should rise.
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The IPL is also far more lucrative in revenue per game, despite the fact that with a mere 74 games, it’s in the bottom 5 of sports leagues by number of games played per season.
Each of those games is a money spinner. Let’s look at how much.
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In terms of total revenue, the IPL ranks a reasonable 13th position among global leagues, despite having so few games in a single season.
There are only three countries on the planet with $1 trillion+ exports - China, USA and Germany. Even Japan is around $850-900B in FY 2021.
India jumped ~10 ranking places to provisionally #8 this year. Positions 4-7 are Japan, UK, France and Netherlands - all $700-900B.
India began 1949 the 9th biggest exporter.
It left the top 10 by end of that year.
It left the top 20 in 1957.
It reentered the top 20 in 2010, but didn't rise further until 2017-18 when it was 18th ranked. 2021-22 saw a big jump to 8th position.
Today was a day of symmetry as the vaccination total hit 175 crore (1.75 billion), exactly on day 400 since start of vaccination. This corresponds to an average of almost 4.4 million vaccinations a day, over four hundred days.
The monthly total to date is now almost 88 million. February should easily cross 100 million and will likely finish at around 125 million. This is expected as it is predominantly second dose + boosters. March will be much higher as the 12+ group becomes eligible.
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With even the second dose numbers not very high now, the daily average over the past week was just 4 million - multiple days towards the end of week being around 3.98 million, and thus still in the 3.x bucket.
So how do you fix sampling error ? You get a small number of people to agree - keeping pop standard deviation down because sample size is tiny.
Disagreement is a problem - if you get half of them to disagree, your sampling error is 20-45% depending on sample size. Oops.
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So let us pretend these experts indeed know what they’re doing. Let us look at the data. This author has the entire VDem dataset, analyzed in detail together with @jai_menon :