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Covid-times Gyaan

‘Invisible women’ in the Covid times

Thalidomide is a medication used to treat a number of cancers and skin conditions including complications of leprosy. The developers of the drug claimed that they “could not find a dose high enough to kill a rat” and so thalidomide was freely available since 1950s in stores as a mild over-the counter medication in many countries.In 1960, doctors began prescribing it to pregnant women who suffered from morning sickness.

It turned out that while the drug didn’t kill rats, it did affect foetal development. Before it was finally taken off the market in 1962, over 10,000 children had been born around the world with thalidomide-related disabilities! You can read a detailed story by NY Times.

Because of this scandal, the FDA (Food and Drug Administration) issued guidelines in 1977 excluding women of childbearing potential from drug trials!

But what happens when you exclude a certain group from clinical trial? Be it pregnant women or women altogether?

Over the last few weeks I have been reading a super insightful book – Invisible Women: Exposing Data Bias in a World Designed for Men by Caroline Criado Perez. Actually, I had started to read it long back (last year I think), but then got overwhelmed by the data / insights and had to take a break. I am glad I resumed. It is still overwhelming but one must read as much as one can.

Caroline essentially quotes examples after examples of how almost everything that is made / designed in the ‘man’-made world, miserably fails to take into account the specific needs of women. This reflects in design of roads to malls to phones to piano to drugs to vaccines.

Just because something works for men in a certain way does not mean it will necessarily work the same way for women. And that’s a problem when women are not represented adequately in any kind of user impact study.

In 2000 for example, the FDA had to force drug manufacturers to remove phenyl-propanolamine, a component of many over-the-counter medications, from all products because of a reported increased risk of bleeding into the brain or into tissue around the brain in women, but not in men.

To understand the difference between male and female bodies, at the most basic level you need to realize that women typically tend to have a higher body-fat percentage than men. This, along with the fact that blood-flow to fat tissue is greater in women, affects how women metabolise certain drugs. Also, male gut transit times are about half the length of women’s! This means women may need to wait for longer after eating before taking medications that need to be absorbed on an empty stomach.

When it comes to vaccine, it is well proven that women develop higher antibody responses and have more frequent and severe adverse reactions to vaccines.

The mechanisms leading to these differences can be:

  • hormonal (i.e. the different effects of testosterone, oestrogens or progesterone);
  • genetic (biological females have two X chromosomes while males have only one); or
  • related to differences in intestinal bacteria.

And yet, most phase 1 clinical trials – a) don’t bother to study sex specific results and b) don’t enroll women in adequate numbers in the first place! How many drugs that would work for women are being ruled out at phase 1 trials just because they don’t work in men? Nobody knows!

Sex matters even in animal trials. In a 2007 analysis of animal studies where rats of both sexes were identified, it was observed that in over half the studies, the drug-effect depended upon the sex of the animal! And yet, most animal tests don’t bother to sex-tag the results (if they at all get enough of male and female animals in the first place).

By the way sex and gender have different implications – and to those not clear on the difference between the two terms, the below figure is self explanatory.

Source

So how are we doing gender / sex wise in terms of analyzing Covid’s effect or vaccine development?

Most states are doing a bad job of reporting sex / gender aggregated data.

Bad quality of sex / gender aggregated data = a vaccine / drug that is designed mostly for men.

Source

In a still to be peer-reviewed study, researchers have found that only 416 of the 2,484 Covid-19 clinical trials mention sex / gender as a recruitment criterion on the ClinicalTrials.gov database. [Source]

During the time of ancient Greeks, the female body was seen as a ‘mutilated male’ body – ovaries were female testicles and didn’t have a name for themselves till the 17th century! For millennia, medicine has functioned on the assumption that male bodies can represent humanity as a whole. A 2008 analysis of a range of textbooks recommended by ‘twenty of the most prestigious universities in Europe, US and Canada’ revealed that across 16,000+ images, male bodies were used three times as often as female!

For things to change in the future, we all need to be at least aware of the implicit data bias that exists in every single aspect of our lives – before enough people can even begin to make noticeable noise about it. I can only hope that happens sooner than later.

If you found the insights in this blog fascinating and yet reading an entire book on this topic is a bit much, at least check out this Guardian article that has a lot more examples of data bias for women and how it affects them, even kills them.

Categories
Covid-times

India Covid deaths weekly projection – 27 Sep update

FOR THE FIRST TIME, THE TOTAL NEW CASES REPORTED IN A WEEK WERE LESS THAN THAT IN THE PREVIOUS WEEK!
HAS INDIA PEAKED ALREADY?
IN ANY CASE, INDIA WILL CROSS 1 LAKH REPORTED COVID DEATHS THIS WEEK (93K+ DEATHS PRESENTLY).


My big question every week (since May) is, when will India cross 1 lakh total reported Covid deaths? It will happen this week as we enter October.

Total cumulative Covid death toll as of yesterday (26 Sep) stands at 93k+ (actual figure could be as high as twice this value, for various reasons documented here).

On a global level, if you just look at total number of reported Covid deaths, you will find that India is at no. 3. But the moment you adjust for population (which makes more sense), you realize that India is in a much better position (the pink line; US is dark blue, Brazil green, UK light blue and Canada red).

93k total deaths means ~68 deaths per million (Brazil is almost ten times that figure). It will take many months for India to reach the kind of deaths per million figures that Brazil or US have already seen (if it does).

The reason I just compared India with literally the worst performers is not so that like Modi, I can claim it’s all good – I am just making sure you see things for how they are. There are many countries doing better then India and there are many others that are doing worse (once you adjust for population and compare).

One could try forecasting the future Covid deaths in India by simply using the existing week-on-week growth in deaths.

Chart created by Amrit Vatsa on 27 Sep 2020 from publicly available data

This is the first time that weekly death growth in India went significantly below zero percent!

Only once earlier, the growth had been negative (just shy of zero – in the 23-29 Aug week). For future average weekly growth estimate, 2% to 10% growth range sounds good?

This is how the forecast looks like, for the following three scenarios.

We will cross 1 lakh Covid deaths this week and then will touch 2 lakh by early December (2 lakh total deaths for India would be equivalent to 144 deaths per million; both US and Brazil are already over 590 per million dead). Even a relatively better performer Canada peaked only after crossing 200 deaths per million.

Alright, let’s now try a slightly more nuanced (albeit indirect) approach to project future deaths. This requires looking first at cases. Cases are important because even when you don’t die, just being infected seems to have its own issues.

From ‘brain fog’ to heart damage, COVID-19’s lingering problems alarm scientists

ScienceMag.Org

At a global level, when adjusted for population – total reported cases for India (pink in the below chart) are low when compared to the worst performers (US – dark blue, Brazil – green) but already higher than Canada (red) and will cross UK (light blue) soon.

Anyway so like deaths, for cases too, if we look at the week-on-week growth, we can have some idea of how it’s probably going to grow in the next few weeks.

~6 lakh total positive cases were detected this week, which is 8% lower than the total cases detected the week before (6.5 lakh)

I will not conclude that India has peaked already unless total cases continue to be negative week after week. This week could just have been an anomaly.

Let’s call this w-o-w growth in cases – ‘X’. X was 2% last week and 14% the week before (see the above chart). X=-8% this week. For my projection, I think I can assume a range of 2 to 10% for X in the coming weeks.

Let me also make sure you understand how growth works – when something grows at 10% every week, it means it will double in less than two months. But if it grows at 2%, it would take almost nine months for it to double!

Now in general, people who die of Covid in a given week, are either tested positive the same week, or the week before (just a basic assumption). Do we have some idea of what %age of such cases die? We do actually.

7,760 Covid deaths were recorded this week, which is basically 1.2% of half of total cases from this week + half of total cases from last week.

Let’s call this %age Y; Y= 1.2%.

For the future, let’s assume a range of 1% to 1.2%?

So we can forecast now – I am going with the following 3 scenarios:

  • X=5%, Y=1.1% (baseline)
  • X=2%, Y=1% (optimistic: slower growth in cases + lesser %ge of deaths)
  • X=10%, Y=1.2% (worse: expecting faster growth in cases)

With the above assumptions, below chart shows the future cumulative death count.

What’s going to happen this week doesn’t change (crossing 1 lakh) but this indirect estimate for deaths tells us that only in the worse case scenario will we cross 2 lakh in 1st week of December.

India will cross 1 lakh total deaths this week and will probably touch 2 lakh in December.

Now, 1 lakh total deaths for India is basically equivalent to 72 deaths per million of the total population (currently we are at 68 per million Covid deaths).

To what extent would the death toll figures keep going up – before it flattens / peaks?

If we look at other countries, death toll for many started to flatten out only after anywhere between 400 to 600 per million of their population died!! Scary, I know!

If we assume that for India, the death toll flattens out even at say 200 deaths per million, that would be equivalent to ~3 lakh total deaths!

It’s difficult to imagine why India would see any less no. of deaths than that. Let’s look at some of our cities / small states.

Y axis represents weeks; 1= the week when the city / state first reached ~10 deaths per million

Pune for example, has crossed 800 deaths per million.

The only populous countries across the globe where death toll flattened at much lower levels (like say Japan and China) happened when they somehow didn’t let the total deaths cross even 5k (Japan for example didn’t even let it cross 1k). We clearly couldn’t control things to that extent in India (most countries haven’t). So now let’s just be hopeful that the total death cap estimate that I am guessing is on the conservative end – otherwise, we could lose even up to 5 lakh people (or 362 deaths per million)!

That’s it for this post. I’ll get back with updated projections next Sunday (04 Oct). Stay safe.

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Newsletter

Vatsap? 2020 Sep 20 Newsletter

Every Sunday, I share with you my creations, weekly discoveries and analysis of how the world works. Subscribe here.


Do things get easier or harder for me in the second phase of my Myanmar shoot?

This is my ongoing BTS video-series that I had finished shooting in Feb. In case you have not yet seen the earlier episodes, here is the link to part 1 and 2. Watch these – you will have fun.

Did you know that when you just watch someone having fun, your own brain registers the same activity as it would do, when you yourself are having fun?

That’s the magic of mirror neurons – that are used to study empathy. Talking about empathy, let me ask you something.

If everyone had friends from minority groups, would there be less bigotry overall?

I explore the answer to this question in my new blog. I also introduce a very interesting 2019 book called ’The War For Kindness’.

Some people don’t think bigotry can be brought down by ‘friendships’ (because friendship is easily faked / can be just superficial). But then, there actually is enough evidence to suggest that bringing people together, does work (CONTACT HYPOTHESIS). Read my blog – there are some very interesting experimental data that I have shared. It’s a short insightful read.

You can also listen to this Contact Hypothesis topic on my podcast. Search for VATSANALYSIS on your favourite podcast-platform and do subscribe.

Not all bigotry is natural of course.

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Why you so predictable Arnab?

A post shared by Vatsap? (@amritvatsa) on

What Arnab does in his show for example, is by design. A performance. I had fun this week creating music to bring out how predictable this asshole is. Watch the above 1 minuter. If you want a more ‘melodic’ take on Arnab’s by-design stupidity, Mayur never disappoints.

Irrespective of what other drama is happening in our country, Covid is still around and only getting stronger. Once again, I do have my weekly death projection updates ready.

Reported death count now stands at over 85k. We will cross 1 lakh deaths in just two more weeks (as per my estimates – that I have explained in my blog). And if the growth continues the way it has been, another 1 lakh will be dead just in October and November!

Check out my full analysis to see how we have no other option but to live under the shadow of Covid for many more months to come. Not all shadows suck this much though.

When shadow becomes art…

While you cannot have a tree inside your room, you can definitely have plants. If you’ve been discouraged from getting plants because your room doesn’t get plenty of sunlight, check out this super useful Twitter thread by @batonthemoon where she shares a wonderful list of plants that you can easily grow in a window-less room! You are almost towards the end of this newsletter now.

Oh by the way, you should also check out what my friend Shweta creates on her @amillionforestdotcom insta channel. Below is a photo I recently took of Princy (my wife), with one of Shweta’s creations – some very cool planty stuff.

Now that we are on the theme of plants and nature, let’s look at what’s happening in one particular rice field and as we watch, let’s exclaim together…

WHAT THE DUCK!!

That’s all for this week. Stay safe, consume less, create more, make this world a better place and I will get back to you next Sunday!

Categories
Covid-times

India Covid deaths weekly projection – 20 Sep update

85K+ HAVE REPORTEDLY DIED ALREADY.
WE WILL CROSS 1 LAKH DEATHS IN 2 MORE WEEKS AND AT THE PRESENT RATE, ANOTHER ONE LAKH WOULD BE DEAD BY NOV END.


My big question every week (since May) is, when will India cross 1 lakh total reported Covid deaths?

Total cumulative Covid death toll as of yesterday (19 Sep) stands at 85k+ (actual figure could be as high as twice this value, for various reasons documented here). Remember that this number was just over forty thousand in first week of August.

On a global level, if you just look at total number of reported Covid deaths, you will find that India is at no. 3. But the moment you adjust for population (which makes more sense), you realize that India is in a much better position (the pink line; US is blue, Brazil green).

85k total deaths means just over 60 deaths per million (Brazil is ten times that figure). It will take many months for India to reach the kind of deaths per million figures that Brazil or US have already seen. The reason I just compared India with literally the worst performers is not so that like Modi, I can claim it’s all good – I am just making sure you see things for how they are. There are many countries doing better then India and there are many others that are doing worse (once you adjust for population and compare).

One could try forecasting the future Covid deaths in India by simply using the existing week-on-week growth in deaths.

Yes there are many ups and downs in the weekly growth of Covid deaths but if one has to extrapolate, a 5% to 15% growth range seems to be a good guess?

This is how the forecast looks like, for the following three scenarios.

We will cross 1 lakh Covid deaths in like two weeks and then will touch 2 lakh sometime in November (2 lakh total deaths for India would be equivalent to 144 deaths per million; both US and Brazil are already over 550 per million dead).

There is another way to forecast future deaths, but let me take a quick light break first.

Alright, let’s now try a slightly more nuanced (albeit indirect) approach to project future deaths. This requires looking first at cases. Cases are important because even when you don’t die, just being infected seems to have its own issues.

From ‘brain fog’ to heart damage, COVID-19’s lingering problems alarm scientists

ScienceMag.Org

At a global level, when adjusted for population – total reported cases for India, as of now are much low. But they are growing – so would they remain low forever?

Anyway so like deaths, for cases too, if we look at the week-on-week growth, we can have some idea of how it’s probably going to grow in the next few weeks.

6.5 lakh total positive cases were detected this week, which is only 2% higher than the total cases detected the week before (6.4 lakh)

Let’s call this w-o-w growth in cases – ‘X’. X was 14% last week and 15% the week before (see the above chart). X=2% this week, possibly because of not enough increase in testing capacity?

Anyway, for my projection, I think I can assume a range of 5 to 15% for X in the coming weeks.

Let me also make sure you understand how growth works – when something grows at 10% every week, it means it will double in a little less than two months (7-8 weeks). But if it grows at 15%, it will double in just five weeks. On the slower end, if something grows at 5%, it would take almost 4 months for it to double!

Now in general, people who die of Covid in a given week, are either tested positive the same week, or the week before (just a basic assumption). Do we have some idea of what %age of such cases die? We do actually.

8,147 Covid deaths were recorded this week, which is basically 1.3% of half of total cases from this week + half of total cases from last week.

Let’s call this %age Y; Y= 1.3%.

For the future, let’s assume a range of 1% to 1.3%?

So we can forecast now – I am going with the following 3 scenarios:

  • X=10%, Y=1.2% (baseline)
  • X=5%, Y=1.1% (optimistic: slower growth in cases + lesser %ge of deaths)
  • X=15%, Y=1.3% (worse: expecting faster growth in cases)

With the above assumptions, below chart shows the future cumulative death count.

The indirect method more or less gives a similar estimate as the direct death projection.

India will cross 1 lakh total deaths in two weeks and by Nov end, the figure is likely to cross 2 lakh (i.e. 1 lakh more deaths will happen just in Oct-Nov).

Now, 1 lakh total deaths for India is basically equivalent to 72 deaths per million of the total population (currently we are at 62 per million Covid deaths).

To what extent would the death toll figures keep going up – before it flattens / peaks?

If we look at other countries, death toll for many started to flatten out only after anywhere between 400 to 600 per million of their population died!! Scary, I know!

If we assume that for India, the death toll flattens out even at say 200 deaths per million, that would be equivalent to ~3 lakh total deaths!

It’s difficult to imagine why India would see any less no. of deaths than that. Let’s look at some of our cities.

Y axis represents weeks; 1= the week when the city first reached ~10 deaths per million

Pune for example, crossed 700 deaths per million last week itself.

The only populous countries across the globe where death toll flattened at much lower levels (like say Japan and China) happened when they somehow didn’t let the total deaths cross even 5k (Japan for example didn’t even let it cross 1k). We clearly couldn’t control things to that extent in India (most countries haven’t). So now let’s just be hopeful that the total death cap estimate that I am guessing is on the conservative end – otherwise, we could lose even up to 5 lakh people (or 362 deaths per million)!

That’s it for this post. I’ll get back with updated projections next Sunday (27 Sep). Stay safe.

Categories
Gyaan

If everyone had friends from minority groups, would there be less bigotry overall?

In one of my Instagram stories, I wrote about the need for more Hindus to have at least one good Muslim friend. Likewise, upper castes should have one good lower-caste friend. This I proposed would make the Bhakts more empathetic (Bhakts I believe are predominantly upper caste Hindu men).

Note: You can also listen to this blog in my podcast (to subscribe to my podcast channel, search for VATSAnalysis on your favourite podcast platform)

To this suggestion, someone pointed out that this may not help at all.

“Having a friend really makes little to no difference to Bhakts / card carrying RSS member for that matter. The hypocrisy is too deep”, P commented and shared the below cartoon.

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A cartoon by @ellisjrosen. #NewYorkerCartoons

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“All these people have friends. But they consider them friends only until outside their doorstep”, P added. “They never give up on rituals and cultural processes. They stay with the family circles and with those, they have constructed beliefs that make a villain out of minorities.”

Since this ‘friendship ain’t gonna do nothing’ theory was primarily coming from the P’s personal experiences, I wanted to find out if there were studies available, where sociologists / social scientists had tried to test this hypothesis.

Life’s real answers are mostly neither here, nor there – they are somewhere in between! 🙂

Jamil Zaki is the director of The Stanford Social Neuroscience Lab. Following are some insights he shares in his book:

  • In all-white housing projects in US, 75% of residents said they’d dislike living alongside blacks; but in mixed projects, only 25% disliked having black neighbors.
  • In all-white platoons in US, 62% of soldiers opposed integrating the armed forces; but among whites who had been in a mixed platoon, only 7% opposed such integration.

Do you now think there is a possibility of an evidence based support for what I was instinctively thinking? In fact, there’s a name for it – the ‘Contact Hypothesis’.

Bigotry often boils down to a lack of acquaintance.

Gordon Allport, The Nature of Prejudice

The antidote to bigotry that the Harvard psychologist Gordon Allport proposed in his 1954 book was simple – Bring people together – which in psychology, came to be known as the ‘Contact Hypothesis‘.

But Contact doesn’t work all the time. In fact, in some cases, it can actually make things worse.

The Boston commuter train experiment

When each morning at the same time, some Latino passengers were ‘planted’ on a Boston commuter train – and this was done for ten days – it was observed that the white commuters who saw Latinos grew less tolerant of immigration than they had been before.

“Goodwill contact without concrete goals accomplishes nothing”, Allport proposed, followed by recommendations to make such Contact initiatives truly effective (things like giving the groups mutual goals, making the interactions personal etc.)

Allport proposed that for most favorable results of such Contact initiatives, groups should be given equal status (even if one group has more power in real life). But now we know it takes more than that (in part thanks to the Sender-Responder experiment).

The Sender-Responder experiment

Emile Bruneau – Director of the Peace and Conflict Neuroscience Lab at University of Pennsylvania – started with the premise that if one group is silenced for most part in real life, perhaps they should be given greater status when the groups come together.

To test this idea, he paired Mexican immigrants and white U.S. citizens who had never met. In each pair,

  • one person was assigned the role of “sender” – who would write a short essay about the hardships facing their group;
  • the second person – the “responder” – would read the essay and then summarize it in their own words and pass it back.

When white Americans acted as responders (reading what Mexicans wrote and then summed it up themselves), they said they felt better about Mexican immigrants. The Mexican immigrants who acted as senders also felt the same.

But when Mexican immigrants acted as responders (where they had to read about hardships of white Americans), they felt worse about the white Americans.

Brunue tried similar experiments in different contexts and settings and the results were the same. The minority group is already well aware of the majority narrative / perspective. In a sit-down where say both men and women are supposed to share their perspectives, men get to gain real insights; women – not so much.

Women are so keenly aware of the male experience because our entire existence had to be kind of through that lens. Whereas men have never had to understand the female experience in order to exist in the world.

Sarah Silverman (from The War for Kindness)

Contact Hypothesis works, but it works best when it reverses the existing power structure, rather than ignoring it in the name of ‘equality’.

Before I end, let me share another story / experiment from the book – this one is on psychopaths. Psychopaths, by definition, have impaired empathy – they simply don’t care about other people’s emotion. So the question is – IS IT POSSIBLE TO ALTER THE EMPATHY LEVEL OF PSYCHOPATHS TOO?

The short answer is yes! I know I know…

Christian Keysers and his colleagues traveled to prisons around the Netherlands and scanned the brains of both psychopathic and non-psychopathic criminals as they were shown images of people in pain.

As expected, psychopaths didn’t show a mirroring response (activation of mirror neurons takes place in our brain when we feel someone else’s feelings / pains / movement). The non-psychopathic criminals showed such mirroring response.

This may suggest that psychopaths’ lack of empathy is “hardwired” into their brains. But then Keysers’s team ran a second version of the study – the result was no more the same!

The psychopaths were now asked to focus on victims’ pain and to do their best to imagine how it felt. And when the psychopaths did this, their brains mirrored suffering in almost exactly the same way as non-psychopaths!

Bottom-line – with the right nudge, anyone can be triggered to show empathy.

The book of course talks a lot about short-term empathy and long-term empathy and what works when and the need for more research in select areas etc. There is no way I can sum all that up in a blog (nor should I). If you like the premise and whatever little that I have shared, it’s definitely a meaningful read.

As I end, let me leave you with a Ted talk by Jamil Zaki where he touches upon few more aspects of empathy (like his Roddenbery hypothesis). That will be all for this blog – hope your learnt something useful. If you like what I write, do subscribe to my Sunday newsletter.

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Newsletter

Vatsap? 2020 Sep 13 Newsletter

This Sunday, I continue my Myanmar Vlog, share the psychology of why you may have felt odd about Trump being nominated for the Nobel Peace Prize, question you on whether you should feel proud of Kalpana Chawla, share something on cancer research + my weekly Covid updates and end with a Shitoon.


The Myanmar saga continues

I hope you enjoyed part 1 that I shared last week. Here is the second part of my four part BTS series. You will see me travel to an island in a crazy ferry ride and chase some stories. Feb was fun!

My Myanmar story is all fun and happy but The New York Times had something serious that it brought out this week – on the killings of the Rohingya Muslims in the country. It’s something really worth reading.

The PM equivalent of Myanmar (State Councellor) is an old lady named Aung San Suu Kyi who was awarded the Nobel Peace Prize in 1991 (when she was under house arrest by the country’s military). This week, when I heard about Trump’s nomination for the coveted prize, I really wanted to understand what was happening.

I read up a little on the Nobel Peace Prize and have some interesting insights to share.

Give it a read?

Also, tell me, did you feel proud as an Indian when Kailash Satyarthi won the Prize in 2014? If yes, why? After all, what was your contribution towards his achievement?

I have thought about this “feeling proud” thing for quite some time. I wondered again when I read the news below, this week.

The phenomenon that describes this is called BIRGing and if you have never heard of it before, do check out my short blog on the topic. And then tell me, is it okay for me to feel proud of IIT Madras just because I graduated from it?

I had made a bunch of videos for IITM last year as part of its 60 years celebration (60 videos X 60 second).

One such 60 seconder was published this week – it is about cancer research and I think what is happening in IIT Madras in this area is fascinating (whether or not I feel proud of it).

Now that we are talking about cancer, let’s also quickly touch upon Covid. My weekly updates and projections are out as usual.

India is at 77k+ reported Covid deaths (56 per million) and would cross 1 lakh by month end / Oct first week. Pune is leading the growth where 700+ per million have already died.

Do check out my full analysis where I also explain till when cases / deaths could keep rising.

Enough of grimness; let’s have some laughs at the expense of Covid now, shall we?

Before I end, I drew something this week, so check that out.

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For all the crazy ones out there. @amritvatsa

A post shared by Shitoon (@vatsap_shitoon) on

This is all that I have to share. If you are in the mood for Netflix reccos – do watch The Social Dilemma on Netflix (thankfully it’s not a series).

Have a great day and a great week, stay safe, spend less time on Social Media, do something good for the world and I will see you again, in a week’s time!

-Amrit

Categories
Covid-times

India Covid deaths weekly projection – 13 Sep update

INDIA IS AT 77K+ REPORTED DEATHS (56 PER MILLION) AS OF NOW AND WILL CROSS 1 LAKH TOTAL DEATHS BY SEP END / OCT FIRST WEEK. PUNE IS LEADING THE GROWTH WITH 700+ PER MILLION DEAD ALREADY.


My big question every week (since May) is, when will India cross 1 lakh total reported Covid deaths? Total cumulative Covid death toll as of yesterday (12 Sep) stands at 77k+ (actual figure could be as high as twice this value, for various reasons documented here).

For the first time, daily reported deaths every single day in a week remained over 1,000. That’s almost six full capacity A320s crashing and killing everyone on board everyday.

One could try forecasting the future deaths by simply using the existing week-on-week growth in ‘total deaths in a week’.

Yes there are many ups and downs in the weekly growth of Covid deaths but if one has to extrapolate, a 5% to 15% growth range seems to be a good guess?

This is how the forecast looks like, for the three scenarios.

We will cross 1 lakh Covid deaths either by this month end or latest by first week of October.

Alright, let’s now try a slightly more nuanced (albeit indirect) approach to project cumulative deaths. This requires looking first at cases. Cases are important because even when you don’t die, just being infected could be troublesome.

From ‘brain fog’ to heart damage, COVID-19’s lingering problems alarm scientists

ScienceMag.Org

Let’s look at the week-on-week growth rate of ‘total cases in a week’.

6.4 lakh total positive cases were detected this week, which is 14% higher than the total cases detected the week before (5.6 lakh)

To make sure you understand how growth works, when something grows at 10% every week, that means it will double in a little less than two months (7-8 weeks). If it grows at 15%, it will double in just five weeks and if it grows as slow as 5%, it would take almost 4 months for it to double.

For my projection, I will assume a range of 5 to 15% (X) for week-on-week growth of cases. That’s my assumption #1.

Now in general, people who die of Covid in a given week, are either tested positive the same week, or the week before. Do we have some idea of what %age (Y) of such cases die? We do actually.

7,891 Covid deaths were recorded this week that is basically 1.3% of half of total cases from this week + half of total cases from last week.

In other words, Y for this week is 1.3%. For the future, let’s assume a range from 1% to 1.3%? That’s my assumption #2.

Let’s forecast now…

Let me consider 3 scenarios:

  • X=10%, Y=1.2% (baseline)
  • X=5%, Y=1.1% (optimistic: slower growth in cases + lesser %ge of deaths)
  • X=15%, Y=1.3% (worse: expecting faster growth in cases)

With the above assumptions, below chart represents the future cumulative death count:

The indirect method more or less gives a similar estimate as the direct death projection.

India will cross 1 lakh total deaths by early October.

Now, 1 lakh total deaths for India is basically equivalent to 72 deaths per million of the total population (currently we are just over 56 per million Covid deaths).

To what extent would the death toll figures keep going up – before it flattens / peaks?

If we look at other countries, death toll for many started to flatten out only after anywhere between 400 to 600 per million of their population died!! Scary, I know!

Y axis = no. of days (all the countries are arranged in a way that starting point of 10 deaths per million is common to all)

If we assume that for India, the death toll flattens out even at say 200 deaths per million, that would be equivalent to ~3 lakh total deaths!

It’s difficult to imagine why India would see any less no. of deaths than that. Let’s look at some of our cities.

Y axis represents weeks; 1= the week when the city first reached ~10 deaths per million

Pune has already crossed 700 deaths per million and is still quite steep in terms of growth.

Delhi has somehow managed to grow much slower and doesn’t look like it will cross even 200 deaths per million. Mumbai may not be growing as fast as Pune, but it is almost at 400 per million deaths and far from peaking. So that’s the overall range we are looking at (to remind you again, at a country level we are at < 60 per million dead so far).

The only populous countries across the globe where death toll flattened at much lower levels (like say Japan and China) happened when they somehow didn’t let the total deaths cross even 5k (Japan for example didn’t even let it cross 1k). We clearly couldn’t control things to that extent in India (most countries haven’t). So now let’s just be hopeful that the total death cap estimate that I am guessing is on the conservative end – otherwise, we could lose even up to 5 lakh people (or 362 deaths per million)!

That’s it for this post. I’ll get back with updated projections next Sunday (20 Sep). Stay safe.

Categories
Gyaan

Nobel Peace prize, Trump and the Halo effect

In 2009, when I heard Obama had won the Nobel peace prize within months of becoming a US President (meaning he must have been nominated much earlier) – it just felt awfully weird. I drew the below Shitoon.

The above will not appear funny unless you remember a 2009 video that had gone viral, where Obama swats a fly. The video is funny.

Anyway, so I didn’t dig deep much into it then.

When they gave one to Malala few years down the line, I did find it amusing, like many others, but again, didn’t bother to investigate much. Until this happened.

This sounded too ridiculous to be true. But true it was. In fact I learnt that Trump had been nominated earlier too (but obviously didn’t win).

I decided it was time to figure out how such ridiculousness creeps in, in something that is apparently so prestigious that Indians have been offended since long that Gandhi never got one.

By the way, as I am writing this blog, I hear that Trump has been nominated again! Shit gets shittier.

Nomination is of course not the same as winning.

After a bit of reading I now understand that the reason nomination can get ridiculous is because just too many people can nominate any person of their choice. The criteria and the link to submit the nomination-form is accessible to everyone here. There are over 300 nominees this year! Trump is just one of them.

Is it possible then that Modi has been nominated too? Going by the criteria for nomination, yes pretty much possible. One can never officially find out though (true for Trump too).

The Nobel Committee does not itself announce the names of nominees, neither to the media nor to the candidates themselves. In certain cases names of candidates appear in the media. These advanced speculations are either the product of sheer speculation or information released by the person or persons behind the nomination.

Neither the names of nominators nor of nominees for the Nobel Peace Prize may be divulged until the start of the year marking the 50th anniversary of the awarding of a particular prize.

Source – Nobel Peace Prize

Although Gandhi never won, even he was nominated a bunch of times. And given that pretty much anyone can be nominated – so were Hitler and Mussolini. Basically, nomination means shit. They are also forged once in a while.

A 2018 NYT article reports that Trump’s nomination has indeed been forged twice.

Anyway, so who selects the winner from all the nominations? Just a bunch of old people (usually 5 or 6).

The Norwegian Nobel Committee 2020. From left: Thorbjørn Jagland, Henrik Syse (vice chair), Berit Reiss-Andersen (chair), Anne Enger, Olav Njølstad (secretary), Asle Toje.

With all these insights, why does anyone care about the Nobel Peace prize, really?

To find an answer, I read a 2019 book (at least the first chapter) by Geir Lundestad. He was the Director of the Norwegian Nobel Institute and the Secretary of the Norwegian Nobel Committee for 25 years (1990 -2014).

Last year (2019), when Lundestad was asked what he thought of Trump ever winning the prize, this is what he said:

I would be extremely surprised if Donald Trump ever received the Nobel Peace Prize. He may say he wants to bring peace to the Middle East or the Korean Peninsula, but he has not accomplished anything. And his policies do not fall into line with the ideas of liberal internationalism.

NYP

As per Lundestad, there are four main reasons that make the Nobel Peace prize “The World’s Most Prestigious Prize”:

  1. It’s 100+ year old
  2. It belongs to a family of prizes (and Nobel Prize for science, economics, literature etc. are hardly as debated + their selection is more sorted / technical)
  3. In spite of few mistakes (not giving one to Gandhi for example – that Lundestad acknowledges in his book) and few controversies here and there (Obama?), the record has mostly been solid.
  4. The prize has proven to be relatively flexible – the peace concept for example has been expanded and the prize has gradually become more global.

The issue with point no. 3 (on track record) is, every time someone like Trump makes a headline, associating himself with the Nobel Peace Prize, it brings down the value of the prize itself. It leads to articles like what ‘The Atlantic’ published today titled “End the Nobel Peace Prize“.

If Trump wins the prize, it will be the fourth Nobel awarded for peace between Israel and its neighbors. That will make Arab-Israeli peace mediators more successful at charming the Nobel Committee than the International Committee of the Red Cross, which has won three times in the prize’s 120-year history, but still less successful than my favorite, which is no one at all. The committee has declined to award a peace prize 19 times.

The record of achievement of the peace laureates is so spotty, and the rationales for their awards so eclectic, that the committee should take a long break to consider whether peace is a category coherent enough to be worth recognizing. Peace had its chance, and blew it.

Graeme Wood, The Atlantic

Before I end this blog, I want to touch upon a different but related topic.

Why did I instinctively find Trump’s nomination ridiculous even before I found out these details?

That’s most likely halo effect at work. Let me explain.

Imagine two random people – Anil and Varun. Following are their traits.

Anil: intelligent-industrious-impulsive-critical-stubborn-envious

Varun: envious-stubborn-critical-impulsive-industrious-intelligent

See how you felt differently about Anil than about Varun (please tell me you did)? I used an illustration from my current favourite book “Thinking, Fast & Slow” (chapter 7) and just added Indian names.

This experiment has been conducted on various people and the conclusion is solid – “the initial traits in the list change the very meaning of the traits that appear later. The stubbornness of an intelligent person is seen as likely to be justified and may actually evoke respect, but intelligence in an envious and stubborn person makes him more dangerous.”

That’s halo effect at work where your brain feels like jumping to a conclusion about a person based on first few information that you gather (you put them in the ‘good’ box or the ‘bad’ box’).

Since Trump is in my ‘bad’ box, my cognitive bias immediately makes me uncomfortable when something like a Nobel Peace prize gets associated with his name.

In fact the halo effect is also a possible explanation for the positive association we have for the Nobel Peace prize itself (point no. 2 from Lundestad – ‘it belongs to a family of prizes’).

The consistently credible Nobel prizes in other disciplines make us view the overall brand in a strong positive light and so even when the nomination / selection and everything else for the Peace prize is totally different, the instinctive part of our brain over-rides the rational, and the brand continues to remain strong!

The below quote from a 2007 Wiki discussions page would be the best way to close this blog.

Some in this discussion have argued that nominations are notable because of the significant publicity given to them. But this is circular: The public gives nominations attention because it mistakenly believes they are notable (as I and many others here believed before looking into it). If Wikipedia decides they are notable because the public does, it will only reinforce the public view that they are notable. If everybody in the world knew all the facts around nominations, it is likely that most would not find them notable.

Source (emphasis is my own)

Hope you learnt something, thanks.


Categories
Gyaan

Are you proud of Kalpana Chawla? Should you be?

So this happened.

If you are an Indian, you must have felt an instinctive emotion of “pride”. We feel proud of the Indian team when they get us the world cup and we feel proud of A R Rahman when he wins an Oscar. But why do we? What have we contributed to their achievements?

What about saying ‘I am proud of my country’?

Religion? Heritage? In all these instances, why are we feeling proud of something to which our contribution has been zilch?

I finally understand it from a cognition / psychological point of view.

Basking in reflected glory (BIRGing) is a self-serving cognition whereby an individual associates themselves with known successful others such that the winner’s success becomes the individual’s own accomplishment.

Wiki

What is the benefit of BIRGing? It boosts your self-esteem.

Disadvantage? BIRGing can be negative when done so extensively that you become delusional or forget the reality that you did not actually accomplish the successful event.

So essentially the ‘instinctive’ reason we BIRG is because the non-rational part of our brain knows it will increase our self-esteem – and that means we can achieve more in life / be more productive etc.

It is only the rational part of the brain, that even asks – ‘but is there any real basis’? Well guess what, no – there is no real basis. At the end of the day, it’s a story that helps us feel good about being part of something bigger than just us.

If you have anything more to add to this, do let me know (other than more jargon like ‘tribalism’, ‘social identity theory’ etc.).

Categories
Shitoon

Shitoon 162 – Live like crazy!

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For all the crazy ones out there. @amritvatsa

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