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What is common to all human beings? Here's one answer. We are all mathematical
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modellers. Think about it. Whenever you do something, you have a sense of how it
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will turn out. And this is based on past experience and knowledge and so on. Like
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when you cross the road, you know that there's a tiny chance that you will be
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run over. But there is a model in your head that tells you that there's a high
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probability of getting to the other side. So off you go, crossing the road,
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along with the proverbial chicken. Now while you're modeling the future, you're
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not explicitly modeling the future. You're not laying down all your assumptions,
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putting numbers to them, assigning probabilities, doing Bayesian thinking,
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none of that. But implicitly you're doing a crude version of modeling. And it has
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always been my contention that refining this crude process in our heads can help
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us think clearly about the world. It can make us more mindful about what we do
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and why. It can give us insight into ourselves. What are our priorities? What
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are our biases? What are we in denial about? That is why for personal growth, if
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nothing else, we should think more about the modeling processes that we adopt. If
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we are not clear about why we are doing what we are doing, then isn't it much
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more likely that we'll make mistakes?
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Welcome to The Seen and the Unseen, our weekly podcast on economics, politics,
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and behavioral science. Please welcome your host, Amit Varma.
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Welcome to The Seen and the Unseen. My guest today is Gautam Menon, a professor
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of physics and biology at Ashoka University and a pioneer in the
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relatively new field of biophysics. Gautam is also a pioneer in the
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mathematical modeling of infectious diseases and was one of the first
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specialists to take COVID-19 seriously before the pandemic hit last year. I have
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gotten tremendous insights from his scholarship and his public work over
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the last few months and I was delighted to have him as a guest on the show. As I
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expected, I got many many insights on COVID-19, the modeling of infectious
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diseases, and the second wave. Gautam is soft-spoken, nuanced, and lucid, the ideal
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professor I feel so jealous of his students at Ashoka. He is also deeply
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passionate about his work. The first half of our conversation was about his
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personal journey and one of the things I loved about it was how he expressed his
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love of science, the joy that he gets out of it. I found it, well, infectious.
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Before we get to the conversation though, let's take a quick commercial break.
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I often think about how little I know about the world that I live in. This is
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not something to lament but something that should excite all of us. There is
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so much to discover and I don't just mean this about the larger-than-life
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phenomena around us, the geopolitics and the economics and our planetary system
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and so on. I mean this also about the microscopic world, the world of viruses
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and bacteria and fungi and so on, that we are basically slave to. To understand
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more about this fascinating world, I'd like to recommend a course for you. Head
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Gautam, welcome to The Scene and The Unseen. I'm very happy to be here Amit.
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Gautam, I always start my conversations by asking people more about their
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background and where they came from and all of that but before we go into the
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distant past, which I like to do, I also want to kind of talk about the present.
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How have the last few months been for you and how have the last couple of
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weeks been for you? Because these are very torrid times for all of us. How have
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you been keeping up? I think they've been difficult for pretty much everybody
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and lucky in that I know relatively few people in my immediate circle of family
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and friends who've been sort of seriously ill with COVID. I know many
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people who've got COVID-19 but when I hear the stories that my friends tell me
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of their own relatives or people close to them who've been very seriously ill,
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had trouble locating oxygen, trouble locating ICU, some of them have passed
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away. It's a heart-wrenching and a very difficult time so at least I'm glad
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that so far this hasn't happened to me but my heart goes out to everyone who
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has lost people close to them during this time. Let's now go back to your
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childhood and all of that because I'm kind of very interested in what shapes
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people, what moulds them, where do they come from. So tell me a bit about your
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childhood. Where were you born? Where did you grow up? What was your childhood like?
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What did you do as a kid? I was born in one of the most beautiful cities in the
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world I think. This is a city called Kuchi. It's known, I mean it's an old name
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and when I used to be there was Kuchin. It's on the Arabian Sea. It has an old
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part to it which is really where I grew up. It's called Fort Kuchin which has a
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history of you know both the Dutch and then the British later. So the
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buildings are old. There is an old, one of the oldest churches in India is over
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there. That's where Vasco da Gama came and was buried initially. It was a
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lovely time. I grew up in the late 60s, early 70s and I spent the first
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16 years roughly speaking of my childhood over there. You know extremely
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beautiful. I lived about 10 meters away from from the sea for pretty much
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I don't know half my life over that period or more probably and I can't
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think of a more beautiful place to grow up. It was a quieter milder time. My
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parents were roughly middle class, maybe a little upper middle class, very aware
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of the importance of education, reading and learning of all sorts and all
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varieties. My father himself taught both econometrics and English literature.
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In university before he decided that he had to look after his own parents and
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moved to a more commercial career but he still had that amazing connect with
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with learning in a sense. So he had a wonderful library. We had about five
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thousand books at home that he had collected of his own collection and I
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grew up surrounded by books. I grew up surrounded by people who read books, who
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discuss them, a whole bunch of friends and his interests were not just apart
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from being literature, philosophy, it was also art, music. So I grew up surrounded
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by all of this. My mother was had initially had learned Carnatic music and
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so was a singer although she never really used that. Grew up in really a
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loving household there's no other way to put it which was very supportive
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anything that I might have wanted to do. I went to school very close to where I
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stayed for much of my part from brief period in boarding school and I think I
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had good teachers overall and particularly a very good chemistry
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teacher and that was probably what stimulated my interest in science. Of
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course at the point in which I sort of left Cochin I was interested in many
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things. I could have been a writer in fact if I think if I was to pursue an
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alternative career it would be as a writer because I really enjoy writing
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enjoy communication and so I do a lot more of that now in terms of
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communicating science the intricacies of what's happening now with the current
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COVID situation that I realized that that plays into a part of me that goes
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back a long way. Couple of things strike me about what you just said and also
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commonalities because my father also had thousands of books at home and he was a
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civil servant he was an IAS officer and I was fortunate to pick up early habits
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of reading and my mother was also a musician but what I'm struck by by what
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you said about your father is that he taught econometrics and literature
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which is a sort of a very unusual combination in the sense that people who
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teach literature are not people you typically think of as you know numbers
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people necessarily and all of that so it's it's a very unusual combination and
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then you said you wanted to be a writer tell me a little bit about the kind of
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books you read as a kid what are the kind of books that shaped you I'm also
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struck by how you mentioned that perhaps your love for the sciences could have
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been influenced by your chemistry teacher being as good as he was which is
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wonderful to hear because you know it's a treasure when you come across a good
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teacher in your childhood so were there any magical moments which kind of
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sparked your love for the sciences or chemistry or you know all of that I mean
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I know I've asked you two separate questions so to go to the first point I
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mean I think the books in our house were in an in quotes adult books there were
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very few that were really intended for children so already I was I was sort of
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used to reading books that were noticeably more advanced my father had a
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love for biography so we had a whole range of biographies at home so reading
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about people who were famous in both the Western and the Indian Canon I grew up
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with that and sort of try to think about people in that way and it never struck
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me that this was unusual that I was actually reading far beyond my age
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because that was all I had to read it was only sort of going out and finding
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out what other people read that it struck me that this was in any way
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different from the experience that other people might normally have had my
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teachers were important certainly but I think it was also the fact that my
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parents supported whatever that I did I mean I was for example I was interested
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in chemistry so they got me a chemistry set and I would do a small little
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chemistry experiments and I think there's nothing like chemistry to quite
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fire a young person's imagination it's just you know you can see it's a very
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tactile feeling you can see colors change you can see crystals form so
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that's a good way of getting people into the sciences but of course there were
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also books and I remember one book that struck me particularly is this book
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called my family and other animals which you may have read and loved yourself as
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a child and that for the introduction to the natural world and how to think
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about the natural world to see what's happening from the point of view of a
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child and you can see Darrell's amazing writing he's writing it as an adult but
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you can see it through the eyes of a child and you know it's really a work of
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literature and you know you can sort of imagine from its popularity and
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from the fact that both of us have read it that it's it combined both what I
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thought was you know beautiful writing as well as the real sharp eye for what a
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child sees in the world around them and I think that was also very pivotal in
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ways that I didn't quite realize until I grew older and so it was these things
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that I grew up with books about adventure of course every child would
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would want to read about that and cricket which I played a lot and also
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books about books about cricket which were the rage at that time for some
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strange reason among my friends and it was these things that surrounded me when
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I grew up with a child. What was your education like at this point you know
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when you're growing up when does your conception of yourself as someone who's
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going to go into the sciences and immerse himself in that world when does
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that begin to form and take shape because I imagine like when we are young
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it's very nebulous and then gradually things begin to form and take shape and
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then there is a road taken and the road not taken and you've already almost
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wistfully spoken about the the writing road as perhaps a road not taken when
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was the road taken taken as it were? So I think around my my 11th standard or 12th
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standard I was beginning to think about what it is that I wanted to do
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and somehow that's the point where I moved away from chemistry into thinking
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about physics and I think I can sort of describe why that happened. It seemed to
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me that physics had a simplicity that the other sciences didn't have. I mean you
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could write down equations that described everything around you on
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essentially a few sheets of paper whereas with you know biology is full of
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exceptions to any general rule there are outstanding and remarkable principles
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those of evolution theory of evolution for example but overall every biological
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system is unique in its own very special way and with chemistry of course there
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was this limitation it's it's a there's a certain number of elements that you
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have to play around with an amazing combination that you can make out of
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these. I think what I've been looking for is always is simplicity that's
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something that I found in physics the fact that there was so much that you
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could understand just from the application of a very very small number
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of physical laws and I think you know that was important which is why I
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decided to do physics as an undergrad but even then I could have you know it
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was sort of physics plus writing physics plus music physics plus lots of things
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and I was still somewhat uncertain at that point it is in principle I could
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still have made a change after that I was perfectly willing to do so but it's
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just that physics seduced me further and further as I went in and finally I
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decided that that's what I really wanted to do and I loved it enough to want to
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do it. So give me a sense of your progression from there like where did
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you study and where did those parts take you? So I studied as I said for the first
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till till the 10th standard in Cochin after that I went to Kodaikanal for two
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years and studied in school there to finish up my 12th standard then I went
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to St. Stephen's College in Delhi to do physics and I spent three wonderful
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years there. I'd lived all my life before that in the south of India so to sort of
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go to the north for the first time was sort of you know sort of brought new
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horizons to me and I had made many friends who I remained close to and you
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know they were people from very different backgrounds from me but we
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happened to be all in hospital together I like them very much and it was again
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very different for me to someone who sort of you know sort of just this sort
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of culture leap from going from the from south to the north I enjoy speaking
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Hindi so that was a certainly something that I enjoyed doing. Delhi at that time
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between the mid 80s was a special place I mean now when you look at it it's
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crowded it's dusty it's got flyovers all over but there's still little sparks of
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that magic around but it was a much more magical place in the 80s than it is now
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in terms of music so my uncle in Delhi was a musicologist and a
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writer on music so he would write for multiple newspapers including the
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Hindustan Times and the Express so together with him I would go to
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practically any significant music concert in Delhi over that period of
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three years I must have attended hundreds of concerts so listening to
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music was very much a part you know dash off from from leave college and then go
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go for a concert somewhere and I met many many musicians at that time of
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really the the most remarkable musicians of that time just because I was in his
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proximity and I managed to do that so again from that point of view Delhi was
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special it was also a turbulent time for India two months after I reached Delhi
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mrs. Gandhi was assassinated so to come to a city and then you'll be on the
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curfew see the smoke rise in the distance far away here for about the
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what the really terrible things that were going on when you when it happens
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you don't know what what actually is going to happen was going to hope what
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the future holds for you all of this goes into making you I remember that
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period as being fundamental in sort of transforming my idea of India what it
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was what it could be in a way that I hadn't really anticipated it in growing
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up in the idyllic south can you elaborate on that or your understanding
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of the idea of India as it were and what did those times have to do with it being
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in one of the better colleges in India and sort of surrounded by people of a
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certain intellectual level plus the fact that there was a lot of activity that was
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going on this even had this practice by which they would invite very eminent
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people to come and speak to them off the record and then they would address
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questions to them so that I saw a whole bunch of people at that time who were
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very impressive and so that understanding how dialogue and thoughts
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about India were formed was important to me my friends were very important
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because through them I got a glimpse of what India was like so one thinks about
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Stephen's as you know a place where which is elite and it certainly that
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is true but if you looked at the physics class that I came from many people came
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from relatively unprivileged backgrounds they studied in signing schools and they
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came because they were just very very clever and very very accomplished in
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what they did so we had the strange mix of people who came from more privileged
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less privileged backgrounds all together learning from each other and many of my
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classmates have of course gone on to do you know very interesting and very are
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already accomplished in their own right now but it's interesting to see that we
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were all formed in that same crucible talking to each other learning from each
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other at that time I think for me when I talked about the idea of India I really
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meant music and the fact that this was such an incredible uniter of people I
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remember Kumar Gandharva concerts late at night in Kamani auditorium or
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wherever it was it you know people just filling the aisles filling the the sides
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of the stage waiting for him to come on in this magical moment about five ten
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minutes when you'd heard the sound of the Tanpura he was mini Tanpura so the
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very beautiful sound that came before he started in this complete concentration
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with both him and the audience were engaged in with the music so the idea
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what makes India special how is it that Indian music and Hindustani music is
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really a part of what makes us its concerns the fact that Hindu Muslim none
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of this makes any difference in terms of our devotion to the idea of music as a
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cultural uniter and the way we express and Kumar Gandharva used to sing with
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folk used to incorporate many folk elements and folk melodies into his music
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and again that's a very different part of the sort of normal very different
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from the normal Khayal Tumri history that is really at the core of it so to
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reconceptualize the various creativity come from in a specific Indian context
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all of these were sort of forming vaguely in my head at that point waiting to be
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crystallized waiting to be put together this is you know such a beautiful
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thought and it's the first time I've heard this sense of Indian music being
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an emblem of an idea of India and the moment you said it I was like wow that's
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actually true because I think the best parts of the idea of India the
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inclusiveness this delightful khichri where people feel free to take from
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whatever tradition and there is no other per se you know there in our music that
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which is such a lovely thought another question to sort of add on to that you
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were mentioning Stevens and how you know so many of your friends there are so
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prominent today and all of that I did an episode with Ram Guha in December Ram of
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course went to college in Delhi in the 70s but perhaps a decade before you
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essentially and one of the strands that came up and this struck me when I was
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reading his latest book on cricket is that so many of the people in college
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with him at that time are prominent elites today and if I remember correctly
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he kind of agreed with the thesis that in those days with opportunities not
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being so many it was this narrow bunch of elites who went to the same bunch of
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you know really good colleges whether they are Stevens in Hindu Xavier's in
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Bombay Presidency in Calcutta who would eventually become prominent in whatever
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fields and that is something that hopefully I would imagine has kind of
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changed today what is your kind of sense of that like when you look at the
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landscape around you when you look at all of the prominent people you know I'm
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guessing that at some level you have some kind of shared background with all
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of them in terms of colleges or common friends or whatever and it's a small
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world but it won't be the case 30 years later where it will be a broader world
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or or will it I would be very happy if it was not the case 30 years later we
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are all children of privilege and I as I grow older I could recognize that much
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more I mean the fact that I came from a family that could afford books that
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could they know that was interested in the idea of learning that could expose
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me to all of these different influences at an early point shaping me it's not
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as though somebody else in that situation would not have done exactly or
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even better than me and I recognize that really as you know I'm grateful for that
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privilege I'm a bit ashamed of it in a sense because I recognize that many
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people would have done better than me in that particular situation had they had
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that particular set of circumstances and I do see going forward I do want to see
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a much more egalitarian India I want to see people from remote parts of India
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not just people who privilege go to one particular college I want to see them in
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the places that I am and whatever it is that one can do one has to do that
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because this is fundamental to justice and fairness another stand that struck
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me from what you were talking about is the time when you reach Delhi and you
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could see smoke rising in the distance and all of that and it's a difficult
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time and very soon the difficult time kind of goes away and it's a return to
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normality for most people you know it's like the old normal is a new normal
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again in a sense and you know I have some memory of the times because I was
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a kid my you know we were in Chandigarh at that time and later we moved to Pune
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but when we were in Chandigarh we used to go to Delhi quite often so I have some
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memory of the time as well and it is that there is this period and it passes
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and then there's a return to normality and it's as if it never happened and we
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heal you know the fact that Manmohan Singh became PM a couple of decades
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later and all of that is there an analog with that for example what is happening
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in these COVID times maybe even between the first and the second wave that at
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some point people just internalize the thing that okay the worst is behind us
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the lockdown is over everything is done and I'm not specifically asking about
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COVID which we'll discuss in detail but just about the mindset that people kind
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of have I think it's essential for sort of psychological survival to make sure
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that you don't carry the weight of terrible events too far ahead and I
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think that's why we all adjust and we all find ways of dealing with this I
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regarding code specifically I think the impact of COVID will last for some time
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because there's never been something that has really been countrywide whether
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you go to Bengal where the Maharashtra where the Karnataka where the Delhi
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whether that the the images of funeral pyres of bodies stacked up waiting to be
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cremated and just you know the fact that now we all know people around us who died
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early who need not have died at that point but they had they contracted
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COVID and because they were in that circumstance at that time they they
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passed away needlessly in some cases I think these scars will last for a long
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time there will be psychological scars and mental health in the aftermath of a
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pandemic not so much while people cope with things while they're happening it's
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after that that the survivors have to cope with what happened before and
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figure out their own psychological mental coping strategies to deal with
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this and this is something that we really need to appreciate going forward
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it's going to be difficult it struck me that we have never seen such a sort of
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collective trauma since partition but in one way this is worse because partition
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was like a few areas whereas this touches you know the remotest part I mean
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just scrolling through Twitter alone just my Twitter timeline every day is
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just completely harrowing for me because the kind of images that one sees are
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stunning and I do not know one single friend who is completely unaffected by
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it somewhere or the other that you know things are happening so it's quite
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horrendous but to sort of move back to your personal journey before we come
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back to COVID again it intrigues me that you are a professor of both physics and
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biology tell me a bit about that tell me a bit about how you know you became an
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expert in both these disciplines instead of just picking one what drove you to do
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that and what does this intersection then imply for you know the kind of work
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you do I mean what sets you apart from the physics guys and what sets you
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apart from the biology guys okay to answer that I'll have to tell you a
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little bit more about my history so so from Delhi after doing a bachelor's BSc
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honors in physics I moved to IIT Kanpur I spent two years in IIT Kanpur doing a
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master's in physics at the end of that I had a sort of choice about what physics
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I wanted to do because physics is of course a very large subject and I'd
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always had a fascination until then for the more theoretical parts of physics
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the much more esoteric and mathematical parts of it quantum gravity was one
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thing that I was interested in or that I felt I ought to be interested in because
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it sort of sounded difficult and seemed to attack fundamental questions etc and
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then in the last semester of my being at IIT Kanpur I had this wonderful course
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taught by a professor called Jayanta Bhattacharji who later became director of
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various well-known institutions in the country but he was an amazing teacher so
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I sat in the class and I said look this is what I really want to do and this
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this is a field called statistical physics which is a very interesting part
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that overlaps many different areas so having done that I said that look I
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first said look can I go and can I come and work with you for a PhD and he said
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fine and I joined IIT Kanpur briefly but I'd also interviewed in a bunch of other
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places including the Indian Institute of Science so the Institute of Science got
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back to me a little late after I had joined IIT Kanpur and said that look you
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know would you be would you consider joining as a PhD student I'd
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interviewed earlier with them and so then I went to Prasavaraj and I said
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look I have an offer here what do you think I should do and he said I think you
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should go there because these are the people who are over there they're so and
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so and so they're they're very good I think you will enjoy it this is a group
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that's a center of activity in in the field of condensed matter physics in
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India so I upped and went there a little later so I had various others of my my
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bachelor already joined and that was a big that was in a sense a pivotal
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decision for me the Indian Institute of Science then had pretty much the best
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physics department in the areas that I was interested in at that point it had
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amazing people so not just as physicists but much of what I am as a person really
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owes to the people who taught me and worked with me at the Institute of
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Science in Bangalore okay so this is maybe a little esoteric for your
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listeners but usually the relationship between a thesis advisor and a student
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is a very personal thing well you know that you work with somebody you
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interact with them you meet them regularly but the Indian Institute of
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Science physics department at that time was a very fluid department you could be
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registered as a student with someone work with someone completely different
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you could work with multiple people there so I was extremely fortunate that
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out of the six people in the department I worked with five of them all on
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different problems together learning from each something special something
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that they could uniquely could uniquely do and contribute so I view that as
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being very very pivotal not just from the point of view of the science and
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the techniques that I learned but also from the quality of the people that the
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honesty uprightness the scientific rigor and the fact that they were you know not
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just boring scientists but had multiple interests across many different areas
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they were very well read on their own so the fact that you didn't be a boring
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scientist you know really really came to me then that it was important for a
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scientist to learn about things that were outside what they were doing to be
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able to keep up that sense of wonder at this and to try and relate what they saw
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outside to the things that they were doing I think these were all important
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lessons that you sort of pick up along the way I had a bunch of very good
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friends at that time who still remain my friends and at that point I was really
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thinking of doing nothing except physics I'd sort of began began to think about
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this field called biophysics because that was the point where many physicists
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had begun to move into this huge gray nebulous area between fields called
#
biophysics so I thought of that but it but you know I hadn't wasn't quite ready
#
to make that decision yet so then from there I went to the Tata Institute
#
of Fundamental Research in Bombay and I worked for two years there as a what's
#
called a postdoctoral fellow and there I was working with largely with an
#
experimental group with both with people who work who did theoretical work and
#
people who did experimental work and that was useful because that was the
#
first time that I actually worked understood what a theoretical person
#
working with an experimental person meant to see results as they came fresh
#
off from whatever work that was done by the student the previous night and we
#
had to look at it and try and understand decipher what all of this meant I made
#
one particular connection with a person who I discussed a whole lot of physics
#
this is Professor Shobha Bhattacharya who later became director of the Tata Institute
#
of Fundamental Research and again the style of doing physics the style of how
#
in experimentalist things was became very important to me and after that I
#
think much of what happened after that is really reflects that interaction I
#
realized that it doesn't make sense of my style of doing physics involves
#
understanding experimental data and trying to put a theoretical cloth on it
#
and trying to understand where the different bones actually fit it together
#
and that's what really is my trajectory after that doing exactly this a few
#
more details which I can fill you in I have some amount of career before I get
#
to the biology from there I moved to Canada I worked in Vancouver for a
#
couple of years and that's when I made the move towards thinking about more
#
biology problems and these were problems that had to do with how cells move
#
things inside inside them and that was a interesting sort of area to mine for a
#
while when I came back to India after that I joined the Institute of
#
Mathematical Sciences I kept both of these up at the same time was doing some
#
sort of regular physics things as well as sort of this introductory biological
#
calculations as well and then I gradually shifted over to thinking more
#
and more about biology that's the point at which I really made the transition
#
and as I said in the beginning it because biology seemed exciting
#
afterwards and because there were these huge questions in it all of this
#
completely unexplored areas that you know physics was sort of well trodden
#
path people knew what to do you have laid out here you had to make up your
#
own calculations as you went you decide what you could calculate and what you
#
could make a difference with and that was very different so I'm just going to
#
you know pick up a couple of strands and think aloud here one you spoke about the
#
importance of the interaction between the theoretical and the practical aspects
#
of physics that as a theoretical person you are constantly getting feedback and
#
having to modify your assumptions and all of that and it strikes me and I want
#
to talk about your modeling also at a later point but it strikes me that when
#
you're modeling also that's an essential part of what you're doing that you can't
#
be married to assumptions you are constantly updating you know whatever is
#
in your model you're constantly updating your theory and therefore a certain
#
humility is a prerequisite for that now I imagine that as individuals one can
#
go in different directions when one is studying whatever one is that you can
#
get a certain amount of knowledge allow that to go to your head form a certain
#
vision of the world get stuck to it become dogmatic follow certain paths you
#
know and and become kind of that kind of person or on the other hand you can just
#
you know retain a lot of humility always be willing to accept that you're wrong
#
which I guess all scientists are to an extent and always be willing to accept
#
that you're wrong sort of keep interfacing with the outside world now
#
what I see kind of in your history is one that you are not just someone doing
#
science you know you've grown up reading you've grown up immersed in the world of
#
music and you pointed out that there were all these wonderful people with
#
varied interests who you also learned a lot from and secondly this kind of
#
interaction where you're keeping an open mind you're sort of in that exciting
#
phase where you're kind of discovering the world and molding your theories to
#
that and to a large extent you know a lot of this could be happenstance you
#
could have been somewhere else interacting with different kinds of
#
people and all of that so am I reading too much into this or is this really
#
kind of essential in that sense could you have gone in a different direction
#
if you were elsewhere and are there people who don't have the similar kind
#
of humility and openness you know the way Kumar gander would take things from
#
here and there and be open to everything I guess some scientists are also like
#
that and some are not you know as someone who doesn't know about the field
#
forgive me for this naive question but what are your thoughts I wouldn't call
#
it so much humility I would say personality okay there are people whose
#
personalities are very focused to understanding one thing they become
#
experts in it that's what they enjoy they they sort of thrive within that
#
that sort of limited area that they have carved out from themselves they make an
#
international name as experts in that particular area I'm more of a gadfly I
#
like to take things from different parts I get bored with doing something for too
#
long I'm interested in new problems it's just a sort of it's really just
#
personalities and every scientist has a sort of unique personality and the way
#
in which they decide how to approach a problem I've often kicked myself and
#
saying why do you want to do all of this new stuff because you know this is
#
stressful you're going to have to learn new things you would have to learn a new
#
language and the great French physicist Pierre Gilles Dejean actually said that
#
changing a field is like going to a new country and learning a new language and
#
that really is true it's stressful you have to sort of decide is it worth it or
#
not am I not just happy here but there are parts of my personality that are fed
#
by the desire to learn something new to see whether what I know can be applied
#
to that and partly I guess you'll get to the COVID-19 a little later but also the
#
desire to do something concrete that will help the country in whatever way it
#
can be done I mean that sounds a little vague and a little too general but that
#
also was a motivation for what I did over the last five to ten years after
#
that after I thought of doing doing all of these physics and biology things that
#
I told you about I mean I guess at some level this is like what Isaiah Berlin
#
said about the fox and the hedgehog but does taking multiple perspectives into
#
these different fields like taking you know whatever you've learned about
#
physics and stats and math and all of that into biology and vice versa does it
#
help you look at that particular field you're going to in a slightly different
#
way in which say a specialist in that field who knows nothing else who knows
#
just that one big thing would kind of look at it do you feel that that kind of
#
helped your perspectives because you're able to commit it from so many different
#
directions and also do you think I mean apart from personality do you think that
#
there is a particular intellectual bent of mind or maybe a kind of rigor which
#
makes a person able to do this for example you know maybe like whenever I
#
try to learn a new subject what I always try to do is kind of go back to first
#
principles and just try to understand it from its fundamental so to say so is
#
that a bent of mind also that is useful to you and that you find is inherent to
#
you that's a great question I think it's certainly true that as a scientist as a
#
physicist someone trained in physics talking to someone in biology I bring a
#
perspective that is very different as a physicist exactly as you said you're
#
trained to look at general generality the general description to strip it away
#
from all extraneous detail to try and say that this is what is going to be
#
important this is crucial and we could forget about all of this the classically
#
trained biologist on the other hand is very much a person of detail they know
#
that these details matter so they will have all of the very detailed complex
#
biochemical understanding or whatever is going on from their observations so the
#
dialogue between a biologist and a physicist always has to try and find a
#
common ground where the physicist has to engage with what is happening with the
#
biology very specifically and the biologist has to be agreeable to saying
#
that there is merit in stepping back and trying to see what the bigger picture is
#
so I think the fact that I had a fairly broad training in physics has been very
#
valuable because I know the standard bunch of tricks that one is trained in
#
in terms of computational mathematics and I can see that when I think about a
#
problem or discuss it somebody where something might fit in what might be
#
optimal what might be what might work in this case and that I think is the
#
advantage of talking to other people because you can pick this up it's good
#
for both sides one as a physicist you find interesting problems that no one
#
has looked at yet and as a biologist on the other side you find different ways
#
of thinking about what it is that you're doing that may be important that may
#
help your intuition but will guide your experiments in a particular direction
#
and I'm still not very good at it and you can and I'm not thought of them a
#
trained biologist who's gone through multiple years of a formal biology
#
classroom but I do understand what is being said now which I would have been
#
sort of earlier more difficult earlier and you know I think it's a sort of been
#
learning on both sides so tell me more about this field of biophysics perhaps
#
I'm not a person of the sciences so I haven't really heard much about this
#
before and it seems a very intriguing combination to me so I'm a layman so
#
explain it in those terms what is biophysics where is you know where do
#
the two come together how would you describe it so that the field that I'm
#
interested in is more appropriately called physical biology so that takes a
#
biological system and ask where does the physics come in at what points is
#
physics important to explain biological phenomena so to give you an example of
#
course it's a big mistake I mean to ask me to explain what I do because I mean
#
I just sort of get very happy and give you a half an hour seminar on that but
#
to give you to give you a very quick summary of the stuff that I'm interested
#
in you're made up of cells I made up of cells we're all made up of cell that's a
#
sort of fundamental basic unit of life the cells that make you and me up have a
#
cell nucleus it's basically a sort of bag with stuff hanging on it but the
#
nucleus is a little compact structure inside that contains the DNA and the
#
DNA contains instructions for making new cells for the whole what makes you you
#
and me me is contained in our DNA apart from some influence from from external
#
sources the DNA is contained in a long molecule called chromosomes and there
#
are 23 pairs of chromosomes in all your cells and all my cells what determines
#
where these chromosomes are placed inside this little nuclear bag and you
#
might think that look they're just long molecules you just mix them it looks
#
like noodles or spaghetti that's the sort of picture that you might have
#
mentally long molecules all together mixed up together what else what's the
#
best analogy it would be easily be baggy noodles inside a bowl it turns out
#
that's not true that there is a certain relationship that different chromosomes
#
bear with respect to each other some of them like to be closer to the inside
#
some of them like to be further towards the outside and this can have very
#
specific impacts on what the cell does what sort of cell it is is it is it a
#
cell taken from your liver is it a cell taken from your skin is it cell taken
#
from your brain so these questions are really questions of physics because
#
they're how do molecules move around how do molecules know where to go what
#
drives that and of course they have no brains so what are the physical
#
principles that tells a molecule of the large chromosome to be near the outside
#
versus near the inside and what does this mean for how we understand the
#
biological functions of the chromosome these are questions that are really set
#
at the boundaries between physics and there are no biological answers to this
#
question and there are no physics answers to the question because it has
#
to come from the phenomena is intrinsically biological phenomena so now
#
you can see why it's necessary to talk to each other and for the physicists to
#
understand what the experiment was what led to this observation and how do we
#
understand this observation from mathematical and a computational point
#
of view so that's my my three-minute elevator spiel on why I'm interested in
#
this topic so I'd love to know more it sounds incredibly fascinating so one
#
what are the implications of this new field and two I imagine this new field
#
would have emerged at a time when it becomes necessary for this new field to
#
emerge to take human knowledge forward so tell me a little bit about you know
#
the fundamental ways in which this is taking our knowledge forward and is
#
actually having a practical impact on lives and the way perhaps we treat
#
diseases or the our understanding of the world tell me a little bit more actually
#
again with this example sort of carrying on with this example just as the
#
prototype of what the field does you know that in cancers you tend to
#
multiply the number of chromosomes inside your nucleus and that's one one
#
gauge one way you can tell if you have a cancer or not just to look for an
#
anomalous number of chromosomes inside it so what effect does that have if I
#
just introduce multiple copies of your chromosomes just say chromosome 2
#
chromosome 5 chromosome 7 what effect does that have on what the cell is
#
doing why is it that those cells might be cancerous as opposed to other cells
#
what's going wrong in the regulation that should normally have existed there
#
are other types of conditions progeria for example it is a premature aging is
#
also linked to the way the chromosomes attached to the outside of the nucleus
#
to the to the walls of the interior walls of the nucleus so now you can
#
understand where these sort of physical connections between molecules what
#
contains them etc mean from a physics context and then ask what does it mean
#
for the biology why do these changes happen and what do they mean
#
biologically for someone with prudy with a condition like progeria or a
#
condition like down syndrome which is a particular chromosome having an extra
#
copy what goes on there what are these questions that relate these anomalies
#
that you can see their biological anomalies but they must have physical
#
origins how do these tie together in general the the background for this is
#
a growing appreciation of the fact that biology isn't just chemistry in a
#
different context for many years it was believed the whole of molecular biology
#
goes back and tries to reduce the behavior of biological systems to
#
molecules and how they interact with each other the DNA molecules and
#
proteins etc etc the physical biology part of it says it's not just molecules
#
it's not just a way they interact with each other but the way forces are
#
exerted inside says and the whole fact that you grew from just one fertilized
#
egg into what you are now with 10 trillion cells is itself an amazing act
#
of how forces must be expanding the baby grows the cells grow but they all go in
#
this very precisely orchestrated way that's called development all of this
#
must involve forces because cells must grow against each other cells must
#
receive mechanical cues from other cells in their vicinity how does how is all of
#
this orchestrated and how do we understand this whole phenomenon of life
#
as being this intersection of physics of chemistry and biology together this is
#
really the exact nature at all no no it's so fascinating you know if I had
#
heard you talk like this when I was 10 years old I might just have gone further
#
into this field because it's so fascinating so tell me something when we
#
look back on medicine in the 19th century and we are like you know the
#
cure for so many things was bloodletting the germ theory of disease wasn't there
#
there's so much we didn't know it seems entirely prehistoric today and we often
#
tend to assume by default as humans because the future is unknown unknowns we
#
often tend to assume that we are at some kind of pinnacle of science that earlier
#
we were ignorant now we know it all but the truth is that if we survive another
#
couple of centuries we'll probably look back on the 21st century and say my god
#
what primitive people they knew nothing so being at the cutting edge of this
#
particular field do you get that sense that we are just exploding open these
#
walls of knowledge figuring out things we didn't know earlier you know people
#
increasingly these days are talking about reversing aging and even the
#
horrible notion of how we could live forever as if I would anyone want to do
#
that what are your thoughts in that I mean just just thinking of it is enough
#
to of course fill one with wonder but you know as someone who is actually you
#
know not a bystander but working in these fields thinking about them
#
understanding them what is your sense of what's going on there's a strange
#
paradox here there's something that we know very very very well we sort of
#
think of the 19th century as being the century of engineering where first the
#
great engineering feats of building huge bridges and roads etc were first
#
accomplished the 20th century is really the century of physics we figured out
#
quantum mechanics we figured out the fundamental laws that govern the world
#
we explored the atom with greater and greater accuracy and we can now
#
calculate properties of physical systems that are to nine decimal places that's
#
an amazing achievement the cosmic background radiation that comes to us
#
from billions of years ago that are received in radio receivers on earth the
#
spectrum of that we can calculate with such accuracy first of all is an
#
experimental measurement the size of the little symbol that you used to plot it
#
is a hundred times larger than the actual accuracy with which you which
#
which that measurement is made and we can fit that perfectly so that's a
#
amazing example of how physics actually works that it works we know
#
amply that it works and it works very well this century is a century of
#
biology and that's where the explosion is really going to happen how do we
#
understand how systems develop how do they start a single cell and become
#
whole beings like you and me can we replicate that at all inside the
#
laboratory where does like where does consciousness sit if consciousness is
#
just a fact of the way our neurons are connected inside our brain if there's
#
something else said what determines that can I just connect in a in a little
#
petri dish a bunch of neurons together will that be conscious will that not be
#
conscious these are really the question especially in a disease which certainly
#
is one of the most important questions of our time where do cancers come from
#
how do we treat them what do we understand what goes wrong in someone
#
growing to that that makes that makes that little error which is an amplified
#
and becomes a cancer what about Alzheimer's disease as you know we're
#
all growing older and older and older average lifespan on earth is increasing
#
as you grow older there are diseases that are associated with aging that are
#
just more likely if you survive till age 80 it's much more likely that you would
#
get dementia than if you were 20 years old how do we understand that where do
#
these diseases come from how do we treat them even if we don't want to live
#
forever how do we ensure that until we until we die we live comfortable healthy
#
lives until that particular point and these are going to be issues going
#
forward and that's where the real frontiers are let's also talk about math
#
because you know math is something that you've dealt deeply into as much as
#
physics or biology though one could argue that it's a part of both and
#
everything is interconnected and I also find that fascinating because one you
#
could argue that a given sufficient knowledge you can explain all of the
#
universe and perhaps even all of what is going to happen through eternity just
#
using math but at the same time because we do not have that knowledge we
#
therefore have to use the math to do a kind of probabilistic thinking like I
#
read this superb book a few years ago called a super forecasting by Philip
#
Tetlock which you know includes stuff on modeling and all of that and at that
#
time I had been like a professional poker player for five years and all of
#
that and it seemed to come to the same conclusion that I had through my years
#
of playing poker that you have to think of the world in a probabilistic way in
#
a human beings love stories that explain the world they want certainty and
#
therefore they'll think of the world in ways that mimic that in non
#
probabilistic ways because after all everything that has happened has
#
happened it's hundred percent that happened right but before it happened
#
there were probabilities involved and I would say that probabilistic thinking
#
is an implicit part of everything we do like I remember I once wrote a column
#
about why we should not stigmatize gambling the poker of course is a game
#
of skill but my point was that everything is gambling that when you
#
choose to cross the road that is a gamble because there are probabilities
#
associated with how often you will get run over or how if you're in Versova the
#
tree in the middle of the road may fall on you extremely low probability and all
#
of that you know if you are a young single person and you choose to go up to
#
someone and introduce yourself there's a probability there there's a range of
#
possibilities as it were you know poker players love to talk in terms of ranges
#
rather than specific hands and it seems to me that one of the beauties of
#
mathematics is that it fits real life in this beautiful way and when you think
#
about math explicitly a lot of the implicit stuff because we are all
#
gambling anyway we are all modelers anyway we are modeling what will happen
#
into tomorrow when we choose to do something today but we're doing it in
#
imprecise ways we are not thinking about it but actually forcing yourself to
#
think about it and think in probabilistic terms makes you much more
#
clear about a lot of things in your head if you start doing that so you know and
#
I know I'm rambling a bit but you know this was one of the big revelations that
#
changed the way I look at the world and maybe the last decade or so now you are
#
someone who is of course into modeling but before you we get into the
#
nitty-gritties of what modeling involves and how you learnt it and how
#
you got into it just give me your sense of overall aspect of it and do you think
#
that you know learning how to model figuring out modeling and all of that
#
also changed you as a person in in in some ways that's a great question and
#
it's also sort of part of what I teach in my undergraduate courses here at
#
Ashoka University the nature of randomness the nature of chance and how
#
to how to deal with it and how to make statistical statements about it so the
#
field in which I was trained which is statistical physics really has all of
#
these components in it it has a lot of randomness and then you ask from this
#
randomness how does some sort of order emerge how can you understand emergent
#
order from structure that are intrinsically very random and I think
#
that's an important philosophical point at which physics begins to connect to
#
many other fields because you know look at us we're extremely ordered structure
#
we have noses and eyes and ears in exact locations but the laws of physics
#
finally are completely you know immutable and don't and you know you
#
need them to put this structure together so how does that actually work is an
#
important and really very crucial point that you made so it's very much a part
#
of what I do and in a sense I really have always been a modeler I know talks
#
I now refer to myself as a mathematical modeler now but I've really always been
#
doing mathematical modeling there's no I mean it's just we didn't talk of it in
#
that way earlier but it's just trying to understand the observations about the
#
world around you in some sort of mathematical framework and this is a
#
framework that as you said uses probability use the statistics as well
#
as well as uses the laws of physics that's really the best way of describing
#
it and just an aside when like when we speak of randomness isn't that
#
randomness really a result of our own ignorance like for example is a free
#
will if you could know everything that was knowable would it not be obvious
#
what is going to happen in the next moment and so on you know the arguments
#
against free will so where do you kind of stand on it oh you're plunging deep
#
into philosophical debate on this question I think the current
#
understanding is that that randomness is at the root of everything it's really
#
that there are fundamentally even though quantum mechanics prescribes in a
#
deterministic manner what's going to unfold here there are many limitations
#
you don't know the quantum mechanics of everything in the universe you just do
#
the little thing that you're studying and finally from that quantum mechanics
#
comes probabilities of events because you cannot talk about the absolute
#
certainty except in very limited senses where you precisely prescribe the nature
#
of the quantum state of a particular particle so it's complicated and I think
#
quantum mechanics has contributed a lot to this debate around what is known what
#
can we know what is knowable and even these ideas of chaos that chaos theory
#
and dynamical systems has told us about the very large sensitivity that even
#
very simple systems can have to how you start them off make tiny little changes
#
and that amplifies this butterfly effect that you can have it tiny changes here
#
can have huge far-reaching changes very far away it's important especially now
#
when people sort of imagine that there is a logical sequence in which an
#
epidemic unfolds and we should have known that a model should have predicted
#
it etc these are at their heart random events they're fundamentally
#
unpredictable and when we talk about COVID-19 maybe we'll get to some of that
#
that particular question but it's important that you raise that because
#
that is really very fundamental to the way we think about how complex systems
#
behave but one final philosophical question at a philosophical level can
#
something be truly random I mean it can be apparently random because we of
#
course don't have knowledge so many things will seem random to us like back
#
in the day I imagine you know whether it would rain tomorrow or not would seem
#
random to a person while today you can kind of model that and have some kind of
#
probabilistic prediction about whether it will rain tomorrow in Andheri for
#
example or Kochi is a term randomness itself an illustration of the
#
limitations of our knowledge or are there things that are actually truly
#
random and even if you knew what every atom in the universe was doing right
#
now that there are still things that would happen the next moment that are
#
actually random and unpredictable so that's a difficult question to answer
#
because you have to answer it at a practical level how well should I know
#
a particular system now in order to be able to predict it do I should I know it
#
to a hundred decimal places or will it will 15 do or should it be a thousand so
#
the whole idea behind chaos is that it doesn't matter how you know you can do
#
it thousand ten thousand five thousand etc decimal places that you can specify
#
that I know the system that well but finally to degenerate into chaos that
#
these the trajectories that the possible system might unfold go further and
#
further away the more the better you know it earlier the more time it will
#
take for that to happen but finally it will happen and at some point it becomes
#
a sort of a debate that cannot be settled is there some truly underlying
#
perfectly deterministic theory of the universe and everything in it or are you
#
always going to be limited by the fact that there's no paper long enough to
#
write down all of the conditions on all of the atoms inside that could enable
#
you to make that decision so right now I think most scientists take a practical
#
point of view that life is fundamentally stochastic and unpredictable the system
#
that you're studying you can control them to some extent and insulate them
#
from external influence for the purpose of your measurement but in most cases in
#
practical world you cannot do that there is too many imponderables which is why
#
you need statistical descriptions of phenomena around you. Fabulous you know if
#
your undergraduate courses were online I'd absolutely take them because this is
#
so fascinating but let's move on to modeling now tell me a little bit more
#
about this and how you've got deeper into modeling and I guess that will at
#
some level bring us to what you've been doing in the last 10 years and just
#
modeling disease and epidemics and so on so good tell me a bit more about that.
#
As I said my training as a theoretical physicist is really a training and as
#
theoretical physicists who work with experiments whether they're physics
#
experiments or biology experiments it's really a question of taking a lot of
#
complexity and trying to distill it into some compact mathematical
#
description which can then be used to predict what might be happening or to
#
understand what might be happening so that's a general theme that every
#
modeler is about. To come more to sort of modeling as I use it now I have to tell
#
you a little bit more about history I think beyond after I spent about five to
#
seven years as a young faculty member doing sort of standard things that
#
faculty young faculty members do in terms of research and teaching I realized
#
that there was some part to me that still needed to be satisfied in terms of
#
the fact that I was doing things that were academic but in some sort of
#
academic sense these were useful academic things to do but I began to
#
wonder whether I could and should do something more for the nation I was
#
embedded in for the people around me so that's when we began thinking a little
#
bit colleagues of mine and I about looking at infectious diseases as a
#
problem that was that one could potentially hope to make some progress
#
with in terms of a modeling framework so between 2005 and 2016 we organized three
#
different meetings so we were just coming at it out of the blue we just
#
said that look you know we have some funding in our Institute to support
#
these meetings just have a meeting on modeling infectious diseases let's get
#
people interested in modeling from all over the country who we knew let's get
#
people clinical people let's get people who work on the public health on the
#
public health front get them together and just see if they can talk to each
#
other so we used to have little tutorial sessions before that and then we used
#
to have a little conference where people would talk about real work and we did
#
this in 2006 2010 2016 etc and by the time the last time we did it we had a
#
whole lot of people coming in from outside we had a bunch of people coming
#
from the London School of Hygiene and Tropical Medicine and Imperial College
#
to come and give lectures and be part of this of this discussion and we slowly
#
began to realize that there wasn't a strong modeling community in the
#
country which was alert to what information was there in the public
#
health domain trying to understand what India could and should do most of the
#
models on Indian data come from outside India and you know there were people in
#
India doing in quotes modeling but these were all somewhat rarefied more
#
mathematical and very little of it had the real connection to to the problems
#
of India specifically in the field of public health infectious diseases so
#
from there that's an imperceptible move in which I was doing biophysics and
#
doing a little bit of this at the time I also taught this course a couple of
#
times I taught modeling infectious diseases and epidemiology a couple of
#
times and in a bunch of different places and then I realized you know what the
#
tools that went into this what what they were what these techniques when I
#
realized they weren't very far away from what the stuff that I had been doing earlier
#
this is also just part of a larger question of how do you begin to model
#
systems and if you're trained in modeling you can understand how this
#
modeling is done then after thinking about these questions I realized that
#
maybe the other interest ought to be policy because finally you can do
#
mathematics you could do modeling you can talk to people you can see what
#
you're doing but the only point at which you can really intersect you can do
#
things useful is when you try and see how you can convert this into some sort
#
of policy policy suggestion in a way that is acceptable in a way that has a
#
chance of working because all of this has to float up until someone implements
#
and decides to choose it so what is the interplay between models and policy I
#
think that's really where I am at now how does one use mathematical modeling
#
of diseases public health situations etc so as to motivate better policy how can
#
policy take in information that is mathematical in nature that projects
#
what might happen in the future that explains what is going on and gives you
#
gives you a sort of analytic framework or bones with which to make decisions now
#
this is hard because policymakers are not necessarily accustomed to looking at
#
mathematical models when they make decisions so there's also a question of
#
how do you describe what you do in such a way that a policymaker understands
#
that it is important understand that it fulfills a certain very definite need
#
and they can then incorporate it into the stuff they're actually doing so that
#
sort of is my history of the last couple of years and part of my reason for my
#
move to Ashoka University where I am now because I felt that talking about policy
#
would be easier in a place that had policy chops in this area and where I
#
could talk to multiple people across multiple departments the whole field of
#
public health is very interdisciplinary you need economics there you need some
#
sense of history of what's happened in the historical background you need
#
people from multiple departments you need to talk to biologists who with
#
experience in immunology if you want to look at what happens in infectious
#
disease so you need to talk to ecologists especially to disease
#
ecologists who want to look at what are the consequences for disease of changes
#
in the ecology so this you know suddenly your your horizon and I discovered that
#
just became so much more broader I was talking to people that I had never
#
imagined I would talk to five years before that or ten years before that and
#
then I began to slowly understand the scope of this field and what exactly
#
this complexity meant and the fact that to be really serious about understanding
#
disease in the Indian context you had to weave a whole lot of different strands
#
together that I was only you know imperfectly aware of earlier and even
#
now I'm somewhat imperfectly aware of. A few questions and you know forgive me if
#
they sound a little naive as I'm trying to kind of get to grips with the subject
#
but the first one is that modeling would basically be that you take a bunch of
#
data and you figure out ways in which the data interact and then you try to
#
model what will happen as time goes by now a couple of elements come into it
#
and the first element is that to improve your model to improve whatever you're
#
coming up with you need to iterate constantly keep changing the data
#
iterate all the time and if you are at the same time interacting with
#
policymakers you know in the real world there's kind of no iteration you need to
#
decide what to do right now how does one solve that problem because you know as
#
we've seen in the past like there's a famous case of how another modeler Neil
#
Ferguson about 15 years ago said that you know when a bird flu happened said
#
that around 200 million people would probably die from it and actually around
#
300 people died from it which is kind of a stark difference and that's obviously
#
that there is something there in the assumptions which is wrong there is
#
something there in the you know the data which is wrong which didn't work out and
#
it's an honest error and can happen and if it's a purely theoretical activity
#
that doesn't really have a huge impact on the real world but when you're going
#
to make policy decisions on the basis of these models you could go terribly wrong
#
for example you could order a crazy stringent lockdown which has massive
#
economic costs on the basis of a model that was flawed to begin with not that
#
I'm saying but that's what happened last year you know as we are seeing now you
#
know without the lockdown things could even have been much worse so I'm not
#
sort of making a specific sort of reference to COVID itself which is
#
hopelessly complicated and we'll only be able to figure out which decisions were
#
good or bad in hindsight and we'll come to that later but in general about
#
modeling like that's my first of kind of two questions that how do we sort of
#
then get past this that we know models are useful and we know that politicians
#
are actually modeling every time they make a decision except that they're not
#
using data they're just using guesswork and assumptions and biases and all of
#
that and then they're deciding we should do this so obviously using a
#
mathematical model is better but at the same time models become better and
#
better and better the more iteration there is where you know in the real
#
world that becomes a limitation so what's your view on that there are great
#
questions and which is why I say that modeling is cannot be an exercise that
#
is done in the vacuum modeling has to refer to a whole bunch of people with a
#
vested interest in understanding what the outcomes might be you must you must
#
talk to the public health specialist who see a disease increasing in numbers you
#
must talk to the immunologist who tried who try and understand you know how does
#
it affect your body and how did the immune system react and is there any
#
pre-existing immunity that you might have to that particular disease let's
#
talk to disease biologists who sort of are familiar with how with how infection
#
might happen consequences of a particular disease you must go back in
#
history to ask what's most similar to this pathogen you must talk to the
#
biologist who say is this a new virus is an old virus how much has changed and
#
you must talk to ecologists who will tell you that this came from animals
#
into human beings so in what's called a spillover event so the important point
#
that I want to make here which I think is crucial to answering your question is
#
that modeling should not be done in vacuums it's not as though mr. Ferguson
#
or whoever it is can sit and write down equations and put new computer programs
#
without being very very very careful about the numbers that enter those
#
models the assumptions that enter those models and that's why you need groups of
#
people working on this together and these groups should not just be a
#
monoculture of people all of who think in the same way you need to have a
#
fertilization of people from very very different areas to try and do the right
#
smell test on what the model is what it predicts what it might do in the future
#
the other statement you made is very correct the politician makes a model but
#
we all make models all of us are sort of thinking if I go out tomorrow will they
#
catch go in if I go to the shop what is the probability will they wear masks do
#
I was there going to be a big crowd is there a line we're all trying to predict
#
what happens in the future based on our experience of the past based on the
#
input that is coming coming to us all the time but there are questions that
#
only models can answer for example how much how you know what is the amount of
#
remdesivir that the state of Kerala should order in given that the state of
#
Kerala orders let's say one month at a time or two months at a time from its
#
suppliers how much would it order now that's a question that really relies on
#
how where do you think cases are going to go if cases have been down trending
#
for the last three weeks you don't want to buy a big order remdesivir and waste
#
all your budget in that if you think they're going up but they're going to
#
come down in a day or two you might structure your order differently if you
#
see it going up relentlessly at a certain rate between now and the end of
#
the month you're going to order a completely different quantity so that's
#
where the numbers begin to matter and that's where the numbers that you put
#
into model cannot be a matter of intuition anymore you can't say okay I'm
#
going to order you know so many strips of of this from my supplier because I
#
have a general feeling that that should be right you don't want to run out of
#
potential you potentially useful medicines and you forget about whether
#
remdesivir is useful outside a very specific clinical context I'm just
#
using it as an example these things need mathematical bones to be put upon them
#
the bones are determined by the way you structure your model what you think is
#
important what you want to leave out of it it may turn out that you left out
#
something very important but then you would wait for people to tell you that
#
and then you publish your model so that people can comment upon it and say look
#
this looks like a terrible mistake when to do this or they can write their own
#
version of your model and say if we tweak it in this way this is a little
#
better representation what was actually going on but no the problem that
#
modelers face especially models of disease is that you're always trying to
#
ask what's going to happen in the future and there are many things that come into
#
that we'll probably talk about it a little later when we talk about more
#
specifically COVID things but if all of those terrible things that ensured
#
actually happened that would be a terrible thing indeed so it's a job of
#
the modeler to be proved wrong whenever the modeler says something that is that
#
is you know that that points to much more to death than devastation of the
#
consequence of a pandemic that's a modeler's job to insist that you do
#
something now so that what happens in the future can be averted and one has to
#
think about models in that way they counterfactual they tell you what might
#
happen if a particular course of action was done or if you did not do
#
particular things at particular times and that should be impetus to do those
#
things at those times for example a lockdown or a quarantine etc so that
#
those things don't happen and then in a sense the model is wrong because what
#
it predicted didn't happen but you're very happy and the modeler is also very
#
happy that they didn't happen because this was a sort of situation where you
#
it was a counterfactual situation if you assume you don't do anything this is
#
what will actually ensue. And it's also you know very hard to say that someone
#
who's making a probabilistic prediction is wrong per se like I might say that X
#
has a 80% chance of happening Y has a 20% chance of happening if Y happens
#
that doesn't mean I was wrong it just means that that exact 20% kind of
#
worked out randomness as it were I mean the other way of looking at it it seems
#
is that when I argue that we should read more I keep telling my writing students
#
in the class that I teach that you know how do we paint a picture of the world
#
we paint it by joining dots and the more dots we have the more high definition our
#
picture will be and the only way to gather dots is by reading more but it
#
seems to me that here also the way of generating the best models is like you
#
said by getting a lot of dots from a lot of places and just doing that and it
#
also worries me that simplistic models in the past that might have gone wrong
#
can just give the whole field of modeling a bad name which is kind of
#
silly because we are all modeling anyways so you might as well try to be
#
as precise as you can the other question I had about modeling in
#
particular is that when you're modeling anything in India your limitation is
#
data because obviously you're feeding in data and you know garbage in garbage out
#
you you need decent data to be able to understand what is happening like one
#
reason people are speculating about why the second wave blindsided so many
#
people is that we didn't have accurate enough data to understand the first wave
#
to begin with now in the context of a place like India where not only is one
#
data not available because of incompetence but it is also increasingly
#
being suppressed because certain narratives you know are more in the
#
interest of the people who govern us or you know govern us quote-unquote so how
#
does one get past this like I assume in many ways like economists in India for
#
example will always have to look for proxies for economic data because the
#
GDP has become such a corrected measure so in the context of say infectious
#
disease in India if you're trying to model that or just what has happened in
#
COVID in the last year how does one deal with this so it's a bit paradoxical that
#
there is some amount of data that is available it's not available in the most
#
convenient way for example the National Center for Disease Control has a weekly
#
report that covers every district in India that reports on how many cases of
#
influenza something else H1N1 whatever it is cholera etc came up so that is
#
very granular data set the data the level of 740 districts in India on a
#
weekly basis this data is put out not in any convenient form it's usually in some
#
PDF file that is located somewhere else so that's again an irritant that data
#
is not made available even when it is collected it's not made available in a
#
form that is useful someone has to then transcribe it literally from one
#
spreadsheet to another spreadsheet from PDF document to something so one
#
irritant is this is conversion of data from the way it is collected to the way
#
it can be used the other problem of course is just a collection of data
#
there is data that we don't collect that a district that are not surveyed well
#
enough and not carefully forget about issues of people making up data and so
#
on you're not even concerned about that at this point and with COVID of course
#
everything has gone completely crazy because we are not testing enough
#
they're not reporting deaths correctly etc these are problems that are
#
particularly hard to deal with in India because the scale of the lack of
#
knowledge is truly it's truly huge compared to sort of a typical Western
#
developed democracy just to give you an example if you just look at deaths in
#
India most people in India die at home even though about 80% of deaths in India
#
are recorded in some way only about 20% of that 80% is what is accompanied by
#
what's called a medically certified cause of death so we actually know only
#
the reasons for a death in terms of what a doctor might have scribbled on a form
#
in 20% of 80% of the Indian population very roughly speaking and it's fairly
#
inhomogeneous across the country and it depends very much on which district you
#
are more far flung remote etc the less accurate that information might be
#
whereas in cities you typically have good recording of this that's a very
#
fundamental thing if you don't know what people are dying off and how many are
#
dying you have no way of understanding what for example coming with COVID
#
currently what is a background against which I must measure an excess how much
#
of an excess are we seeing twice as many people die as would normally have died
#
in a non-covid year is it five times as more and that's very crucial to
#
understand the impact on the country where are people dying how are they
#
dying are they dying typically more of respiratory ailments that have not been
#
classified as COVID because no COVID test was done we need to put all of this
#
and this is a challenge for modeling it's a challenge for public health more
#
generally even if you didn't talk about models to understand and to make
#
decisions about public health you need data the problem is of course there is
#
an incentive there's a subtle incentive to not report it because you may think
#
it reflects bad upon badly upon your district badly upon your state badly
#
upon your chief minister badly upon your government etc so that's going to be an
#
issue in the future and it should have been an issue for many years how do you
#
change the incentives around so that the incentive to to not report bad data to
#
basically report good data as far as possible is there inside the system that
#
there are checks and balances there are sniff tests that you can apply to the
#
data to make sure that they are actually sensible so we do need I think that a
#
slow progress towards this I think COVID has thrown light on for many people the
#
natures of the inadequacies in the data that we deal with and hopefully that is
#
one thing that will come out the need for transparency of data the need to
#
make data available while protecting of course the identities of any individuals
#
who contract disease etc but even without that the level of granularity
#
with which you understand disease in the United States or the UK or the France
#
or Germany we're not nowhere near that and that's where we should strive to be
#
so the question that you just asked and you know how do we incentivize people to
#
you know produce better data and all that what are the candidate answers to
#
that I wish I knew I really wish I knew what what would be that the right way to
#
do that how do you ensure that people don't feel that they're subject to
#
political pressure that they have independence and that they're being
#
monitored in a sense to ensure the data that they provide is good part of it is
#
just you know giving people a sense of pride in what they do in the sense that
#
this will be added up and decisions will be taken on the basis of a report that
#
is made but this is something that really calls more expert knowledge of
#
health systems and this is not an area that I that I'm knowledgeable about so
#
I wouldn't I won't be able to answer that question well enough fair enough so
#
tell me a little bit more about this latest phase of your career as it were
#
the last few years of modeling infectious disease and so on you know
#
you've already sort of mentioned what drew you to it but you know tell me
#
about discovering what the field was like what were your early challenges did
#
people in your fields you know see something like go it coming I mean of
#
course a lot of truly large numbers states that at some point there will be
#
a pandemic like this and so on that's inevitable but did it take you
#
completely by surprise or you know was it on the cards give me a sense of what
#
the last few years in this field have been like for you so I moved to
#
Sonipat about six months before the pandemic actually began four or five months with the
#
intention of potentially setting up a group of people interested in disease
#
modeling and to you make a sort of node identify people across the country who
#
would be interested in contributing to improving disease and public health
#
model in the country that's what I was here of course I will join the physics
#
and a biology department so as that is I had regular teaching responsibilities
#
there research responsibility of students to supervise etc etc but this
#
was my larger goal so it came as a shock almost or a surprise to realize that the
#
world had changed sometime around January about four or five months after I'd
#
come to Delhi and that there was this new sort of pathogen around that everyone
#
was suddenly taking much more seriously that could be potentially this the seed
#
of a pandemic that could sweep across the world so I think I must have given
#
one of the first public talks on COVID-19 remember that the first case in
#
India was on the 30th of January on the 7th or 8th of every I gave a talk in I
#
think Hyderabad to a public audience saying that look this is what we know
#
about the virus at this point so what we know about how it spreads this is a
#
larger socio-political historical background what do we know historically
#
about pandemics of infectious diseases what my what might this mean long term
#
if you if China's lockdown what does it mean to supply chains what is the use of
#
language why is it that you know you move from calling it the Wuhan virus at
#
that point at that point the WHO had not been declared it to be a pandemic so
#
even though in my title I gave the COVID-19 pandemic I was sort of very
#
conflicted about that and said you know should I say this they haven't called it
#
a pandemic we don't know what it is yet etc and after that I was reading up very
#
intensively about it following developments in Hong Kong Singapore
#
China etc watching it spread first from there to Italy watching the start of the
#
Italian pandemic first couple of cases in India and also talking about it
#
because I found that people did want to know and the sort of perspective that I
#
had which wasn't just a modeling perspective but presenting it in a
#
historical context telling you about the many areas in which and I used to say
#
then and I still do now that there is no area as interdisciplinary as a study of
#
infectious diseases it deals with health it deals with biology deals with
#
modeling it deals with economics it deals with but geopolitics as well even
#
if you forget about made the whole field of medicine and public health and all of
#
these other areas so it's that that I wanted to convey to people you should
#
think about this in much larger terms of how it's going to affect your life how
#
is it going to affect the lives of other people around you so that's when we
#
began to model and very early on colleagues from from the organization
#
that I had taken leave from in order to come to Ashoka University which is the
#
Institute of Mathematical Sciences we had started a group there along with a
#
bunch of colleagues from across India called the Indian scientists response to
#
COVID-19 and this was really intended to give a scientific explanations to the
#
public to try and and describe to the public in lay terms what the science
#
was saying what are the right precautions that you should take what do
#
we know about the disease and to translate it into multiple languages
#
because we didn't want to just be an English the dominated how do you design
#
good graphics that could describe to people who may not even read to what what
#
the situation was so we did that very successfully for we continue to do it
#
now curating and producing local language material that describes
#
everything about vaccines for COVID and current knowledge of COVID how COVID
#
spreads wash and washing etc etc but part of that was also setting up a group
#
that did model so that's when we began working a group of about eight or nine
#
of us came together to try and think what were models for COVID-19 that were
#
being discussed at that time how would we put that into mathematical framework
#
and what would we want to do so this was a period of intense discussions
#
surrounding that model that we tried to put it together and that point many
#
people around India were also involved in COVID modeling all of sort of varying
#
qualities there was some good and some bad but we were particularly we wanted
#
to develop model that would be general enough and powerful enough to be able to
#
answer the sort of granular questions that we wanted to answer in this
#
particular district with this population with so many cases by the beginning of
#
March what would you predict for what might happen in the future if there was
#
a lockdown what would you predict what is the best way to come out of a lockdown
#
what about lockdown that are on and off so you have a lockdown for a week then
#
we offer for a week then on for a week is this a way of getting people back to
#
work can you segment the population that one-third of the people go to work two
#
thirds remain at home and every week they interchange these are all ideas
#
that we were asked to in some cases we were asked to by government to look at
#
these and to try and suggest different ways out of the lockdown that existed at
#
that point and these others were sort of general interest what what was the
#
international scientific community thinking about lockdowns in general and
#
how does one cope with that and could we track the behavior of COVID-19 across
#
Indian cities across India as a whole as they develop between the months of
#
about March when the lockdown happened too much later in the year so that's
#
really what we were engaged in that's where my my first intersection with
#
COVID-19 modeling came about I'll tell you a little bit more but we can go on
#
so my sort of assumption about pandemics would be that there is a point early in
#
a pandemic in fact before a pandemic is called a pandemic as you put it where
#
you don't really know which way it's going to go you're thinking
#
probabilistically and it could be a pandemic or it may not be a pandemic
#
like in the bird flu case where you know Neil Ferguson forecasts 200 million
#
deaths and 300 people died and you know it goes one way instead of the other
#
way so at what point does it begin to become apparent that this is a big deal
#
and also a dual question that would that then have something to do with the
#
nature of the virus itself in the sense that is this like a game theory optimal
#
virus in in in that it is a perfect mix of infectious and lethal you know
#
unlike Ebola it is not so lethal that it is not infectious enough and unlike a
#
lot of common flus or whatever it is not incredibly infectious but not lethal at
#
all so why worry about it did this kind of hit the sweet spot is there a sweet
#
spot what is your sense of this and would tracking this have helped you
#
realize earlier and kind of change the probabilities that yes this is a big
#
deal this is going to be serious at what point did you realize that oh shit this
#
is something oh that's a great question and so just to go back to the sort of
#
previous point that you've made it really hits a sweet spot because it's
#
highly transmissible it goes by respiratory so all you do have to is to
#
breathe in the same air that someone close to you is breathing out and then
#
if enough virus particles get into your body and multiply then you have
#
COVID-19 but as you pointed out the other thing is that most people who have
#
COVID-19 will not know it because we know that somewhere between 50 to 80% of
#
people who contract COVID-19 are asymptomatic for the disease they may
#
feel mildly ill for a few hours maybe a day or two sometimes in many cases not
#
that at all they don't know that they have the disease and are capable of
#
transmitting it to other people it's only a small fraction that has symptoms and
#
of them even a smaller fraction will wind up in an ICU or wind up in a
#
morgue at the end of that so the combination of highly transmissible yet
#
killing people at just the right rate is a sort of unique combination there and
#
and and this is really no it's it's it's a perfect example of what a
#
highly evolved pathogen ought to be like if you really wanted to design one that
#
received across the world you design something like this but much of the
#
transmission happens before people show symptoms unlike its relative which is
#
the original SARS virus which is closely related they're both coronaviruses which
#
are related to each other that killed you at a much larger rate it killed it
#
had infection fatality rate of about 8% so 8 out of every 100 people who
#
contacted it would die of it this another coronaviruses called MERS MERS
#
the numbers are more even more scary it's about 30% so you had a 30% chance of
#
dying if you contracted it none of these diseases do we have a vaccine do we have
#
any sort of treatment any antiviral nothing so you effectively you catch it
#
your odds really depend upon that particular number whereas we know now
#
that for the SARS-CoV-2 the virus that causes COVID-19 that number is
#
probably somewhere around 0.1 to 0.3 percent compared to 8 percent and
#
compared to 30 percent so it's really you know it's sort of it kills you but
#
it kills you at a sufficiently small rate that you're constantly having to
#
understand this trade-off in your head at what point should I open up how many
#
numbers will die as a consequence where a younger population maybe we won't die
#
so much but then you have to compare that with the fact that people who die
#
are people who belong to our population there's somebody's nephew somebody's
#
uncle somebody's aunt and at the individual level it's always going to
#
be a tragic issue so this was debated much more earlier when what these
#
numbers were weren't completely clearly understood people thought it was much
#
more like the regular flu and the argument was if it's a flu then everybody
#
gets a flu sooner or later in a year maybe if you're elderly etc somebody
#
dies of it but that's a risk that we're willing to take but then it took some
#
while to figure out that this is not the flu that you know it's certainly not the
#
flu the older you get the further and further it gets away from being a flu
#
the younger you get you should it's kind of a little more flu like but
#
nevertheless it has long-term consequences that the flu doesn't have
#
but this thought being an evolving understanding and that's a very nice
#
point that you made the fact that this is really hits a sweet spot in terms of
#
in terms of its behavior as a potential pathogen that influences human beings
#
when did I first think about the importance of this it took a long time
#
for me to really figure out because the numbers themselves weren't clear the
#
distinction between a case fatality ratio the infection fatality ratio the
#
fact that many so such a substantial fraction of people were asymptomatic for
#
the disease the fact that age the way age is distributed in a population makes
#
a huge difference India is a young population the Italy is an old
#
population so it hits both of us very very differently all of these things I
#
sort of slowly began to realize as I went forward and so it's a learning
#
experience for me as well and and you know when you reached that intersection
#
of modeling how an infectious disease will spread and modeling what the
#
consequences of say a public policy which is intended to cut counter that it
#
gets complicated like the issue with something like a lockdown is that sure
#
it is possible to you know with the best data that you have to come to some kind
#
of a probabilistic estimate of if we do a lockdown we'll save these many lives a
#
curve will flatten in this way maybe it will be prolonged all of that what is
#
much harder to model are the downstream economic impacts for example what is
#
going to be the effect on migrant labor if we lose 20% of GDP because of the
#
lockdown that is also going to kill a lot of people and the problem with any
#
public policy measure in this particular case is really a case of the seen and
#
the unseen in the sense that the the bad consequences of people who died at a
#
scene and the people you are saving that is unseen therefore you know as far as
#
any measure is concerned whether you do a lockdown you don't do a lockdown you
#
do a you know a lockdown and gradations it's something that if you can ever tell
#
whether it was a wise move or not it is only well well well in hindsight at the
#
moment you're in the fog of war there's simply no information so as a modeler
#
there's this extra dimension that you're suddenly having to deal with and you're
#
having to deal with say the incentives therefore of politicians which can go in
#
different directions like one of course will be the status quo bias that rather
#
than do something which harms people I'd rather just you know not do anything
#
and pass a buck and control the narrative tell me about this challenge
#
like how does one deal with this so this is a challenge and exactly you've
#
really explained it very beautifully and this is unfortunately not an area that
#
I've got into yet how do I combine epidemiological models for what might
#
happen with four with together with economic models that examine the
#
consequences of particular actions such as a lockdown etc this is much harder to
#
do we now are working on a bunch of models that hopefully will be will
#
provide one way of thinking about this question these are called agent-based
#
models where you try to model every individual in say the Pune City or the
#
Greater Bombay area or also the NCR region etc or even potentially across a
#
whole state or the whole country and then you say if the first thing I'm
#
going to do is to make a synthetic population for that region instead of you
#
know if I knew every detail about everybody I could just sort of write
#
person in one line so and so you know her Kirat Singh age 65 this comorbidity
#
earning agricultural labor etc etc etc but what I have to do is to make a
#
population that is a synthetic populations are made up computer population
#
that has all the features of the population that I want to study in rural
#
Punjab or in Bhatinda or in Kerala or wherever it is and then these models are
#
big computational models that try and put in elements of how people make
#
decisions about that where do they make trade-offs between economics and health
#
where do you account for people who are vaccine hesitant some fraction of the
#
population is vaccine hesitant can I put that in and simulate what might happen
#
in this case so I have people who go to work and come back go to work and come
#
back on a regular schedule kids go to school and then I introduce the disease
#
in that population and this disease begins to spread and people see the
#
numbers of the disease and then begin to alter their own decisions accordingly so
#
you have infection that rise and come down you can have vaccinations you can
#
have different variants all of this you can test out on your computational
#
population these models have earlier been used to study really how disease
#
spreads in a population that is somewhat semi realistic but now you can also use
#
them to understand economic decisions you can hard code these in the way you
#
describe these how people make decisions on the basis of the economy and then
#
from that try to extract the information that you talked about all of this is a
#
pipe tree which in the future but we made some steps towards that we're
#
actually defining many of these units that go into this and that I think is
#
going to be the most important that we're going to be doing in the next
#
couple of years and you know your point is completely valid and again it goes
#
back to what I said about this being the most interdisciplinary area that I could
#
possibly think of it has any public policy must include all of this effects
#
about the disease how it spreads people circumstances poverty etc etc loss of
#
livelihood together with with country level economic macroeconomic consequences
#
for for the ability of the Indian state to create jobs when its budget is
#
already depleted by by the strains of coping with a pandemic for a year year
#
and a half fantastic all my listeners would be expert forecasters in at least
#
one area by now which is knowing when a commercial break comes up that as we
#
pass an hour the probability of a commercial break keeps rising and we
#
have finally got there so let's take a quick break and when we come back let's
#
dive a little deeper into kovat 19 long before I was a podcaster I was a writer
#
in fact chances are that many of you first heard of me because of my blog
#
India uncut which was active between 2003 and 2009 and became somewhat
#
popular at the time I love the freedom the form gave me and I feel I was shaped
#
by it in many ways I exercise my writing muscle every day and was forced to think
#
about many different things because I wrote about many different things well
#
that phase in my life ended for various reasons and now it is time to revive it
#
only now I'm doing it through a newsletter I have started the India
#
uncut newsletter at India uncut dot subtract comm where I will write
#
regularly about whatever catches my fancy I'll write about some of the themes
#
I cover in this podcast and about much else so please do head on over to India
#
uncut dot subtract comm and subscribe it is free once you sign up each new
#
installment that I write will land up in your email inbox you don't need to go
#
anywhere so subscribe now for free the India uncut newsletter at India uncut
#
dot subtract comm thank you welcome back to the scene in the unseen I'm chatting
#
with Gotham Mennon about his work on physics biology modeling and go with 19
#
so tell me a little bit about how our understanding of this particular virus
#
began to evolve through the early months of last year in particular you know when
#
we look back on history with hindsight even if it's recent history as recent as
#
a few months ago you know the pictures that we somehow paint in our minds tend
#
to be very clear pictures but actually you know in the moment there's much
#
more hesitation and uncertainty and all of that so give me a picture of your
#
personal sort of journey in understanding the virus in understanding
#
the pandemic in figuring out what's happening how serious it is all of that
#
you know while you are of course doing the modeling work how are those models
#
kind of evolving give me a picture of you know that period so I think the
#
first and the most important thing that I had to learn was the difference between
#
a case fatality ratio and infection fatality ratio and case fatality is the
#
number of people who died divided by the number of cases of confirmed COVID-19
#
that you get the infection fatality ratio the number of people who died
#
divided by the number of people infected with the SARS-CoV-2 virus and normally
#
these numbers are sort of close to each other but the main step in certainly my
#
understanding was understanding that these two numbers could be very
#
different from each other because of the large reservoir of people who would show
#
no symptoms of the disease were asymptomatic so they would have to be
#
counted among the infected but out of that much larger number only a small
#
number would be counted as cases because they actually showed symptoms or landed
#
up for testing etc etc etc and this is of course the distinction is also a
#
function of how much you test if you could test everybody then you would catch
#
every case and catch every infection you will wind up invariably missing some
#
fraction of these and exactly the extent to which you're missing cases is an
#
important question right now. The models that people began to write down I think
#
fairly early on not too early but maybe about a month and a half two months into
#
the epidemic people realized that you had to account for these asymptomatic
#
cases and that they were important in the sense that they could infect other
#
people with showing infections showing symptoms themselves and the other point
#
was that even for the people who were going to show symptoms earlier they were
#
most infectious before they began to show these symptoms and that's again
#
something unusual about this disease that it has a longish period of a day
#
or two days but maybe more in which you can be infectious to other people while
#
not knowing that you actually had the disease so then people began to develop
#
a whole bunch of models that included this particular feature it included the
#
fact that you know some fractional people would go to hospital some of them
#
would die and began to flesh these with numbers what is the probability that if
#
you were in ICU then you would die what is the probability of being in an ICU if
#
you were hospitalized for some case of COVID how many what fraction were mild
#
cases as opposed to severe cases and then it began to be even fleshed out
#
even more if you were between the ages of 80 and 90 what is the probability that
#
you that you would be a severe case which would then who would then die or
#
require ventilation of some sort etc so this age stratification as it's called
#
became slowly began to be understand that this is an important feature of how
#
we think about the disease and how we might understand the numbers that might
#
ensue if you allow this to sweep through a population or even if you impose some
#
sort of a constraint such as a lockdown how would these numbers change and that
#
was very important because it's a very differential way in which people of
#
different age groups are affected so once we had all of that in place I'm
#
guessing this is about three to four months maybe five months into the
#
pandemic then the question was is India different in any way and this is how
#
haunted people who modeling for quite a bit it seemed as though in fact if you
#
just look at the numbers fewer Indians are dying than people elsewhere if you
#
look at the deaths per million population that say then in the Indian
#
number is at least about a factor of 10 to 15 smaller then the analogous number
#
for the UK or for the US let's say that we can understand the UK and the US are
#
older populations why is that number so different from Brazil on sort of
#
nominally you would have thought that Indian Brazil are not that different
#
we're all sort of growing LMIC roughly speaking economies why is it that
#
Brazil is seeing such a large number of deaths whereas India is not now we
#
realize that this question is sort of holds many questions inside it are we
#
counting debts correctly in Brazil sort of records all of its debts and has a
#
cause of death India as I pointed out doesn't do that are we missing debts
#
outside the major cities in the major cities you could say that maybe you know
#
you're counting all of the deaths more or less accurately but is the real bulk
#
of that's happening outside so then you can go back and ask these numbers in a
#
more specific district specific city specific city area specific to see what
#
you what you're actually missing out then this question of you know the fact
#
that the ages how sensitive is this to the way you distribute people across the
#
ages how sensitive it is to the structure of families India has
#
multi-generational families where both young children as well as elderly
#
grandparents live in the same house and that certainly affects transmission we
#
are a society that tends to protect elderly people in whatever way we can so
#
there is automatically at the coming of the virus there is some level of
#
distancing once you know that old people and people with comorbidities are likely
#
to be more affected then families have automatically reconfigured wherever they
#
could to be able to protect older people of course also in India there's also
#
survivorship in the sense that if you've already lived up to 70 the
#
likelihood is that your nutrition was better that your your educational status
#
was better that you come from a higher socioeconomic class etc so that's
#
something that again goes into the description you have to try and
#
disentangle it from from this whole massive data that actually exists so
#
slowly we began to get a better understanding the only real question
#
being was there anything special about the Indian population that protected it
#
and this is something that has really bugged many people in this field
#
including very very good Indian scientists who pointed out that you know
#
according to their their view of things something does protect the Indian
#
population and this is likely to be the fact that Indians on average grew up in
#
more unhygienic and unplanetary surroundings than people elsewhere let's
#
say in Japan or France or Germany etc this exposure to a variety of pathogens
#
when you grew up in your childhood etc makes your immune system respond a
#
little less a little more bluntly to things that are challenging it we know
#
that many of the bad consequences of COVID-19 really come from an excess of
#
immune response the immune response is overreacting to the presence of this new
#
invaded inside your your body so if you can stamp that down if you can prevent
#
inflammation and that levels of steroids for example do that that's one way of
#
coping with the disease but maybe it's inbuilt in India the fact that we sort
#
of grew up and so we we have this whole exposure wherever we go because don't
#
really truly insulate ourselves from our environment that was something that
#
people did to a very exhaustive statistical study suggests now it seems
#
that those arguments were just plain wrong because from an immunological
#
point of view there's no real reason to believe that for this particular viral
#
disease a prior exposure made a difference but it's a valid scientific
#
point of view to take just now unfortunately seems to be wrong because
#
this very very steep rise now that we're seeing he had we been protected we
#
would not have seen it so it's clear that that argument was wrong but it's a
#
legitimate scientific argument that many of us considered at some point over the
#
last year so many many strands to pursue here and the first one is this that it
#
strikes me that when we try to arrive at the truth about this virus there are
#
sort of two parts that we can take which both make sense and which at some point
#
will come together to do a whole picture one is we look at what the numbers tell
#
us how is it spreading where is it spreading what is it doing and the other
#
is we look at the inherent biology of the virus itself where you know we
#
encode the genome and blah blah blah and we look at that another way of putting
#
it would be that you look at what it does and you also look at what it is and
#
why it is what it is which I guess are questions of physics as well then you
#
know just taking this analogy further for example one of the big learning
#
moments for me couple of years ago about artificial intelligence was you know
#
when AlphaZero did what it did it destroyed stockfish in chess and
#
stockfish was of course the older sort of engine which at a rating of about
#
3500 was way stronger than the strongest human Magnus Carlsen but this new
#
machine learning program called AlphaZero just came learnt taught itself
#
to play chess in 24 hours and destroyed stockfish and one of the great
#
learnings there was you didn't know how it did it you just know that it did it
#
you saw what it did but you didn't know the why you didn't know the inherent
#
logic behind it and which is true of a lot of machine learning that you don't
#
get the inherent logic but you know that it's figuring out some remarkable
#
truths so therefore you know in this aspect of figuring out what it does
#
figuring out what the numbers tell you is there a point at which machine
#
learning also becomes applicable in terms of figuring out what it is and do
#
these two ways of learning about a virus converge at some point or do they go in
#
different directions and I imagine that in the early days when the biology is
#
still kind of figuring itself out though even there I mean it's it's a freaking
#
miracle that all these vaccines came that the mRNA vaccines were practically
#
designed on a computer in January itself which is mind-boggling progress there
#
but in terms of the numbers like what are your thoughts on these people have
#
applied machine learning to trying to predict the course of the pandemic and
#
that's something that's a sort of minor cottage industry itself it's a bit
#
difficult to do that with any degree of real credibility the reason is that the
#
the way these IBM computers I guess figured out what the right machine
#
learning strategy was that you just fed it every chess game ever played and you
#
and then it basically used that information played against itself
#
multiple times to see what would optimal choices strategy actually with
#
AlphaZero they didn't do that they just started the rules of the game so earlier
#
computers would have heuristics built in and it would have the entire database of
#
everything ever played like stockfish but AlphaZero approach was you teach it
#
the rules and then it plays against itself a few million times and figures
#
out its own heuristic which you have no access to yeah it's like a black box I
#
agree that's and corrected on that that's the way in which sort of plays
#
against itself figures out we'll figure out what has to be done and then see
#
what is an optimal strategy at the end of it but here it's not clear what the
#
rules are I mean the rules are profoundly the way disease spread is
#
really at its root a social phenomenon and who is talking to who's interacting
#
with who in what density where at what no where does the person in this in the
#
slum intersect the person of the in the high-rise building what are the points
#
of this intersection what are the nature of that interaction who's wearing a mask
#
who's not wearing a mask because the complexities are social and not
#
biological or physical this becomes a far far harder problem so I don't think
#
there's been any approaches to disease modeling that actually take the more
#
modern approach of this adversarial network approach that you mentioned
#
it's more based on traditional studies of of how disease transmission has
#
happened records across multiple countries records across multiple
#
districts and try to understand is there a logic behind that but here it's as though
#
you're playing chess with where you're not told that the you know your there's
#
sort of fuzziness around the position of every of every chess piece it could be
#
somewhere in this region but you're not sure where and whether given that
#
fuzziness there is useful information to be had that in terms of being able to
#
project forward is not very clear to me they don't have any neural network or AI
#
based or machine learning based algorithm that actually manages to do
#
that in any credible way and also at the other point is that as you said these
#
are not interpretable or explainable you don't know why it does it it sort of
#
says it'll go up here and it will come down a little later but you have to be
#
careful it's not being biased by the data that you fed it in earlier which
#
showed lots of stuff that went down for reasons that may have been completely
#
different from reason that prevail in the current situation so the models that
#
I work with and the models that I sort of I like to use I had a bunch of
#
different classes of model but they all rely on a very classic description by
#
two Scottish mathematicians called Kermack and McKendrick and they devised
#
a model called the SIR model and that model and the way of thinking about it
#
is at the basis of all research in infectious diseases nowadays it's really
#
remarkable to see what a profound impact that has had and the idea is that the
#
simplest way to think about disease is just put everybody in the population
#
into three boxes or compartments you're either susceptible to the disease you're
#
infected by the disease and infectious to other people or you're recovered from
#
the disease and you can sort of choose different versions of these that are put
#
in more compartment but the idea is everybody in the population you can put
#
into these boxes then understanding how the numbers of infected changes just as
#
simply a question of saying once a susceptible person contacts an infectious
#
person they move from the susceptible to the infected box once they recover they
#
move from the infected box to the recovered box so it's just movement
#
across these boxes or compartments that is described by this mathematical model
#
and the twist here is that you need susceptible people to interact with
#
infected people for the susceptible person to be infected if there was no
#
infected person at all surrounding you you would remain susceptible for
#
infinite amount of time for as long as it takes so that's these are simple
#
ideas that say that we need we can forget about people's names ages colors
#
whatever etc for the purpose of disease all we need to understand these
#
compartments and there is no simpler version of this model there are more
#
complicated versions but no simpler versions and people have worked with
#
these models these models were roughly about a hundred years old now and there
#
have been many sort of versions of higher-order versions of these models
#
that have a lot of complexity let me give you one example because it's an
#
interesting example so you can have a susceptible infected recovered dead
#
removed etc all of these categories the dead category is an interesting one
#
because if you think about Ebola we understood that even dead Ebola patients
#
can be infectious and the way you handle the body in a traditional African
#
burial is one that allows the dead person to transfer the Ebola the live
#
Ebola virus to you to a body fluid so changes in burial practices is really a
#
consequence of really superb epidemiological thinking about this
#
problem and in order to do that you have to make a little compartment for the
#
dead compartment which could potentially infect someone who was who was
#
susceptible if you didn't if you didn't sort of tap that down and make sure that
#
that didn't happen so you have depending upon the disease you can devise the
#
compartments appropriately and now there are compartmental models for the for
#
COVID-19 which are fairly good and they really put in the sort of information
#
that you need to understand how the disease is going to spread at a large
#
scale what will the peak be what will a lockdown do what will multiple
#
lockdowns do etc the main intuition that you get you have is the first port of
#
call is that particular model or the version of that model that's most
#
appropriate your disease so I think it's a very beautiful and a powerful way of
#
thinking about it in a sense and it's interesting that that that intuition that
#
fundamental intuition these two guys worked out so many years ago is really
#
what we rely on when we think about modeling. You know you mentioned the
#
change in burial practices because of Ebola and that reminds me of you know
#
just a stray bit of trivia I'll throw out there which was in fact in the preview
#
episode of the Seen and the Unseen when it started more than four years ago
#
which is how the Parsis changed their burial practices like the Parsis of
#
Mumbai used to leave bodies for you know vultures and the reason that stopped is
#
that in the early 1990s when cows started getting a certain kind of
#
stomachache or whatever vets would prescribe something called
#
diclofenac so the cows would take diclofenac and then later the dead cows
#
would be eaten by vultures and because of the diclofenac in the dead cows the
#
vultures above Mumbai or wherever in the area started getting liver disease and
#
started dying out and because there weren't enough vultures the Parsis had
#
to move away from leaving the bodies in the tower of silence for vultures
#
because there simply were no vultures and of course we're talking about real
#
vultures here not human ones otherwise there's no shortage of those. It's a particularly
#
lovely story because it's the intersection of culture you know culture
#
and traditional practices with some with you know with veterinary health and in a
#
sort of strange and complicated ways it's really a lovely example. Classic example
#
of the Seen and the Unseen who would have thunk cow gets a stomachache and
#
Parsis change burial practices it's insane you know as all this is happening
#
you are tracing how the disease is doing whatever it is doing in the population
#
and there are all these social elements and interaction and you're figuring out
#
the virus but as you're figuring out all of this the virus is also evolving right
#
and my earlier understanding which might be a simplistic one so maybe now you can
#
correct me and elaborate on that my earlier understanding was that a virus
#
will typically evolve in a way that makes it more infectious and less lethal
#
like you know earlier flu viruses like some of the flus which caused the
#
pandemics of the past evolved to becoming completely harmless and
#
seasonal variants similarly you know I think one of the two coronavirus
#
variants which causes a kind of flu completely harmless again was Anirban
#
Mahapatra was telling me in the episode I did with him caused something a couple
#
of centuries ago so one would have imagined that okay this virus will go
#
in that way in the sense it will become more infectious and less lethal and over
#
a period of time it will be a seasonal thing and we'll just get COVID and it's
#
fine we'll you know we'll get over after two or three days of not feeling well
#
now one how hard is it to model something like this when all these
#
variants are springing up and to the variant that seems to have struck in the
#
second wave I mean a lot of the deaths in the second wave in India are really
#
because of the failure of the dysfunctional state the scarcity of
#
oxygen and many of those lives could no doubt have been saved but at the same
#
time while it does seem to have become far more infectious we are not sure yet
#
whether it is less lethal people are dying all over the place because of it
#
like I you know I I find that I don't have a single friend who's unaffected by
#
it in some way or the other in terms of family or friends or you know extended
#
circles so what is your take on what's going on here so you have to draw a
#
distinction between evolutionary time scales and the sort of shorter time
#
scales on which we are observing the behavior of the virus it's true that the
#
ultimate job of every entered living entity on earth is to be able to
#
propagate itself and to find the smoothest way of doing it and to prevent
#
itself from being sort of known from that chain of propagation being stopped
#
coupled with the idea of natural selection that whatever enables you to
#
fit better into this certain to the niche into which you are born as an
#
organism is one that will enable you to propagate better it give rise to more
#
progeny which will then expand further this is complicated by several things
#
first of all viruses mutate at random there the virus doesn't know what's a
#
good mutation what's a bad mutation some things might be it might kill the host
#
immediately some things might let the host survive something might be so my
#
that the host won't notice it at all it's only over a much longer time scale
#
that you can allow for the selection that you slowly prune out those that
#
will tend to be dead ends in terms of dying out they kill the host too fast so
#
they can't propagate versus those who manage to survive within the community
#
for a longer time already there's an interesting selection that's happening
#
here already you can argue that with masking which with physical distancing
#
etc you're selecting for those viruses that pass more easily between people
#
that's an interesting point of view from an ecological point of view that the
#
only ones that survived are the ones that were moved more easily because you
#
already by fiat force people to sort of stay further away from each other right
#
now I think it hasn't been enough time for these ecological scales to play out
#
for things to for the virus the virulence of the virus to decrease
#
sufficiently so that it becomes like a cold which you know may or may not be
#
the final trajectory that it actually chooses to take because who knows what
#
is trying to optimize in this particular case it could jump hosts for example for
#
example found a different animal host in which it was perfectly happy it might
#
move back to that host and that would be a different evolutionary trajectory that
#
it might choose regarding this question of you know we all know people who've
#
been affected by kovat in this way compared to the previous wave and so the
#
assumption here is that it's worse this time compared to what it was earlier but
#
all of this is still really anecdotal we don't know whether at the level of
#
numbers this is just the same old virus in effective terms as it was earlier it's
#
just that because every number is being multiplied by a factor of 10 all the
#
people in the 10 to 20 age group, 20 to 30 age group, 30 to 40 age group because all of that is
#
being multiplied by 10 we're seeing the adverse consequences the larger numbers
#
of people dying even though that the fraction of people dying may be the
#
same as in the earlier case so right now there isn't enough data to tell whether
#
this is more virulent so it's just it's just currently this is completely
#
anecdotal hopefully you know sort of going forward looking back which is
#
always the best way to see these things you'll be able to understand this
#
question a little bit. Also when you're modeling can you look into the past you
#
know is there a natural curve to pandemics for example I had done an
#
episode with Chinmay Tumbey on past pandemics and we've been plagued with
#
pandemics as it were for a while but nevertheless if you just count the
#
number of pandemics humanity has had the sample size is really small can one
#
generalize from that in what a pandemic will necessarily do can one figure out
#
that this is a natural curve can one then say that this intervention like say
#
intervention X which is lockdown or intervention Y which is no lockdown will
#
have this effect on the curve and therefore we should do this what's a
#
what's the state of thinking on this? So the answer is that the original SIR
#
model of Garmack and McKendrick was actually applied retrospectively to
#
plague data in Bombay from the 1905 to 1906 pandemic and so that's when they
#
found that they could perfectly fit the rise in the following cases over a 30
#
week period using their mathematical model and that gave them certain
#
confidence that they should apply more generally to pandemics so this has been
#
used to model data earlier as well as pretty much anything any sort of current
#
increase and decrease in cases in the case of infectious disease so we're
#
confident enough that the elements the mathematical elements that are there
#
actually work you can use it you can impose lockdowns see what the effect of
#
the lockdowns are etc etc usually what happens is that the lockdown nearly
#
reduces the number of cases but spreads it out over a longer time it doesn't
#
sort of you know it doesn't eliminate the cases all together but just by
#
spreading it out over a longer time you remove the sort of pressure that you're
#
seeing right now on hospitals on the public health system and thereby saving
#
lives that would otherwise have been have been destroyed because they just
#
weren't able to access medical treatment in time so we are confident enough
#
about these models we know what they do we can explore a bunch of different
#
interventions in them typically the harder question is asking how does a
#
particular intervention translate into changing a particular number in the
#
model or parameter in the model how does mask wearing by 50% of the population
#
change that number versus 70% how does a lockdown change that particular number
#
from let's say from 0.2 to 0.5 these are much more delicate because they're
#
really statements about how population behave as an aggregate and as I said is
#
driven by social behavior and these are the hardest things suppose 10% of
#
people in Bombay decides that look this is all the hocus pocus I'm just going to
#
go out and behave normally as I would so then the assumptions about physical
#
distancing apply to the entire population begin to break down and then
#
you have to ask how do I put that into a model some fraction of people who just
#
will not obey the any instruction any reasonable public health instruction
#
that they're given what do you do in that context so these this is a gray
#
area of models how do I take social public behavior and convert it into the
#
numbers that must enter must enter my mathematical model once I'm confident to
#
that transition I can run my mathematical model and say this is that
#
this is what you will get at the end of it after lockdown after imposing mask
#
wearing after relaxing mask wearing for so long and you can begin to think that
#
look this may represent the numbers a little better and I could keep going
#
back and calibrating against the numbers of cases numbers of deaths numbers of
#
hospitalization to make sure that you're satisfying that basic smell test for the
#
validity and the quality of that model but the back and forth is very very
#
important to make sure that you have everything in this mapping is you've
#
done correctly between these things so in the process of kind of figuring out
#
what was happening during the first wave so to say you know it's so tragic that
#
we're almost speaking of in the past tense of first wave you know what
#
happened in those months of last year like we begin in a fog of war right you
#
know certain things but you don't know so many other things what are those sort
#
of big TIL moments about the virus at different points like I imagine for
#
example once you start looking for antibodies in the population you do your
#
seroprevalence surveys and all of that you understand the big gap between
#
infection fatality rate and case fatality rate as you said that that is a
#
TIL there and more TILs gradually come and then some TILs might be theories
#
which turn out to be false you know such as why India is not affected so much by
#
the virus so you know what were those big sort of steps in learning about the
#
virus for you as we went through the months last year? Certainly as I said
#
before the importance of the asymptomatic category was very important
#
and then figuring out the numbers and what is the right number for the
#
infection fatality ratio you know we sort of looked through a whole bunch of
#
different literature they tried to optimize it looked at what the numbers
#
are try to fit the Indian data with respect to that and finally came up with
#
a range that we think is correct and then we're able to compare to more and
#
more modern research regarding that's a very important number because in a
#
sense that's the most specific consequence someone near you you know
#
dies of the particular disease we want to be able to estimate that and
#
from that how many people will need hospitalization or an ICU because some
#
fraction of that will go into will have to die at the end of it. I think the
#
basic elements of what should go into the model were clear enough they were
#
clear enough by certainly in the first four months of the disease three to four
#
months across the world and beyond that it didn't change maybe people would have
#
tuned a number here or there but not not all of that much what
#
changed was the development of other types of models for example the agent
#
based model that I told you about about network models all of which were are
#
intended to answer different types of questions have different levels of
#
granularity from the simple compartmental models so people began to
#
work on those models as much more detailed versions of these and they put
#
in aspects of people's behavior that you can in a network model or an agent
#
based model that you cannot in the sort of simple model where I'm trying to take
#
this parameter and tune it there and to understand what this means so this is
#
part of the evolution of thinking about any disease you start off with a simpler
#
model you put dress it up as much as you can but side by side the whole bunch of
#
different models statistical models prediction model the various are AI
#
machine learning all these proceed together and somewhere at the end of it
#
you you sort of get a better idea what these models are saying so one thing I
#
can point out that if you look at how we ought to have been thinking about
#
modeling in the country we should have been looking much more at what is called
#
an ensemble model right now the Department of Science and Technology
#
funded and supported a model called this DSG supermodel built by various
#
computer scientists and engineers and one army medical doctor but that it sort of
#
said that this is going to be our model that's always a mistake because no
#
single model is often good enough its behavior fluctuates what the US UK and
#
many other countries used to build an ensemble of models so they have 10
#
people or 20 people in different universities across the country each of
#
them coming up with a different model for what's going on they pool this
#
information together and between week and week they'd find out which is doing
#
better which is doing worse they wait those that are doing better
#
historically is somewhat more than the ones who are doing worse and then they
#
come out with say a combined suggestion from all of these models thing over the
#
next week or 10 days this is what's going to happen this usually is good at
#
eliminating biases that individual modelers might have it includes you
#
know information that some models do better things that some models do better
#
versus things that some models do worse and then you get to a much better
#
prediction then you would have the wisdom of crowds in a sense in this
#
particular context but that that's how one ought to have done it that's not how
#
we've done it in India but one of the things that you've done and I heard you
#
mention it in an interview is that your model is effectively open source so
#
anyone can go in there and tweak the assumptions and put in their own data
#
and and just see kind of what it does so if my listeners want to kind of play
#
around with it is there a URL they can go to and sort of have a look at it so
#
the model right now is not open source but the description of the model is open
#
source so anyone can use that and so on what is what exists is a website on
#
which you can play around with it which where it is hard-coded so you can figure
#
out in Maharashtra state if I imposed a lockdown between such and such a time
#
then what would happen so this is that this is linked from the website of the
#
Indian scientists response to COVID-19 in psych of within that there's a link
#
that goes to the modeling group and that modeling group has a link that goes to
#
the dashboard as we call it so you can people are willing to do that I'll link
#
it from my show notes on a general point of principle we plan to make this public
#
although the current version of the model because it's evolved to become
#
much more technically more complicated etc and it's my belief that there should
#
be completely transparent so we will make this available publicly to people
#
who want to download it play with it or whatever this will take about a week to
#
ten days before we do it right now we're trying to finalize a bunch of papers
#
that have to relate to the work that we've been doing along with that as we
#
push that out we push this out of them great one more question now when we're
#
looking back at what happened last year you did the modeling then you saw what
#
happened the models kept getting tweaked along the way so on and so forth what
#
lessons can we learn from the progression of COVID-19 in the first
#
wave which are applicable to us today like tell me a bit about what the
#
current models say about you know where we are going with this I mean obviously
#
what happened last year is irrelevant even if this variant is different in
#
certain ways you know what should we expect and did did this take everyone by
#
surprise were we all kind of blindsided because just a couple of months back you
#
know Siddharth Mukherjee had this great piece in the New Yorker about why has
#
India escaped and what are the possible reasons we haven't got it which I'll
#
also ask you about shortly but just in general like were we blindsided and now
#
that all of this is unfolding you know we had cases go up and then
#
hospitalizations go up and deaths are going up you know of course our health
#
minister announced that the end game with COVID had happened a couple of
#
months back but what is now sort of the end game as it were where are we headed
#
you've asked me multiple questions I'll have to try and disentangle I think it's
#
correct to say that most people were blindsided by this and that's because of
#
the gap between the decline of the peak of the first wave and the increase of
#
the second wave where we where we actually took it off that's a very large
#
gap compared to the gaps that most other countries saw so how did we think about
#
it then what some people did was to say we've reached herd immunity we've in
#
both 60% plus of our population is already infected and that's why we can
#
expect that the disease will not bother us anymore
#
unlike the US unless UK France etc whether at 10% or 20% they're nowhere
#
near our numbers we should have been a little careful about that because they
#
were saying the same thing in Brazil as well that we've had you know in the
#
region of Manaus we've already have 60% 70% plus and so therefore nothing is
#
going to happen over there we're out of the woods etc what our work did over the
#
last year by going to about September October November when I was talking
#
about this stuff what I was doing was pointing out that according to the
#
models that we had we were not near herd immunity we could say that around 40 to
#
50 percent of India in the cities was infected but maybe 30 to 40 outside in
#
the rural areas so on an average maybe about 40% of India was infected with
#
COVID-19 that's nowhere near the 60% plus that was imagined to be the herd
#
immunity threshold so we said that look what likely to happen is that the cities
#
may not show big outbreaks but certainly the rest of the end is just there many
#
millions of people hundreds of millions of people yet to be infected which is
#
why we should be careful as more and more time wore on and particularly for
#
me one turning point was the fact that we didn't see a rise of cases during the
#
puja time so in November which is Diwali puja in in in Bengal the wedding season
#
etc that's in a time and also the onset of winter so that's at a time when not
#
just myself pretty much every epidemiologist around would have said
#
this is the time when things are going to blow up there are people to be
#
infected people are going to come into contact they're going to disregard every
#
sort of physical distancing they're going to the puja you know what a puja
#
in Calcutta is actually like and there's no way anybody can regulate that
#
therefore this is when we're going to see a peak and I like other people said
#
this but of course I couched it differently I said that look we should
#
be wary of or we should be careful about this etc we just there's just a little
#
blip in that data it goes up a little bit and then comes down but otherwise you
#
can sort of it's if you just ignore that little blip it's pretty much downhill
#
the way so that I think got me and other people thinking that maybe there is some
#
truth to this maybe there is maybe the Indian population is protected in some
#
way by exactly as Siddhartha Mukherjee pointed out why why in this number and
#
why this was this such large gap a bunch of things changed in I think February
#
and they changed in ways that perhaps we should have anticipated but I don't think
#
one can be blamed for missing it given what we knew at that time the suddenly
#
there appeared across multiple parts of the country various variants of the
#
virus that were just more efficient to transmitting between people and by just
#
more I mean by a factor of maybe factor of two or three times more than the
#
previous version this also it's likely that any protection due to previous
#
infection had begun to wear off by then that the antibodies that were generated
#
if you're infected in July or August typically have a six to eight month
#
lifetime in your body so by the time maybe you got to February or March those
#
antibodies were at a low enough level that potentially you could be reinfected
#
now here is a huge question because the immune system is not just antibodies
#
reacting made by a prior infection there are many different parts to it and you
#
know there's a lovely quote I think from Ed Yong that says immunology is where
#
intuition goes to die and you know as from a scientific point of view that's
#
true it's incredibly complex the nature of the human immune response to an
#
external invader that comes that it encounters so declining immunity from
#
antibodies levels going low new variants much more efficient to transmitting
#
between each other and in general relaxations and already I think by the
#
first of February probably schools outside Bombay had begun to open in more
#
and more the hinterland in schools are always where you should watch because
#
that's where infections move very rapidly between families all you need is
#
one person in a family child gets it goes to school and then finally everybody in
#
the class takes it back home and that's when it spreads and that's when you know
#
it began to be noticed in first in rural Maharashtra and then moving to the
#
cities in Maharashtra and then you saw this incredibly sharp wave taking off of
#
new cases there so to come to your specific questions I don't think we
#
could have predicted it we know that viruses mutate all the time we don't know
#
we know most of the time that mutations don't do anything so you can be as
#
alert to it as possible but you know you can keep writing them down every time
#
they occur but most of the time they're actually irrelevant to this some
#
specific mutation then we've only begun to understand which they are over the
#
last three to four months turn out to be specifically important because the
#
effect of the virus attaches to cells it's these mutations that have happened
#
in the in the Maharashtra that the one six one seven variant and that's where
#
things have taken off but to complicate matters there's a different variant
#
that is responsible for cases in Punjab and Delhi there's a different variant
#
that is responsible for cases in the south of India and Bangalore and
#
Telangana and there's a different variant that is responsible for the east of India
#
which seems to be a melting pot of multiple variants including
#
potentially one from Bangladesh and so on so the situation is complicated it
#
could not have been anticipated what could have been anticipated is government
#
response the lessons of the first wave of COVID-19 were that our cell systems
#
is potentially highly stressed this is a time to build it up so that nothing
#
happens in the future let's hope nothing happens in the future but it's also an
#
excuse to do something better by public health then we've done in the past to
#
put much more money into it then we've done in the past and also guard against
#
future future consequences of a rise in cases which we are seeing now so a
#
question before that and aside like you mentioned schools as you know places
#
were obviously they'll spread because one kid gets it all the kids get it and
#
then you know half the cities got it like that and I was listening to Anoop
#
Malani on an episode of the Grand Tamasha with Milan Vaishan of the
#
podcast which you know just came out recently and Anoop was talking about
#
the dilemma of shutting schools down that if now the government decides that
#
hey let's shut the schools down because you know for these valid reasons but the
#
cost that it has on your human capital where kids don't go to school for a year
#
and the downstream effects the unseen effects as it were is also going to be
#
devastating and these things are absolutely incalculable and the problem
#
is that you know whatever decision a policymaker is to take now you will
#
only see the negative effects of that you won't see the positives in terms of
#
the things that didn't happen so it is just such an incredibly difficult choice
#
now my question is this that you know it's easy to say that oh ten years ago
#
we could have done something about this taken it seriously build this
#
infrastructure build that build this it's easy to say that after the first
#
wave we could have you know built 30 more oxygen plants and blah blah blah now
#
when I think of a politicians incentives the politicians incentives given that
#
there is a limited money that they have at their behest and that all that
#
spending has an opportunity cost will obviously be not to ward off the low
#
probability high-impact event which may happen ten years in the future but
#
cater to the election cycle and do whatever will win them votes in the next
#
election which for most people is probably five years but actually for a
#
party it's almost like every day because there's always an election happening
#
somewhere or the other so the incentives of politicians would not be at all to
#
ward off future pandemics even now that we know what we know you know we will I
#
think forever have the security theater of hand sanitization and all that even
#
though you know COVID-19 doesn't even spread by surfaces as we know but apart
#
from that it's very hard to foresee like even from the data that we have from
#
last year and now as various people including you have pointed out that
#
possibly a lot of the data we collected last year was faulty which is why we
#
couldn't prepare for this well enough but the sort of oxygen shortages that
#
were coming up nobody mentioned anywhere in the public domain say in February or
#
before that we are going to have oxygen shortages it's something that we now
#
realize and we now say that oh we should have planned for this earlier and oh why
#
didn't the Modi government do this or why did the KJV government spend 400
#
crores on advertising instead of building oxygen plants all of which are
#
in and of themselves of course valid and the response of various governments in
#
obfuscating and trying to change the narrative is shocking and unforgivable
#
and I hope the citizens of this country will remember this and not forgive them
#
for that but it's just that in hindsight all this seems obvious ki yeh karna chahiye tha
#
but in the moment your incentives are completely against it how does one deal
#
with this and as a as a scientist who is at different levels interfacing with the
#
government talking to policymakers what is the sense you get from there like are
#
they asking that oh this has happened what should we do now to make this
#
problem go away or are they thinking that hey tell me about could there be a
#
second wave next year could there be a third wave after that what do we do
#
about it now what are those mindsets like again that's sort of difficult to
#
answer it's a go back to the first question that you asked about the point
#
that you made about schools it is a very difficult policy choice and you know the
#
consequences the psychological consequences of having children out to
#
appear group for a year here and more we don't this is incalculable we really
#
don't know this yet and from and what we will only see the consequences the
#
pandemic across multiple fronts only much later I think if you ask me
#
politicians should given what they're seeing now it would probably be have
#
been in the politicians best interest to have prepared three months ago for a
#
possible event like this I think had you said that this would happen but this
#
might potentially have happened this is not the course that they might have
#
chosen to take so it's not clear to me with the government who is giving it
#
advice and on what fronts this is a government that we know is somewhat
#
inward-looking if there is a large group of IS officers in the prime minister's
#
in the PMO who advise it so prime minister Modi seems to be taking a lot
#
of technocrats around him and people who are in a sense reliant on him in many
#
ways from whom he takes advice it's not clear that the government system
#
currently is geared towards taking independent advice from people outside
#
except very informally and without even telling anybody that they
#
actually did it so we don't know who's supplying information we don't know the
#
quality of the information that is being supplied because none of this is in the
#
public domain we know that this is largely Delhi centered there all of this
#
that any committee that you can think of that is a public health committee has
#
seven people out of ten who are from Delhi this makes no sense at all and so
#
who's saying what at what time what the minutes were what the recommendations
#
were what are they based upon we have no idea so we really don't know what
#
people were telling the government so it's no use telling people that look
#
why didn't you say this why didn't you say that I mean people may have said it
#
or not have said it but there's no guarantee that anyone was lifting to
#
them and we don't know what the internal committees at the government formed at
#
that time were telling them I think the government had a difficult choice to
#
make because economic consequences of the lockdown of this of essentially
#
shutting down large parts of country in various ways for something like ten
#
months were very strong and so this getting the economy back on track
#
getting people back to work must have weighed very heavily on them and that
#
takes money that takes injection of physical stimulus at various points in
#
the economy how do you do that and you know it was a hard choice to make and
#
presumably at that point they said that look it seems to be over something has
#
saved India they sort of said is the Prime Minister has saved India which
#
didn't seem quite to be true at that point but something has saved India we
#
dodged a bullet we had a good example for the world we're not actually we are
#
not a good example for the world in some ways certainly in other ways
#
certainly yes it what I feel more is that a bunch of people around that time
#
after the peak when the peak had begun to subside were saying let's use this
#
experience to be for public health in the country we have seen what
#
devastating consequences both on the health front as well as on the
#
economic front are of not controlling a disease that is liable to spread in the
#
population this is something that affects everybody rich or poor wherever
#
you happen to be north or south east or west let's just say strengthen
#
especially primary care public health centers across the country so that
#
whatever happens in the future we will be prepared for it you could argue that
#
much of what we are seeing is because we didn't do that little step and that
#
would have called for rethinking about public health which is traditionally
#
been not a particularly high priority in the country in a different way but
#
informed by our experience of what a pandemic can actually do had we done
#
that we would have been in a better situation now that as I wasn't done
#
forget about the delays and there are you know questions of why do the tenders
#
go out in October even though they should have been issued several months
#
earlier all of these are massive screw-ups on a bureaucratic and a
#
political scale but even apart from all of that I think just listening to what
#
people were saying about the possibilities of reconfiguring a
#
public health system in the wake in the aftermath of the decrease in cases after
#
the first wave is something we should have done more seriously actually want
#
to underscore a point that you've made a couple of times which is a point of
#
transparency like you speak about how you want your model to be transparent
#
and of course how models become better is that they are transparent and there
#
are feedback loops and so on and they keep improving and they open to
#
improvement and the problem with whatever is happening in the PMO and in
#
the decision-making of the country is that lack of transparency like they have
#
processes now how will those processes improve if there is no transparency and
#
we don't even know what they are like of course you do not judge a process
#
always by short-term results we know that but the point is you should be able
#
to see the process for what it is and you know and and that is simply not
#
there like even a person like me you know I'll have processes for example for
#
doing my podcast and it you know if I mess it up I'll have to change those
#
processes but it seems that the government just needs to control the
#
narrative and not give a damn about what the reality actually is and their
#
processes for controlling narrative are outstanding though in many cases you
#
know just as the dysfunctional state last year had only the blunt tool of the
#
lockdown their narrative machine seems to have the blunt tool of calling
#
everybody who's against Modi anti-national and filing cases and they
#
couldn't blame Nehru so they tried to blame Biden for this which is just like
#
so completely bizarre to me my other question is this what is tomorrow's
#
oxygen and what I mean by that is that after the second wave is gone we are
#
never going to be in a situation where people die because of lack of oxygen
#
because this particular proximate problem they would have solved it is the
#
same way as after there was that shoe bomber on a flight they now check
#
everybody's shoes right so in the future oxygen I am fairly confident won't be a
#
problem but the point is oxygen is just a symptom of bad planning and not
#
thinking hard enough about public health and whatever you know what is the next
#
shortage what is tomorrow's oxygen where could we go like what should we be
#
thinking of now for the future not necessarily just a third wave and inshallah
#
if enough of us get vaccinated and all that you know which is a whole separate
#
screw up but inshallah there won't be a third wave but future pandemics because
#
there will be future pandemics so you know what are the systemic things and
#
see I'm using the word system again what are the sort of systemic lessons that we
#
need to learn so that in future we don't get into this kind of situation where
#
people are not dying because of a disease but because of you know bad
#
governance or the scarcities that need not have arisen. So I can tell you about my
#
experience with being vaccinated so I went to I was in Chennai at that point
#
and I was since I have some co-morbidities together with being
#
above 45 I got a letter from my doctor testing that and I made a appointment on
#
the on the website very very smooth experience you know I was I was first
#
checked my documentation was checked my number was just saying everybody wore a
#
mask I was asked to I was given the injection after everything was entered
#
into this I was asked to wait for half an hour to 40 minutes there was no one
#
around me wonderful experience this was in a public health center a friend of
#
mine called and spoke to me a couple of days after that saying that look you
#
know I went to another one I had a fantastic experience I have no idea I
#
would normally have gone to a private hospital if I had to do anything but
#
the fact that this was so beautifully organized minimally crowded careful this
#
this is a care that I expect in a high-end by private hospital but I'm
#
getting that in a government facility so we can do it I guess that's the point
#
that I'm making that you know we tend from where we come from we tend to
#
think of government facilities as being dirty dingy smelly etc etc we don't want
#
to go there be crowded it'll be awful let's pay extra money and go to place X
#
or Y where we can get a sort of experience that we think is a little
#
more is more pleasant and pleasurable because anyway no face it no one likes
#
to go to hospital anyway so you don't want to add mental stress on top of that
#
why can't we do this under normal circumstances why can't we have a
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public health system that is the first choice so I've lived for some years in
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Singapore where you have a public health system and that's an automatic thing
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where you go and you go and you get each and everybody gets treated exactly the
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same way it's facilities are clean your insurance pays for it you're looked
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after well whether you are you know the the son of the Prime Minister whether
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you're a common person you know off the street you're entitled to the same
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gamut of facilities that anybody else's I don't think you can actually you just
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be playing whack-a-mole if you try to anticipate which virus is going to come
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at which point which way when this etc let's just concentrate on the basics
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and let's just make sure that the basics of a strong public health system across
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the country let's not sort of look look at cities where all of us come from
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let's just look at remote areas and until a place small village in Jharkhand
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has the same level of excess as someone in in in Borivali or someone in in
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Vasanth Vihar does that I think would be a real index of where we moved as a
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society as a sort of larger questions of equity in our society I agree with you
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on it being an ideal end goal almost a utopian end goal I mean the big question
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is how do we get here from there because we know what the incentives are we know
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that our state is fundamentally a dysfunctional state the few things that
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we ought to do well like rule of law public health we don't do at all we're
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completely absent which has become more stark in you know in this current crisis
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whereas we interfere in so many things where we should not actually be present
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so I mean but that is a whole question of political economy and possibly beyond
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the scope of this let's kind of talk about India for a moment like Siddhartha
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Mukherjee's piece of course speculated on some of the possible plausible reasons
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why India escape last year you know whether is a BFG vaccines whether it is
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that we have a relatively younger population and the virus targets older
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people you also have made a great point about the survivorship bias which I'll
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quickly spell out for my listeners you mentioned it earlier but the point that
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Gautam is effectively making there is that because of the state of our public
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health system and all of that our older people tend to you know succumb to
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diseases other diseases quicker than people in the West do the older people
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who are left are actually the stronger hardier sort of people who therefore
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also have a better capacity of fighting the COVID virus when it comes which is
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you know a fascinating and very logical theory and there was also this bizarre
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theory that you know the heat will kill the virus now none of that kind of seems
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to apply so you know is it just that they applied to the previous to the
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original SARS-CoV-2 but this variant just does all these other things
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differently or is it that our understanding was flawed we somehow got
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lucky or is it that it did actually hit us really hard but the numbers don't
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show it that the data isn't accurate my feeling is that it did actually hit us
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hard and this was a nice I was on a panel yesterday where I think Ramanand
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Akshay Narayan pointed out that to get to about a million deaths in India if you
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sort of distributed it out over all the districts of India it's unclear that you
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would pick up that little fluctuation because at the district level given what
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the numbers are an extra three to five deaths per district etc added up across
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across the whole year you would not have attracted that much attention what you
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really need is understand what the background is in a good year of all of
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the deaths and then check to see in the COVID year how that changed but for that
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you must have better records but right now at least my thinking is that it's a
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combination of the young population a combination of the fact that we don't
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record dates well outside plus multiple no pressures on people families not not
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telling the society doctors clinical people that this is potentially a
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COVID patient because there's a stigma attached to COVID in the country so
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people may not want to do that deaths being unrecorded across much of the
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country is probably a large factor in this and I think it's a combination if
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you added all of these up then we don't do so badly I think I mean our numbers
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are floating up towards Brazil's numbers or towards the UK numbers once you
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adjust for all that the largest single part of this is just age and that's
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certainly true whether how much of it extra beyond that is some biological you
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know your microbiome prior infection coronavirus hygiene hypothesis etc etc
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or BCG or whatever it is it remains that remains a question mark but I think
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about you get to about 80% plus just with by accounting for age and just by
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accounting for the under counting of deaths. Fascinating and tell me about the
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second wave now like how worried should we be what exactly is going on I think
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many people are just bewildered like on Twitter I get this constant reaction
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from people which I kind of share that we are you know there's this new term
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doom-scrolling that I'll often just find myself it's just you know you reach a
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stage of numbness like you know I wake up every morning and Gautam honestly for
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the first half an hour I'm just writing condolences on Twitter or you know take
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care and all of that or get well soon and whatever and and of course I get
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your point that you know the first wave may not have hit people in our elite
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circles so hard that you know seroprevalence study showed that they
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were much harder in the slums and Tharvi for example and all of that and
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therefore PLU people like us as it were kind of escaped it and now we are you
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know really it's come home so to say you know how do we make sense of this like
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this is very virulent very infectious what is a natural curve we should expect
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obviously I think that things have become much worse because of bad
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governance and all the scarcities like of oxygen like people just dying because
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there's no oxygen is like would have been so unthinkable and dystopian a year
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ago you know where are we going with this where is a peak going to come like
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I know different models have shown you know middle of May end of May so on and
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so forth what is a best-case scenario and the worst-case scenario for this I
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can tell you one major source of uncertainty and that is to what extent
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are reinfections important if the infections are very important then
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having caught it once makes no difference to getting catching it again
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here and that's very crucial because that expands a susceptible population to
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what it was in January of last year we basically be back to square one if on
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the other hand it's somewhat protective every bit of protection that you get
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from a prior infection or a vaccination helps okay so we don't know that at all
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yet the best models now the models if you sort of average as I said in an
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ensemble sense of multiple model predictions that would suggest by
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about the middle of May is when you can expect to see a downturn there are many
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reasons why you will not see it then one reason is just testing already testing
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across the country has pretty much gone down to levels that are just not
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sufficient to cope with this increase in cases in Delhi it can take you something
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like two to three days or four days for someone to come to your house to test
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you it can take another three days for you to get the test result so this gap
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between falling sick and getting tested is becoming larger and larger and
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larger the rate at which tests are going is quite slow compared to the rate at
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which cases are growing already Delhi test positivity is 35% plus for a
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country as a whole is 20% plus so look at the number of cases that you're
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missing of people who ought to be tested and people who've decided that look I'm
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not going to get tested at all because I suspect that I have it I'm just going to
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lock myself up in my room and not bother to test so you'll never get an idea of
#
what the real number is especially now precisely because of these reasons and
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because you're not testing enough you will not see the 8 lakhs and the 10
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lakhs and that people have been protecting these sort of doomsday
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predictions that people have been making because you will see your testing will
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saturate and you will see something that appears to be coming down God knows
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what's happening inside the background so my best guess based on the models
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that we have another is that you will see a genuine downturn around the middle
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of a middle of me how will we survive until then that is completely open to I
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mean it's very hard to imagine that's another at least another another two
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weeks plus from here it may be shifted a little forward it may be shifted a
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little back unlikely that it will go on deep into into into June is my feeling
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it really I would really put some money on on on the middle of me from what we
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have we're recording on April 28 by the way for my listeners
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yeah so that's about so that's about two weeks from now and what you're saying
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about the gap in testing is also crazy because one way in which people are
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dying is that hospitals are at times refusing to admit people to their
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covert ICU if they haven't tested covert positive and they are covert
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positive but the tests aren't coming in time so it's really dead by a bureaucracy
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as it were yes exactly which is just a horrendous state of affairs you know in
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my show notes I'll direct people to the other like the panel discussion you
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mentioned which I also saw in which was illuminating bunch of different experts
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apart from you also giving great insights there and to all the things
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you've written and so on I'll ask you now you know as we leave this episode to
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also for a moment move away from kovat and but just just generally talk about
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you know for those who in the first half of this episode might have been turned
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on to the joy and wonder of science in a certain sense we live in magical times
#
the most magical times because it's so much of knowledge is just opening up to
#
us in various different fields you know one of the memes for my show kind of is
#
that people you know people have actually posted pictures of seen and
#
unseen bookshelves of books I bought from recommendations on the show so I'm
#
going to ask you to recommend you know your favorite books which convey some of
#
this joy in the different areas that you're passionate about whether it's
#
physics or biology or modeling or maths or even music that that you know you
#
feel enthusiastic about and would love to share with the world the book called
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spillover by David Quammen an American writer on nature and diseases is a
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particularly important book because in a sense it was very prescient it foresaw
#
the fact that in the next few years we would have a what is called a spillover
#
event from an animal virus from possibly from bats coming over to human beings
#
and sort of examining what the long-term consequences of that were
#
contagion by Adam Kucharski is another very good book and that again is the
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idea of how epidemics spread in non disease context as well how do
#
pandemics of financial collapse happen and how do what how what is how do
#
social rumors spread how do gangs form and what is so that suddenly expands the
#
way in which you think about how how you how small something in very small
#
initially is amplified into something that can transform society the Egyptian
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spring the Africans at the spring in terms of the movements that that try to
#
overthrow repressive governments was another is another example of a
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collective effect starting from from small groups of people beginning to
#
organize that I think is really the important idea behind a lot of what I
#
think about how and it's also very physics idea how does collective
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behavior come from what are initially somewhat random behaviors of individuals
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that suddenly seem to build up into some sort of coherent all that's as true
#
for examples in physics as it is for examples in epidemiology
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fascinating and and these are epidemiology and disease other stuff
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music what's your favorite fiction are there any books you go back to from
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your younger days or your childhood and like to reread to tell you the truth
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I'm kind of blanking on it right now because I've not been reading anything
#
but scientific papers for the past for the past several months so I will think
#
about this and supply an appropriate note so I'll have a separate section in
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my show notes for your recommendation thank you so much for your time and your
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insights I know especially in these busy times where you're playing such an
#
important role for you to take out this time for me is quite something so I'm
#
really grateful for that thank you so much thank you it has been wonderful
#
talking to you and you are such great questions and I really enjoyed being on
#
the show if you enjoyed listening to this conversation head on over to the
#
show notes where there are tons of links and you can enter rabbit holes at will
#
you can follow Gotham at Twitter at men and biophysics that's all one word men
#
and biophysics you can follow me on Twitter at Amit Varma a MIT BA RMA you
#
can browse past episodes of the scene and the unseen at scene unseen dot I n
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thank you for listening did you enjoy this episode of the scene and the
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unseen if so would you like to support the production of the show you can go
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