WEBVTT - Clay Travis Sits Down With Avik Roy 0:00:02.080 --> 0:00:05.760 This is Wins and Losses with Clay Travis. Clay talks 0:00:05.800 --> 0:00:09.879 with the most entertaining people in sports, entertainment and business. 0:00:10.160 --> 0:00:20.600 Now here's Clay Travis. Welcome in Wins and Losses Podcast. 0:00:20.640 --> 0:00:22.600 I am Clay Travis, and we're about to be joined 0:00:22.600 --> 0:00:24.759 by O vic Roy, who I think you guys are 0:00:24.800 --> 0:00:28.280 really going to enjoy. He's been doing fantastic work looking 0:00:28.440 --> 0:00:32.680 at the data surrounding the coronavirus, making recommendations on so 0:00:32.720 --> 0:00:35.280 many different levels, been writing for the Wall Street Journal, 0:00:35.320 --> 0:00:39.159 among other locations. We have never actually spoken before, but 0:00:39.240 --> 0:00:41.159 I am impressed by the work that he's done. I 0:00:41.200 --> 0:00:44.680 found him on social media over the last several months, 0:00:44.800 --> 0:00:47.720 and we bring him in now. Oh vic Roy, let 0:00:47.720 --> 0:00:49.960 me go ahead and start here. How can people find 0:00:50.040 --> 0:00:53.200 you on social media? How can they read your work? Ovic? 0:00:53.200 --> 0:00:56.560 And thanks for joining us. Hey, thanks Clay, Well, thanks 0:00:56.600 --> 0:00:59.280 to my eccentric parents. My name is spelled A v 0:00:59.560 --> 0:01:01.800 i K, not Ovic, O v i K. It's a 0:01:01.920 --> 0:01:03.960 v i K and that's my Twitter handle, A v 0:01:04.080 --> 0:01:06.360 I K, just like it sounds a viasm Victor I K. 0:01:06.920 --> 0:01:09.240 All right, So your background as we get into so 0:01:09.240 --> 0:01:11.800 many different interesting topics that I want to discuss with you. 0:01:11.920 --> 0:01:16.400 But what is your educational background that led you into 0:01:16.520 --> 0:01:18.440 the profession that you have now and what do you 0:01:18.440 --> 0:01:21.120 do for a living. It's a bit of a zig 0:01:21.200 --> 0:01:24.440 zag path. My undergraduate degree was in molecular biology at 0:01:24.600 --> 0:01:27.600 M I. T. And then I went to medical school 0:01:27.600 --> 0:01:29.840 at Yale. And then instead of becoming a doctor or 0:01:29.840 --> 0:01:33.480 a scientist, I went into biotechnology investing, where I invested 0:01:33.520 --> 0:01:36.920 in companies trying to develop new treatments for diseases, vaccines, 0:01:36.959 --> 0:01:39.479 all that sort of thing. And then I got really 0:01:39.520 --> 0:01:41.760 interested in healthcare re form and that led me down 0:01:41.760 --> 0:01:44.880 the rabbit hole of healthcare policy and economic policy in general, 0:01:44.920 --> 0:01:47.800 and worked on a bunch of presidential campaigns and and 0:01:47.840 --> 0:01:50.920 now I run a think tank in Austin, Texas called 0:01:50.960 --> 0:01:53.360 the Foundation for Research on Equal Opportunity, where we come 0:01:53.440 --> 0:01:56.520 up with ideas to help more Americans climb up the 0:01:56.520 --> 0:01:59.720 economic ladder of success. All right, So I'm fascinated by 0:01:59.720 --> 0:02:01.920 several different things. You've already told this, So what is 0:02:01.960 --> 0:02:04.680 the reaction. You go to M I T. And then 0:02:04.720 --> 0:02:06.600 you go to Yale Medical School and I don't know 0:02:06.640 --> 0:02:08.520 the answer to this. I went to law school and 0:02:08.520 --> 0:02:11.600 I practiced law for a couple of years, and even 0:02:11.639 --> 0:02:15.120 not practicing law was considered to be a sort of 0:02:15.120 --> 0:02:17.280 a risky choice by many people, because you have a 0:02:17.320 --> 0:02:20.760 good profession that's out there. What percentage of your classmates 0:02:20.880 --> 0:02:23.280 or people who go to a medical school as good 0:02:23.320 --> 0:02:29.400 as Yale end up not actually practicing medicine when they graduate, Well, 0:02:29.440 --> 0:02:31.839 it's a small percent at most schools, it's probably close 0:02:31.919 --> 0:02:34.840 to zero. But Yale was a particular place where they 0:02:34.840 --> 0:02:38.200 actually encourage you to to pursue your interests outside of 0:02:38.240 --> 0:02:40.360 medical school. It was like a path Baale system and 0:02:40.440 --> 0:02:42.720 things like that. And so I'd say in a typical 0:02:42.840 --> 0:02:46.320 Yale class, which is a hundred people per class, about 0:02:46.600 --> 0:02:49.200 five to ten end up doing something that's sort of 0:02:49.240 --> 0:02:50.840 like you know, in law school, it's very typical, right, 0:02:50.880 --> 0:02:52.520 A lot of lawyers go into things of the law, 0:02:52.560 --> 0:02:56.280 and especially yeah, especially after a few years. Most people 0:02:56.320 --> 0:02:58.639 have to go in and make money initially, but then 0:02:58.639 --> 0:03:01.000 they'll start to filter out. Everybody I always say who's 0:03:01.000 --> 0:03:03.480 a lawyer has got a dream of not being a lawyer. 0:03:03.720 --> 0:03:05.320 But most people who go to medicine, That's why I 0:03:05.320 --> 0:03:07.680 was interested. Most people who go to medical school go 0:03:07.760 --> 0:03:09.800 on in practice. So is that something you came to 0:03:10.120 --> 0:03:13.520 a decision before you even started medical school, or what 0:03:13.600 --> 0:03:16.320 was it about, sort of the capitalistic economy I guess 0:03:16.360 --> 0:03:19.840 of the biotechnology universe that attracted you more than being 0:03:19.880 --> 0:03:23.760 a traditional doctor. Well, my dad was also a scientist. 0:03:23.840 --> 0:03:26.480 He was a biochemist, and so I grew up around 0:03:26.919 --> 0:03:29.560 all these incredible people who had been like the people 0:03:29.600 --> 0:03:33.600 who had characterized DNA and RNA, and we're the pioneers 0:03:33.600 --> 0:03:36.120 in this modern field of genetics and biology that we're 0:03:36.120 --> 0:03:38.640 now living in. So I always had this real excitement 0:03:38.640 --> 0:03:40.240 about it. I thought I wanted to be a scientist. 0:03:40.280 --> 0:03:43.120 And then you know what the problem is, Like, you know, 0:03:43.400 --> 0:03:45.280 at m I T a core of the faculty has 0:03:45.360 --> 0:03:47.480 Nobel Prize that I'm walking around and I'm like, there 0:03:47.560 --> 0:03:49.160 is no way I'm going to win a Nobel Prize. 0:03:49.160 --> 0:03:51.160 I'm not smart enough. So how am I going to 0:03:51.200 --> 0:03:53.560 actually do something useful to the world. I don't know. 0:03:53.680 --> 0:03:55.280 So I struggled with it for a while, and I thought, 0:03:55.320 --> 0:03:57.560 you know what, maybe I can invest in biotech companies. 0:03:57.560 --> 0:03:59.920 I can help build the latest new cure for some disease. 0:04:00.040 --> 0:04:02.200 That would be something useful I could do with my life, 0:04:02.200 --> 0:04:04.480 and that let me down that path, and here I 0:04:04.520 --> 0:04:06.720 am doing this now because obviously health care reform and 0:04:07.120 --> 0:04:09.280 economic policy in general effects so many people. A lot 0:04:09.280 --> 0:04:12.040 of people struggle to find affordable health insurance. Uh. And 0:04:12.080 --> 0:04:13.360 there are a lot of cures that we need to 0:04:13.400 --> 0:04:15.120 have for for disease that people have, and and not 0:04:15.200 --> 0:04:17.119 just in health care, a lot of oarliious higher education. 0:04:17.160 --> 0:04:18.960 How do you afford college? How do you afford to 0:04:19.040 --> 0:04:20.840 keep the lights on in your house? There are lots 0:04:20.839 --> 0:04:22.680 of things where we need new ideas, and it's been 0:04:22.720 --> 0:04:27.159 fun to work on work on trying to develop those ideas. Okay, yeah, no, 0:04:27.240 --> 0:04:29.320 it's so how do you decide? So I'm kind of 0:04:29.320 --> 0:04:33.360 fascinated by the concept of being an investor in biotechnology 0:04:33.440 --> 0:04:37.520 companies because you obviously have to be sophisticated to even 0:04:37.680 --> 0:04:40.440 understand as an investor, like for people out there who 0:04:40.480 --> 0:04:42.799 don't really think about it very much, being a quote 0:04:42.839 --> 0:04:47.120 unquote sophisticated investor is typically requires a certain net worth, 0:04:47.480 --> 0:04:50.080 but you can invest in a variety of different companies, 0:04:50.080 --> 0:04:54.280 sometimes for better or ill uh. And but biotechnology, I 0:04:54.320 --> 0:04:57.480 would imagine there's a lot of I I you tell me, 0:04:57.520 --> 0:04:59.560 but I would imagine there's a lot of snake oil 0:04:59.640 --> 0:05:02.479 cells out there who are trying to peddle things that 0:05:02.600 --> 0:05:04.800 may or may not make a lot of sense. So 0:05:04.839 --> 0:05:08.039 I would think a medical degree like yours would basically, 0:05:08.120 --> 0:05:10.320 for lack of a better way to describe it, allow 0:05:10.440 --> 0:05:12.880 you to speak the language so that you're may be 0:05:12.880 --> 0:05:15.719 better able to understand. And maybe also your dad being 0:05:15.760 --> 0:05:18.360 involved help with that as well. But what was your 0:05:18.400 --> 0:05:21.640 process like in terms of investing and finding companies that 0:05:21.720 --> 0:05:25.719 you found to be worthy of putting money behind. Yeah, 0:05:25.760 --> 0:05:28.919 it's it's so it's great and and really relevant that 0:05:28.920 --> 0:05:31.320 you're bringing this up, actually because it has a lot 0:05:31.360 --> 0:05:33.560 to do with how I ended up being a contrarian 0:05:33.560 --> 0:05:38.080 on COVID stuff. So yes, I I UH entered the 0:05:38.080 --> 0:05:41.120 workforce a finished school in two thousands, and that was 0:05:41.200 --> 0:05:44.320 right around the time that the Human Genome Project had 0:05:44.320 --> 0:05:47.160 been completed. So for those who remember those days, the 0:05:47.240 --> 0:05:50.520 Human Genome Project was at the time this gigantic UH 0:05:50.800 --> 0:05:54.800 scientific enterprise to sequence the entire human genome. Every d 0:05:54.960 --> 0:05:57.640 N a piece of DNA and you're comprise of the 0:05:57.720 --> 0:06:00.840 human genetic code from beginning to end, because that had 0:06:00.880 --> 0:06:02.839 never been done before. And that was finally finished in 0:06:02.880 --> 0:06:05.680 two thousand and one, and there was a big dot 0:06:05.720 --> 0:06:07.640 com boom in the nineties when the Internet as we 0:06:07.680 --> 0:06:09.880 know at first came into being. And right after that 0:06:09.960 --> 0:06:12.839 dot com bubble burst, there was this basically this genomics bubble. 0:06:12.880 --> 0:06:16.520 All these stocks called this genomics and that genomics were 0:06:16.320 --> 0:06:19.280 We're getting multibillion dollar market caps and nobody knew what 0:06:19.320 --> 0:06:21.440 they did and a lot of it was hyped. And 0:06:21.520 --> 0:06:24.159 so UH an investment firm I've never heard of called 0:06:24.160 --> 0:06:26.320 Bain Capital. I have to know a couple of people 0:06:26.320 --> 0:06:27.560 who worked there, and one of them reached out to 0:06:27.600 --> 0:06:29.000 me and said, hey, can you help us figure out 0:06:29.040 --> 0:06:31.960 all this genomics stuff because we're just a bunch of MBAs. 0:06:31.960 --> 0:06:34.200 We don't know anything about genomics, and we figured you 0:06:34.279 --> 0:06:36.200 can teach us. You have a degree in molecro biology, 0:06:36.200 --> 0:06:37.560 you can teach us about this stuff, and we can 0:06:37.600 --> 0:06:39.159 teach you how to read a balance sheet and then 0:06:39.480 --> 0:06:41.480 maybe you can be useful. And I'm like, wow, that's 0:06:42.640 --> 0:06:45.440 not really knowing the first thing about about how to 0:06:45.480 --> 0:06:48.000 do any of that. When I started UH that, I 0:06:48.040 --> 0:06:50.560 got recruited to to Bain Capital, moved to Boston and 0:06:50.640 --> 0:06:53.440 started investing in bi tech companies. I basically became part 0:06:53.480 --> 0:06:58.279 of this first generation of people with scientific and medical 0:06:58.320 --> 0:07:01.760 backgrounds m d s and PhD is mostly who started 0:07:01.800 --> 0:07:04.039 investing in biotech companies because it ended up it's not 0:07:04.120 --> 0:07:06.200 like a normal stock thing, where like normally, if you 0:07:06.320 --> 0:07:08.240 turn on c NBC or something, it's like, well the 0:07:08.320 --> 0:07:10.720 pe ratio is this right, or if you look at 0:07:10.720 --> 0:07:13.520 those conventional things. But biotech it's not like that at all, 0:07:13.600 --> 0:07:16.680 because the clinical trial turns out positive or negative in 0:07:16.760 --> 0:07:19.239 terms of your your latest jurif for breast cancer or whatever, 0:07:19.640 --> 0:07:21.960 and that stock goes to zero if it fails, or 0:07:22.000 --> 0:07:25.400 it triples if it succeeds. I mean, it's total volatility. 0:07:25.480 --> 0:07:28.239 It's crazy, a lot of losses, a lot of winds. 0:07:28.360 --> 0:07:31.120 It's it's kind of like baseball, where if you're batting average, 0:07:31.160 --> 0:07:33.240 it doesn't if you're batting averages below five dred you're 0:07:33.240 --> 0:07:35.120 probably not gonna survive. But if you if but you're 0:07:35.120 --> 0:07:37.720 gonna lose, you're gonna be wrong plenty. And you have 0:07:37.800 --> 0:07:41.280 to put an enormous amount of effort into statistics, right 0:07:41.280 --> 0:07:43.200 because at the end of the day, what what my 0:07:43.320 --> 0:07:45.360 job ended up becoming and a lot of people who 0:07:45.360 --> 0:07:49.480 were like me, is we ended up being incredibly intense statisticians, 0:07:49.480 --> 0:07:51.600 because what you end up doing is you're looking at 0:07:51.600 --> 0:07:53.680 a say, a breast cancer trial, new new drug for 0:07:53.680 --> 0:07:56.320 breast cancer. It's being tested in fifty women who have 0:07:56.360 --> 0:08:00.320 breast cancer. Well, fifty women is not a lot. So 0:08:00.480 --> 0:08:02.520 if there's fifty women who got the drug and fifty 0:08:02.520 --> 0:08:06.320 women who didn't, and let's say that trial got really hyped, Oh, 0:08:06.320 --> 0:08:08.080 this drug really worked in the fifty women who got 0:08:08.080 --> 0:08:10.200 the drug where they really did better, they lived longer, 0:08:10.240 --> 0:08:13.320 their breast tumors went away. But what if it's what 0:08:13.400 --> 0:08:15.520 if what happened was actually the people who were in 0:08:15.520 --> 0:08:17.440 the placebo arm of the trial were actually sicker in 0:08:17.480 --> 0:08:20.400 the beginning and that excuse the results. Or maybe they 0:08:20.520 --> 0:08:23.160 measured the tumors in the wrong way. So there's all 0:08:23.200 --> 0:08:26.280 sorts of subtleties about a way a clinical trial operates 0:08:26.320 --> 0:08:28.240 that you can then make a bet and say, Okay, 0:08:28.400 --> 0:08:31.880 is this drug overhyped or under hyped? Are people overhyping 0:08:31.920 --> 0:08:33.719 the drug as you said, like is it snake? Or 0:08:33.920 --> 0:08:36.079 there's this trial that's been published in the New England 0:08:36.120 --> 0:08:38.400 Journal of Medicine that says this this drug really works. 0:08:38.559 --> 0:08:41.000 But then in a larger trial with thousands of patients 0:08:41.080 --> 0:08:43.480 is going to fail or is it the other way around. 0:08:43.480 --> 0:08:45.480 Maybe it's a drug that didn't do that well at 0:08:45.520 --> 0:08:47.880 early state trials, but in bigger trials there is a 0:08:47.960 --> 0:08:50.840 signal there and it ends up being really successful. Those 0:08:50.840 --> 0:08:52.559 are the kinds of things that people like me we're 0:08:52.600 --> 0:08:55.720 making bets on, betting tens of in fact, sometimes hundreds 0:08:55.760 --> 0:08:58.600 of millions of dollars, trying to figure out which side 0:08:58.640 --> 0:09:02.200 was right. How do you do? I did all right? 0:09:02.280 --> 0:09:05.040 I did all right. Uh. And that's what kind of 0:09:05.040 --> 0:09:07.160 gave me the freedom to to start a nonprofit on 0:09:07.280 --> 0:09:11.480 a lark. Yeah, a financial cushion, all right. So that 0:09:11.679 --> 0:09:15.280 is all interesting and fascinating background. And you said something 0:09:15.480 --> 0:09:18.079 a couple of minutes ago. You said that your work 0:09:18.120 --> 0:09:20.560 at Bain Capital, and your work and going to m 0:09:20.559 --> 0:09:22.840 I T and also going to Yale Medical School and 0:09:22.840 --> 0:09:26.199 putting it to work molecular biology, looking at the genome projects, 0:09:26.200 --> 0:09:30.440 all of these different things obviously lead you to being 0:09:30.480 --> 0:09:33.080 able to look at data and figure out what I 0:09:33.080 --> 0:09:35.600 would call, for lack of a better way, what the 0:09:35.679 --> 0:09:39.199 signal is versus what the noise is. So there's so 0:09:39.280 --> 0:09:42.480 much noise on a day to day basis, regardless of 0:09:42.559 --> 0:09:45.440 what you do for a living, whatever people are doing 0:09:45.480 --> 0:09:47.920 for livings where they listen to us out there, most 0:09:48.000 --> 0:09:51.400 of it is external noise that isn't really getting to 0:09:51.480 --> 0:09:55.400 the essence of what you do. Figuring out the signal 0:09:55.520 --> 0:09:58.960 is essentially what you had to do for these biochemistry 0:09:59.280 --> 0:10:03.120 uh and and investigations for lack of another way of 0:10:03.160 --> 0:10:06.800 putting it. That also then corresponds in an incredibly unique 0:10:06.840 --> 0:10:10.960 way with what's happened with the coronavirus, where every day, 0:10:11.040 --> 0:10:14.640 it seems to me, we are deluged with data, with news, 0:10:14.640 --> 0:10:19.439 with viral stories meaning not necessarily virus stories, but literally 0:10:19.440 --> 0:10:22.240 stories that go viral about the virus. How do you 0:10:22.320 --> 0:10:25.360 cut through that noise and figure out what the real 0:10:25.559 --> 0:10:30.440 signal is? So let's go in. The story starts in January. 0:10:30.559 --> 0:10:34.160 When did you first become aware of the coronavirus and 0:10:34.200 --> 0:10:39.000 start to read and pay attention to it in China? Well, 0:10:39.080 --> 0:10:42.360 I started hearing the stories right away in in you know, 0:10:42.480 --> 0:10:46.320 just January February when the news started the break, But 0:10:46.320 --> 0:10:48.800 but at that time we didn't know that it was coming. 0:10:48.800 --> 0:10:50.760 I think it wasn't. One was the first case in 0:10:50.760 --> 0:10:52.840 in Washington, stated was I want to say February. It 0:10:52.920 --> 0:10:55.640 was late January, so we started to hear about this 0:10:55.679 --> 0:10:58.880 thing in Wuhan. There was a lot of suppression and uh, 0:10:58.960 --> 0:11:00.959 and then we started to see the in Washington and 0:11:01.360 --> 0:11:03.880 certainly followed that all the way through because healthcare is 0:11:04.000 --> 0:11:07.040 health care policy, and healthcare stuff is on my radar 0:11:07.080 --> 0:11:09.600 always because as part of my job. So you said 0:11:09.640 --> 0:11:12.800 you were comfortable early on with looking into the data 0:11:12.960 --> 0:11:16.720 and maybe becoming somewhat of a contrarian in terms of 0:11:17.240 --> 0:11:19.920 what your analysis has done. Such a good job of, 0:11:20.040 --> 0:11:21.960 I think is for people who don't know, and again 0:11:21.960 --> 0:11:24.439 I would encourage them to go follow you on Twitter 0:11:24.600 --> 0:11:27.880 at a v I k oh vic Roy we are 0:11:27.920 --> 0:11:31.319 talking to right now. Uh. He does incredible work looking 0:11:31.320 --> 0:11:33.800 at the data and putting it in context. And I 0:11:33.840 --> 0:11:36.360 believe the first time I started to see your data 0:11:36.800 --> 0:11:39.599 percolate on my feed and start to follow it aggressively 0:11:40.240 --> 0:11:43.520 was when you were looking at risk factors for college 0:11:43.520 --> 0:11:47.880 age kids, comparing let's say the seasonal flu and also 0:11:47.960 --> 0:11:51.560 with kids who are elementary school age and saying, hey, 0:11:51.640 --> 0:11:53.480 I understand that you know there's a lot of attention 0:11:53.480 --> 0:11:56.640 on the coronavirus right now, but the data would reflect 0:11:56.800 --> 0:11:59.960 that most young people are under greater danger from the 0:12:00.040 --> 0:12:04.960 seasonal flu or pneumonia. That's a counterintuitive data. Fect When 0:12:05.000 --> 0:12:08.160 did you start to dive into the numbers in such 0:12:08.160 --> 0:12:10.360 a way, Because this is eerily similar, I would think, 0:12:10.400 --> 0:12:12.520 and I bet you would agree with what you were 0:12:12.559 --> 0:12:16.400 doing looking at these biotech trials where the headline might 0:12:16.400 --> 0:12:18.839 be one thing, but when you actually go and look 0:12:19.160 --> 0:12:22.320 underneath the surface and start to examine it, some of 0:12:22.360 --> 0:12:25.000 the data is telling a different story maybe than the 0:12:25.480 --> 0:12:27.760 data that the media is sharing at the top line 0:12:27.840 --> 0:12:32.360 level of reporting. Yeah, that's that's exactly right, Clay. I mean, 0:12:32.360 --> 0:12:34.079 you've hit it on the head. And I would say also, 0:12:34.120 --> 0:12:36.120 by the way, I think one of the reasons when 0:12:36.120 --> 0:12:39.120 I listen to your your show and your podcast, you 0:12:39.240 --> 0:12:42.320 do an amazing job of just walking people through the 0:12:42.360 --> 0:12:45.520 real statistical situation. And I think that goes to show 0:12:45.559 --> 0:12:48.840 that there's an analogous, analogous situation of sports. Right you 0:12:48.840 --> 0:12:51.280 think about this explosion of sports analytics today and what 0:12:51.440 --> 0:12:53.120 is that all about. It's all about the fact that 0:12:53.200 --> 0:12:57.800 conventional ways of measuring performance in sports, starting with baseball, 0:12:57.800 --> 0:12:59.360 but the true in it, it's true in every sport 0:12:59.640 --> 0:13:03.720 don't necessarily accurately measure how a player or a team 0:13:03.840 --> 0:13:06.640 is performing, and so there's been this explosion of trying 0:13:06.640 --> 0:13:09.200 to be much more rigorous about how to measure that. 0:13:09.720 --> 0:13:11.840 And on Wall Street that's basically what you're doing on 0:13:11.840 --> 0:13:14.360 Wall Street two. You're you're trying to say, at least 0:13:14.400 --> 0:13:17.080 in biotech investing, and it's not just true in biotech investing, 0:13:17.120 --> 0:13:20.120 but especially true in biotech investing. What you're trying to 0:13:20.120 --> 0:13:22.760 figure out is, Okay, this stock is really hyped. What 0:13:22.840 --> 0:13:25.680 are people getting wrong? This stock is down in the dumps? 0:13:25.880 --> 0:13:29.040 What are people getting wrong? You're you're you're training. If 0:13:29.040 --> 0:13:31.760 you try to be good, try to outperform is to 0:13:31.800 --> 0:13:34.120 always try to swim against the tide and try to 0:13:34.240 --> 0:13:37.280 identify what other people aren't thinking about when they look 0:13:37.320 --> 0:13:40.440 at the data, and that habit of mind or cast 0:13:40.480 --> 0:13:43.160 of mind, if you want to call it, that's how 0:13:43.200 --> 0:13:45.080 That's what's driven all my policy work too. When I 0:13:45.080 --> 0:13:47.160 write about public policy, when I write about healthcare, when 0:13:47.200 --> 0:13:49.640 I write about COVID, it's all about, Okay, what are 0:13:49.640 --> 0:13:54.480 people missing? And the obvious thing right away was this 0:13:54.679 --> 0:13:59.319 real skew in the age distribution of who was getting 0:13:59.360 --> 0:14:02.040 really sick and who was dying from COVID. There was 0:14:02.080 --> 0:14:05.840 a huge skew towards the elderly. And I started writing 0:14:05.840 --> 0:14:08.240 about this pretty early on it. The early data out 0:14:08.280 --> 0:14:10.920 of China, the early data out of Italy, the places 0:14:10.960 --> 0:14:13.120 that really got hit a little bit before the US 0:14:13.240 --> 0:14:16.400 got hit, they all were showing the same skew where 0:14:17.040 --> 0:14:20.120 in the case of the US, literally of the people 0:14:20.160 --> 0:14:22.680 who died of COVID are over the age of sixty five. 0:14:23.240 --> 0:14:25.280 And I would start writing about this and tweeting about 0:14:25.280 --> 0:14:26.720 it and putting in our in our work at free 0:14:26.720 --> 0:14:29.080 out dot org, which is our think tank, and some 0:14:29.120 --> 0:14:31.880 people would ask me like, well, okay, that's that's interesting. 0:14:32.600 --> 0:14:34.640 People over are the ones that dying or are the 0:14:34.640 --> 0:14:37.480 ones over sixty five? Isn't that true? Most things don't, 0:14:37.560 --> 0:14:40.080 don't Most things end up, you know, basically killing the 0:14:40.120 --> 0:14:41.760 elderly more than they kill young people. So I was 0:14:41.800 --> 0:14:45.040 actually curious to say, okay, let's let's actually try to 0:14:45.120 --> 0:14:47.160 dig into that and figure out, Okay, how does the 0:14:47.200 --> 0:14:50.880 skew work for a more conventional infectious disease like influenza 0:14:51.320 --> 0:14:53.840 compared to COVID, Because if you look at the history 0:14:53.880 --> 0:14:57.960 of influenza pandemics, they actually do hurt and kill children. 0:14:57.960 --> 0:15:00.960 They do kill young people. The famous uh influenza pandemic 0:15:00.960 --> 0:15:04.480 of nineteen eighteen killed a lot of soldiers. That was, 0:15:04.800 --> 0:15:08.000 more soldiers died because of influenza than died at least 0:15:08.040 --> 0:15:11.560 American soldiers then died actually fighting in the trenches in 0:15:11.600 --> 0:15:14.440 World War One. So a lot of young people die. 0:15:14.480 --> 0:15:16.600 Then that's why we closed the schools if there's like 0:15:16.640 --> 0:15:20.280 a crazy influenza pandemic. So so I was interested in 0:15:20.280 --> 0:15:22.120 this and what I found. If you actually look at 0:15:22.280 --> 0:15:26.400 CDC data, official government data over a ten year period 0:15:26.440 --> 0:15:28.640 from two thousand seven to two thousand seventeen, which is 0:15:28.680 --> 0:15:31.400 our most recent cut of the data, and you compare 0:15:31.440 --> 0:15:33.960 the number of people who die from influenza by age 0:15:34.000 --> 0:15:36.560 bracket to those who die from COVID, you see a 0:15:36.760 --> 0:15:39.480 much worse skew for COVID. In other words, COVID is 0:15:39.640 --> 0:15:42.000 much more skewed in terms of serious illness or death 0:15:42.000 --> 0:15:46.480 towards the elderly than your typical infectious disease or any 0:15:46.480 --> 0:15:49.920 other kind of disease. So this is interesting to me 0:15:50.040 --> 0:15:54.840 too because one of the sort of aphorisms I would 0:15:54.840 --> 0:15:58.080 say about war and people who listen to this know 0:15:58.240 --> 0:16:00.120 also that I'm a history buff and I love of 0:16:00.280 --> 0:16:04.040 you know, studying American and and other history a lot. 0:16:04.800 --> 0:16:10.000 You typically end up fighting the last war, sorry, the 0:16:10.000 --> 0:16:13.320 most recent war, with the technology from the previous war, right, 0:16:13.360 --> 0:16:17.000 because everybody who has everybody who has studied all the 0:16:17.080 --> 0:16:20.040 things that have happened in warfare is going to apply 0:16:20.160 --> 0:16:24.520 the lessons that have applied from prior war. But you're then, 0:16:24.680 --> 0:16:26.440 you know, let's use the Civil War for an example, 0:16:26.480 --> 0:16:28.680 which is a particular interest of mine. You're using the 0:16:28.760 --> 0:16:35.680 Napoleon Napoleonic tactics, but now the technology, the ability of rifles, cannons, 0:16:36.280 --> 0:16:39.680 all of the rapid fire weapons have changed. So the 0:16:40.440 --> 0:16:44.720 mortality rates skyrockets, right, because you're fighting, and gradually you 0:16:44.760 --> 0:16:47.200 adjust and people learn, hey, maybe we should be fighting 0:16:47.200 --> 0:16:48.880 in a different way. The reason why I used that 0:16:48.880 --> 0:16:52.040 as an example is so many people and this frustrated me, 0:16:52.240 --> 0:16:54.240 and I bet it frustrated you when you actually looked 0:16:54.240 --> 0:16:57.760 at the data. So many people used data from the 0:16:57.880 --> 0:17:02.920 flu to justify shutting down schools based on the coronavirus. 0:17:02.960 --> 0:17:06.720 But this particular covid infection was not like the flu 0:17:06.960 --> 0:17:09.960 in that the age range of the impacted were different. 0:17:10.359 --> 0:17:12.960 So the decision to shut down schools might well have 0:17:13.080 --> 0:17:16.639 made sense in the pandemic a hundred years ago, and 0:17:16.720 --> 0:17:18.520 you study it and you say, oh, that's the lesson 0:17:18.560 --> 0:17:21.360 we should take away. But it's not the same virus. 0:17:21.720 --> 0:17:25.280 So you're fighting a new virus which has never existed 0:17:25.320 --> 0:17:28.440 before with the tactics that would have worked against the 0:17:28.520 --> 0:17:32.280 virus a hundred years ago. That's a misfit, right, But 0:17:32.359 --> 0:17:35.840 most people aren't sophisticated enough to think about that. You were, 0:17:36.160 --> 0:17:37.840 But that has to be frustrating to you from a 0:17:37.840 --> 0:17:41.240 public policy perspective to see it and not be able 0:17:41.280 --> 0:17:43.480 to cut through the noise and make people realize the 0:17:43.560 --> 0:17:47.359 data and what you've seen. Well, first of all, let 0:17:47.400 --> 0:17:49.960 me just say you're absolutely right, a thousand percent. This 0:17:50.119 --> 0:17:53.600 issue of fighting the last war is exactly what's going 0:17:53.640 --> 0:17:56.800 on here. And and so there's so many different dimensions 0:17:56.840 --> 0:17:59.159 of how that's true. Clay, I'll give you one. So 0:17:59.200 --> 0:18:01.840 you've heard people say, well, there were these plans in 0:18:01.920 --> 0:18:05.080 place from the George W. Bush administration and the Obama administration, 0:18:05.119 --> 0:18:09.640 why didn't Trump use those plans to fight the coronavirus. Well, 0:18:09.640 --> 0:18:13.320 those plans were not designed for coronavirus. They were designed 0:18:13.359 --> 0:18:16.359 for influenza. In fact, if you actually look at the 0:18:16.400 --> 0:18:19.600 cover page of the reports, they say things like our 0:18:19.760 --> 0:18:24.720 plan for dealing with a novel influenza pandemic. Now, influenza 0:18:24.800 --> 0:18:29.160 is a different virus from coronaviruses like COVID stars kobe 0:18:29.160 --> 0:18:31.800 to the virus that causes COVID nineteen. And again this 0:18:31.880 --> 0:18:34.560 is part of having that molecular biology backrouprom m I 0:18:34.560 --> 0:18:37.680 T I understand the difference between different types of viruses 0:18:37.720 --> 0:18:40.360 and how they actually infect you and how they're they're 0:18:40.440 --> 0:18:43.280 how they actually work in the body. They're very different, 0:18:43.320 --> 0:18:45.240 and so it's very important to understand that not all 0:18:45.359 --> 0:18:47.520 viruses are the same. The way they behave in your body, 0:18:47.520 --> 0:18:51.639 their lethality, their virulence can be very different. And so 0:18:51.760 --> 0:18:54.639 to your point, yes, you closing schools and a severe 0:18:54.680 --> 0:18:58.400 influenza pandemic makes sense because the young children and young 0:18:58.440 --> 0:19:02.560 adults do get killed old from really bad influenza pandemics 0:19:02.600 --> 0:19:05.199 in the in the case of nineteen eighteen especially, But 0:19:05.320 --> 0:19:08.640 this is not an influenza pandemic, and it's also true 0:19:08.640 --> 0:19:11.200 of all these sort of public health epidemiologists types. So 0:19:11.560 --> 0:19:13.280 one of the things you'll see a lot of people say, 0:19:13.320 --> 0:19:16.399 particularly on social media as well, how dare you write 0:19:16.480 --> 0:19:19.000 or tweet about COVID. You don't have a right to 0:19:19.040 --> 0:19:22.080 have an opinion because you're not an epidemiologist. Now, the 0:19:22.160 --> 0:19:26.560 problem is, first of all, epidemiology is we can get 0:19:26.600 --> 0:19:28.280 in to have a long discussion about what you actually 0:19:28.320 --> 0:19:31.920 learn in epidemiology school or epidemology grad school of public 0:19:31.960 --> 0:19:33.879 health school. But a lot of what you do, a 0:19:33.880 --> 0:19:36.439 lot of how epidemiologists or public health officials, how they 0:19:36.440 --> 0:19:39.760 cut their teeth is studying things like influenza pandemic. So 0:19:39.800 --> 0:19:43.199 a lot of the pronouncements that they're making with this 0:19:43.400 --> 0:19:45.640 incredible certainty in the sense, well you, if you don't 0:19:45.680 --> 0:19:49.880 listen to me, you're against science, are based on historical 0:19:50.000 --> 0:19:53.760