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Speaker 1: This is Wins and Losses with Clay Travis. Clay talks

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Speaker 1: with the most entertaining people in sports, entertainment and business.

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Speaker 1: Now here's Clay Travis. Welcome in Wins and Losses Podcast.

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Speaker 1: I am Clay Travis, and we're about to be joined

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Speaker 1: by O vic Roy, who I think you guys are

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Speaker 1: really going to enjoy. He's been doing fantastic work looking

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Speaker 1: at the data surrounding the coronavirus, making recommendations on so

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Speaker 1: many different levels, been writing for the Wall Street Journal,

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Speaker 1: among other locations. We have never actually spoken before, but

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Speaker 1: I am impressed by the work that he's done. I

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Speaker 1: found him on social media over the last several months,

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Speaker 1: and we bring him in now. Oh vic Roy, let

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Speaker 1: me go ahead and start here. How can people find

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Speaker 1: you on social media? How can they read your work? Ovic?

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Speaker 1: And thanks for joining us. Hey, thanks Clay, Well, thanks

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Speaker 1: to my eccentric parents. My name is spelled A v

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Speaker 1: i K, not Ovic, O v i K. It's a

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Speaker 1: v i K and that's my Twitter handle, A v

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Speaker 1: I K, just like it sounds a viasm Victor I K.

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Speaker 1: All right, So your background as we get into so

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Speaker 1: many different interesting topics that I want to discuss with you.

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Speaker 1: But what is your educational background that led you into

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Speaker 1: the profession that you have now and what do you

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Speaker 1: do for a living. It's a bit of a zig

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Speaker 1: zag path. My undergraduate degree was in molecular biology at

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Speaker 1: M I. T. And then I went to medical school

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Speaker 1: at Yale. And then instead of becoming a doctor or

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Speaker 1: a scientist, I went into biotechnology investing, where I invested

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Speaker 1: in companies trying to develop new treatments for diseases, vaccines,

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Speaker 1: all that sort of thing. And then I got really

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Speaker 1: interested in healthcare re form and that led me down

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Speaker 1: the rabbit hole of healthcare policy and economic policy in general,

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Speaker 1: and worked on a bunch of presidential campaigns and and

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Speaker 1: now I run a think tank in Austin, Texas called

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Speaker 1: the Foundation for Research on Equal Opportunity, where we come

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Speaker 1: up with ideas to help more Americans climb up the

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Speaker 1: economic ladder of success. All right, So I'm fascinated by

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Speaker 1: several different things. You've already told this, So what is

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Speaker 1: the reaction. You go to M I T. And then

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Speaker 1: you go to Yale Medical School and I don't know

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Speaker 1: the answer to this. I went to law school and

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Speaker 1: I practiced law for a couple of years, and even

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Speaker 1: not practicing law was considered to be a sort of

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Speaker 1: a risky choice by many people, because you have a

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Speaker 1: good profession that's out there. What percentage of your classmates

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Speaker 1: or people who go to a medical school as good

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Speaker 1: as Yale end up not actually practicing medicine when they graduate, Well,

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Speaker 1: it's a small percent at most schools, it's probably close

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Speaker 1: to zero. But Yale was a particular place where they

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Speaker 1: actually encourage you to to pursue your interests outside of

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Speaker 1: medical school. It was like a path Baale system and

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Speaker 1: things like that. And so I'd say in a typical

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Speaker 1: Yale class, which is a hundred people per class, about

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Speaker 1: five to ten end up doing something that's sort of

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Speaker 1: like you know, in law school, it's very typical, right,

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Speaker 1: A lot of lawyers go into things of the law,

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Speaker 1: and especially yeah, especially after a few years. Most people

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Speaker 1: have to go in and make money initially, but then

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Speaker 1: they'll start to filter out. Everybody I always say who's

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Speaker 1: a lawyer has got a dream of not being a lawyer.

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Speaker 1: But most people who go to medicine, That's why I

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Speaker 1: was interested. Most people who go to medical school go

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Speaker 1: on in practice. So is that something you came to

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Speaker 1: a decision before you even started medical school, or what

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Speaker 1: was it about, sort of the capitalistic economy I guess

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Speaker 1: of the biotechnology universe that attracted you more than being

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Speaker 1: a traditional doctor. Well, my dad was also a scientist.

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Speaker 1: He was a biochemist, and so I grew up around

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Speaker 1: all these incredible people who had been like the people

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Speaker 1: who had characterized DNA and RNA, and we're the pioneers

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Speaker 1: in this modern field of genetics and biology that we're

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Speaker 1: now living in. So I always had this real excitement

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Speaker 1: about it. I thought I wanted to be a scientist.

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Speaker 1: And then you know what the problem is, Like, you know,

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Speaker 1: at m I T a core of the faculty has

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Speaker 1: Nobel Prize that I'm walking around and I'm like, there

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Speaker 1: is no way I'm going to win a Nobel Prize.

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Speaker 1: I'm not smart enough. So how am I going to

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Speaker 1: actually do something useful to the world. I don't know.

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Speaker 1: So I struggled with it for a while, and I thought,

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Speaker 1: you know what, maybe I can invest in biotech companies.

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Speaker 1: I can help build the latest new cure for some disease.

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Speaker 1: That would be something useful I could do with my life,

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Speaker 1: and that let me down that path, and here I

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Speaker 1: am doing this now because obviously health care reform and

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Speaker 1: economic policy in general effects so many people. A lot

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Speaker 1: of people struggle to find affordable health insurance. Uh. And

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Speaker 1: there are a lot of cures that we need to

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Speaker 1: have for for disease that people have, and and not

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Speaker 1: just in health care, a lot of oarliious higher education.

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Speaker 1: How do you afford college? How do you afford to

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Speaker 1: keep the lights on in your house? There are lots

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Speaker 1: of things where we need new ideas, and it's been

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Speaker 1: fun to work on work on trying to develop those ideas. Okay, yeah, no,

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Speaker 1: it's so how do you decide? So I'm kind of

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Speaker 1: fascinated by the concept of being an investor in biotechnology

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Speaker 1: companies because you obviously have to be sophisticated to even

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Speaker 1: understand as an investor, like for people out there who

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Speaker 1: don't really think about it very much, being a quote

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Speaker 1: unquote sophisticated investor is typically requires a certain net worth,

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Speaker 1: but you can invest in a variety of different companies,

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Speaker 1: sometimes for better or ill uh. And but biotechnology, I

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Speaker 1: would imagine there's a lot of I I you tell me,

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Speaker 1: but I would imagine there's a lot of snake oil

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Speaker 1: cells out there who are trying to peddle things that

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Speaker 1: may or may not make a lot of sense. So

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Speaker 1: I would think a medical degree like yours would basically,

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Speaker 1: for lack of a better way to describe it, allow

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Speaker 1: you to speak the language so that you're may be

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Speaker 1: better able to understand. And maybe also your dad being

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Speaker 1: involved help with that as well. But what was your

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Speaker 1: process like in terms of investing and finding companies that

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Speaker 1: you found to be worthy of putting money behind. Yeah,

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Speaker 1: it's it's so it's great and and really relevant that

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Speaker 1: you're bringing this up, actually because it has a lot

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Speaker 1: to do with how I ended up being a contrarian

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Speaker 1: on COVID stuff. So yes, I I UH entered the

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Speaker 1: workforce a finished school in two thousands, and that was

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Speaker 1: right around the time that the Human Genome Project had

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Speaker 1: been completed. So for those who remember those days, the

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Speaker 1: Human Genome Project was at the time this gigantic UH

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Speaker 1: scientific enterprise to sequence the entire human genome. Every d

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Speaker 1: N a piece of DNA and you're comprise of the

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Speaker 1: human genetic code from beginning to end, because that had

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Speaker 1: never been done before. And that was finally finished in

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Speaker 1: two thousand and one, and there was a big dot

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Speaker 1: com boom in the nineties when the Internet as we

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Speaker 1: know at first came into being. And right after that

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Speaker 1: dot com bubble burst, there was this basically this genomics bubble.

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Speaker 1: All these stocks called this genomics and that genomics were

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Speaker 1: We're getting multibillion dollar market caps and nobody knew what

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Speaker 1: they did and a lot of it was hyped. And

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Speaker 1: so UH an investment firm I've never heard of called

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Speaker 1: Bain Capital. I have to know a couple of people

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Speaker 1: who worked there, and one of them reached out to

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Speaker 1: me and said, hey, can you help us figure out

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Speaker 1: all this genomics stuff because we're just a bunch of MBAs.

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Speaker 1: We don't know anything about genomics, and we figured you

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Speaker 1: can teach us. You have a degree in molecro biology,

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Speaker 1: you can teach us about this stuff, and we can

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Speaker 1: teach you how to read a balance sheet and then

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Speaker 1: maybe you can be useful. And I'm like, wow, that's

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Speaker 1: not really knowing the first thing about about how to

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Speaker 1: do any of that. When I started UH that, I

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Speaker 1: got recruited to to Bain Capital, moved to Boston and

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Speaker 1: started investing in bi tech companies. I basically became part

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Speaker 1: of this first generation of people with scientific and medical

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Speaker 1: backgrounds m d s and PhD is mostly who started

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Speaker 1: investing in biotech companies because it ended up it's not

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Speaker 1: like a normal stock thing, where like normally, if you

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Speaker 1: turn on c NBC or something, it's like, well the

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Speaker 1: pe ratio is this right, or if you look at

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Speaker 1: those conventional things. But biotech it's not like that at all,

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Speaker 1: because the clinical trial turns out positive or negative in

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Speaker 1: terms of your your latest jurif for breast cancer or whatever,

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Speaker 1: and that stock goes to zero if it fails, or

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Speaker 1: it triples if it succeeds. I mean, it's total volatility.

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Speaker 1: It's crazy, a lot of losses, a lot of winds.

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Speaker 1: It's it's kind of like baseball, where if you're batting average,

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Speaker 1: it doesn't if you're batting averages below five dred you're

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Speaker 1: probably not gonna survive. But if you if but you're

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Speaker 1: gonna lose, you're gonna be wrong plenty. And you have

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Speaker 1: to put an enormous amount of effort into statistics, right

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Speaker 1: because at the end of the day, what what my

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Speaker 1: job ended up becoming and a lot of people who

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Speaker 1: were like me, is we ended up being incredibly intense statisticians,

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Speaker 1: because what you end up doing is you're looking at

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Speaker 1: a say, a breast cancer trial, new new drug for

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Speaker 1: breast cancer. It's being tested in fifty women who have

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Speaker 1: breast cancer. Well, fifty women is not a lot. So

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Speaker 1: if there's fifty women who got the drug and fifty

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Speaker 1: women who didn't, and let's say that trial got really hyped, Oh,

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Speaker 1: this drug really worked in the fifty women who got

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Speaker 1: the drug where they really did better, they lived longer,

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Speaker 1: their breast tumors went away. But what if it's what

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Speaker 1: if what happened was actually the people who were in

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Speaker 1: the placebo arm of the trial were actually sicker in

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Speaker 1: the beginning and that excuse the results. Or maybe they

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Speaker 1: measured the tumors in the wrong way. So there's all

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Speaker 1: sorts of subtleties about a way a clinical trial operates

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Speaker 1: that you can then make a bet and say, Okay,

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Speaker 1: is this drug overhyped or under hyped? Are people overhyping

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Speaker 1: the drug as you said, like is it snake? Or

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Speaker 1: there's this trial that's been published in the New England

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Speaker 1: Journal of Medicine that says this this drug really works.

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Speaker 1: But then in a larger trial with thousands of patients

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Speaker 1: is going to fail or is it the other way around.

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Speaker 1: Maybe it's a drug that didn't do that well at

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Speaker 1: early state trials, but in bigger trials there is a

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Speaker 1: signal there and it ends up being really successful. Those

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Speaker 1: are the kinds of things that people like me we're

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Speaker 1: making bets on, betting tens of in fact, sometimes hundreds

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Speaker 1: of millions of dollars, trying to figure out which side

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Speaker 1: was right. How do you do? I did all right?

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Speaker 1: I did all right. Uh. And that's what kind of

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Speaker 1: gave me the freedom to to start a nonprofit on

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Speaker 1: a lark. Yeah, a financial cushion, all right. So that

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Speaker 1: is all interesting and fascinating background. And you said something

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Speaker 1: a couple of minutes ago. You said that your work

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Speaker 1: at Bain Capital, and your work and going to m

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Speaker 1: I T and also going to Yale Medical School and

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Speaker 1: putting it to work molecular biology, looking at the genome projects,

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Speaker 1: all of these different things obviously lead you to being

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Speaker 1: able to look at data and figure out what I

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Speaker 1: would call, for lack of a better way, what the

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Speaker 1: signal is versus what the noise is. So there's so

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Speaker 1: much noise on a day to day basis, regardless of

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Speaker 1: what you do for a living, whatever people are doing

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Speaker 1: for livings where they listen to us out there, most

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Speaker 1: of it is external noise that isn't really getting to

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Speaker 1: the essence of what you do. Figuring out the signal

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Speaker 1: is essentially what you had to do for these biochemistry

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Speaker 1: uh and and investigations for lack of another way of

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Speaker 1: putting it. That also then corresponds in an incredibly unique

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Speaker 1: way with what's happened with the coronavirus, where every day,

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Speaker 1: it seems to me, we are deluged with data, with news,

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Speaker 1: with viral stories meaning not necessarily virus stories, but literally

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Speaker 1: stories that go viral about the virus. How do you

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Speaker 1: cut through that noise and figure out what the real

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Speaker 1: signal is? So let's go in. The story starts in January.

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Speaker 1: When did you first become aware of the coronavirus and

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Speaker 1: start to read and pay attention to it in China? Well,

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Speaker 1: I started hearing the stories right away in in you know,

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Speaker 1: just January February when the news started the break, But

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Speaker 1: but at that time we didn't know that it was coming.

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Speaker 1: I think it wasn't. One was the first case in

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Speaker 1: in Washington, stated was I want to say February. It

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Speaker 1: was late January, so we started to hear about this

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Speaker 1: thing in Wuhan. There was a lot of suppression and uh,

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Speaker 1: and then we started to see the in Washington and

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Speaker 1: certainly followed that all the way through because healthcare is

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Speaker 1: health care policy, and healthcare stuff is on my radar

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Speaker 1: always because as part of my job. So you said

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Speaker 1: you were comfortable early on with looking into the data

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Speaker 1: and maybe becoming somewhat of a contrarian in terms of

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Speaker 1: what your analysis has done. Such a good job of,

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Speaker 1: I think is for people who don't know, and again

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Speaker 1: I would encourage them to go follow you on Twitter

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Speaker 1: at a v I k oh vic Roy we are

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Speaker 1: talking to right now. Uh. He does incredible work looking

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Speaker 1: at the data and putting it in context. And I

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Speaker 1: believe the first time I started to see your data

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Speaker 1: percolate on my feed and start to follow it aggressively

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Speaker 1: was when you were looking at risk factors for college

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Speaker 1: age kids, comparing let's say the seasonal flu and also

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Speaker 1: with kids who are elementary school age and saying, hey,

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Speaker 1: I understand that you know there's a lot of attention

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Speaker 1: on the coronavirus right now, but the data would reflect

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Speaker 1: that most young people are under greater danger from the

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Speaker 1: seasonal flu or pneumonia. That's a counterintuitive data. Fect When

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Speaker 1: did you start to dive into the numbers in such

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Speaker 1: a way, Because this is eerily similar, I would think,

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Speaker 1: and I bet you would agree with what you were

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Speaker 1: doing looking at these biotech trials where the headline might

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Speaker 1: be one thing, but when you actually go and look

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Speaker 1: underneath the surface and start to examine it, some of

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Speaker 1: the data is telling a different story maybe than the

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Speaker 1: data that the media is sharing at the top line

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Speaker 1: level of reporting. Yeah, that's that's exactly right, Clay. I mean,

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Speaker 1: you've hit it on the head. And I would say also,

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Speaker 1: by the way, I think one of the reasons when

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Speaker 1: I listen to your your show and your podcast, you

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Speaker 1: do an amazing job of just walking people through the

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Speaker 1: real statistical situation. And I think that goes to show

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Speaker 1: that there's an analogous, analogous situation of sports. Right you

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Speaker 1: think about this explosion of sports analytics today and what

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Speaker 1: is that all about. It's all about the fact that

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Speaker 1: conventional ways of measuring performance in sports, starting with baseball,

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Speaker 1: but the true in it, it's true in every sport

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Speaker 1: don't necessarily accurately measure how a player or a team

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Speaker 1: is performing, and so there's been this explosion of trying

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Speaker 1: to be much more rigorous about how to measure that.

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Speaker 1: And on Wall Street that's basically what you're doing on

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Speaker 1: Wall Street two. You're you're trying to say, at least

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Speaker 1: in biotech investing, and it's not just true in biotech investing,

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Speaker 1: but especially true in biotech investing. What you're trying to

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Speaker 1: figure out is, Okay, this stock is really hyped. What

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Speaker 1: are people getting wrong? This stock is down in the dumps?

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Speaker 1: What are people getting wrong? You're you're you're training. If

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Speaker 1: you try to be good, try to outperform is to

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Speaker 1: always try to swim against the tide and try to

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Speaker 1: identify what other people aren't thinking about when they look

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Speaker 1: at the data, and that habit of mind or cast

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Speaker 1: of mind, if you want to call it, that's how

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Speaker 1: That's what's driven all my policy work too. When I

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Speaker 1: write about public policy, when I write about healthcare, when

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Speaker 1: I write about COVID, it's all about, Okay, what are

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Speaker 1: people missing? And the obvious thing right away was this

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Speaker 1: real skew in the age distribution of who was getting

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Speaker 1: really sick and who was dying from COVID. There was

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Speaker 1: a huge skew towards the elderly. And I started writing

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Speaker 1: about this pretty early on it. The early data out

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Speaker 1: of China, the early data out of Italy, the places

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Speaker 1: that really got hit a little bit before the US

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Speaker 1: got hit, they all were showing the same skew where

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Speaker 1: in the case of the US, literally of the people

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Speaker 1: who died of COVID are over the age of sixty five.

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Speaker 1: And I would start writing about this and tweeting about

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Speaker 1: it and putting in our in our work at free

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Speaker 1: out dot org, which is our think tank, and some

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Speaker 1: people would ask me like, well, okay, that's that's interesting.

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Speaker 1: People over are the ones that dying or are the

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Speaker 1: ones over sixty five? Isn't that true? Most things don't,

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Speaker 1: don't Most things end up, you know, basically killing the

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Speaker 1: elderly more than they kill young people. So I was

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Speaker 1: actually curious to say, okay, let's let's actually try to

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Speaker 1: dig into that and figure out, Okay, how does the

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Speaker 1: skew work for a more conventional infectious disease like influenza

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Speaker 1: compared to COVID, Because if you look at the history

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Speaker 1: of influenza pandemics, they actually do hurt and kill children.

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Speaker 1: They do kill young people. The famous uh influenza pandemic

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Speaker 1: of nineteen eighteen killed a lot of soldiers. That was,

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Speaker 1: more soldiers died because of influenza than died at least

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Speaker 1: American soldiers then died actually fighting in the trenches in

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Speaker 1: World War One. So a lot of young people die.

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Speaker 1: Then that's why we closed the schools if there's like

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Speaker 1: a crazy influenza pandemic. So so I was interested in

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Speaker 1: this and what I found. If you actually look at

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Speaker 1: CDC data, official government data over a ten year period

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Speaker 1: from two thousand seven to two thousand seventeen, which is

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Speaker 1: our most recent cut of the data, and you compare

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Speaker 1: the number of people who die from influenza by age

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Speaker 1: bracket to those who die from COVID, you see a

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Speaker 1: much worse skew for COVID. In other words, COVID is

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Speaker 1: much more skewed in terms of serious illness or death

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Speaker 1: towards the elderly than your typical infectious disease or any

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Speaker 1: other kind of disease. So this is interesting to me

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Speaker 1: too because one of the sort of aphorisms I would

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Speaker 1: say about war and people who listen to this know

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Speaker 1: also that I'm a history buff and I love of

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Speaker 1: you know, studying American and and other history a lot.

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Speaker 1: You typically end up fighting the last war, sorry, the

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Speaker 1: most recent war, with the technology from the previous war, right,

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Speaker 1: because everybody who has everybody who has studied all the

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Speaker 1: things that have happened in warfare is going to apply

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Speaker 1: the lessons that have applied from prior war. But you're then,

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Speaker 1: you know, let's use the Civil War for an example,

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Speaker 1: which is a particular interest of mine. You're using the

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Speaker 1: Napoleon Napoleonic tactics, but now the technology, the ability of rifles, cannons,

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Speaker 1: all of the rapid fire weapons have changed. So the

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Speaker 1: mortality rates skyrockets, right, because you're fighting, and gradually you

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Speaker 1: adjust and people learn, hey, maybe we should be fighting

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Speaker 1: in a different way. The reason why I used that

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Speaker 1: as an example is so many people and this frustrated me,

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Speaker 1: and I bet it frustrated you when you actually looked

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Speaker 1: at the data. So many people used data from the

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Speaker 1: flu to justify shutting down schools based on the coronavirus.

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Speaker 1: But this particular covid infection was not like the flu

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Speaker 1: in that the age range of the impacted were different.

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Speaker 1: So the decision to shut down schools might well have

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Speaker 1: made sense in the pandemic a hundred years ago, and

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Speaker 1: you study it and you say, oh, that's the lesson

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Speaker 1: we should take away. But it's not the same virus.

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Speaker 1: So you're fighting a new virus which has never existed

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Speaker 1: before with the tactics that would have worked against the

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Speaker 1: virus a hundred years ago. That's a misfit, right, But

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Speaker 1: most people aren't sophisticated enough to think about that. You were,

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Speaker 1: But that has to be frustrating to you from a

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Speaker 1: public policy perspective to see it and not be able

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Speaker 1: to cut through the noise and make people realize the

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Speaker 1: data and what you've seen. Well, first of all, let

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Speaker 1: me just say you're absolutely right, a thousand percent. This

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Speaker 1: issue of fighting the last war is exactly what's going

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Speaker 1: on here. And and so there's so many different dimensions

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Speaker 1: of how that's true. Clay, I'll give you one. So

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Speaker 1: you've heard people say, well, there were these plans in

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Speaker 1: place from the George W. Bush administration and the Obama administration,

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Speaker 1: why didn't Trump use those plans to fight the coronavirus. Well,

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Speaker 1: those plans were not designed for coronavirus. They were designed

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Speaker 1: for influenza. In fact, if you actually look at the

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Speaker 1: cover page of the reports, they say things like our

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Speaker 1: plan for dealing with a novel influenza pandemic. Now, influenza

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Speaker 1: is a different virus from coronaviruses like COVID stars kobe

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Speaker 1: to the virus that causes COVID nineteen. And again this

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Speaker 1: is part of having that molecular biology backrouprom m I

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Speaker 1: T I understand the difference between different types of viruses

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Speaker 1: and how they actually infect you and how they're they're

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Speaker 1: how they actually work in the body. They're very different,

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Speaker 1: and so it's very important to understand that not all

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Speaker 1: viruses are the same. The way they behave in your body,

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Speaker 1: their lethality, their virulence can be very different. And so

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Speaker 1: to your point, yes, you closing schools and a severe

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Speaker 1: influenza pandemic makes sense because the young children and young

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Speaker 1: adults do get killed old from really bad influenza pandemics

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Speaker 1: in the in the case of nineteen eighteen especially, But

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Speaker 1: this is not an influenza pandemic, and it's also true

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Speaker 1: of all these sort of public health epidemiologists types. So

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Speaker 1: one of the things you'll see a lot of people say,

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Speaker 1: particularly on social media as well, how dare you write

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Speaker 1: or tweet about COVID. You don't have a right to

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Speaker 1: have an opinion because you're not an epidemiologist. Now, the

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Speaker 1: problem is, first of all, epidemiology is we can get

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Speaker 1: in to have a long discussion about what you actually

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Speaker 1: learn in epidemiology school or epidemology grad school of public

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Speaker 1: health school. But a lot of what you do, a

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Speaker 1: lot of how epidemiologists or public health officials, how they

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Speaker 1: cut their teeth is studying things like influenza pandemic. So

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Speaker 1: a lot of the pronouncements that they're making with this

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Speaker 1: incredible certainty in the sense, well you, if you don't

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Speaker 1: listen to me, you're against science, are based on historical

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Speaker 1: evidence of what has worked or what has happened with influenza.

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Speaker 1: And this is a completely new virus that we've never

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Speaker 1: seen before, and so a lot of that expertise doesn't

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Speaker 1: really work because you're dealing with something completely different. And

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Speaker 1: you I want to circle back right now, because you

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Speaker 1: just said something, somebody says, oh, you're not an epidemiologist,

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Speaker 1: you're not a virologist. I try to share intelligent people.

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Speaker 1: You went to m I T. You went to Yale

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Speaker 1: Medical School. I've got decent degree background as well. But ultimately,

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Speaker 1: intelligent people who are contrarians or who are skeptics are

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Speaker 1: very often right, and people who think that they are

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Speaker 1: the quote unquote experts very often are wrong when you

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Speaker 1: actually look at the data. Right. And so when you say, hey,

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Speaker 1: we're only going to listen to scientists or we're only

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Speaker 1: going to listen to quote unquote experts, I mean the

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Speaker 1: data is telling us a story that it shouldn't matter

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Speaker 1: who's telling the story, right, Like when when you are

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Speaker 1: coming out and sharing your data on why schools should

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Speaker 1: be back open based on looking at the direct c

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Speaker 1: DC data, that's more valid than somebody who studied uh

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Speaker 1: the influenza outbreak a hundred years ago, and it is

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Speaker 1: trying to draw lessons from there. Yet it seems to

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Speaker 1: me like in the public media sphere there's more benefit

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Speaker 1: given to those epidemiologists. Maybe then would be justified based

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Speaker 1: on the data. That's a kind of a long winded question,

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Speaker 1: but you have to see that on a regular basis

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Speaker 1: with what you've been writing and talking about. Well, I

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Speaker 1: would you're I would agree with you, but I would

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Speaker 1: also describe it in a different way because there is

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Speaker 1: not consensus in the scientific community and and it is

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Speaker 1: actually anti scientific to demand that alternative hypotheses be thrown

432
00:21:38,640 --> 00:21:43,040
Speaker 1: out for no reason. There's actual evidence about what's happening,

433
00:21:43,080 --> 00:21:45,760
Speaker 1: and the evidence heavily waits in one direction another. That's

434
00:21:45,760 --> 00:21:47,960
Speaker 1: one thing. But in a situation where you're dealing with

435
00:21:48,000 --> 00:21:50,960
Speaker 1: an unknown virus that we've never seen before and that's

436
00:21:50,960 --> 00:21:55,040
Speaker 1: spreading in a way that has unique characteristics, then you

437
00:21:55,480 --> 00:21:58,000
Speaker 1: as a scientist and again my dad was in scientist

438
00:21:58,040 --> 00:22:00,920
Speaker 1: I group around size my whole life. Uh, as a scientist,

439
00:22:01,000 --> 00:22:05,000
Speaker 1: you're obligated not to throw out any theory, any hypothesis

440
00:22:05,080 --> 00:22:08,679
Speaker 1: until you can convincingly with the evidence disprove it. And

441
00:22:08,720 --> 00:22:10,840
Speaker 1: in fact, there are lots of there's a lot of

442
00:22:10,840 --> 00:22:14,840
Speaker 1: evidence that that plausible scientific theories are not are being suppressed.

443
00:22:15,240 --> 00:22:17,520
Speaker 1: I'll give you an example. There's an epidemails or a

444
00:22:17,520 --> 00:22:21,399
Speaker 1: biomathematician who does a lot of things around population health

445
00:22:21,880 --> 00:22:25,240
Speaker 1: named Gabriella Gomet and she tweeted a couple of weeks

446
00:22:25,240 --> 00:22:27,879
Speaker 1: ago that she actually has been trying to write some

447
00:22:27,920 --> 00:22:31,760
Speaker 1: stuff about her immunity population immunity and how that population

448
00:22:31,760 --> 00:22:35,120
Speaker 1: immunity may maybe closer at hands than other people think.

449
00:22:35,600 --> 00:22:38,440
Speaker 1: And she can't get the work published in scientific journals

450
00:22:38,480 --> 00:22:40,480
Speaker 1: because some people have shared with her. The editors of

451
00:22:40,520 --> 00:22:43,359
Speaker 1: these journals say, well, if we if we publish your

452
00:22:43,400 --> 00:22:47,880
Speaker 1: work and people become less scared of COVID, then maybe

453
00:22:47,960 --> 00:22:50,240
Speaker 1: that will lead people to not wear masks and stuff.

454
00:22:50,280 --> 00:22:52,800
Speaker 1: And we don't want that. Therefore, we've got to keep

455
00:22:52,840 --> 00:22:56,960
Speaker 1: this kind of optimistic take off off the table. Now

456
00:22:57,119 --> 00:23:01,840
Speaker 1: that's not science, right, It's not science when you artificially suppressed,

457
00:23:01,880 --> 00:23:06,000
Speaker 1: for subjective or political reasons, alternative hypothesis of what's going on.

458
00:23:06,400 --> 00:23:08,680
Speaker 1: And that's the lesson I really want to drive home

459
00:23:08,720 --> 00:23:10,720
Speaker 1: here is a lot of the people who are screaming

460
00:23:10,760 --> 00:23:15,200
Speaker 1: the loudest about trusting the science are not actually acting scientifically.

461
00:23:15,400 --> 00:23:18,800
Speaker 1: Because if you're acting scientifically, you're looking very hard at

462
00:23:18,800 --> 00:23:22,000
Speaker 1: the data. You're not ruling out any theory until you've

463
00:23:22,000 --> 00:23:26,680
Speaker 1: got got convincing evidence that it's wrong. That's really well

464
00:23:26,720 --> 00:23:30,600
Speaker 1: said and much better than my sort of haphazard question

465
00:23:30,680 --> 00:23:33,960
Speaker 1: that I asked there. Why do you think that is? So?

466
00:23:34,400 --> 00:23:37,360
Speaker 1: Why do you think it is? And that's hugely important.

467
00:23:37,400 --> 00:23:42,800
Speaker 1: I think the scientific method is a rigorous adversarial system. Right.

468
00:23:42,840 --> 00:23:45,000
Speaker 1: There are a lot of people out there who believe

469
00:23:45,440 --> 00:23:48,359
Speaker 1: science only has one answer, right because we've proven, say,

470
00:23:48,440 --> 00:23:51,159
Speaker 1: what the boiling point of water is or what the

471
00:23:51,200 --> 00:23:54,560
Speaker 1: freezing point is, but that had to be tested over time, right,

472
00:23:54,640 --> 00:23:58,800
Speaker 1: And when you have these rigorous battles over what might

473
00:23:58,960 --> 00:24:02,400
Speaker 1: or might not be the ruth, that's how science advances.

474
00:24:02,440 --> 00:24:04,840
Speaker 1: But when you don't allow that battle to me it

475
00:24:04,920 --> 00:24:07,159
Speaker 1: kind of ties in with the marketplace of ideas and

476
00:24:07,160 --> 00:24:10,359
Speaker 1: why I'm such a huge First Amendment absolutist. When you

477
00:24:10,480 --> 00:24:15,000
Speaker 1: constrict sort of the available universe of argument or discussion,

478
00:24:15,480 --> 00:24:19,119
Speaker 1: you are actually penalizing our ability to arrive at a

479
00:24:19,240 --> 00:24:23,920
Speaker 1: truth or a universally ultimately recognize truth. Right. I mean

480
00:24:24,040 --> 00:24:27,400
Speaker 1: the entire purpose of science is I've got this hypothesis,

481
00:24:27,480 --> 00:24:30,480
Speaker 1: let me test it. When you start saying to people, oh,

482
00:24:30,560 --> 00:24:34,840
Speaker 1: that hypothesis makes people uncomfortable, we can't discuss it, you're

483
00:24:34,880 --> 00:24:40,680
Speaker 1: actually combating science. That's exactly right. And one one important

484
00:24:40,680 --> 00:24:43,600
Speaker 1: element of this that's that's that's essential to really think

485
00:24:43,600 --> 00:24:46,080
Speaker 1: about and where you can really get your spiddy sense

486
00:24:46,160 --> 00:24:51,560
Speaker 1: up in a sense, is when people conflate predictions with facts.

487
00:24:52,320 --> 00:24:54,680
Speaker 1: A prediction is about something that may happen in the future.

488
00:24:54,680 --> 00:24:59,880
Speaker 1: And look, it may be more probable that Alabama wins

489
00:24:59,880 --> 00:25:04,800
Speaker 1: the national championship then Michigan State, Uh, but it isn't

490
00:25:04,840 --> 00:25:08,159
Speaker 1: guaranteed that Alabama is going to win the national championship, right,

491
00:25:08,400 --> 00:25:13,000
Speaker 1: And so similarly, uh. In science, you hear a lot

492
00:25:13,000 --> 00:25:14,960
Speaker 1: of people say, well, it's a fact that X will

493
00:25:15,000 --> 00:25:18,000
Speaker 1: happen in the future, but we don't know because the

494
00:25:18,080 --> 00:25:20,119
Speaker 1: world is a very complex place and there are a

495
00:25:20,160 --> 00:25:22,840
Speaker 1: lot of variable to go into whether something happens or not,

496
00:25:22,920 --> 00:25:25,280
Speaker 1: especially when you're talking about a novel virus that no

497
00:25:25,280 --> 00:25:28,480
Speaker 1: one has ever seen before. And so that's where there's

498
00:25:28,640 --> 00:25:32,000
Speaker 1: particularly been a poisonous climate where if you have a

499
00:25:32,040 --> 00:25:34,560
Speaker 1: different view as to what may happen in the future

500
00:25:35,040 --> 00:25:38,400
Speaker 1: in a situation where there's a lot of uncertainty, there's

501
00:25:38,400 --> 00:25:40,280
Speaker 1: been a lot of suppression debate at that because we

502
00:25:40,320 --> 00:25:42,879
Speaker 1: can't we can't, we can't give any anybody reason to

503
00:25:42,920 --> 00:25:45,959
Speaker 1: be optimistic, because if you're optimistic, then maybe you know

504
00:25:46,080 --> 00:25:47,840
Speaker 1: you'll you'll hang out at a bar with your friends

505
00:25:47,840 --> 00:25:49,880
Speaker 1: and communicate the disease to other people. And we can't,

506
00:25:49,920 --> 00:25:52,359
Speaker 1: we can't have that, And the problem is if you

507
00:25:52,480 --> 00:25:55,399
Speaker 1: engage in that kind of let's call it dishonest suppression,

508
00:25:56,359 --> 00:25:58,479
Speaker 1: then people don't listen to you because they don't trust you.

509
00:25:59,080 --> 00:26:00,760
Speaker 1: If they don't trust they're gonna say, you know what,

510
00:26:00,920 --> 00:26:03,240
Speaker 1: I don't trust that guy who's telling me not to

511
00:26:03,280 --> 00:26:05,159
Speaker 1: do all this stuff because he's been wrong half the

512
00:26:05,160 --> 00:26:07,800
Speaker 1: time anyway, and he's demanding that I listened to because

513
00:26:07,800 --> 00:26:09,639
Speaker 1: he's a scientist, or that I'm not going to believe

514
00:26:09,640 --> 00:26:12,480
Speaker 1: in science. And that's actually more dangerous for science and

515
00:26:12,520 --> 00:26:15,959
Speaker 1: the scientific enterprise that people cloak themselves in the words

516
00:26:16,000 --> 00:26:19,639
Speaker 1: science but they're not actually being scientific, because then people

517
00:26:19,640 --> 00:26:21,360
Speaker 1: out there say, well, if that's what scientists and I'm

518
00:26:21,359 --> 00:26:24,120
Speaker 1: not for it. Fox Sports Radio has the best sports

519
00:26:24,119 --> 00:26:26,919
Speaker 1: talk lineup in the nation. Catch all of our shows

520
00:26:26,960 --> 00:26:29,879
Speaker 1: at Fox Sports Radio dot com and within the I

521
00:26:29,960 --> 00:26:32,920
Speaker 1: Heart Radio apps. Search f s R to listen live.

522
00:26:33,160 --> 00:26:35,439
Speaker 1: We're talking to O vic Roy. I'm Clay Travis. This

523
00:26:35,560 --> 00:26:39,399
Speaker 1: is Wins and Losses. I mean, now this obviously, this

524
00:26:39,440 --> 00:26:43,280
Speaker 1: subject has utterly fascinated me on several different levels. And

525
00:26:43,320 --> 00:26:45,480
Speaker 1: you know that I've spent a lot of time talking

526
00:26:45,520 --> 00:26:47,680
Speaker 1: about this from the sports perspective and We're going to

527
00:26:47,760 --> 00:26:50,359
Speaker 1: circle around to it on a sports perspective. But it

528
00:26:50,440 --> 00:26:53,119
Speaker 1: seems to me you've talked about the analytics revolution that

529
00:26:53,160 --> 00:26:55,760
Speaker 1: we've seen in sports. It seems to me that the

530
00:26:55,960 --> 00:27:00,240
Speaker 1: essence of why our national conversation about the coronavirus has

531
00:27:00,280 --> 00:27:03,240
Speaker 1: been so bad ultimately boils down to something you were

532
00:27:03,240 --> 00:27:06,400
Speaker 1: just talking about, which is, there's a very poor understanding

533
00:27:06,520 --> 00:27:09,520
Speaker 1: of statistics and probability in this country. And I'm gonna

534
00:27:09,520 --> 00:27:11,080
Speaker 1: give people out there, and I want to give you

535
00:27:11,119 --> 00:27:13,520
Speaker 1: a chance to tee off on this too. But and

536
00:27:13,640 --> 00:27:15,880
Speaker 1: and really it kind of goes to why you've been

537
00:27:15,920 --> 00:27:19,360
Speaker 1: able to be successful looking at biotech companies. I think

538
00:27:19,400 --> 00:27:22,160
Speaker 1: it goes to why I've been successful in my chosen

539
00:27:22,359 --> 00:27:25,760
Speaker 1: field of of life. Um, it's because I tend to

540
00:27:25,840 --> 00:27:29,600
Speaker 1: be skeptical of consensus opinion and actually look at the

541
00:27:29,680 --> 00:27:32,760
Speaker 1: data myself. But you talked about, you know, Alabama playing

542
00:27:32,760 --> 00:27:36,399
Speaker 1: Michigan State. Sports fans are universally bad about this. In

543
00:27:36,560 --> 00:27:40,399
Speaker 1: college football in particular, there's an idea that if a

544
00:27:40,480 --> 00:27:43,480
Speaker 1: team plays and one team beats the other, when that

545
00:27:43,520 --> 00:27:47,320
Speaker 1: means that team was quote unquote better. But the reality is,

546
00:27:47,359 --> 00:27:49,600
Speaker 1: and this has been something I love thinking about you know,

547
00:27:49,640 --> 00:27:53,080
Speaker 1: if you played a million minute game instead of a

548
00:27:53,160 --> 00:27:56,680
Speaker 1: sixty minute game, the team that played for a million minutes,

549
00:27:56,880 --> 00:27:59,640
Speaker 1: you know, is probably going to be the better team

550
00:27:59,680 --> 00:28:02,879
Speaker 1: if it wins, because your data sample size is a

551
00:28:02,960 --> 00:28:05,960
Speaker 1: million games. But when you play sixty minutes of a

552
00:28:05,960 --> 00:28:09,520
Speaker 1: football game, any one of those sixty minutes that you

553
00:28:09,560 --> 00:28:12,359
Speaker 1: pull out of the million minutes could go so many

554
00:28:12,440 --> 00:28:17,240
Speaker 1: different directions. And sports fans it seems to me kind

555
00:28:17,280 --> 00:28:19,440
Speaker 1: of understand this in the context of, oh, well, that's

556
00:28:19,440 --> 00:28:22,199
Speaker 1: why we play a seven game series, because over the

557
00:28:22,200 --> 00:28:24,800
Speaker 1: course of a seven game series, the inferior team might

558
00:28:24,840 --> 00:28:27,520
Speaker 1: win by thirty one game, but they might lose the

559
00:28:27,520 --> 00:28:29,440
Speaker 1: other four, right, and we don't look at the sum

560
00:28:29,480 --> 00:28:32,879
Speaker 1: total of the of the games. The coronavirus, it seems

561
00:28:32,920 --> 00:28:35,119
Speaker 1: to me, and the way that the media has covered it,

562
00:28:35,520 --> 00:28:38,280
Speaker 1: so many people in my industry are bad at math. Right.

563
00:28:38,480 --> 00:28:41,560
Speaker 1: One of the reasons why I think people become journalists

564
00:28:41,760 --> 00:28:44,400
Speaker 1: is they're good at reading and writing, they're bad at math,

565
00:28:44,560 --> 00:28:47,160
Speaker 1: and they're running from math and science. And I'm not

566
00:28:47,280 --> 00:28:49,360
Speaker 1: great at math and science. I'm not pretending to be

567
00:28:49,480 --> 00:28:51,560
Speaker 1: incredible at it, but I'm better than most people in

568
00:28:51,560 --> 00:28:57,200
Speaker 1: my industry. And so the failure of understanding probability and statistics,

569
00:28:57,200 --> 00:29:00,720
Speaker 1: particularly in a social media age, where you say, oh,

570
00:29:00,760 --> 00:29:03,560
Speaker 1: this thirty four year old woman was completely healthy and

571
00:29:03,560 --> 00:29:07,680
Speaker 1: then she died. That story goes viral all over social media.

572
00:29:08,040 --> 00:29:11,120
Speaker 1: Even though it's an outlier. It's in no way representative

573
00:29:11,160 --> 00:29:13,480
Speaker 1: of what happens when the average thirty four year old

574
00:29:13,600 --> 00:29:16,080
Speaker 1: or twenty four year old or sixteen year old get

575
00:29:16,200 --> 00:29:20,320
Speaker 1: sick with the coronavirus. Yet people believe it because it's

576
00:29:20,360 --> 00:29:23,560
Speaker 1: a story that they want to believe. So I've talked

577
00:29:23,600 --> 00:29:26,040
Speaker 1: a lot. They're kind of setting the table, but I

578
00:29:26,040 --> 00:29:28,400
Speaker 1: want to circle back around to the original premise. How

579
00:29:28,480 --> 00:29:32,200
Speaker 1: much of our national failure with the coronavirus has to

580
00:29:32,240 --> 00:29:37,720
Speaker 1: do with a national failure to understand probability and statistics. Well,

581
00:29:37,960 --> 00:29:40,840
Speaker 1: I would say there's no doubt that our response to

582
00:29:41,000 --> 00:29:45,800
Speaker 1: the coronavirus has been utterly and badly damaged by a

583
00:29:45,840 --> 00:29:48,960
Speaker 1: failure to understand statistics. By the way you're selling yourself shortly,

584
00:29:49,000 --> 00:29:50,480
Speaker 1: I mean, I've looked to your show. You do an

585
00:29:50,560 --> 00:29:54,160
Speaker 1: amazing job of communicating what's really going on from a

586
00:29:54,240 --> 00:29:57,120
Speaker 1: quantitative standpoint to your audience, and you're doing an incredible

587
00:29:57,320 --> 00:29:59,240
Speaker 1: public service, because you have such a big audience and

588
00:29:59,240 --> 00:30:01,440
Speaker 1: you're sharing this data with a lot of people who

589
00:30:01,440 --> 00:30:04,040
Speaker 1: otherwise wouldn't get it from anywhere else. So I want

590
00:30:04,040 --> 00:30:06,240
Speaker 1: to thank you for that. I'm trying, but by the way,

591
00:30:06,280 --> 00:30:08,760
Speaker 1: I get crushed for it, right, Like I get crushed

592
00:30:08,800 --> 00:30:11,280
Speaker 1: because people are like, oh my god, you're a you know,

593
00:30:11,320 --> 00:30:13,480
Speaker 1: sports guy who went to law school. Why in the world.

594
00:30:13,560 --> 00:30:15,840
Speaker 1: And the reality is because I want sports to come back.

595
00:30:16,120 --> 00:30:18,720
Speaker 1: But when I see something that I believe is factually

596
00:30:18,760 --> 00:30:22,920
Speaker 1: inaccurate and being discussed poorly by media, it just draws

597
00:30:23,000 --> 00:30:25,800
Speaker 1: me and I want to try to get real facts

598
00:30:25,880 --> 00:30:28,120
Speaker 1: out there in a way that they're not being So

599
00:30:28,120 --> 00:30:30,480
Speaker 1: I appreciate you saying that, but I'm sure you get

600
00:30:30,480 --> 00:30:32,280
Speaker 1: this all the time, and I get it certainly, Like

601
00:30:32,320 --> 00:30:35,000
Speaker 1: you don't care about people dying. No, I wish nobody

602
00:30:35,040 --> 00:30:37,440
Speaker 1: ever died, right. I wish we were all immortal. I

603
00:30:37,440 --> 00:30:41,120
Speaker 1: wish your grandma, my grandma, everybody's kids, every everybody was

604
00:30:41,160 --> 00:30:44,120
Speaker 1: safe forever. That's not the reality of the world in

605
00:30:44,160 --> 00:30:46,960
Speaker 1: which we live. And I am troubled by what I

606
00:30:47,000 --> 00:30:52,200
Speaker 1: would say, is this very poor ability to discuss complex

607
00:30:52,240 --> 00:30:54,680
Speaker 1: issues where it's like people either like Hey, we've got

608
00:30:54,760 --> 00:30:58,080
Speaker 1: to completely shut down, nobody can leave their homes, or

609
00:30:58,120 --> 00:31:01,000
Speaker 1: we gotta be completely wide open. And and it's like

610
00:31:01,080 --> 00:31:03,120
Speaker 1: the nuances. We need to be somewhere in the middle.

611
00:31:03,160 --> 00:31:05,560
Speaker 1: We need to be living our lives but not allowing

612
00:31:05,560 --> 00:31:08,520
Speaker 1: the coronavirus to destroy our world. If that makes sense,

613
00:31:09,640 --> 00:31:12,440
Speaker 1: well totally. And I can give you some concrete examples

614
00:31:12,480 --> 00:31:14,800
Speaker 1: of how this is played out in real time. So

615
00:31:14,880 --> 00:31:17,840
Speaker 1: one example is school closures. Right, we're seeing all these

616
00:31:18,280 --> 00:31:20,320
Speaker 1: and let's leave let's leave colleges aside for the moment,

617
00:31:20,360 --> 00:31:24,959
Speaker 1: I'm talking about pre k kindergarten, primary school, elementary school.

618
00:31:25,560 --> 00:31:29,400
Speaker 1: That the overwhelming, overwhelming scientific evidence at this point. I

619
00:31:29,400 --> 00:31:33,320
Speaker 1: mean basically it's anti scientific to argue that kids are

620
00:31:33,360 --> 00:31:36,240
Speaker 1: at risk, uh, you know, in a meaningful way of

621
00:31:36,640 --> 00:31:40,040
Speaker 1: obviously there's a handful. There's literally like thirty nine kids

622
00:31:40,080 --> 00:31:42,440
Speaker 1: aged one fifteen who died of COVID in the United

623
00:31:42,480 --> 00:31:46,320
Speaker 1: States out of fifty million in that repeat that again,

624
00:31:46,400 --> 00:31:48,120
Speaker 1: because I think it's a big it's a big deal.

625
00:31:48,640 --> 00:31:52,040
Speaker 1: And according to the most recent CDC data, kids fifteen

626
00:31:52,040 --> 00:31:55,880
Speaker 1: and under thirty nine have died of the coronavirus between

627
00:31:55,880 --> 00:32:00,400
Speaker 1: the ages of one and by the way that's with

628
00:32:00,800 --> 00:32:03,120
Speaker 1: that's with the coronavirus, because I bet if you went

629
00:32:03,160 --> 00:32:05,360
Speaker 1: into those thirty nine what you would find is they

630
00:32:05,400 --> 00:32:09,080
Speaker 1: have significant health issues on top of whatever they got

631
00:32:09,120 --> 00:32:12,719
Speaker 1: from the COVID impact. Right, that's right. It's people who

632
00:32:12,760 --> 00:32:15,400
Speaker 1: have died who have tested positive for COVID. Whether the

633
00:32:15,480 --> 00:32:17,920
Speaker 1: actual cause of death was COVID or not, we don't know.

634
00:32:18,280 --> 00:32:21,760
Speaker 1: But thirty nine kids. And I guess how many kids

635
00:32:21,920 --> 00:32:24,040
Speaker 1: live in the United States who are aged one, uh

636
00:32:24,320 --> 00:32:27,400
Speaker 1: fourteen or one to fifteen. I mean, there's what three

637
00:32:27,600 --> 00:32:30,160
Speaker 1: d and thirty ish million people in the United States.

638
00:32:30,160 --> 00:32:32,560
Speaker 1: I would guess that there's got to be what fifty

639
00:32:32,640 --> 00:32:37,160
Speaker 1: or sixty million kids at that age, fifty seven million,

640
00:32:37,760 --> 00:32:42,280
Speaker 1: fifty seven millions, always saying thirty nine out of fifty

641
00:32:42,360 --> 00:32:45,200
Speaker 1: seven million kids. And we're shutting down schools. And by

642
00:32:45,200 --> 00:32:47,800
Speaker 1: the way, you know what that means. Shutting down school

643
00:32:47,840 --> 00:32:51,280
Speaker 1: it's not exactly good for children, particularly low income kids

644
00:32:51,320 --> 00:32:54,960
Speaker 1: who have no other alternative. If you're a single mom

645
00:32:55,000 --> 00:32:56,800
Speaker 1: and you have to work, Let's say you work at

646
00:32:56,840 --> 00:32:58,800
Speaker 1: a pharmacy or grocery store, what are you gonna do?

647
00:32:58,880 --> 00:33:00,400
Speaker 1: Are you gonna go to work and lead just three

648
00:33:00,440 --> 00:33:04,760
Speaker 1: year old at home you can't. Uh, there's forty estimated

649
00:33:04,840 --> 00:33:07,320
Speaker 1: about forty thousand cases of child abuse that are going

650
00:33:07,400 --> 00:33:11,160
Speaker 1: unreported in the United States because schools are closed right now,

651
00:33:12,920 --> 00:33:17,000
Speaker 1: and let alone the mental healthy, educational deficits, the emotional development.

652
00:33:17,360 --> 00:33:20,360
Speaker 1: It's just incredible costs. So, like we often talk about

653
00:33:20,400 --> 00:33:22,440
Speaker 1: this purely in terms of what's your risk of getting COVID,

654
00:33:22,440 --> 00:33:24,640
Speaker 1: what's your risk of not getting COVID, and we don't

655
00:33:24,640 --> 00:33:27,520
Speaker 1: talk about the costs on the other side of the equation.

656
00:33:27,840 --> 00:33:30,360
Speaker 1: The cost to a kid who doesn't get to go

657
00:33:30,440 --> 00:33:33,680
Speaker 1: to school. Uh, the cost to a business that shuts

658
00:33:33,680 --> 00:33:37,000
Speaker 1: down permanently. It's estimated that over a hundred thousand businesses,

659
00:33:37,040 --> 00:33:40,400
Speaker 1: maybe even more have shut down permanently because they didn't

660
00:33:40,440 --> 00:33:42,520
Speaker 1: have the cash cushion. Once you start losing your revenue,

661
00:33:42,520 --> 00:33:44,880
Speaker 1: but you can't keep your payroll going, you can't pay

662
00:33:44,960 --> 00:33:48,200
Speaker 1: the rent for your building, and you're done and you quit. Uh.

663
00:33:48,240 --> 00:33:50,280
Speaker 1: That's not good for a lot of people. And people say, well,

664
00:33:50,320 --> 00:33:52,200
Speaker 1: it's just about dollars. No, it's not just about dollars.

665
00:33:52,240 --> 00:33:54,160
Speaker 1: Is actually a lot of evidence that shows that when

666
00:33:54,200 --> 00:33:57,920
Speaker 1: you have a massive economic dislocation or a massive recession

667
00:33:58,000 --> 00:34:01,320
Speaker 1: or a massive disruption that leads to shorten life expectancy

668
00:34:01,360 --> 00:34:03,600
Speaker 1: as well for a lot of different reasons. Think about

669
00:34:03,640 --> 00:34:06,880
Speaker 1: the opioid crisis, where is that happening economically depressed parts

670
00:34:06,880 --> 00:34:10,400
Speaker 1: of the country to a significant degree. Why do you

671
00:34:10,520 --> 00:34:14,640
Speaker 1: think that all of those facts which are so incredibly

672
00:34:14,719 --> 00:34:18,440
Speaker 1: important are not able to cut through the noise. That's

673
00:34:18,480 --> 00:34:20,640
Speaker 1: a big question that I have because it's frustrating to me.

674
00:34:20,680 --> 00:34:22,960
Speaker 1: I understand the audience and we have I'm fortunate to

675
00:34:23,000 --> 00:34:26,000
Speaker 1: have a substantial audience that we have built up, but

676
00:34:26,120 --> 00:34:29,359
Speaker 1: I'm still a pinprick of the overall media audience. Right,

677
00:34:29,760 --> 00:34:32,440
Speaker 1: Why do you think that data that you just shared

678
00:34:32,480 --> 00:34:34,360
Speaker 1: and by the way, credit to the Wall Street Journal

679
00:34:34,440 --> 00:34:37,359
Speaker 1: for carrying your story was on the front page. I mean,

680
00:34:38,000 --> 00:34:39,960
Speaker 1: your work is getting out there, and I think you're

681
00:34:39,960 --> 00:34:42,440
Speaker 1: doing an incredible job of it. But why do you

682
00:34:42,560 --> 00:34:46,800
Speaker 1: think those stories, those facts are having such difficulty cutting

683
00:34:46,840 --> 00:34:49,399
Speaker 1: through the noise. And there are so many people out

684
00:34:49,480 --> 00:34:53,160
Speaker 1: there with kids that are terrified that their kids are

685
00:34:53,160 --> 00:34:56,240
Speaker 1: going to die of COVID that would not think twice

686
00:34:56,360 --> 00:34:58,880
Speaker 1: about ever pulling their kid out of school from the

687
00:34:58,920 --> 00:35:02,080
Speaker 1: seasonal flu, even though the seasonal flew is far more

688
00:35:02,160 --> 00:35:04,960
Speaker 1: dangerous and by the way, don't even think twice about

689
00:35:04,960 --> 00:35:08,160
Speaker 1: sending their kid out to a swimming pool without parental

690
00:35:08,200 --> 00:35:11,400
Speaker 1: supervision when their kid is far more likely to drown

691
00:35:11,520 --> 00:35:16,720
Speaker 1: there than they ever already get COVID. It's totally right, um.

692
00:35:16,760 --> 00:35:18,680
Speaker 1: And you know, just like you were saying, like, I'm lucky,

693
00:35:18,719 --> 00:35:21,040
Speaker 1: I have I have a platform. I'm the policy editor

694
00:35:21,080 --> 00:35:22,680
Speaker 1: at Forbes. I can put my stuff there, I can

695
00:35:22,719 --> 00:35:24,560
Speaker 1: put my stuff on Twitter, I can put my stuff

696
00:35:24,719 --> 00:35:26,479
Speaker 1: at the Wall Street Journal when they when they asked

697
00:35:26,480 --> 00:35:28,360
Speaker 1: me to. And so I've been lucky and then that

698
00:35:28,400 --> 00:35:31,160
Speaker 1: I've had those opportunities to get the word out there.

699
00:35:31,160 --> 00:35:34,760
Speaker 1: But you're right, it's it's overwhelmed by the NonStop wild

700
00:35:34,840 --> 00:35:38,720
Speaker 1: wall alarmism, uh, coming from the people who think everything

701
00:35:38,719 --> 00:35:40,520
Speaker 1: should be shut down all the time. And there's a

702
00:35:40,560 --> 00:35:43,279
Speaker 1: couple of different you know, I have a couple of

703
00:35:43,280 --> 00:35:45,520
Speaker 1: different hypotheses that I think are pretty plausible as to

704
00:35:45,560 --> 00:35:48,840
Speaker 1: wise it's happening. The first is the media has always

705
00:35:48,880 --> 00:35:52,120
Speaker 1: been about alarmism. I mean, the thing we used to

706
00:35:52,120 --> 00:35:54,600
Speaker 1: talk about if you ever took a statistics class, the

707
00:35:54,640 --> 00:35:56,480
Speaker 1: thing that people used to always talking about in statistics

708
00:35:56,520 --> 00:35:59,920
Speaker 1: class was well, people are often more afraid of flying

709
00:36:00,200 --> 00:36:03,960
Speaker 1: that they are driving their car because every plane crash

710
00:36:04,160 --> 00:36:08,359
Speaker 1: ever gets plastered all over the newspaper and plaster TV. Right,

711
00:36:08,480 --> 00:36:10,080
Speaker 1: So a lot of people have this impression that's not

712
00:36:10,160 --> 00:36:12,480
Speaker 1: safe to fly, when in fact, your chances of dying

713
00:36:12,520 --> 00:36:16,399
Speaker 1: in a plane crash are orders of magnitude lower than

714
00:36:16,440 --> 00:36:18,560
Speaker 1: your chances of dying in a car accident or even

715
00:36:18,600 --> 00:36:22,440
Speaker 1: crossing the street in a busy intersection. So that's an

716
00:36:22,440 --> 00:36:25,839
Speaker 1: example of where the media because it's the disaster of

717
00:36:25,880 --> 00:36:28,840
Speaker 1: the plane crashes. You remember that the Malaysia when that

718
00:36:28,880 --> 00:36:32,280
Speaker 1: Malaysia plane went Malaysian Airline plane went disappear and nothing

719
00:36:32,320 --> 00:36:36,240
Speaker 1: else for like four days, right, for like four months, honestly,

720
00:36:36,280 --> 00:36:39,040
Speaker 1: Malaysian three. And I was fascinated by that too because

721
00:36:39,040 --> 00:36:41,799
Speaker 1: it felt like when that thing disappeared, uh, you know,

722
00:36:41,880 --> 00:36:44,160
Speaker 1: maybe there was something other. You know, we still don't

723
00:36:44,160 --> 00:36:47,279
Speaker 1: know acent it seems like the pilot was involved. But

724
00:36:47,520 --> 00:36:49,680
Speaker 1: that story was such a mystery. It was not only

725
00:36:49,719 --> 00:36:53,120
Speaker 1: a plane disappearing, it was not knowing why the plane disappeared,

726
00:36:53,120 --> 00:36:55,439
Speaker 1: which is probably the greatest thing ever. Another example would

727
00:36:55,480 --> 00:36:58,080
Speaker 1: be shark attacks. Right, every time somebody gets attacked by

728
00:36:58,080 --> 00:37:01,440
Speaker 1: a shark, you hear about it. Uh, and so everybody

729
00:37:01,440 --> 00:37:04,439
Speaker 1: who goes into the ocean summer vacation time right now,

730
00:37:04,800 --> 00:37:06,919
Speaker 1: everybody is thinking, oh my god, I'm gonna get eaten

731
00:37:06,960 --> 00:37:10,840
Speaker 1: by a shark. Yeah. The old school adage among you know,

732
00:37:10,920 --> 00:37:14,320
Speaker 1: the newspaper hands is if it bleeds, it leads. Anything

733
00:37:14,320 --> 00:37:17,960
Speaker 1: that's sort of catastrophic or disastrous gets that headline. And

734
00:37:18,200 --> 00:37:20,000
Speaker 1: we all know that, we all we all consume the news.

735
00:37:20,000 --> 00:37:22,000
Speaker 1: We we understand that that's part of it. So that's

736
00:37:22,480 --> 00:37:25,279
Speaker 1: definitely it's like this is cat nipped to that kind

737
00:37:25,320 --> 00:37:28,120
Speaker 1: of journalism. Right. So that's that's number one. I think

738
00:37:28,480 --> 00:37:32,160
Speaker 1: number two is uh for for certain people who are

739
00:37:32,200 --> 00:37:38,000
Speaker 1: more politically politically oriented. Uh, it's clearly a situation where, um,

740
00:37:38,040 --> 00:37:40,279
Speaker 1: you know, if you if you're a journalist who hates

741
00:37:40,280 --> 00:37:42,680
Speaker 1: Trump and you didn't you know, you were frustrated by

742
00:37:42,680 --> 00:37:45,920
Speaker 1: the fact that the economy was roaring along. Record low

743
00:37:46,000 --> 00:37:49,840
Speaker 1: unemployment in the winter last winter for all races, not

744
00:37:49,920 --> 00:37:52,399
Speaker 1: just whites, but also blacks and Hispanics and Asians, record

745
00:37:52,440 --> 00:37:56,160
Speaker 1: lownemployment for everybody, record low disparities in the in the

746
00:37:56,160 --> 00:37:59,560
Speaker 1: difference between employment unemployment of blacks and whites, for example.

747
00:37:59,760 --> 00:38:03,200
Speaker 1: So the economy was doing incredibly well. So people were like, well,

748
00:38:03,239 --> 00:38:05,200
Speaker 1: you know, boy, that's annoying because we hate Trump and

749
00:38:05,239 --> 00:38:06,960
Speaker 1: we want them to lose. And now you have a

750
00:38:06,960 --> 00:38:08,799
Speaker 1: story that you can say, this is all Trump's fault.

751
00:38:08,880 --> 00:38:11,040
Speaker 1: Trump is the reason why a hundred seventy thousand people

752
00:38:11,120 --> 00:38:15,080
Speaker 1: have died in America. And so there's there's an enthusiasm

753
00:38:15,160 --> 00:38:18,200
Speaker 1: in a sense for the negative take on the government response.

754
00:38:18,200 --> 00:38:20,480
Speaker 1: And I'm not trying to say the government response doesn't

755
00:38:20,480 --> 00:38:23,960
Speaker 1: have things to criticize about it, whether federal, state, or local. Uh,

756
00:38:24,160 --> 00:38:27,040
Speaker 1: but but it is to say that that that has

757
00:38:27,080 --> 00:38:29,200
Speaker 1: been a huge part of the story. And I'll give

758
00:38:29,239 --> 00:38:32,560
Speaker 1: you some examples of why that is, or examples of

759
00:38:32,600 --> 00:38:35,680
Speaker 1: how why I think that is. There if you if

760
00:38:35,719 --> 00:38:37,319
Speaker 1: you ask the average person on the street, you know

761
00:38:37,360 --> 00:38:39,640
Speaker 1: who who watch the CNN or read the New York Times,

762
00:38:39,680 --> 00:38:42,400
Speaker 1: They'll say, you know what, why can't every governor handle

763
00:38:42,680 --> 00:38:45,239
Speaker 1: COVID like Andrew Cuomo, the governor of New York handled it.

764
00:38:45,280 --> 00:38:47,280
Speaker 1: He's just done such a great job. Why can't everyone

765
00:38:47,400 --> 00:38:49,520
Speaker 1: feel like him? In fact, Andrew Cuomo just published a

766
00:38:49,560 --> 00:38:54,719
Speaker 1: book about his triumph in conconquering COVID and wrestling it

767
00:38:54,760 --> 00:38:58,360
Speaker 1: to the ground. Now, this makes absolutely no sense according

768
00:38:58,400 --> 00:39:02,040
Speaker 1: to the data, because New York has been by far

769
00:39:02,280 --> 00:39:07,080
Speaker 1: the worst performing state by a country mile. California, Texas,

770
00:39:07,080 --> 00:39:10,680
Speaker 1: and Florida combined have had far fewer deaths from COVID

771
00:39:10,800 --> 00:39:13,920
Speaker 1: nineteens than New York has, whether per capita or not.

772
00:39:14,400 --> 00:39:16,959
Speaker 1: And yet somehow New York is portrayed as this success story.

773
00:39:16,960 --> 00:39:18,320
Speaker 1: It's not a success story at all. It's been a

774
00:39:18,360 --> 00:39:22,040
Speaker 1: complete catastrophic failure. Arguably, of any states that have done

775
00:39:22,080 --> 00:39:24,120
Speaker 1: pretty well, it's been the Texas and the Florida is

776
00:39:24,160 --> 00:39:27,120
Speaker 1: that never completely locked down their economy, and while they

777
00:39:27,160 --> 00:39:29,040
Speaker 1: have had death from COVID, it's been at a far

778
00:39:29,120 --> 00:39:32,440
Speaker 1: lower scale than New York. But you wouldn't know that

779
00:39:32,480 --> 00:39:34,399
Speaker 1: from the coverage. And that goes to this point about

780
00:39:34,400 --> 00:39:36,440
Speaker 1: the politics, Like if we were just looking at the data,

781
00:39:36,800 --> 00:39:38,880
Speaker 1: and we would have a lot more questions, we'd be

782
00:39:38,880 --> 00:39:41,120
Speaker 1: asking about build the Blasio, the mayor of New York City,

783
00:39:41,440 --> 00:39:43,600
Speaker 1: and Eventrew Cuomo, the governor of New York, and on

784
00:39:43,640 --> 00:39:45,400
Speaker 1: a bunch of his neighbors by the way, like Murphy

785
00:39:45,400 --> 00:39:49,640
Speaker 1: and New Jersey. It's so true, and I look at

786
00:39:49,640 --> 00:39:52,920
Speaker 1: the data and do you think that the media that

787
00:39:53,040 --> 00:39:57,520
Speaker 1: is praising Andrew Cuomo and Murphy who is next to him,

788
00:39:57,560 --> 00:39:59,600
Speaker 1: and by the way, to kind of put into context,

789
00:39:59,719 --> 00:40:02,680
Speaker 1: the day to New York and New Jersey's death rate

790
00:40:03,239 --> 00:40:07,880
Speaker 1: is twice the worst country in the world from COVID

791
00:40:07,960 --> 00:40:10,080
Speaker 1: so far the most recent numbers that I looked at,

792
00:40:10,120 --> 00:40:13,600
Speaker 1: Belgium was the worst, and New York and New Jersey

793
00:40:13,680 --> 00:40:17,440
Speaker 1: work twice what Belgium was. Okay, do you and this

794
00:40:17,520 --> 00:40:20,960
Speaker 1: gets into a hypothesis situation again, because we really don't know.

795
00:40:21,000 --> 00:40:22,440
Speaker 1: But this is something that I just think about a

796
00:40:22,440 --> 00:40:26,560
Speaker 1: great deal I can forgive people who are ignorant because

797
00:40:26,600 --> 00:40:29,719
Speaker 1: they are listening to media that is telling them things

798
00:40:29,760 --> 00:40:32,200
Speaker 1: that are not true. Right, Like, So, if you read

799
00:40:32,200 --> 00:40:34,640
Speaker 1: the New York Times and you have convinced yourself that

800
00:40:34,719 --> 00:40:38,239
Speaker 1: Andrew Cuomo and Governor Murphy did an incredible job, that's

801
00:40:38,280 --> 00:40:40,799
Speaker 1: because those journalists are telling you that, right. You are

802
00:40:40,840 --> 00:40:43,760
Speaker 1: being told that is not a truth. But you trust

803
00:40:43,840 --> 00:40:46,319
Speaker 1: the New York Times or you trust CNN to get

804
00:40:46,360 --> 00:40:50,560
Speaker 1: that right, and so you are misapprehending what the data

805
00:40:50,600 --> 00:40:54,600
Speaker 1: is actually saying. I don't like I'm not as bothered

806
00:40:54,640 --> 00:40:57,919
Speaker 1: by people who believe things that are untrue because they're

807
00:40:57,960 --> 00:41:01,520
Speaker 1: listening to people in positions of authority. I am desperately

808
00:41:01,600 --> 00:41:05,000
Speaker 1: bothered by people in positions of authority in my industry,

809
00:41:05,040 --> 00:41:08,799
Speaker 1: in the media who are sharing untruths about New York

810
00:41:08,800 --> 00:41:11,640
Speaker 1: and New Jersey such that people are willing to buy

811
00:41:11,680 --> 00:41:14,400
Speaker 1: a book that suggests it's supposed to come out in

812
00:41:14,400 --> 00:41:19,319
Speaker 1: October that Andrew Cuomo triumphed over the coronavirus when he

813
00:41:19,520 --> 00:41:24,040
Speaker 1: literally did the worst job of any politician, arguably in

814
00:41:24,080 --> 00:41:27,800
Speaker 1: the world. I mean, it's just such an upside down story.

815
00:41:27,880 --> 00:41:30,760
Speaker 1: So do you think the journalists are not sophisticated enough

816
00:41:31,120 --> 00:41:33,759
Speaker 1: to actually look at the data? Do you think they're

817
00:41:33,800 --> 00:41:39,160
Speaker 1: intentionally misleading their audiences? How is it possible for something

818
00:41:39,200 --> 00:41:43,800
Speaker 1: that is so untrue to become so widely believed such

819
00:41:43,840 --> 00:41:47,000
Speaker 1: that I believe right now Andrew Cuomo has the highest

820
00:41:47,000 --> 00:41:50,520
Speaker 1: popularity rating of almost any governor in the country, despite

821
00:41:50,600 --> 00:41:54,200
Speaker 1: clear evidence that he probably did a worse job than

822
00:41:54,239 --> 00:41:59,800
Speaker 1: any politician in the entire world with the coronavirus. Well,

823
00:41:59,800 --> 00:42:02,520
Speaker 1: you know, it's it's it's this phol Andrew Cuomo thing

824
00:42:02,640 --> 00:42:05,520
Speaker 1: is like one of the craziest aspects of this whole

825
00:42:05,680 --> 00:42:08,719
Speaker 1: six months, it's just been what is going on, there's

826
00:42:08,760 --> 00:42:12,040
Speaker 1: such a disconnect between what his actual performance has been

827
00:42:12,480 --> 00:42:14,239
Speaker 1: and not just in terms of the numbers, in terms

828
00:42:14,280 --> 00:42:17,000
Speaker 1: of COVID, in terms of his actual decisions, because a

829
00:42:17,000 --> 00:42:20,839
Speaker 1: lot of his actual decisions are the distress, yes, which

830
00:42:20,920 --> 00:42:24,800
Speaker 1: you know about that. I'm gonna ask you about that directly,

831
00:42:24,840 --> 00:42:26,400
Speaker 1: which is because this is the other place where I

832
00:42:26,440 --> 00:42:29,959
Speaker 1: really started seeing your work. We knew early on, when

833
00:42:29,960 --> 00:42:34,359
Speaker 1: the infection first was recognized in a Washington nursing home,

834
00:42:34,800 --> 00:42:37,600
Speaker 1: that the elderly people, when you looked at the data

835
00:42:37,680 --> 00:42:43,040
Speaker 1: from Italy, that elderly people were particularly susceptible to this virus,

836
00:42:43,080 --> 00:42:46,760
Speaker 1: and that therefore the most susceptible people in the entire

837
00:42:46,840 --> 00:42:50,240
Speaker 1: country were people in nursing homes. And so what happened

838
00:42:50,239 --> 00:42:53,480
Speaker 1: in New York although they're not sharing their honest data,

839
00:42:53,600 --> 00:42:56,080
Speaker 1: and I think you've been you've been looking at this too,

840
00:42:56,200 --> 00:42:58,359
Speaker 1: but you went out and looked and said, okay, where

841
00:42:58,400 --> 00:43:01,560
Speaker 1: are people actually dying? And you found out that the

842
00:43:01,640 --> 00:43:05,200
Speaker 1: death rate inside of nursing homes was just I mean,

843
00:43:05,239 --> 00:43:07,279
Speaker 1: like I think in Canada, for instance, not just the

844
00:43:07,360 --> 00:43:10,560
Speaker 1: United States. The data that I saw of all deaths

845
00:43:10,560 --> 00:43:13,600
Speaker 1: in Canada have been inside nursing homes. Uh. And so

846
00:43:13,719 --> 00:43:17,040
Speaker 1: New York believed these forecasts that they were gonna need

847
00:43:17,080 --> 00:43:20,400
Speaker 1: a hundred and forty thousand hospital beds. They ended up peaking.

848
00:43:20,400 --> 00:43:21,759
Speaker 1: And you can correct me on some of this data

849
00:43:21,760 --> 00:43:23,120
Speaker 1: if I'm wrong, because I'm doing it off the top

850
00:43:23,160 --> 00:43:26,080
Speaker 1: of my head. They ended up right around nineteen thousand,

851
00:43:26,360 --> 00:43:29,920
Speaker 1: uh actually hospital beds. So the order of the forecast

852
00:43:30,080 --> 00:43:34,279
Speaker 1: was way off. But as a result, Cuomo sent all

853
00:43:34,320 --> 00:43:38,400
Speaker 1: of these infected patients back into nursing homes, which was

854
00:43:38,480 --> 00:43:41,440
Speaker 1: like putting kindling, you know, right beside a forest fire

855
00:43:41,800 --> 00:43:44,040
Speaker 1: and it exploded. And the same thing happened in New

856
00:43:44,120 --> 00:43:48,160
Speaker 1: Jersey and in Michigan and in all these other states

857
00:43:48,200 --> 00:43:51,239
Speaker 1: that had early outbreaks and followed his lead. It wasn't

858
00:43:51,280 --> 00:43:53,560
Speaker 1: just that he made a poor decision. It was that

859
00:43:53,640 --> 00:43:57,239
Speaker 1: all these liming governors followed his lead and end up

860
00:43:57,239 --> 00:44:00,400
Speaker 1: making disastrous decisions. So how is that all not a

861
00:44:00,440 --> 00:44:02,600
Speaker 1: primary point of story, because to me, it's the biggest

862
00:44:02,600 --> 00:44:07,319
Speaker 1: story of the coronavirus outbreak from a death perspective. Yeah,

863
00:44:07,360 --> 00:44:09,480
Speaker 1: I mean, what's really important to understanding about what you

864
00:44:09,560 --> 00:44:13,760
Speaker 1: just described, Clay, is that Andrew Cuomo forced these forced

865
00:44:13,760 --> 00:44:17,040
Speaker 1: these nursing homes. His Health Department issued in order forcing

866
00:44:17,400 --> 00:44:20,000
Speaker 1: the nursing homes to accept COVID infected patients, and the

867
00:44:20,080 --> 00:44:23,759
Speaker 1: nursing home operators screamed, bloody murder. There's a there's a

868
00:44:23,800 --> 00:44:26,239
Speaker 1: great article from like the March twenty seven of the

869
00:44:26,239 --> 00:44:28,400
Speaker 1: Wall Street Journal that you can find if you just

870
00:44:28,480 --> 00:44:31,480
Speaker 1: google nursing home Andrew Cuomo March, you might be able

871
00:44:31,520 --> 00:44:34,960
Speaker 1: to find the article. The nursing homes knew that this

872
00:44:35,040 --> 00:44:39,560
Speaker 1: was a potentially fatal decision, literally fatal decisions if people

873
00:44:39,560 --> 00:44:41,759
Speaker 1: were screaming bloody murder bout at the time, and he

874
00:44:41,840 --> 00:44:45,960
Speaker 1: did it anyway because the experts that he was talking to,

875
00:44:46,080 --> 00:44:49,080
Speaker 1: quote unquote experts told them, well, gosh, what we know

876
00:44:49,160 --> 00:44:51,960
Speaker 1: from influenza, You've got to keep those hospital beds clear.

877
00:44:52,000 --> 00:44:53,480
Speaker 1: We don't have to worry about the nursing homes. Well,

878
00:44:53,480 --> 00:44:55,360
Speaker 1: we really have to worry about it as the hospitals,

879
00:44:55,400 --> 00:44:59,960
Speaker 1: which is totally backwards, because if you actually infect everyone

880
00:45:00,000 --> 00:45:01,520
Speaker 1: the nursing home, where do you think they're gonna end

881
00:45:01,560 --> 00:45:05,600
Speaker 1: up in the hospital definitely sick of COVID. So that

882
00:45:05,760 --> 00:45:09,000
Speaker 1: was an incredibly bad decision that was in part that

883
00:45:09,040 --> 00:45:12,000
Speaker 1: was it was Andrew mcuhma's decision. But it was also

884
00:45:12,080 --> 00:45:15,560
Speaker 1: a failure of experts who advised him to make that decision.

885
00:45:15,600 --> 00:45:17,600
Speaker 1: And that's part of the reason why you're not seeing

886
00:45:17,640 --> 00:45:20,080
Speaker 1: the accountabilit because those experts don't want to, you know,

887
00:45:20,200 --> 00:45:22,759
Speaker 1: take credit for that for that advice. Be sure to

888
00:45:22,800 --> 00:45:25,760
Speaker 1: catch live editions about Kicked the coverage with Clay Travis

889
00:45:25,800 --> 00:45:29,120
Speaker 1: week days at six am Eastern, three am Pacific. We're

890
00:45:29,120 --> 00:45:31,120
Speaker 1: talking to O vic Roy. I'm Clay Travis. This is

891
00:45:31,120 --> 00:45:34,520
Speaker 1: Wins and Losses. Sorry to cut you off. Continue, Oh please,

892
00:45:34,560 --> 00:45:36,560
Speaker 1: do you know I was going to bring up another

893
00:45:37,480 --> 00:45:40,000
Speaker 1: element of this phenomena we were talking before about just

894
00:45:40,080 --> 00:45:42,680
Speaker 1: the news coverage and and how distorted is. Let me

895
00:45:42,719 --> 00:45:45,360
Speaker 1: give you an example that's that's not related to what's

896
00:45:45,360 --> 00:45:47,440
Speaker 1: been happening in the US with COVID, but is related

897
00:45:47,440 --> 00:45:50,279
Speaker 1: to the US media coverage of the whole thing. There

898
00:45:50,320 --> 00:45:53,080
Speaker 1: was a story published on July eighteenth in the New

899
00:45:53,160 --> 00:45:55,839
Speaker 1: York Times by a poor bum on Mondabilia. I think

900
00:45:55,880 --> 00:45:58,959
Speaker 1: I'm pronouncing that correctly. The headline of the articles older

901
00:45:59,040 --> 00:46:02,720
Speaker 1: children spread the ronavirus just as much as adults. Large

902
00:46:02,760 --> 00:46:05,600
Speaker 1: study finds the study of nearly sixty five thousand people

903
00:46:05,600 --> 00:46:09,040
Speaker 1: in South Korea, suggests that school reopenings will trigger more

904
00:46:09,080 --> 00:46:13,160
Speaker 1: out backs, and the whole articles about the study by

905
00:46:13,160 --> 00:46:17,080
Speaker 1: there the South Korean c DC that actually didn't look

906
00:46:17,080 --> 00:46:19,799
Speaker 1: at sixty five thousand kids. It looked at a couple

907
00:46:19,800 --> 00:46:23,360
Speaker 1: of hundred kids and found that there were some adults

908
00:46:23,360 --> 00:46:26,400
Speaker 1: in those households who also had COVID. So she published

909
00:46:26,400 --> 00:46:28,600
Speaker 1: this very long article. I it was probably on the

910
00:46:28,600 --> 00:46:30,680
Speaker 1: front page and there are time certainly very prominent place

911
00:46:30,719 --> 00:46:32,040
Speaker 1: and I read it online, so I don't know what

912
00:46:32,120 --> 00:46:35,919
Speaker 1: page in the newspaper it actually appeared on. And well,

913
00:46:36,040 --> 00:46:37,600
Speaker 1: what's interesting about it is that so that was a

914
00:46:37,640 --> 00:46:39,359
Speaker 1: story that was being cited by everyone, Oh, you can't

915
00:46:39,360 --> 00:46:42,480
Speaker 1: open schools because there's a South Korea study that shows

916
00:46:42,480 --> 00:46:45,960
Speaker 1: that even young kids can infect everybody, even though in

917
00:46:46,000 --> 00:46:48,279
Speaker 1: nobody in Europe has seen this effect. Nobody in the

918
00:46:48,320 --> 00:46:49,839
Speaker 1: rest of the world where they've opened schools to see

919
00:46:49,840 --> 00:46:52,200
Speaker 1: in this fact, in South Korea there's a study that

920
00:46:52,320 --> 00:46:55,160
Speaker 1: shows that kids will affect adults, and so we got

921
00:46:55,160 --> 00:46:57,520
Speaker 1: to keep the schools close. That was the takeaway. And

922
00:46:57,560 --> 00:47:00,719
Speaker 1: you saw all this chatter in other news papers, other

923
00:47:00,760 --> 00:47:02,920
Speaker 1: media on social media about this article of the New

924
00:47:02,960 --> 00:47:06,120
Speaker 1: York Times saying this, well, fast forward a couple of

925
00:47:06,120 --> 00:47:08,640
Speaker 1: weeks later, and what do we find when the full

926
00:47:08,800 --> 00:47:12,160
Speaker 1: data set is actually released by the Korean CDC. It

927
00:47:12,239 --> 00:47:16,440
Speaker 1: turns out forty of the forty one cases of kids

928
00:47:16,440 --> 00:47:19,239
Speaker 1: and adults having COVID in the same household, they were

929
00:47:19,280 --> 00:47:22,680
Speaker 1: infected simultaneously. It wasn't the kids infecting the adults. The

930
00:47:22,840 --> 00:47:26,320
Speaker 1: kids and the adults in that household were simultaneously infected

931
00:47:26,320 --> 00:47:29,319
Speaker 1: by somebody else. So forty or forty one cases were

932
00:47:29,320 --> 00:47:32,120
Speaker 1: not actually of kids infecting adults. There kids just getting

933
00:47:32,160 --> 00:47:37,400
Speaker 1: infected by other people. The one case of COVID, uh,

934
00:47:37,440 --> 00:47:40,640
Speaker 1: somebody who was a child infecting someone else. A teenage

935
00:47:40,760 --> 00:47:44,600
Speaker 1: girl infected her younger sister. And that's it. One case

936
00:47:44,680 --> 00:47:48,000
Speaker 1: in the entire country of South Carota, of South Korea.

937
00:47:49,120 --> 00:47:51,319
Speaker 1: But you're not gonna see a front page store in

938
00:47:51,320 --> 00:47:53,880
Speaker 1: the New York Times saying, hey, guess what, everybody, that

939
00:47:54,000 --> 00:47:56,279
Speaker 1: South Korea study that we touted a couple of weeks

940
00:47:56,320 --> 00:48:01,040
Speaker 1: ago totally misconceived, totally misinterpreted and out of deal. You're

941
00:48:01,040 --> 00:48:03,680
Speaker 1: not going to see that story. And that's an example

942
00:48:03,719 --> 00:48:08,120
Speaker 1: of where a factual situation, just by the way it's

943
00:48:08,160 --> 00:48:12,480
Speaker 1: being covered completely distorts a very very important policy question,

944
00:48:12,480 --> 00:48:15,319
Speaker 1: which is do we bring sixty million kids and young

945
00:48:15,360 --> 00:48:18,640
Speaker 1: adults back to school this fall? What would the data

946
00:48:18,680 --> 00:48:21,160
Speaker 1: tell us we should have done? All? Right, So let's

947
00:48:21,200 --> 00:48:25,360
Speaker 1: pretend that that we had all this data that we

948
00:48:25,440 --> 00:48:27,880
Speaker 1: have now. And for people out there who are listening

949
00:48:27,920 --> 00:48:31,240
Speaker 1: to us, I'm talking to O vic Roy Clay Travis

950
00:48:31,239 --> 00:48:34,239
Speaker 1: here wins and losses, and we're talking in I think

951
00:48:34,280 --> 00:48:36,359
Speaker 1: it's August twenty five days all run together. I think

952
00:48:36,360 --> 00:48:39,759
Speaker 1: it's August August twenty one, whatever it is now, with

953
00:48:39,920 --> 00:48:43,240
Speaker 1: the benefit of hindsight, right, everybody always likes say hindsights.

954
00:48:44,239 --> 00:48:46,640
Speaker 1: With all of the data that you have out there

955
00:48:46,719 --> 00:48:51,440
Speaker 1: right now, what would have been the appropriate and smartest

956
00:48:51,480 --> 00:48:56,239
Speaker 1: decision in March and April in May? Because it's one

957
00:48:56,280 --> 00:48:58,320
Speaker 1: thing to say, and I think you would probably agree.

958
00:48:58,600 --> 00:49:00,840
Speaker 1: Maybe there's the fog of are in March when we

959
00:49:00,880 --> 00:49:03,359
Speaker 1: shut down a lot of people don't know what's going on.

960
00:49:03,560 --> 00:49:06,000
Speaker 1: I would argue there was enough data out there to

961
00:49:06,120 --> 00:49:09,680
Speaker 1: suggest that shutting completely down wasn't the smart move. But

962
00:49:09,840 --> 00:49:12,480
Speaker 1: let's pretend that you have all the data, all the

963
00:49:12,480 --> 00:49:15,839
Speaker 1: time that you've spent looking at everything what is the

964
00:49:15,960 --> 00:49:20,239
Speaker 1: right thing to do right now and what would have

965
00:49:20,320 --> 00:49:23,239
Speaker 1: been Let's pretend that we could have been flawless and

966
00:49:23,280 --> 00:49:27,840
Speaker 1: we could have executed perfectly. The appropriate response to coronavirus

967
00:49:28,239 --> 00:49:33,080
Speaker 1: based on what we know now is what. I'll give

968
00:49:33,120 --> 00:49:37,360
Speaker 1: you three core ideas of the core concepts or core frameworks.

969
00:49:37,680 --> 00:49:42,319
Speaker 1: Number one, we should have reopened schools, particularly for kids

970
00:49:42,360 --> 00:49:45,000
Speaker 1: under the age of twelve in the sprint, like Europe did,

971
00:49:45,440 --> 00:49:48,840
Speaker 1: and we definitely should be opening schools for younger kids

972
00:49:49,000 --> 00:49:52,160
Speaker 1: starting now. Uh, that's something that Europe did. They had

973
00:49:52,280 --> 00:49:55,200
Speaker 1: enormous success with it. They had no problems of kids,

974
00:49:55,320 --> 00:49:58,919
Speaker 1: no problems of kids infecting adults. And in fact, part

975
00:49:58,920 --> 00:50:01,239
Speaker 1: of why Europe maybe haven't so much more success in

976
00:50:01,280 --> 00:50:04,280
Speaker 1: the US in terms of the course of the pandemic

977
00:50:04,680 --> 00:50:06,879
Speaker 1: is because kids went back to school. Because the kids

978
00:50:06,880 --> 00:50:10,200
Speaker 1: all probably got some low level exposure, the virus developed

979
00:50:10,200 --> 00:50:13,720
Speaker 1: immunity and also transmitted immunity to others. Effect in Germany,

980
00:50:13,800 --> 00:50:16,520
Speaker 1: that's what they think. They think that reopening schools acted

981
00:50:16,600 --> 00:50:20,319
Speaker 1: as a break on the transmission of COVID nineteen. So

982
00:50:20,360 --> 00:50:23,520
Speaker 1: that's a counterintuitive. Yeah, that's a counterintuitive thought. Sorry to

983
00:50:23,560 --> 00:50:26,279
Speaker 1: cut you off, but we there's a strong argument to

984
00:50:26,320 --> 00:50:30,279
Speaker 1: be made that reopening schools, rather than leading to mass outbreaks,

985
00:50:30,360 --> 00:50:33,520
Speaker 1: actually makes them less likely. So in addition to the

986
00:50:33,560 --> 00:50:37,000
Speaker 1: fact that kids obviously benefit from being in school, there's

987
00:50:37,040 --> 00:50:40,000
Speaker 1: an argument that being in school actually makes us safer

988
00:50:40,320 --> 00:50:42,080
Speaker 1: as opposed to more dangerous. I just want to cut

989
00:50:42,120 --> 00:50:44,640
Speaker 1: you off because that's a counterintuitive take that you would

990
00:50:44,640 --> 00:50:46,920
Speaker 1: hear almost nowhere else in the media. Sorry, Okay, So

991
00:50:46,960 --> 00:50:50,319
Speaker 1: that's point one. Yeah, and if you get if you

992
00:50:50,320 --> 00:50:51,880
Speaker 1: dig into my Twitter feed and if you read the

993
00:50:51,880 --> 00:50:54,000
Speaker 1: Wall Street Journal article, you'll you'll see the links to

994
00:50:54,080 --> 00:50:57,200
Speaker 1: the German scientists in particular who have been making this argument.

995
00:50:57,760 --> 00:51:00,879
Speaker 1: So that's point one. Point two. We should have done

996
00:51:00,920 --> 00:51:02,879
Speaker 1: a lot more, and we should still do a lot

997
00:51:02,920 --> 00:51:07,520
Speaker 1: more to protect people who live in nursing homest of

998
00:51:07,560 --> 00:51:09,920
Speaker 1: all the deaths in the United States from COVID nineteen

999
00:51:10,560 --> 00:51:13,840
Speaker 1: or with no COVID nineteen have taken place among residents

1000
00:51:13,880 --> 00:51:17,160
Speaker 1: in nursing homes and other assisted living facilities that house

1001
00:51:17,600 --> 00:51:20,600
Speaker 1: zero point six percent of the U S population. Now,

1002
00:51:20,760 --> 00:51:24,240
Speaker 1: in any normal crisis, the fact that forty five percent

1003
00:51:24,320 --> 00:51:27,000
Speaker 1: of the death were occurring in zero point six percent

1004
00:51:27,040 --> 00:51:29,440
Speaker 1: of the population that would be the headline every day.

1005
00:51:29,480 --> 00:51:33,200
Speaker 1: Every day we'd be seeing on CNN some anchors asking

1006
00:51:33,280 --> 00:51:35,440
Speaker 1: a politician, what are you doing to protect people in

1007
00:51:35,520 --> 00:51:37,919
Speaker 1: nursing homes today? That's what we'd be talking about every

1008
00:51:37,920 --> 00:51:40,719
Speaker 1: hour of every day, but we're not. Why is that?

1009
00:51:41,120 --> 00:51:43,520
Speaker 1: That's one of the again, the craziest things about this

1010
00:51:43,560 --> 00:51:45,520
Speaker 1: whole situation. So we should be doing, we should have

1011
00:51:45,640 --> 00:51:47,680
Speaker 1: done all along a lot more to protect people in

1012
00:51:47,760 --> 00:51:49,759
Speaker 1: nursing homes, and we still have a ways to go,

1013
00:51:50,360 --> 00:51:51,960
Speaker 1: uh to do that, to get to a point where

1014
00:51:51,960 --> 00:51:54,160
Speaker 1: we can really say that people in nursing homes are protected.

1015
00:51:54,200 --> 00:51:56,200
Speaker 1: Were made a lot of progress in a in a

1016
00:51:56,280 --> 00:51:58,520
Speaker 1: sense of like to you know, a little bit too late.

1017
00:51:58,680 --> 00:52:00,600
Speaker 1: We should have gotten there much early, or particularly at

1018
00:52:00,600 --> 00:52:02,880
Speaker 1: the state level, as we've talked about with Andrew Cuomo.

1019
00:52:03,320 --> 00:52:05,000
Speaker 1: But but that's an area where we still need to

1020
00:52:05,000 --> 00:52:06,880
Speaker 1: do more. So that's that would be like And by

1021
00:52:06,920 --> 00:52:10,880
Speaker 1: the way, that figure is probably low because the data

1022
00:52:10,920 --> 00:52:13,360
Speaker 1: that you have on New York, the way they classify

1023
00:52:13,520 --> 00:52:18,200
Speaker 1: nursing home deaths likely drastically undercut the number of people

1024
00:52:18,239 --> 00:52:21,319
Speaker 1: who actually died who were in nursing homes in New York. Right,

1025
00:52:21,320 --> 00:52:25,200
Speaker 1: it's probably the case like I mentioned earlier, candidates, it's

1026
00:52:25,200 --> 00:52:27,520
Speaker 1: probably over half in the United States. Right if you

1027
00:52:27,600 --> 00:52:31,440
Speaker 1: had the best possible statistical data of all to be

1028
00:52:31,480 --> 00:52:35,160
Speaker 1: able to put together, that's not a crazy hypothesis, right. No.

1029
00:52:35,280 --> 00:52:37,080
Speaker 1: And in fact, if if you want to dig into

1030
00:52:37,080 --> 00:52:38,480
Speaker 1: the data, if any of your listeners want to dig

1031
00:52:38,480 --> 00:52:40,160
Speaker 1: into the data, they can go to our website f

1032
00:52:40,360 --> 00:52:43,040
Speaker 1: R E O P P free op dot org and

1033
00:52:43,080 --> 00:52:47,640
Speaker 1: there's an article They're titled, uh Nursing Homes and Assisted

1034
00:52:47,680 --> 00:52:51,200
Speaker 1: living Facilities account for COVID nineteen deaths, and we we

1035
00:52:51,280 --> 00:52:52,520
Speaker 1: put all the data in there. I also have a

1036
00:52:52,560 --> 00:52:55,400
Speaker 1: Forbes article about it. But the free dot org articles

1037
00:52:55,440 --> 00:52:57,920
Speaker 1: the WOE that has the most updated information you can

1038
00:52:57,960 --> 00:52:59,799
Speaker 1: dig through at the state level how your state has

1039
00:52:59,840 --> 00:53:02,040
Speaker 1: been doing. And by the way, when we first started

1040
00:53:02,080 --> 00:53:04,319
Speaker 1: reporting on this, we basically compiled all the data from

1041
00:53:04,640 --> 00:53:08,960
Speaker 1: the state health departments and thirteen states weren't even reporting

1042
00:53:08,960 --> 00:53:11,879
Speaker 1: the data. This is in like June. Thirteen states weren't

1043
00:53:11,880 --> 00:53:14,600
Speaker 1: even reporting the data. It wasn't until I basically humiliated

1044
00:53:14,600 --> 00:53:17,000
Speaker 1: them by writing this article in Forbes that got like

1045
00:53:17,080 --> 00:53:20,080
Speaker 1: one point two million page views that all of a sudden,

1046
00:53:20,120 --> 00:53:22,200
Speaker 1: the state departments started to say oh yeah, actually, here's

1047
00:53:22,200 --> 00:53:24,480
Speaker 1: our you know, nursing home deat. So you know, it

1048
00:53:24,520 --> 00:53:26,719
Speaker 1: was it was it was just this crazy thing where again,

1049
00:53:26,880 --> 00:53:29,839
Speaker 1: forty five or half the desk maybe more are coming

1050
00:53:29,880 --> 00:53:32,279
Speaker 1: in in in nursing homes and assistant living facilities, and

1051
00:53:32,360 --> 00:53:34,359
Speaker 1: yet some states weren't even reporting the data. They didn't

1052
00:53:34,400 --> 00:53:37,200
Speaker 1: even know what percentage of the people in their states

1053
00:53:37,200 --> 00:53:39,600
Speaker 1: that were dying were in nursing homes, and some parts

1054
00:53:39,640 --> 00:53:42,239
Speaker 1: of the country it's even higher than fift In Minnesota,

1055
00:53:42,680 --> 00:53:46,160
Speaker 1: it's like it's like Canada, it's and and so that

1056
00:53:46,239 --> 00:53:48,200
Speaker 1: was one of the things that's like absolutely a thing

1057
00:53:48,239 --> 00:53:50,919
Speaker 1: that we could have done better than we certainly could

1058
00:53:50,920 --> 00:53:54,040
Speaker 1: be doing better now. And then the third bucket is

1059
00:53:54,600 --> 00:53:59,080
Speaker 1: the economic lockdown. So you'll remember when Texas and Florida,

1060
00:53:59,320 --> 00:54:02,359
Speaker 1: well Florida ever really completely locked down, but Texas did

1061
00:54:02,400 --> 00:54:05,520
Speaker 1: lockdown in May and then they reopened in June. And

1062
00:54:05,560 --> 00:54:08,360
Speaker 1: there were all these predictions in in national newspapers and

1063
00:54:08,400 --> 00:54:11,799
Speaker 1: other media organizations about how Texas, hundreds of thousands people

1064
00:54:11,800 --> 00:54:13,719
Speaker 1: are gonna die. I was gonna be terrible, it was

1065
00:54:13,760 --> 00:54:17,080
Speaker 1: gonna be apocalyptic. You know, these these rednecks from Texas

1066
00:54:17,120 --> 00:54:19,239
Speaker 1: didn't know what they were doing. And if you go

1067
00:54:19,320 --> 00:54:21,840
Speaker 1: by those predictions of tens or hundreds of thousands of

1068
00:54:21,840 --> 00:54:26,000
Speaker 1: people dying compared to what actually happened. Yes, there were

1069
00:54:26,080 --> 00:54:28,120
Speaker 1: there have been people who died of COVID in Texas.

1070
00:54:28,160 --> 00:54:30,799
Speaker 1: There was a rise in cases and hospitalizations and deaths

1071
00:54:31,000 --> 00:54:34,960
Speaker 1: in the late summer, but much much less so than

1072
00:54:34,960 --> 00:54:37,840
Speaker 1: it was predicted, far less though we were never in Texas.

1073
00:54:37,880 --> 00:54:39,799
Speaker 1: It never turned into New York, and Florida never turned

1074
00:54:39,800 --> 00:54:43,520
Speaker 1: into New York. These places that reopened in the Sun Belt,

1075
00:54:43,719 --> 00:54:45,840
Speaker 1: they never turned into New York. And yet there was

1076
00:54:45,880 --> 00:54:49,280
Speaker 1: this kind of almost rooting for for that failure to happen.

1077
00:54:49,280 --> 00:54:53,000
Speaker 1: And that's I think what that shows is that there

1078
00:54:53,120 --> 00:54:55,040
Speaker 1: is a balance to be struck. And there's a lot

1079
00:54:55,040 --> 00:54:57,359
Speaker 1: of actually academic research on this, and her really gets

1080
00:54:57,440 --> 00:55:02,480
Speaker 1: talked about that there's diminished returns to a total lockdown. Yes,

1081
00:55:03,000 --> 00:55:05,160
Speaker 1: it may or may not make sense to close bars.

1082
00:55:05,160 --> 00:55:08,120
Speaker 1: It certainly probably makes sense to not have large gatherings

1083
00:55:08,160 --> 00:55:10,520
Speaker 1: of like sporting events or conventions or things like that.

1084
00:55:10,560 --> 00:55:12,759
Speaker 1: We have a hundred thousand people packed into a state,

1085
00:55:12,800 --> 00:55:17,000
Speaker 1: and that's probably a thing you want to avoid. But uh,

1086
00:55:17,400 --> 00:55:20,440
Speaker 1: things like allowing restaurants to open at half capacity, allowing

1087
00:55:20,480 --> 00:55:23,480
Speaker 1: people to go into a shop with a mask on.

1088
00:55:23,920 --> 00:55:26,040
Speaker 1: That's not a big deal, right, Let the car wash

1089
00:55:26,120 --> 00:55:27,839
Speaker 1: it's open up. Why do it? Just because the car

1090
00:55:27,880 --> 00:55:30,920
Speaker 1: watch is not a quote unquote essential business doesn't mean

1091
00:55:30,960 --> 00:55:32,400
Speaker 1: that a car wash has to be shut down. You

1092
00:55:32,400 --> 00:55:34,720
Speaker 1: can drive your car for the car washing. It's basically fine.

1093
00:55:35,160 --> 00:55:38,320
Speaker 1: So there is a balance to be struck, and Texas

1094
00:55:38,320 --> 00:55:40,799
Speaker 1: and Florida clearly struck that balance in a way that

1095
00:55:40,840 --> 00:55:43,400
Speaker 1: the total lockdown states did not. And exhibit A by

1096
00:55:43,440 --> 00:55:46,759
Speaker 1: the way, on that clay, it's California. Look at California.

1097
00:55:46,840 --> 00:55:50,840
Speaker 1: California has had the same spike in cases and hospitalizations

1098
00:55:50,840 --> 00:55:54,400
Speaker 1: and deaths that Texas and Florida did. But California lockdown.

1099
00:55:54,440 --> 00:55:57,799
Speaker 1: California did all the things that all the people in

1100
00:55:57,920 --> 00:56:00,479
Speaker 1: the sort of the quote unquote at the pro quote

1101
00:56:00,520 --> 00:56:04,520
Speaker 1: unquote pro quote science unquote class says we should do,

1102
00:56:05,200 --> 00:56:08,160
Speaker 1: and yet California still had an outbreak. Why is that right?

1103
00:56:08,239 --> 00:56:10,840
Speaker 1: So why is it that California right now the situation

1104
00:56:10,880 --> 00:56:13,560
Speaker 1: is arguably worse. California had a complete breakdown of their

1105
00:56:13,600 --> 00:56:16,799
Speaker 1: data systems. They don't even know how many people have

1106
00:56:17,000 --> 00:56:19,760
Speaker 1: COVID or of tested positive because they're testing data center

1107
00:56:19,800 --> 00:56:24,359
Speaker 1: broke down. So all that to say that, Uh, the

1108
00:56:24,400 --> 00:56:26,120
Speaker 1: third bucket I'd say in terms of what we need

1109
00:56:26,160 --> 00:56:29,360
Speaker 1: to do better is we need to identify. We need

1110
00:56:29,400 --> 00:56:32,920
Speaker 1: to be very objective about what measures have worked and

1111
00:56:32,960 --> 00:56:36,360
Speaker 1: what measures have not worked in terms of limiting the

1112
00:56:36,440 --> 00:56:39,120
Speaker 1: spread flattening the curved center. It's pretty clear at this

1113
00:56:39,160 --> 00:56:42,960
Speaker 1: point that the Texas Florida model strikes the write balance.

1114
00:56:43,000 --> 00:56:44,279
Speaker 1: And by the way, as you know, Clay, I don't

1115
00:56:44,280 --> 00:56:46,080
Speaker 1: have to remind you. I probably don't have to remind

1116
00:56:46,080 --> 00:56:49,440
Speaker 1: your listeners. When we originally locked down in May, the

1117
00:56:49,560 --> 00:56:53,480
Speaker 1: ar gament was not that we were gonna obliterate COVID nineteen.

1118
00:56:53,920 --> 00:56:55,600
Speaker 1: It was that we were going to flatten the curve

1119
00:56:55,680 --> 00:56:59,480
Speaker 1: so that the hospitals weren't overwhelmed. Well, no hospitals are

1120
00:56:59,480 --> 00:57:04,000
Speaker 1: getting over filmed anywhere. Today we're talking to Ovicroy. You

1121
00:57:04,000 --> 00:57:06,440
Speaker 1: can follow him on Twitter at a v I K

1122
00:57:07,040 --> 00:57:09,120
Speaker 1: encourage you to go read all of his work. He

1123
00:57:09,200 --> 00:57:12,319
Speaker 1: went to m I TEO Medical School. Um, and this

1124
00:57:12,360 --> 00:57:16,200
Speaker 1: is wins and losses. I'm Clay Travis. Alright, so this, uh,

1125
00:57:16,640 --> 00:57:18,960
Speaker 1: there's a lot I could still unpack about what you

1126
00:57:19,040 --> 00:57:22,920
Speaker 1: just said. For your for your ideas about how we

1127
00:57:22,920 --> 00:57:24,760
Speaker 1: should be responding today. I love that you've got the

1128
00:57:24,760 --> 00:57:28,680
Speaker 1: three pronged there. How much of what we're doing is

1129
00:57:28,720 --> 00:57:32,440
Speaker 1: cosmetic theater And what I mean by that is in

1130
00:57:32,520 --> 00:57:35,880
Speaker 1: New York, if you look at the rates of infection,

1131
00:57:36,360 --> 00:57:38,880
Speaker 1: it seems like, based on the recent data that the

1132
00:57:38,920 --> 00:57:41,800
Speaker 1: Governor of Florida has shared, there are many parts of

1133
00:57:41,840 --> 00:57:45,000
Speaker 1: Florida with similar rates of infection to New York. It

1134
00:57:45,120 --> 00:57:47,640
Speaker 1: seems like there is a curve, a steep curve, and

1135
00:57:47,640 --> 00:57:50,360
Speaker 1: then it starts back down. In fact, the rates of

1136
00:57:50,400 --> 00:57:53,800
Speaker 1: infection in the Northeast, if you look at the rates

1137
00:57:53,840 --> 00:57:56,440
Speaker 1: per million or whatever the heck it is, it's almost

1138
00:57:56,520 --> 00:57:59,080
Speaker 1: identical to what we've eventually seen in the South. And

1139
00:57:59,160 --> 00:58:04,080
Speaker 1: while everybody was panicking on some level, isn't a virus

1140
00:58:04,320 --> 00:58:07,320
Speaker 1: going to be a virus no matter what we do?

1141
00:58:07,880 --> 00:58:11,160
Speaker 1: And that even if you shut down for a long time,

1142
00:58:11,320 --> 00:58:14,560
Speaker 1: eventually people are going to go back outside and the

1143
00:58:14,640 --> 00:58:18,400
Speaker 1: virus is going to uh to spread again. Is it

1144
00:58:18,480 --> 00:58:21,720
Speaker 1: that like to me? There's early on I think and look,

1145
00:58:21,760 --> 00:58:23,600
Speaker 1: I'm I'm far from an expert, but there's only two

1146
00:58:23,600 --> 00:58:26,200
Speaker 1: ways to end the virus. One is by vaccine, and

1147
00:58:26,280 --> 00:58:28,000
Speaker 1: I'll ask you about a vaccine in a little bit.

1148
00:58:28,240 --> 00:58:30,720
Speaker 1: The other is about herd immunity, and it seems to

1149
00:58:30,760 --> 00:58:32,760
Speaker 1: me like there is a lot of data out there

1150
00:58:32,800 --> 00:58:37,680
Speaker 1: now which would suggest that the herd immunity requirement is

1151
00:58:37,800 --> 00:58:40,760
Speaker 1: way lower than we were initially told. Initially, and you'll

1152
00:58:40,760 --> 00:58:42,760
Speaker 1: know better than me, but it's like, hey, you need

1153
00:58:42,840 --> 00:58:45,760
Speaker 1: seventy or eighty percent of the population to be exposed

1154
00:58:45,760 --> 00:58:48,280
Speaker 1: to it. There's no way we can actually do that,

1155
00:58:48,800 --> 00:58:53,040
Speaker 1: And the reality is maybe it's only ten to But

1156
00:58:53,120 --> 00:58:55,880
Speaker 1: if you even are willing to discuss that, it's like, oh,

1157
00:58:55,880 --> 00:58:58,560
Speaker 1: you don't care about somebody's grandma dying? How dare you?

1158
00:58:58,840 --> 00:59:02,200
Speaker 1: Which seems to circle back again around to your initial point,

1159
00:59:02,240 --> 00:59:05,480
Speaker 1: which is science is not science. It's like it's got

1160
00:59:05,480 --> 00:59:08,800
Speaker 1: to have a certain negative bit to it or else

1161
00:59:08,840 --> 00:59:11,560
Speaker 1: you're not allowed to to share it. So what data

1162
00:59:11,600 --> 00:59:15,200
Speaker 1: are you seeing about her immunity and what would you

1163
00:59:15,360 --> 00:59:18,400
Speaker 1: surmise based on that data as we speak in in

1164
00:59:18,600 --> 00:59:22,280
Speaker 1: mid to late August. Well, before I get to that,

1165
00:59:22,360 --> 00:59:24,840
Speaker 1: let me talk about the other piece of your question,

1166
00:59:24,880 --> 00:59:27,800
Speaker 1: which was, well, how how can we get through this crisis?

1167
00:59:27,800 --> 00:59:29,520
Speaker 1: What are the what are the ways you mentioned two

1168
00:59:29,520 --> 00:59:31,600
Speaker 1: of them you mentioned mentioned you herd immunity, you mentioned

1169
00:59:31,680 --> 00:59:34,600
Speaker 1: the vaccine. There's actually a third, which is you could

1170
00:59:34,640 --> 00:59:38,680
Speaker 1: have a drug that treats the disease, apply the virus

1171
00:59:38,800 --> 00:59:41,400
Speaker 1: in a way that that he doesn't require racing. For example,

1172
00:59:41,440 --> 00:59:45,400
Speaker 1: hepatitis C. There's no vaccine for hepatitis C, but in

1173
00:59:45,480 --> 00:59:49,240
Speaker 1: recent years there have emerged treatments that are effectively cures

1174
00:59:49,240 --> 00:59:52,360
Speaker 1: for hepatitis C. That still the virus still bounces around

1175
00:59:52,440 --> 00:59:55,760
Speaker 1: the country, but you won't Your liver will not fail.

1176
00:59:55,840 --> 00:59:57,640
Speaker 1: You will not need a liver transplant if you take

1177
00:59:57,640 --> 00:59:59,760
Speaker 1: the drugs. And the same thing would be for HIV

1178
01:00:00,200 --> 01:00:04,200
Speaker 1: right where we have tents, but there's no vaccine. The

1179
01:00:04,200 --> 01:00:05,800
Speaker 1: smartest people in the world have been working on an

1180
01:00:05,920 --> 01:00:08,680
Speaker 1: HIV vaccine for forty years. We still don't have an

1181
01:00:08,800 --> 01:00:12,040
Speaker 1: HIV vaccine, but we do have effective treatments. That means

1182
01:00:12,040 --> 01:00:13,440
Speaker 1: that it used to be you know, you and I

1183
01:00:13,440 --> 01:00:15,880
Speaker 1: know because we're of that age in the eighties, if

1184
01:00:15,920 --> 01:00:17,760
Speaker 1: you had HIV it was a death sentence. It's not

1185
01:00:17,800 --> 01:00:20,080
Speaker 1: a death sentence. To day, people are living a pretty

1186
01:00:20,320 --> 01:00:23,680
Speaker 1: uh long lives even if they have HIV, well controlled

1187
01:00:23,680 --> 01:00:27,480
Speaker 1: by these drugs that are not vaccines. So you'll remember,

1188
01:00:27,720 --> 01:00:30,160
Speaker 1: Clay that when we first locked down in the spring,

1189
01:00:31,200 --> 01:00:33,720
Speaker 1: that was the argument actually was, well, we're only going

1190
01:00:33,800 --> 01:00:35,320
Speaker 1: to have to lock down for a couple of weeks

1191
01:00:35,320 --> 01:00:37,600
Speaker 1: because there are a bunch of biotech companies that are

1192
01:00:37,640 --> 01:00:41,360
Speaker 1: developing these drugs, uh that are gonna end up curing

1193
01:00:41,360 --> 01:00:43,080
Speaker 1: the disease, and we're not gonna have to worry about it.

1194
01:00:43,080 --> 01:00:45,439
Speaker 1: It's only gonna be a couple of weeks. Fifteen days

1195
01:00:45,440 --> 01:00:47,720
Speaker 1: to slow the spread was one of the phrases, catchphrases

1196
01:00:47,720 --> 01:00:49,640
Speaker 1: was out there, and I was writing at the time

1197
01:00:49,640 --> 01:00:51,960
Speaker 1: my my original cover story in the Wall Street Journal

1198
01:00:51,960 --> 01:00:55,080
Speaker 1: and COVID from from April was about this fact that actually,

1199
01:00:55,160 --> 01:00:57,440
Speaker 1: as somebody who's invested in a lot of biotech companies,

1200
01:00:57,680 --> 01:01:02,000
Speaker 1: people are totally overestimating a probability of success here. Most

1201
01:01:02,040 --> 01:01:06,959
Speaker 1: drugs that enter clinical trials fail by far. Like so

1202
01:01:07,320 --> 01:01:09,640
Speaker 1: the idea that we're just gonna, you know, flip the

1203
01:01:09,640 --> 01:01:11,360
Speaker 1: switch or snap our fingers and we're gonna have a

1204
01:01:11,360 --> 01:01:14,080
Speaker 1: cure for COVID, it's not gonna necessarily work that way.

1205
01:01:14,120 --> 01:01:17,440
Speaker 1: It maybe months or even years before we have a

1206
01:01:17,560 --> 01:01:21,480
Speaker 1: drug like we do now for happetitis or HIV drugs

1207
01:01:21,520 --> 01:01:24,360
Speaker 1: took years, in decades to develop this idea that we're

1208
01:01:24,360 --> 01:01:27,200
Speaker 1: gonna wait for a cure in terms of a drug

1209
01:01:28,080 --> 01:01:30,920
Speaker 1: to to be on the market before we reopen the economy.

1210
01:01:31,040 --> 01:01:34,480
Speaker 1: We could be waiting years. We could basically destroy the

1211
01:01:34,520 --> 01:01:36,960
Speaker 1: economy permanently if we do that, So that that was

1212
01:01:37,000 --> 01:01:39,280
Speaker 1: one of my arguments early on, and not just mine,

1213
01:01:39,320 --> 01:01:42,240
Speaker 1: with my co authors too, which included some people who

1214
01:01:42,240 --> 01:01:44,080
Speaker 1: are on you know, both sides of the political aisles,

1215
01:01:44,080 --> 01:01:46,400
Speaker 1: so to speak. We were saying, look, you can't destroy

1216
01:01:46,480 --> 01:01:50,240
Speaker 1: the economy for that long because businesses will permanently close

1217
01:01:50,480 --> 01:01:53,960
Speaker 1: and there's no assurance that effective treatments will come along.

1218
01:01:54,040 --> 01:01:56,240
Speaker 1: So that was back in the spring. That theory has

1219
01:01:56,240 --> 01:01:58,680
Speaker 1: been proven right, right, there is still not any drug

1220
01:01:58,720 --> 01:02:02,120
Speaker 1: on the market that cures COVID. Obviously, their treatments that

1221
01:02:02,160 --> 01:02:05,520
Speaker 1: people are more hopeful about than others, but nothing is

1222
01:02:05,560 --> 01:02:09,280
Speaker 1: incontrovertibly a cure. So then let's talk about the vaccine.

1223
01:02:09,320 --> 01:02:12,439
Speaker 1: So you hear a lot of hype about vaccines. Um, well,

1224
01:02:12,480 --> 01:02:14,000
Speaker 1: we're gonna have a vaccine by the end of the year.

1225
01:02:14,040 --> 01:02:16,480
Speaker 1: Some people say, now, look, we all hope that's true,

1226
01:02:16,960 --> 01:02:20,600
Speaker 1: but it's very important to understand that the world record

1227
01:02:20,920 --> 01:02:24,240
Speaker 1: for the fastest vaccine ever developed for a novel virus,

1228
01:02:24,760 --> 01:02:27,840
Speaker 1: it's five years. It took five years to develop a

1229
01:02:27,920 --> 01:02:30,720
Speaker 1: vaccine for the Boula virus. That was the record up

1230
01:02:30,720 --> 01:02:34,560
Speaker 1: to now. So we're talking about having a vaccine in

1231
01:02:34,640 --> 01:02:37,680
Speaker 1: less than a year, which would be five x but

1232
01:02:38,120 --> 01:02:41,640
Speaker 1: the world record for speed the vaccine development. Now, there's

1233
01:02:41,640 --> 01:02:43,480
Speaker 1: a lot of advances in technology, there are a lot

1234
01:02:43,480 --> 01:02:45,000
Speaker 1: of people working on this. There's been a lot of

1235
01:02:45,000 --> 01:02:48,120
Speaker 1: money put to work, so it's possible that all that

1236
01:02:48,360 --> 01:02:51,200
Speaker 1: ends up working, but we we we can't be assured

1237
01:02:51,240 --> 01:02:53,400
Speaker 1: of that. And if the idea is that we're going

1238
01:02:53,440 --> 01:02:56,800
Speaker 1: to keep schools closed and businesses closed and the economy

1239
01:02:56,840 --> 01:02:59,880
Speaker 1: shut down until we have a vaccine, what if we

1240
01:03:00,040 --> 01:03:03,360
Speaker 1: are waiting two, three, four or five years for vaccine.

1241
01:03:03,640 --> 01:03:06,000
Speaker 1: We just can't. So we have to have a plan

1242
01:03:06,120 --> 01:03:09,800
Speaker 1: B in case of vaccine failed. And now that gets

1243
01:03:09,800 --> 01:03:11,560
Speaker 1: to the third thing that you mentioned, which is the

1244
01:03:11,600 --> 01:03:15,600
Speaker 1: herd immunity, your population immunity. Is it possible that, like

1245
01:03:15,640 --> 01:03:17,920
Speaker 1: a forest fire which eventually rages for a while but

1246
01:03:17,920 --> 01:03:21,200
Speaker 1: then eventually runs out of dry wood to burn, could

1247
01:03:21,240 --> 01:03:25,439
Speaker 1: we end up in a situation where COVID eventually flames out,

1248
01:03:26,360 --> 01:03:29,520
Speaker 1: and it is possible, and that is that is I

1249
01:03:29,520 --> 01:03:31,920
Speaker 1: think my personal view is that's what we're seeing in

1250
01:03:31,960 --> 01:03:34,680
Speaker 1: New York City, that's what we're seeing in Sweden. That

1251
01:03:34,760 --> 01:03:38,200
Speaker 1: you had high death whole high death tolls early on,

1252
01:03:38,560 --> 01:03:42,640
Speaker 1: but over time, the people who are susceptible succumbed tragically

1253
01:03:42,720 --> 01:03:45,400
Speaker 1: unfortunately to the virus, and the people who are left

1254
01:03:45,440 --> 01:03:49,600
Speaker 1: standing are not that susceptible. And that's why that's not

1255
01:03:49,600 --> 01:03:52,960
Speaker 1: not Andrew Cuomo flexing his biceps, but actually the fact

1256
01:03:52,960 --> 01:03:55,480
Speaker 1: that the susceptible people in New York are already dead.

1257
01:03:56,840 --> 01:03:59,919
Speaker 1: And do you buy into that this idea that bay

1258
01:04:00,160 --> 01:04:03,640
Speaker 1: on the data, you're seeing that the herd immunity threshold

1259
01:04:03,640 --> 01:04:07,440
Speaker 1: by which you start to see substantial protections is maybe

1260
01:04:07,480 --> 01:04:10,400
Speaker 1: a lot lower than what was initially told by the

1261
01:04:10,480 --> 01:04:14,920
Speaker 1: quote unquote experts. There's there's good evans so that we

1262
01:04:14,920 --> 01:04:18,280
Speaker 1: were talking earlier in the show Clay about Gabriella Gomez,

1263
01:04:18,320 --> 01:04:20,800
Speaker 1: a scientist who has modeled this out and can't get

1264
01:04:20,840 --> 01:04:24,880
Speaker 1: her research published by scientific journals because it's not alarmist enough.

1265
01:04:25,480 --> 01:04:28,360
Speaker 1: And there's there's been some publications and other medical and

1266
01:04:28,360 --> 01:04:31,120
Speaker 1: scientific journals to suggested that people don't know right that

1267
01:04:31,440 --> 01:04:36,080
Speaker 1: there's the The sort of more negative view is that

1268
01:04:36,400 --> 01:04:40,800
Speaker 1: you need of a population to be infected in order

1269
01:04:40,880 --> 01:04:43,640
Speaker 1: for her immunity to to take place. Now, for those

1270
01:04:43,680 --> 01:04:45,360
Speaker 1: who don't know what her immunity is, let me just

1271
01:04:45,400 --> 01:04:48,360
Speaker 1: maybe pause and explain it. So, what her immunity is

1272
01:04:48,360 --> 01:04:51,600
Speaker 1: is the idea that so many people have been exposed

1273
01:04:51,640 --> 01:04:55,480
Speaker 1: to the virus that the virus itself can't explode. Like,

1274
01:04:55,520 --> 01:04:58,560
Speaker 1: for the virus to really explode, infect other people. You know,

1275
01:04:58,760 --> 01:05:01,360
Speaker 1: you as the affected person, have to wander around and

1276
01:05:01,440 --> 01:05:03,520
Speaker 1: counter a bunch of other people who are not yet affected.

1277
01:05:03,520 --> 01:05:06,919
Speaker 1: It spread the virus to those other people. Now, if

1278
01:05:07,840 --> 01:05:11,520
Speaker 1: two out of three of those people are already immune

1279
01:05:11,640 --> 01:05:15,439
Speaker 1: because they've been infected in the past, then maybe there's

1280
01:05:15,440 --> 01:05:18,520
Speaker 1: a chance that that third person gets infected, but the

1281
01:05:18,560 --> 01:05:21,680
Speaker 1: probabilities are lower. The analogy might be trying to throw

1282
01:05:21,680 --> 01:05:24,280
Speaker 1: a golf ball through a chain link fence. Yeah, that

1283
01:05:24,280 --> 01:05:26,520
Speaker 1: the whole in the chain link fence fence is big

1284
01:05:26,640 --> 01:05:28,680
Speaker 1: enough for you to throw the golf ball through it.

1285
01:05:28,720 --> 01:05:30,120
Speaker 1: But if you ever tried to throw a golf ball

1286
01:05:30,160 --> 01:05:32,520
Speaker 1: through a chain link fence, there's at least a fift

1287
01:05:33,000 --> 01:05:35,520
Speaker 1: chance or more that you hit one of the links

1288
01:05:35,520 --> 01:05:38,320
Speaker 1: in the fence and the ball falls to the ground right.

1289
01:05:38,560 --> 01:05:42,120
Speaker 1: So similarly, here, if a bunch of people are already immune,

1290
01:05:42,520 --> 01:05:45,000
Speaker 1: the probability or the ability of the virus to really

1291
01:05:45,040 --> 01:05:48,400
Speaker 1: spread cuts down dramatically. So that's what herd immunity is.

1292
01:05:48,440 --> 01:05:53,680
Speaker 1: It's basically a virus basically failing to spread because enough

1293
01:05:53,720 --> 01:05:57,440
Speaker 1: people have already been immune. And what's very very interesting,

1294
01:05:58,000 --> 01:06:00,320
Speaker 1: and this is something that you see a lot in

1295
01:06:00,360 --> 01:06:02,600
Speaker 1: the background of the scientific literature, but it doesn't get

1296
01:06:02,600 --> 01:06:06,040
Speaker 1: the hype, is that there may be more herd immunity,

1297
01:06:06,560 --> 01:06:08,640
Speaker 1: or the threshold you need to get to her immunity

1298
01:06:08,680 --> 01:06:12,720
Speaker 1: for COVID is a lot lower than what what the

1299
01:06:12,800 --> 01:06:16,160
Speaker 1: what the maybe the more negative side thinks, And the

1300
01:06:16,200 --> 01:06:20,120
Speaker 1: reason for that is that the common cold is also

1301
01:06:20,160 --> 01:06:23,880
Speaker 1: a coronavirus, and so it may be that there are

1302
01:06:23,880 --> 01:06:26,920
Speaker 1: a bunch of other very mild coronaviruses that are out

1303
01:06:26,960 --> 01:06:29,640
Speaker 1: there that people have gotten over the last winter or

1304
01:06:29,720 --> 01:06:34,120
Speaker 1: longer that have given them enough immunity. Two stars Kobe

1305
01:06:34,120 --> 01:06:38,200
Speaker 1: to the virus that causes scovide nineteen, that herd immunity

1306
01:06:38,440 --> 01:06:41,200
Speaker 1: from COVID from actually being infected with stars Scobe two

1307
01:06:41,240 --> 01:06:44,240
Speaker 1: or the novel coronavirus, that threshold is a lot lower

1308
01:06:44,280 --> 01:06:47,920
Speaker 1: because there's already enough community to other coronavirus is out

1309
01:06:47,920 --> 01:06:52,880
Speaker 1: there in the population that that combination means that we're

1310
01:06:52,920 --> 01:06:56,000
Speaker 1: actually a much closer to her immunity today than than

1311
01:06:56,080 --> 01:06:58,600
Speaker 1: we otherwise thought. And that would be the most hopeful

1312
01:06:58,600 --> 01:07:02,200
Speaker 1: case that the thing runs out and uh and we

1313
01:07:02,400 --> 01:07:04,560
Speaker 1: before even there is a vaccine, and a vaccine that

1314
01:07:04,640 --> 01:07:09,320
Speaker 1: gets widely distributed, the virus has already done, as you

1315
01:07:09,320 --> 01:07:12,040
Speaker 1: put at, the forest fire has already burned through the

1316
01:07:12,120 --> 01:07:14,480
Speaker 1: dry wood. And again, that's still a tragedy. I don't

1317
01:07:14,520 --> 01:07:17,120
Speaker 1: mean to minimize the people who are dying from COVID nineteen.

1318
01:07:17,120 --> 01:07:20,280
Speaker 1: It's an incredible tragedy what's happened. But it may be

1319
01:07:20,520 --> 01:07:24,520
Speaker 1: that we're closer to the end than we than we

1320
01:07:24,600 --> 01:07:28,840
Speaker 1: might otherwise believe or be led to believe. What would

1321
01:07:28,840 --> 01:07:33,640
Speaker 1: you say the likelihood is of another situation like this

1322
01:07:33,800 --> 01:07:37,240
Speaker 1: arising during our life? You you look at you know,

1323
01:07:37,360 --> 01:07:42,360
Speaker 1: the the data over history history, but obviously you are

1324
01:07:42,840 --> 01:07:46,480
Speaker 1: at times a skeptic, at times a contrarian. Are you

1325
01:07:46,560 --> 01:07:50,160
Speaker 1: optimistic that in forty years, let's assume that a large

1326
01:07:50,200 --> 01:07:52,280
Speaker 1: percentage of our audience is still going to be alive

1327
01:07:52,320 --> 01:07:54,680
Speaker 1: and they'll be eighty or ninety years old or younger

1328
01:07:54,760 --> 01:07:57,919
Speaker 1: seventy sixty. Is this something that happens again in our

1329
01:07:57,960 --> 01:08:00,600
Speaker 1: lives or is this something that only comes up every

1330
01:08:00,680 --> 01:08:04,120
Speaker 1: hundred years. What is the likelihood that we have another

1331
01:08:04,240 --> 01:08:09,040
Speaker 1: situation like this anytime in the next several generations. You

1332
01:08:09,120 --> 01:08:13,040
Speaker 1: definitely can't rule it out. I mean, pandemics, particularly influenza

1333
01:08:13,080 --> 01:08:16,439
Speaker 1: influenza based pandemics, do happen from time to time. They're

1334
01:08:16,479 --> 01:08:18,840
Speaker 1: not usually as severe or as bad as this one,

1335
01:08:19,160 --> 01:08:21,519
Speaker 1: but they definitely do happen from time to time. The

1336
01:08:21,600 --> 01:08:24,080
Speaker 1: one thing that we can hope for is if if

1337
01:08:24,120 --> 01:08:28,720
Speaker 1: in say twenty fifty or we have another situation like this,

1338
01:08:29,320 --> 01:08:33,439
Speaker 1: that science and technology have advanced to the point where

1339
01:08:33,439 --> 01:08:35,960
Speaker 1: we don't have to wait a year or five years

1340
01:08:35,960 --> 01:08:38,760
Speaker 1: to develop a vaccine. We don't have a bureaucracy like

1341
01:08:38,800 --> 01:08:42,519
Speaker 1: the CDC and the FDA preventing people in February in

1342
01:08:42,600 --> 01:08:46,840
Speaker 1: Washington State from testing for the novel coronavirus. We can

1343
01:08:46,880 --> 01:08:49,720
Speaker 1: distribute those tests rapidly. We figured out how to do

1344
01:08:49,760 --> 01:08:53,439
Speaker 1: all that so that basically in your home, you know,

1345
01:08:53,479 --> 01:08:55,920
Speaker 1: you have your own little kind of lab instrument in

1346
01:08:55,960 --> 01:08:58,120
Speaker 1: your home, and you can just basically test for all

1347
01:08:58,160 --> 01:09:00,479
Speaker 1: sorts of things with that without having to actually go

1348
01:09:00,520 --> 01:09:02,960
Speaker 1: to a doctor or go to a CBS or what

1349
01:09:03,040 --> 01:09:05,840
Speaker 1: have you. So I think the more we can invest

1350
01:09:05,880 --> 01:09:07,760
Speaker 1: in that kind of infrastructure, the more we can have

1351
01:09:08,479 --> 01:09:11,439
Speaker 1: data and real time data reporting from nursing homes and

1352
01:09:11,479 --> 01:09:14,960
Speaker 1: other places where vulnerable populations live, we should be able

1353
01:09:14,960 --> 01:09:18,599
Speaker 1: to respond to a virus like this better. But you know,

1354
01:09:19,080 --> 01:09:22,560
Speaker 1: it's always theoretically possible that a virus comes along this

1355
01:09:22,720 --> 01:09:27,080
Speaker 1: even more virulent, more lethal than COVID nineteen or stars

1356
01:09:27,120 --> 01:09:30,120
Speaker 1: copy two, especially if you factor in the possibility of

1357
01:09:30,120 --> 01:09:32,760
Speaker 1: bio warfare. Right, So, I don't think we can ever

1358
01:09:32,840 --> 01:09:36,320
Speaker 1: rule out the possibility that something worse comes along. And

1359
01:09:36,400 --> 01:09:39,000
Speaker 1: that's all the more reason why we have to be

1360
01:09:39,120 --> 01:09:42,920
Speaker 1: so objective and so serious about the lessons we learned

1361
01:09:42,920 --> 01:09:44,800
Speaker 1: from this crisis, Because if the only lesson we learned

1362
01:09:44,880 --> 01:09:47,439
Speaker 1: is we hate Trump and we throw them out, then

1363
01:09:47,439 --> 01:09:51,360
Speaker 1: we haven't learned anything. You mentioned testing. There's been a

1364
01:09:51,400 --> 01:09:55,880
Speaker 1: massive amount of discussion about testing for the coronavirus throughout

1365
01:09:56,000 --> 01:09:59,439
Speaker 1: the last several months. What do we need to know

1366
01:09:59,640 --> 01:10:03,480
Speaker 1: what should we know about testing its viability? It's important.

1367
01:10:04,240 --> 01:10:06,599
Speaker 1: What would you say the essence of the takeaway about

1368
01:10:06,600 --> 01:10:10,639
Speaker 1: testing should be, Well, there's there's a couple of things

1369
01:10:10,680 --> 01:10:13,120
Speaker 1: I'd say. First, it's important for people understanding because you

1370
01:10:13,120 --> 01:10:15,479
Speaker 1: hear people say, well, it's a failure of the US

1371
01:10:15,600 --> 01:10:19,240
Speaker 1: government that there aren't more tests today. That's not actually true.

1372
01:10:19,280 --> 01:10:21,920
Speaker 1: The US is testing more people than any other country

1373
01:10:21,960 --> 01:10:24,120
Speaker 1: in the world. We're testing a lot of people. The

1374
01:10:24,160 --> 01:10:26,880
Speaker 1: problem is and I wrote about this free opt dot org.

1375
01:10:26,920 --> 01:10:29,920
Speaker 1: The original paper that we published it free opt dot org,

1376
01:10:29,920 --> 01:10:33,080
Speaker 1: our think tank on the pandemic. It's called a New

1377
01:10:33,200 --> 01:10:39,000
Speaker 1: Strategy for Reopening the Economy. During COVID nineteen, we talked,

1378
01:10:39,000 --> 01:10:41,200
Speaker 1: we walked through all the science of this and how

1379
01:10:41,680 --> 01:10:44,400
Speaker 1: the tests that we have today they're not perfect. They're

1380
01:10:44,400 --> 01:10:46,800
Speaker 1: not like the pregnancy tests that people are most used to,

1381
01:10:47,080 --> 01:10:48,280
Speaker 1: where you just kind of take it to home and

1382
01:10:48,280 --> 01:10:50,720
Speaker 1: you know whether you're pregnant or not with incredible accuracy.

1383
01:10:51,040 --> 01:10:53,639
Speaker 1: The COVID tests are not that accurate, and they take

1384
01:10:53,680 --> 01:10:55,519
Speaker 1: a long time to get you the results back. So

1385
01:10:55,520 --> 01:10:57,160
Speaker 1: if you have to wait a week to get the

1386
01:10:57,280 --> 01:11:00,840
Speaker 1: results from a COVID test, even if the test is available,

1387
01:11:01,800 --> 01:11:03,240
Speaker 1: you're not, it's not useful because then what are you

1388
01:11:03,280 --> 01:11:04,760
Speaker 1: gonna do for that week while you sit around wait

1389
01:11:04,760 --> 01:11:06,360
Speaker 1: for the test. You might have gotten positive even if

1390
01:11:06,479 --> 01:11:08,000
Speaker 1: even if the test is negative, you might have gotten

1391
01:11:08,040 --> 01:11:12,000
Speaker 1: positive in the intervening couple of days. So testing alone

1392
01:11:12,520 --> 01:11:16,040
Speaker 1: doesn't matter. Uh. And and testing it doesn't matter, it's

1393
01:11:16,040 --> 01:11:20,200
Speaker 1: not as central at this point as as people think.

1394
01:11:20,240 --> 01:11:22,080
Speaker 1: What really matters is some of the other things that

1395
01:11:22,080 --> 01:11:26,519
Speaker 1: we've talked about, herd immunity, social distancing, washing your hands,

1396
01:11:26,760 --> 01:11:30,960
Speaker 1: basic stuff to have hygiene. Uh. But it did matter

1397
01:11:31,000 --> 01:11:35,760
Speaker 1: early on. If we had enough testing early on, we

1398
01:11:35,800 --> 01:11:37,760
Speaker 1: could have maybe niff this thing in the bud like

1399
01:11:37,840 --> 01:11:41,040
Speaker 1: some countries have done up to this point, in particularly

1400
01:11:41,120 --> 01:11:44,400
Speaker 1: in the Pacific Rim, the New Zealand, the Taiwan. Now,

1401
01:11:44,680 --> 01:11:47,360
Speaker 1: how what would that have looked like? What happened wasn't

1402
01:11:47,360 --> 01:11:49,000
Speaker 1: There was a great story I think in the Washington

1403
01:11:49,040 --> 01:11:52,960
Speaker 1: Post about this several months ago where when the when

1404
01:11:52,960 --> 01:11:55,920
Speaker 1: the stay to Washington, when they first started seeing cases

1405
01:11:55,920 --> 01:11:58,240
Speaker 1: of COVID nineteen or what they saw was an unexplained

1406
01:11:58,240 --> 01:12:03,120
Speaker 1: pneumonia and scientists in local academic centers and other labs,

1407
01:12:03,120 --> 01:12:05,599
Speaker 1: we're trying to figure this out and they actually developed

1408
01:12:05,640 --> 01:12:09,320
Speaker 1: their own kind of homebrew test of the novel coronavirus

1409
01:12:09,360 --> 01:12:14,080
Speaker 1: because the Chinese actually had published online the genetic sequence

1410
01:12:14,520 --> 01:12:17,479
Speaker 1: of the stars Kobe to coronavirus, So if you were

1411
01:12:17,560 --> 01:12:20,719
Speaker 1: a scientist in Washington, you could actually take that genetic

1412
01:12:20,760 --> 01:12:24,040
Speaker 1: sequence and they use that to develop a test. And

1413
01:12:24,120 --> 01:12:26,840
Speaker 1: so they actually started doing that, and the FDA and

1414
01:12:26,840 --> 01:12:29,680
Speaker 1: the CDC came in. The bureaucracy came in and said, no,

1415
01:12:29,960 --> 01:12:33,200
Speaker 1: that's illegal, you can't do that, and basically Squashing said,

1416
01:12:33,200 --> 01:12:36,400
Speaker 1: not only the CDC is legally allowed to develop the test,

1417
01:12:36,520 --> 01:12:39,360
Speaker 1: we were basically waiting for the government, the federal government,

1418
01:12:39,400 --> 01:12:41,160
Speaker 1: to develop the test. And it turned out the CDC

1419
01:12:42,439 --> 01:12:44,439
Speaker 1: waited a month. They had delays of a month in

1420
01:12:44,439 --> 01:12:47,720
Speaker 1: developing a test because their lab was contaminated. So that

1421
01:12:47,920 --> 01:12:53,040
Speaker 1: was a really really disastrous uh results, and what we

1422
01:12:53,080 --> 01:12:54,840
Speaker 1: should have had and what we hopefully will have in

1423
01:12:54,840 --> 01:12:57,719
Speaker 1: the future as a system in which testing for novel

1424
01:12:57,760 --> 01:13:00,799
Speaker 1: viruses is not dependent on the government. You have private

1425
01:13:00,800 --> 01:13:05,479
Speaker 1: sector labs, biotech companies, university scientists, all with the ability

1426
01:13:05,760 --> 01:13:09,040
Speaker 1: to develop tests and and compare them against each other

1427
01:13:09,120 --> 01:13:13,320
Speaker 1: tests for accuracy, crowdsource that test development rather than depending

1428
01:13:13,360 --> 01:13:16,120
Speaker 1: on a single lab in Atlanta to do it for you.

1429
01:13:16,400 --> 01:13:18,519
Speaker 1: So that's a huge lesson that we should have learned earlier.

1430
01:13:18,560 --> 01:13:21,920
Speaker 1: But in terms of scale of testing today, testing is

1431
01:13:21,920 --> 01:13:24,680
Speaker 1: not going to be this panacea that everyone thinks it is.

1432
01:13:25,000 --> 01:13:26,280
Speaker 1: And I'll give you an example of why. You know

1433
01:13:26,320 --> 01:13:28,360
Speaker 1: you're a lot of people talk about, well, look at

1434
01:13:28,360 --> 01:13:30,840
Speaker 1: the cases, the cases are rising, but as you pointed

1435
01:13:30,840 --> 01:13:33,439
Speaker 1: out with Florida, right, the cases are rising in Florida,

1436
01:13:33,640 --> 01:13:36,400
Speaker 1: but not as many people died. Why is that? Well,

1437
01:13:36,439 --> 01:13:38,439
Speaker 1: A big part of the reason is that a number

1438
01:13:38,439 --> 01:13:40,960
Speaker 1: of those cases we're in younger people. A big part

1439
01:13:40,960 --> 01:13:43,200
Speaker 1: of that is that people were getting tested who had

1440
01:13:43,320 --> 01:13:46,160
Speaker 1: very mild symptoms, and a big part of that was

1441
01:13:46,200 --> 01:13:48,680
Speaker 1: that you were actually reporting all that data. I'll give

1442
01:13:48,680 --> 01:13:51,960
Speaker 1: you an example from Europe. In France, they had exactly

1443
01:13:52,000 --> 01:13:53,640
Speaker 1: the same number of cases. If you look at the

1444
01:13:53,880 --> 01:13:56,519
Speaker 1: chart in terms of how many cases of COVID nineteen

1445
01:13:56,520 --> 01:13:59,479
Speaker 1: they had in France, it looks almost exactly like the chart,

1446
01:13:59,520 --> 01:14:02,320
Speaker 1: and neighbor in Germany the same number of cases. But

1447
01:14:02,360 --> 01:14:05,599
Speaker 1: guess what in France, four times as many people died

1448
01:14:05,640 --> 01:14:08,800
Speaker 1: of COVID then died in Germany. Why is that? Was

1449
01:14:08,800 --> 01:14:12,280
Speaker 1: the virus magically four times as lethal in France as

1450
01:14:12,320 --> 01:14:16,240
Speaker 1: it was in Germany. No, it's that the people Germany

1451
01:14:16,360 --> 01:14:20,240
Speaker 1: was testing were different than the people the French were testing.

1452
01:14:20,320 --> 01:14:22,720
Speaker 1: You have an example Denmark. In Denmark, they basically only

1453
01:14:22,720 --> 01:14:26,439
Speaker 1: tested people who are hospitalized for COVID, so they basically

1454
01:14:26,560 --> 01:14:29,320
Speaker 1: underestimate the number of cases there were. So all that

1455
01:14:29,400 --> 01:14:33,160
Speaker 1: to say that testing is helpful, but it's not this panasy.

1456
01:14:33,240 --> 01:14:37,080
Speaker 1: We're getting better. The federal government has actually moved mountains

1457
01:14:37,120 --> 01:14:40,000
Speaker 1: to try to increase the supply of good test The

1458
01:14:40,080 --> 01:14:43,080
Speaker 1: f D is working over time. They had those initial missteps,

1459
01:14:43,120 --> 01:14:45,839
Speaker 1: but they're now trying to recover from that initial misstep

1460
01:14:46,240 --> 01:14:49,040
Speaker 1: and and do a lot better at at rapidly improving

1461
01:14:49,040 --> 01:14:51,479
Speaker 1: better tests. This test that you probably talked about on

1462
01:14:51,520 --> 01:14:54,559
Speaker 1: your show, Clay that's alive, a test that the NBA

1463
01:14:54,680 --> 01:14:57,920
Speaker 1: helped pioneer that could be a real improvement on what

1464
01:14:57,960 --> 01:15:00,000
Speaker 1: we have up to this point. All is to say

1465
01:15:00,479 --> 01:15:03,040
Speaker 1: testing is good, and it's good that we have more tests,

1466
01:15:03,160 --> 01:15:05,360
Speaker 1: but testing alone isn't going to solve the problems. What

1467
01:15:05,439 --> 01:15:07,720
Speaker 1: you really have to do is get her immunity or

1468
01:15:07,760 --> 01:15:11,000
Speaker 1: a vaccine. Those are the only reliable ways to really

1469
01:15:11,000 --> 01:15:14,000
Speaker 1: get this virus under control. Fox Sports Radio has the

1470
01:15:14,040 --> 01:15:16,960
Speaker 1: best sports talk lineup in the nation. Catch all of

1471
01:15:17,000 --> 01:15:20,519
Speaker 1: our shows at Fox Sports Radio dot com and within

1472
01:15:20,560 --> 01:15:23,000
Speaker 1: the I Heart Radio app search f s R to

1473
01:15:23,160 --> 01:15:25,639
Speaker 1: listen live. We're talking to O vic Roy. You can

1474
01:15:25,640 --> 01:15:27,840
Speaker 1: follow him on Twitter at A v I K. I'm

1475
01:15:27,880 --> 01:15:31,080
Speaker 1: Clay Travis. This is the Wins and Losses Podcast. Couple

1476
01:15:31,160 --> 01:15:33,800
Speaker 1: of final questions for you, and you've been phenomenal here.

1477
01:15:34,360 --> 01:15:36,920
Speaker 1: I believe you went to school. You told me off

1478
01:15:36,960 --> 01:15:40,000
Speaker 1: airs we were starting with Chris Webber, you were a

1479
01:15:40,080 --> 01:15:43,320
Speaker 1: year behind him. You grew up outside of Detroit, where

1480
01:15:43,320 --> 01:15:46,360
Speaker 1: my wife also grew up. She went to the University

1481
01:15:46,400 --> 01:15:49,200
Speaker 1: of Michigan. I believe you're a University of Michigan fan.

1482
01:15:49,320 --> 01:15:52,200
Speaker 1: Is that right? I am. Yeah. It's been a it's

1483
01:15:52,200 --> 01:15:54,880
Speaker 1: been a tough decade at least on the football side,

1484
01:15:54,920 --> 01:15:57,920
Speaker 1: but you know, yeah, no doubt. Okay, So when you

1485
01:15:58,000 --> 01:16:01,280
Speaker 1: see the Big ten and the pack well shut down

1486
01:16:01,439 --> 01:16:07,960
Speaker 1: fall Sports from a obviously a perspective with which you

1487
01:16:08,000 --> 01:16:10,680
Speaker 1: are looking at this, did they get it right or

1488
01:16:10,720 --> 01:16:14,120
Speaker 1: did they get it wrong? Well, first of all, if

1489
01:16:14,160 --> 01:16:16,040
Speaker 1: Chris Webber is is listening, I just want to tell

1490
01:16:16,240 --> 01:16:18,879
Speaker 1: Chris I'm so happy that you've you've come back to Michigan,

1491
01:16:18,880 --> 01:16:22,360
Speaker 1: the Fab five as has reunited. Uh and and and

1492
01:16:22,600 --> 01:16:25,400
Speaker 1: those those wounds from so long ago and heal all

1493
01:16:25,439 --> 01:16:28,040
Speaker 1: the love to you. And I'm so glad that that

1494
01:16:28,160 --> 01:16:30,719
Speaker 1: all that is is getting better now after all this time.

1495
01:16:31,520 --> 01:16:33,840
Speaker 1: But in terms of the Big Ten and the pack

1496
01:16:34,120 --> 01:16:36,840
Speaker 1: of the packs, well stuff. Uh, it's been such an

1497
01:16:36,880 --> 01:16:39,479
Speaker 1: interesting story to follow, especially over the last week, as

1498
01:16:39,720 --> 01:16:43,160
Speaker 1: as you've obviously talked about a lot on your show. Uh.

1499
01:16:43,320 --> 01:16:47,280
Speaker 1: Really remarkable. Um. And I think there's a couple of

1500
01:16:47,280 --> 01:16:52,280
Speaker 1: things to say about it. One the uh, the illogic

1501
01:16:52,360 --> 01:16:56,000
Speaker 1: of saying you're gonna have tens of thousands of students

1502
01:16:56,040 --> 01:16:58,880
Speaker 1: come to campus, but it's too dangerous for people to

1503
01:16:58,920 --> 01:17:01,679
Speaker 1: play football. I mean, how do you think they're actually

1504
01:17:01,680 --> 01:17:05,040
Speaker 1: gonna get COVID? It's from the other students, right, So

1505
01:17:05,200 --> 01:17:08,479
Speaker 1: like does that make any sense? Um? And obviously there

1506
01:17:08,479 --> 01:17:10,839
Speaker 1: are some colleges that have given up on having a season.

1507
01:17:11,439 --> 01:17:14,599
Speaker 1: But I actually take the opposite, uh takeaway from that,

1508
01:17:14,600 --> 01:17:17,600
Speaker 1: which is COVID nineteen. You know, we talked about this

1509
01:17:17,640 --> 01:17:19,920
Speaker 1: in some of our work at Prep. COVID nineteen in

1510
01:17:20,920 --> 01:17:25,080
Speaker 1: the college population is not lethal. Yes, of course there

1511
01:17:25,080 --> 01:17:28,680
Speaker 1: are very rare cases of serious illness or death. But

1512
01:17:28,840 --> 01:17:32,120
Speaker 1: it's very very rare. And you know, you've you've heard

1513
01:17:32,160 --> 01:17:34,719
Speaker 1: this talk. And the talk that really reportedly has scared

1514
01:17:35,160 --> 01:17:37,439
Speaker 1: the chancellors and presidents in the Big ten inpacts well

1515
01:17:37,520 --> 01:17:41,200
Speaker 1: has been uh, myocarditis inflammation of the heart muscle as

1516
01:17:41,200 --> 01:17:43,760
Speaker 1: a result of COVID nineteen. There have been a couple

1517
01:17:43,800 --> 01:17:47,560
Speaker 1: of cases of that, um, again a serious illness that

1518
01:17:47,600 --> 01:17:50,200
Speaker 1: we should be concerned about. But the studies that have

1519
01:17:50,280 --> 01:17:54,799
Speaker 1: been used to scare the presidents and chancellors are studies

1520
01:17:54,920 --> 01:17:58,280
Speaker 1: of fifty year olds in other countries. Um. They're not

1521
01:17:58,360 --> 01:18:02,760
Speaker 1: of actually college aged athletes. UM. And in fact, the

1522
01:18:03,080 --> 01:18:07,240
Speaker 1: likelihood that a college ash athlete gets inflammatory myocarditis from

1523
01:18:07,280 --> 01:18:10,280
Speaker 1: COVID nineteen, as far as we know, is extremely low.

1524
01:18:10,320 --> 01:18:12,559
Speaker 1: And that's not to say you shouldn't be concerned about

1525
01:18:12,600 --> 01:18:14,200
Speaker 1: and you shouldn't try to be careful and you shouldn't

1526
01:18:14,200 --> 01:18:17,439
Speaker 1: try to protect the athletes. Of course you should, uh.

1527
01:18:17,520 --> 01:18:21,400
Speaker 1: But there there is again a lot of alarmism here

1528
01:18:21,600 --> 01:18:25,840
Speaker 1: rather than rigorous scientific examination of the real risk. And

1529
01:18:25,880 --> 01:18:28,880
Speaker 1: football players in particular play with a lot of risks

1530
01:18:28,920 --> 01:18:31,600
Speaker 1: every day. You know, they're they're football players who are

1531
01:18:31,640 --> 01:18:35,519
Speaker 1: permanently disabled because of playing football in college. So they're

1532
01:18:35,680 --> 01:18:38,400
Speaker 1: they're they're aware of risk. And what's been I think

1533
01:18:38,439 --> 01:18:41,519
Speaker 1: particularly disappointing is that the players were not part of

1534
01:18:41,520 --> 01:18:45,160
Speaker 1: the conversation. Right. This was being determined by commissioners and

1535
01:18:45,240 --> 01:18:48,400
Speaker 1: presidents without their input in almost every case. And that's

1536
01:18:48,400 --> 01:18:50,240
Speaker 1: not to say that maybe if their input had been

1537
01:18:50,240 --> 01:18:53,120
Speaker 1: in there, the decision wouldn't have been dissimilar. Maybe the parents,

1538
01:18:53,240 --> 01:18:54,560
Speaker 1: there are a lot of there's a maybe there's a

1539
01:18:54,560 --> 01:18:57,200
Speaker 1: silent majority of parents who are concerned. I don't want

1540
01:18:57,200 --> 01:18:59,200
Speaker 1: their kids playing, And that's fine. If they don't want

1541
01:18:59,200 --> 01:19:02,120
Speaker 1: to play, they shouldn't up to But these decisions were

1542
01:19:02,120 --> 01:19:05,680
Speaker 1: being made behind closed doors with in some cases what

1543
01:19:05,720 --> 01:19:09,040
Speaker 1: appears to be sketchy scientific evidence. And I'd like to

1544
01:19:09,080 --> 01:19:11,519
Speaker 1: see a more open discussion where we really do go

1545
01:19:11,600 --> 01:19:15,559
Speaker 1: through the evidence. Why did the experts get so much wrong?

1546
01:19:15,960 --> 01:19:19,360
Speaker 1: I started talking about this early, using sports as a prism,

1547
01:19:19,360 --> 01:19:21,600
Speaker 1: talking about the difficulty and I'm sure you've dealt with

1548
01:19:21,640 --> 01:19:25,480
Speaker 1: this as well. Of knowing both the numerator and the denominator,

1549
01:19:25,600 --> 01:19:29,400
Speaker 1: in other words, projecting a death rate or an infection rate,

1550
01:19:29,720 --> 01:19:32,320
Speaker 1: you need to know how many infections there were, and

1551
01:19:32,400 --> 01:19:34,559
Speaker 1: you need to know, uh, you know, how many of

1552
01:19:34,560 --> 01:19:38,439
Speaker 1: those people are actually dying and because of the virus

1553
01:19:38,520 --> 01:19:41,640
Speaker 1: as opposed to dying with the virus. And so you

1554
01:19:41,680 --> 01:19:45,400
Speaker 1: know this this Imperial College forecast out of out of England,

1555
01:19:45,400 --> 01:19:47,720
Speaker 1: which forecast over two million people would die in the

1556
01:19:47,760 --> 01:19:51,080
Speaker 1: United States. It doesn't I probably surprise you that the

1557
01:19:51,080 --> 01:19:55,559
Speaker 1: worst case scenario forecast got way more attention in the media.

1558
01:19:56,000 --> 01:20:00,160
Speaker 1: But the so called experts, the epidemiologists, the virologists, their

1559
01:20:00,240 --> 01:20:04,000
Speaker 1: forecast were completely for the most part, worthless early on

1560
01:20:04,080 --> 01:20:07,000
Speaker 1: when decisions were being made here, Why do you think

1561
01:20:07,000 --> 01:20:10,920
Speaker 1: they got so much wrong? Wow? Let's uh, we could

1562
01:20:10,920 --> 01:20:13,000
Speaker 1: spend an hour on that tap at topic. Before I

1563
01:20:13,000 --> 01:20:14,880
Speaker 1: get to that, let me say one thing else about

1564
01:20:14,880 --> 01:20:17,040
Speaker 1: the Big ten. A school to keep an eye on

1565
01:20:17,160 --> 01:20:20,720
Speaker 1: is Perdue. Perdue is run by Mitch Daniels, who is

1566
01:20:20,960 --> 01:20:23,040
Speaker 1: a really smart guy who used to be the governor

1567
01:20:23,080 --> 01:20:26,240
Speaker 1: of Indiana, was the budget chief in the White House

1568
01:20:26,280 --> 01:20:30,320
Speaker 1: under George W. Bush, really really smart, talented, thoughtful guy

1569
01:20:30,520 --> 01:20:33,479
Speaker 1: about the stuff testified before the Senate about why he

1570
01:20:33,560 --> 01:20:36,360
Speaker 1: was going to reopen Perdue. And they've had they've done

1571
01:20:36,360 --> 01:20:39,400
Speaker 1: a lot of really interesting and sophisticated things to try

1572
01:20:39,439 --> 01:20:42,479
Speaker 1: to make a fall semester at Purdue work. And let

1573
01:20:42,479 --> 01:20:43,680
Speaker 1: me go through some of them. We don't know if

1574
01:20:43,680 --> 01:20:45,759
Speaker 1: it's gonna work, but he's certainly doing some really interesting

1575
01:20:45,800 --> 01:20:47,920
Speaker 1: things that are worth keeping an eye. And first he

1576
01:20:48,040 --> 01:20:52,200
Speaker 1: required that everybody test negative for COVID before coming to campus,

1577
01:20:52,200 --> 01:20:56,040
Speaker 1: and they sponsored the testing. They tested over thirty produced

1578
01:20:56,080 --> 01:20:59,280
Speaker 1: students and they had a positivity rate of less than

1579
01:20:59,320 --> 01:21:02,920
Speaker 1: one percent of that student body. UM, and they have

1580
01:21:03,040 --> 01:21:06,000
Speaker 1: some other plans in place, so like let's say, UH,

1581
01:21:06,040 --> 01:21:08,479
Speaker 1: you do get sick, or you do you have had COVID,

1582
01:21:08,520 --> 01:21:11,800
Speaker 1: so you have immunity. Let's put those Let's rum those

1583
01:21:11,920 --> 01:21:14,760
Speaker 1: people who are now immune with the people who might

1584
01:21:14,800 --> 01:21:17,479
Speaker 1: have preexisting conditions or other vulnerabilities so that they can

1585
01:21:17,520 --> 01:21:21,840
Speaker 1: be even more safe in those particular UH facilities. So

1586
01:21:21,840 --> 01:21:23,439
Speaker 1: there are a lot of things that Perdue was doing

1587
01:21:23,439 --> 01:21:25,000
Speaker 1: that I think are worth watching to see if you

1588
01:21:25,000 --> 01:21:27,040
Speaker 1: compare and contrast that to u n C, which famously

1589
01:21:27,080 --> 01:21:30,160
Speaker 1: shut down. UNC actually only tested a thousand of their

1590
01:21:30,160 --> 01:21:33,040
Speaker 1: students prior to opening up the campus. And of course,

1591
01:21:33,040 --> 01:21:35,400
Speaker 1: and they had a bunch of outbreaks that they breaked

1592
01:21:35,400 --> 01:21:37,439
Speaker 1: out about and decided to shut everything down again, which

1593
01:21:37,479 --> 01:21:39,400
Speaker 1: was just stupid on every level. They should have had

1594
01:21:39,400 --> 01:21:42,120
Speaker 1: a better plan because people are going to test positive,

1595
01:21:42,400 --> 01:21:44,640
Speaker 1: and having having people test positive should not be a

1596
01:21:44,680 --> 01:21:46,519
Speaker 1: reason to shut down the campus if you have a

1597
01:21:46,560 --> 01:21:50,080
Speaker 1: plan in place to handle those positive cases. So those

1598
01:21:50,120 --> 01:21:52,719
Speaker 1: are two schools I think that are like examples of

1599
01:21:52,720 --> 01:21:54,360
Speaker 1: of a good of a good way to handle the

1600
01:21:54,400 --> 01:21:56,679
Speaker 1: thoughtful way to handle it, and maybe a thought less

1601
01:21:57,000 --> 01:21:59,640
Speaker 1: way to handle it. Um and then so let me

1602
01:21:59,640 --> 01:22:01,439
Speaker 1: then just just go onto your your question about the

1603
01:22:01,479 --> 01:22:04,439
Speaker 1: math modeling. I mean, it's just been This is another

1604
01:22:04,479 --> 01:22:06,719
Speaker 1: part of the story that does not get enough attention

1605
01:22:06,760 --> 01:22:08,800
Speaker 1: today that will over time. And part of it, as

1606
01:22:08,800 --> 01:22:10,760
Speaker 1: you said, or maybe the majority of it, as you said,

1607
01:22:10,800 --> 01:22:13,719
Speaker 1: is that journalists aren't really good at math. But people

1608
01:22:13,800 --> 01:22:16,400
Speaker 1: really need to dig into what these models are. There's

1609
01:22:16,439 --> 01:22:19,640
Speaker 1: a tendency for the average person understandably say, well, that

1610
01:22:19,680 --> 01:22:22,320
Speaker 1: guy's got a PhD in statistics, he's got a PhD

1611
01:22:22,320 --> 01:22:24,960
Speaker 1: in maths, he's smarter than me. I should just defer

1612
01:22:25,000 --> 01:22:27,679
Speaker 1: to him, he's the expert. But if you actually dig

1613
01:22:27,720 --> 01:22:32,080
Speaker 1: into what these models are, they're incredibly simplistic, or they're

1614
01:22:32,080 --> 01:22:36,360
Speaker 1: based on incredibly flimsy uh data points about what's going

1615
01:22:36,400 --> 01:22:39,160
Speaker 1: to happen. It would be like saying, to use the

1616
01:22:39,160 --> 01:22:41,719
Speaker 1: college football analogy again, who's going to be the national

1617
01:22:41,840 --> 01:22:47,360
Speaker 1: champion of college football in right? Like you can make

1618
01:22:47,400 --> 01:22:49,759
Speaker 1: some guesses about that, but we all know the college

1619
01:22:49,760 --> 01:22:53,800
Speaker 1: football landscape shifts and and and flows over over a

1620
01:22:53,840 --> 01:22:57,519
Speaker 1: decade or two different powers emerge, So you're not gonna

1621
01:22:57,520 --> 01:23:00,679
Speaker 1: necessarily have any idea. And yet these models are being

1622
01:23:01,080 --> 01:23:03,320
Speaker 1: put forward with this level of conviction like, well, if

1623
01:23:03,320 --> 01:23:05,519
Speaker 1: you don't agree with me, you're against science. And of

1624
01:23:05,560 --> 01:23:08,400
Speaker 1: course that model from the Imperial College London that predicted

1625
01:23:08,439 --> 01:23:12,120
Speaker 1: two million dust turned out to be completely wrong. And

1626
01:23:12,120 --> 01:23:14,400
Speaker 1: a part of what's been happening here is that what

1627
01:23:14,479 --> 01:23:16,439
Speaker 1: people do, what a lot of the dirty secret of

1628
01:23:16,439 --> 01:23:18,600
Speaker 1: a lot of models, Clay, is what they'll do is

1629
01:23:18,640 --> 01:23:20,720
Speaker 1: they'll take a set of data, like they'll take the

1630
01:23:20,840 --> 01:23:24,160
Speaker 1: curve of how COVID evolved in wuha, how many cases

1631
01:23:24,160 --> 01:23:26,840
Speaker 1: on this day versus that day versus that day, and

1632
01:23:26,840 --> 01:23:30,880
Speaker 1: then they'll just find a mathematical equation that if you

1633
01:23:30,960 --> 01:23:35,320
Speaker 1: chart that mathematical equation looks like that curve and then say, okay,

1634
01:23:35,400 --> 01:23:37,519
Speaker 1: we're gonna use that equation to predict what happens in

1635
01:23:37,520 --> 01:23:41,160
Speaker 1: the future, Well that that doesn't make any sense. Just

1636
01:23:41,400 --> 01:23:43,519
Speaker 1: you know, the thing we were talking about stocks earlier,

1637
01:23:43,680 --> 01:23:46,200
Speaker 1: what do they say in all the stock brokerage commercially,

1638
01:23:46,200 --> 01:23:49,959
Speaker 1: they say past performance is not a guarantee of future results.

1639
01:23:50,400 --> 01:23:52,719
Speaker 1: And the same is true with viruses. Right, just because

1640
01:23:52,960 --> 01:23:55,200
Speaker 1: the curve has looked this way in the past doesn't

1641
01:23:55,240 --> 01:23:57,240
Speaker 1: mean it's going to look a certain way in the future.

1642
01:23:57,600 --> 01:24:00,360
Speaker 1: And yet this very simplistic way of modeling where you say, well,

1643
01:24:00,760 --> 01:24:03,240
Speaker 1: you know, there's this equation and it kind of looks

1644
01:24:03,240 --> 01:24:07,360
Speaker 1: on a graph like the way COVID nineteen evolved in Wuhan.

1645
01:24:07,439 --> 01:24:09,240
Speaker 1: So I'm gonna use that as my model predict theres

1646
01:24:09,240 --> 01:24:14,440
Speaker 1: two million dats. That's not science. That's basically fancy guesswork.

1647
01:24:15,040 --> 01:24:20,920
Speaker 1: And yet that fancy guesswork dramatically affected policy in Western economies.

1648
01:24:22,000 --> 01:24:26,160
Speaker 1: Not only that, arguably that fancy guests work literally caused

1649
01:24:26,200 --> 01:24:29,760
Speaker 1: a lot more deaths because that fancy guest work was

1650
01:24:29,840 --> 01:24:33,920
Speaker 1: the quote unquote expert forecast, which I think Andrew Cuomo,

1651
01:24:34,000 --> 01:24:37,000
Speaker 1: if we gave him truth serum, would say he believed,

1652
01:24:37,080 --> 01:24:39,799
Speaker 1: which is why he sent those patients back into nursing

1653
01:24:39,800 --> 01:24:42,280
Speaker 1: homes right like, he believed they were going to need

1654
01:24:42,320 --> 01:24:45,720
Speaker 1: a hundred and forty thousand beds because of those forecasts.

1655
01:24:46,160 --> 01:24:49,519
Speaker 1: Actually they only needed nineteen thousand. But if he had

1656
01:24:49,520 --> 01:24:52,120
Speaker 1: known they were only gonna need nineteen thousand, it's likely

1657
01:24:52,200 --> 01:24:55,280
Speaker 1: that tens of thousands of people might well be alive

1658
01:24:55,439 --> 01:24:59,719
Speaker 1: in this country today. They actually followed those forecast advice

1659
01:24:59,760 --> 01:25:02,960
Speaker 1: would were wildly off and as a result ended up

1660
01:25:03,000 --> 01:25:05,920
Speaker 1: with arguably way more people dead than would have died

1661
01:25:06,280 --> 01:25:10,120
Speaker 1: if they had not. And you know another example, we

1662
01:25:10,120 --> 01:25:13,559
Speaker 1: were talking earlier on the show Clay about about how

1663
01:25:13,920 --> 01:25:18,000
Speaker 1: all the public health experts they were educated and trained

1664
01:25:18,040 --> 01:25:20,519
Speaker 1: on influenza pandemics, and so they basically went back to

1665
01:25:20,520 --> 01:25:22,400
Speaker 1: that they were fighting the last war they were. They

1666
01:25:22,400 --> 01:25:27,000
Speaker 1: were drawing from those influenza pandemic playbooks to talk about

1667
01:25:27,000 --> 01:25:28,880
Speaker 1: COVID nineteen or how to deal with COVID nineteen. A

1668
01:25:28,880 --> 01:25:32,639
Speaker 1: great example was people don't talk about anymore, but remember

1669
01:25:32,680 --> 01:25:34,519
Speaker 1: how we were all terrified that we were going to

1670
01:25:34,600 --> 01:25:37,599
Speaker 1: run out of ventilators. They're always talking about in the spring,

1671
01:25:37,680 --> 01:25:39,400
Speaker 1: Oh gosh, we don't have enough ventilators. What are we

1672
01:25:39,400 --> 01:25:42,280
Speaker 1: gonna do? Well, it turned out in New York City

1673
01:25:42,800 --> 01:25:45,599
Speaker 1: of the people who were put on ventilators died because

1674
01:25:45,640 --> 01:25:49,599
Speaker 1: the ventilators actually made the disease worse, because it wasn't

1675
01:25:49,640 --> 01:25:53,240
Speaker 1: fundamentally a respiratory disease. It was fundamentally and inflammatory disease.

1676
01:25:53,800 --> 01:25:57,439
Speaker 1: And that's an example of where, uh, to your point,

1677
01:25:57,520 --> 01:25:59,880
Speaker 1: the the the expert opinion about well, this is just

1678
01:26:00,080 --> 01:26:01,800
Speaker 1: like influenza. We got to do what we would do

1679
01:26:01,840 --> 01:26:06,040
Speaker 1: in influenza. Pandemics turned out to be a fatal, fatal decision.

1680
01:26:07,040 --> 01:26:09,600
Speaker 1: We're talking to Vic Roy. I'm Clay Travis. This is

1681
01:26:09,640 --> 01:26:13,200
Speaker 1: wins and losses. What letter grade would you give the

1682
01:26:13,320 --> 01:26:16,879
Speaker 1: United States media for the way that they have covered

1683
01:26:17,240 --> 01:26:22,000
Speaker 1: this pandemic? I mean, I wish I could give him

1684
01:26:22,000 --> 01:26:26,840
Speaker 1: a G because it's not even enough, right because, like

1685
01:26:27,439 --> 01:26:29,599
Speaker 1: you know, you could graduate from high school with an

1686
01:26:29,640 --> 01:26:34,160
Speaker 1: F in a in a particular class. But uh, what's

1687
01:26:34,160 --> 01:26:38,559
Speaker 1: happened here, the the the distortion and the misrepresentation of

1688
01:26:38,600 --> 01:26:40,880
Speaker 1: what's been going on in Again, I don't think that

1689
01:26:40,920 --> 01:26:44,040
Speaker 1: distortion has always been intentional. I think some people, uh,

1690
01:26:44,080 --> 01:26:47,479
Speaker 1: you know, are are getting a certain weird pleasure out

1691
01:26:47,520 --> 01:26:49,599
Speaker 1: of out of making things look better or worse than

1692
01:26:49,600 --> 01:26:51,000
Speaker 1: they are. But I think a lot of people are

1693
01:26:51,040 --> 01:26:54,519
Speaker 1: just genuinely scared, and they're writing articles that reflect how

1694
01:26:54,560 --> 01:26:58,800
Speaker 1: scared they are. Uh. But that that that inability to

1695
01:26:58,840 --> 01:27:02,599
Speaker 1: put numbers in contains up. There was a Indiana Junior

1696
01:27:02,640 --> 01:27:05,680
Speaker 1: high that shut down because one person had COVID. I mean,

1697
01:27:05,960 --> 01:27:08,679
Speaker 1: you know, you'll see these numbers, aren't well. Texas today

1698
01:27:08,720 --> 01:27:12,120
Speaker 1: had five cases. What does that mean? How many people

1699
01:27:12,160 --> 01:27:14,040
Speaker 1: live in Texas? How many those people are getting sick,

1700
01:27:14,040 --> 01:27:17,120
Speaker 1: how many those people are dying? You never see that information.

1701
01:27:17,400 --> 01:27:21,120
Speaker 1: There's just been so much like that. I just you know,

1702
01:27:21,920 --> 01:27:24,840
Speaker 1: it's it's really been. It's been very, very bad. And

1703
01:27:24,880 --> 01:27:26,840
Speaker 1: the only saving grace at the end of the day

1704
01:27:26,880 --> 01:27:29,719
Speaker 1: has been, in a sense, the existence of the Internet.

1705
01:27:29,800 --> 01:27:32,679
Speaker 1: Because for all the things they're terrible about social media

1706
01:27:32,720 --> 01:27:35,880
Speaker 1: we can complain about or whatever, it has allowed people

1707
01:27:35,960 --> 01:27:39,360
Speaker 1: like you and and people who are these epidemiologists who

1708
01:27:39,400 --> 01:27:41,800
Speaker 1: are have this sort of con contrary and opinion, they're

1709
01:27:41,840 --> 01:27:44,599
Speaker 1: able to express themselves. They they're able to put research

1710
01:27:44,640 --> 01:27:47,120
Speaker 1: out there, They're able to put analyzes out there that

1711
01:27:47,320 --> 01:27:50,080
Speaker 1: people who want to look at the evidence can examine.

1712
01:27:50,120 --> 01:27:53,040
Speaker 1: And I think that has enabled, in a sense, a

1713
01:27:53,120 --> 01:27:56,320
Speaker 1: kind of an end around around that more traditional gatekeeping process.

1714
01:27:56,720 --> 01:27:59,600
Speaker 1: It has been perfect. There have been websites, uh, and

1715
01:27:59,680 --> 01:28:01,960
Speaker 1: big internet companies have tried to say, well, if you

1716
01:28:02,040 --> 01:28:04,599
Speaker 1: disagree with the World Health Organization, we're gonna shut down

1717
01:28:04,600 --> 01:28:07,120
Speaker 1: your account, Like the World Health Organization is kind of

1718
01:28:07,200 --> 01:28:09,280
Speaker 1: lot wrong. And I think some of the tech companies

1719
01:28:09,280 --> 01:28:12,519
Speaker 1: have realized that that was a mistake. But how that's

1720
01:28:12,520 --> 01:28:14,479
Speaker 1: an important thing to to to keep an eye and

1721
01:28:14,520 --> 01:28:17,799
Speaker 1: make sure that there's always channels for alternative views because

1722
01:28:18,240 --> 01:28:19,840
Speaker 1: I don't think that problem is gonna get better. We're

1723
01:28:19,840 --> 01:28:21,960
Speaker 1: not going to magically wake up with a different news

1724
01:28:22,040 --> 01:28:25,439
Speaker 1: media ecosystem which everyone's got a degree in statistics. Yeah,

1725
01:28:25,560 --> 01:28:27,559
Speaker 1: and this goes to my biggest issue. And I see

1726
01:28:27,600 --> 01:28:31,520
Speaker 1: so many connections across so many different fabrics of American

1727
01:28:31,600 --> 01:28:35,200
Speaker 1: society and world society today, and the one that really

1728
01:28:35,280 --> 01:28:38,200
Speaker 1: kind of connects it all to me is science is

1729
01:28:38,200 --> 01:28:42,320
Speaker 1: about combat, and we don't think enough about the combat

1730
01:28:42,479 --> 01:28:45,680
Speaker 1: of ideas. Right, you come up with a theory or

1731
01:28:45,720 --> 01:28:48,400
Speaker 1: a hypothesis, however you want to classify it, and the

1732
01:28:48,479 --> 01:28:54,439
Speaker 1: scientific method requires an adversarial response to that, because only

1733
01:28:54,560 --> 01:28:59,519
Speaker 1: by challenging hypotheses or theories can we determine what the

1734
01:28:59,560 --> 01:29:03,120
Speaker 1: truth really is what I see far too often in

1735
01:29:03,160 --> 01:29:05,320
Speaker 1: our society today. And this is why I was saying

1736
01:29:05,320 --> 01:29:08,920
Speaker 1: earlier I'm a first amendmad absolutist. Is it seems like

1737
01:29:08,960 --> 01:29:14,400
Speaker 1: we are creating arenas where only acceptable opinions are allowed

1738
01:29:14,439 --> 01:29:16,920
Speaker 1: to go head to head with each other. And I

1739
01:29:16,960 --> 01:29:20,479
Speaker 1: think we've seen that with the coronavirus, where science all

1740
01:29:20,479 --> 01:29:24,040
Speaker 1: of a sudden has become so political that if you

1741
01:29:24,160 --> 01:29:27,160
Speaker 1: have the belief, hey, we need to figure out a

1742
01:29:27,160 --> 01:29:29,599
Speaker 1: way to live with the coronavirus, we need to figure

1743
01:29:29,640 --> 01:29:32,240
Speaker 1: out a way to live with COVID, maybe all of

1744
01:29:32,280 --> 01:29:36,720
Speaker 1: these dire forecasts aren't true. You were considered to be

1745
01:29:36,880 --> 01:29:41,960
Speaker 1: contributing to and almost an accessory to death. That is

1746
01:29:42,000 --> 01:29:43,960
Speaker 1: scary to me, and I bet it's scary to you,

1747
01:29:44,040 --> 01:29:47,000
Speaker 1: as somebody who makes a living in many ways actually

1748
01:29:47,040 --> 01:29:50,160
Speaker 1: diving into the data. If you can't share that data

1749
01:29:50,240 --> 01:29:53,759
Speaker 1: and have a legitimate debate with someone without being accused

1750
01:29:53,840 --> 01:29:57,040
Speaker 1: of facilitating death in the country or even God forbid,

1751
01:29:57,160 --> 01:30:01,080
Speaker 1: rooting for it, that's an inherent flaw our debate in

1752
01:30:01,200 --> 01:30:06,120
Speaker 1: our national discourse. You know, I think if there's if

1753
01:30:06,280 --> 01:30:09,960
Speaker 1: that's probably what you're just describing, Clay is the single

1754
01:30:10,040 --> 01:30:13,200
Speaker 1: most important thing that our country and the world, but

1755
01:30:13,240 --> 01:30:16,200
Speaker 1: particularly our country has to get better at. You we

1756
01:30:16,320 --> 01:30:20,879
Speaker 1: have to have an ecosystem, a way of debating all ideas,

1757
01:30:20,880 --> 01:30:24,800
Speaker 1: but particularly scientific ideas, but all of these economic policy politics,

1758
01:30:25,439 --> 01:30:27,800
Speaker 1: you know, racial issues we have. We have to have

1759
01:30:28,040 --> 01:30:30,760
Speaker 1: a way of talking about all these issues in in

1760
01:30:30,800 --> 01:30:33,799
Speaker 1: a in a way that where we're competing theories, competing

1761
01:30:33,880 --> 01:30:38,760
Speaker 1: hypotheses can be addressed and considered and not suppressed. That's

1762
01:30:38,800 --> 01:30:42,000
Speaker 1: incredibly important. We've always got to make sure that that

1763
01:30:42,000 --> 01:30:47,160
Speaker 1: that alternative, that contrarian approached ideas, uh, is there. And

1764
01:30:47,160 --> 01:30:49,040
Speaker 1: by the way, like we were talking earlier about my

1765
01:30:49,120 --> 01:30:51,640
Speaker 1: by my time as an investor, that's the essence of

1766
01:30:51,760 --> 01:30:55,280
Speaker 1: every great successful investor. Is they big when everybody else

1767
01:30:55,320 --> 01:30:58,240
Speaker 1: is zagging? Right? You think about Billy Bean, same thing, right,

1768
01:30:58,840 --> 01:31:02,840
Speaker 1: the most success full Uh. You know it's true. And

1769
01:31:02,880 --> 01:31:05,280
Speaker 1: in football especially right, you think about the people who

1770
01:31:05,320 --> 01:31:07,839
Speaker 1: have come up with creative ways of new new offensive

1771
01:31:07,960 --> 01:31:11,040
Speaker 1: or defensive schemes. Sometimes those offensive and defensive schemes are

1772
01:31:11,040 --> 01:31:13,720
Speaker 1: actually were invented a hundred ten years ago, but they've

1773
01:31:13,800 --> 01:31:16,000
Speaker 1: just fallen out of a style, so people don't adjust

1774
01:31:16,000 --> 01:31:18,479
Speaker 1: to them. Right, So there's there's a real need for

1775
01:31:18,600 --> 01:31:22,240
Speaker 1: contrarian thinking at all times, and we've got to find

1776
01:31:22,240 --> 01:31:25,640
Speaker 1: a way to ensure the people who disagree with the

1777
01:31:25,720 --> 01:31:29,680
Speaker 1: majority point of view on a scientific issue are not

1778
01:31:30,160 --> 01:31:34,360
Speaker 1: characterized as anti science merely for disagreeing with one take

1779
01:31:34,400 --> 01:31:36,799
Speaker 1: on the evidence. Because you can have a very evidence

1780
01:31:36,880 --> 01:31:41,000
Speaker 1: driven view that's different from what another person who's looking

1781
01:31:41,000 --> 01:31:43,439
Speaker 1: to the same evidence concludes. And unless we have a

1782
01:31:43,479 --> 01:31:47,120
Speaker 1: system in which that's possible and that's allowed, we're not

1783
01:31:47,160 --> 01:31:50,080
Speaker 1: going to be truly scientific. We're not going to be

1784
01:31:50,160 --> 01:31:52,679
Speaker 1: pro science, and we're not going to actually do right

1785
01:31:52,840 --> 01:31:56,240
Speaker 1: by our country. Final question for you and I, and

1786
01:31:56,280 --> 01:31:58,760
Speaker 1: this is obviously predictive in nature, but I'm curious what

1787
01:31:58,840 --> 01:32:02,040
Speaker 1: you think to a there is a national consensus that

1788
01:32:02,120 --> 01:32:05,360
Speaker 1: the Vietnam War was mismanaged and that we shouldn't have

1789
01:32:05,439 --> 01:32:09,280
Speaker 1: been involved in Vietnam. I think there's also a consensus that,

1790
01:32:09,360 --> 01:32:12,840
Speaker 1: maybe among most people, that the Iraq War did not

1791
01:32:13,000 --> 01:32:16,240
Speaker 1: make make sense when you consider the cost in lives,

1792
01:32:16,400 --> 01:32:20,679
Speaker 1: in economics, all those different things, and that sometimes takes

1793
01:32:20,720 --> 01:32:22,760
Speaker 1: and you know this as well as I do. The

1794
01:32:22,880 --> 01:32:26,120
Speaker 1: retrospective arc of history, we have to go back in

1795
01:32:26,200 --> 01:32:29,479
Speaker 1: twenty five years from now, thirty years, fifteen years, whatever

1796
01:32:29,520 --> 01:32:32,800
Speaker 1: the math, maybe we have a clearer vision of what

1797
01:32:32,960 --> 01:32:37,000
Speaker 1: the full story was right, and that's ultimately what ends

1798
01:32:37,080 --> 01:32:41,120
Speaker 1: up being written. Will people look back on our response

1799
01:32:41,160 --> 01:32:46,160
Speaker 1: to the coronavirus in ten years and right honest portrayals

1800
01:32:46,280 --> 01:32:48,719
Speaker 1: of what we got right and what we got wrong?

1801
01:32:49,000 --> 01:32:53,439
Speaker 1: Or is the media so left wing convinced, Because what's

1802
01:32:53,439 --> 01:32:57,040
Speaker 1: fascinating is the left wingers ended up being right in

1803
01:32:57,120 --> 01:33:00,000
Speaker 1: many ways about the Iraq War and about Vietnam, right

1804
01:33:00,479 --> 01:33:03,400
Speaker 1: without getting into the particulars of those wars, but that

1805
01:33:03,520 --> 01:33:07,519
Speaker 1: made them willing to acknowledge for the public, Hey, we

1806
01:33:07,640 --> 01:33:10,960
Speaker 1: got this wrong. I think the left wing has gotten

1807
01:33:11,000 --> 01:33:14,519
Speaker 1: the coronavirus completely wrong, right. I think that has been

1808
01:33:14,560 --> 01:33:18,320
Speaker 1: a failure, more so by left wing media than anyone.

1809
01:33:18,960 --> 01:33:22,559
Speaker 1: Will we get an honest appraisal of the coronavirus and

1810
01:33:22,640 --> 01:33:25,639
Speaker 1: our response to it in the next ten or twenty

1811
01:33:25,720 --> 01:33:29,280
Speaker 1: years or is it so intensely political that no one

1812
01:33:29,400 --> 01:33:32,080
Speaker 1: is ever going to admit what they got wrong and

1813
01:33:32,120 --> 01:33:37,200
Speaker 1: what we failed from as a country. Well, Clay, it's

1814
01:33:37,240 --> 01:33:40,320
Speaker 1: so interesting that you bring up the Vietnam War because

1815
01:33:40,360 --> 01:33:43,439
Speaker 1: there was a famous book written about the Vietnam War

1816
01:33:43,840 --> 01:33:47,160
Speaker 1: by David Halberstam called the Best and the Brightest Yes,

1817
01:33:47,320 --> 01:33:51,160
Speaker 1: And it's all about how the Vietnam War was prosecuted

1818
01:33:51,600 --> 01:33:56,320
Speaker 1: by the greatest science oriented experts of the country at

1819
01:33:56,320 --> 01:33:59,599
Speaker 1: that time. They were all Harvard and Yale graduates. They

1820
01:33:59,600 --> 01:34:05,599
Speaker 1: all had impressive resumes, lots of degrees. The main general

1821
01:34:05,680 --> 01:34:08,439
Speaker 1: or the Secretary of Defense, Robert McNamara, was one of

1822
01:34:08,479 --> 01:34:12,160
Speaker 1: these guys. He he pioneered, you know, analytic thinking and

1823
01:34:12,240 --> 01:34:14,920
Speaker 1: the way the Forward Motor Company worked in the middle

1824
01:34:14,920 --> 01:34:17,240
Speaker 1: of the twentieth century. And that's what you know. He

1825
01:34:17,280 --> 01:34:20,640
Speaker 1: tried to basically apply those lessons of you know, quantitation

1826
01:34:20,720 --> 01:34:23,040
Speaker 1: and metrics for everything to the to the way the

1827
01:34:23,200 --> 01:34:25,880
Speaker 1: army operated in Vietnam. It turned out to be a

1828
01:34:25,880 --> 01:34:32,160
Speaker 1: complete catastrophic failure. Uh. And and so my hope is that, uh,

1829
01:34:32,160 --> 01:34:36,520
Speaker 1: we have some similar examination of what happened in this crisis,

1830
01:34:36,800 --> 01:34:39,360
Speaker 1: and we draw the lessons from this crisis that David

1831
01:34:39,439 --> 01:34:42,360
Speaker 1: Halbert Stam was able to pull out of the Vietnam

1832
01:34:42,439 --> 01:34:44,920
Speaker 1: War when he wrote that book, Because that is an

1833
01:34:44,920 --> 01:34:46,920
Speaker 1: incredibly important part. We have to have a lot more

1834
01:34:47,280 --> 01:34:50,760
Speaker 1: humility around what we call expertise, and we have to

1835
01:34:50,800 --> 01:34:57,200
Speaker 1: have a genuinely, genuinely sincere uh openness, particularly in this

1836
01:34:57,280 --> 01:34:59,800
Speaker 1: era of big data. So much information is online, so

1837
01:35:00,000 --> 01:35:01,560
Speaker 1: many people can take a look at that data and

1838
01:35:01,640 --> 01:35:03,519
Speaker 1: come up with it. We've got to have a much

1839
01:35:03,560 --> 01:35:07,559
Speaker 1: more open source, crowdsourced approach to thinking about evidence, and

1840
01:35:07,600 --> 01:35:10,320
Speaker 1: if we do that, we'll do a much better job

1841
01:35:10,439 --> 01:35:13,400
Speaker 1: for vulnerable populations in this country and many others. Do

1842
01:35:13,439 --> 01:35:14,920
Speaker 1: you want to write a book on this to help

1843
01:35:14,960 --> 01:35:17,639
Speaker 1: tell the story after this is all done, because at

1844
01:35:17,680 --> 01:35:20,240
Speaker 1: some point we're going to go back to normalcy, But

1845
01:35:20,280 --> 01:35:23,120
Speaker 1: if we don't learn from the errors that we have made.

1846
01:35:23,120 --> 01:35:26,280
Speaker 1: And I think there's a strong argument that the worst

1847
01:35:26,320 --> 01:35:28,840
Speaker 1: decision in my life was the war in Iraq, right,

1848
01:35:28,880 --> 01:35:30,960
Speaker 1: I think you can make that argument, or the twenty

1849
01:35:31,000 --> 01:35:34,120
Speaker 1: one century as Vietnam was before you and I. But

1850
01:35:34,200 --> 01:35:36,640
Speaker 1: I think for older people who are listening to that,

1851
01:35:36,640 --> 01:35:40,320
Speaker 1: that's probably the biggest failure in American policy. I feel

1852
01:35:40,360 --> 01:35:43,479
Speaker 1: like the coronavirus has is in that level. But I

1853
01:35:43,520 --> 01:35:45,800
Speaker 1: wonder if it's ever going to be acknowledged in the

1854
01:35:45,840 --> 01:35:50,960
Speaker 1: same way. Well, you know, uh, I thought about writing

1855
01:35:50,960 --> 01:35:52,519
Speaker 1: a book about it, and and maybe I will. I

1856
01:35:52,520 --> 01:35:55,360
Speaker 1: got to find a spare time because they're no kidding

1857
01:35:55,360 --> 01:35:58,040
Speaker 1: our think tank free up dot org. But I but

1858
01:35:58,120 --> 01:36:00,280
Speaker 1: maybe I will and and and you're right to that

1859
01:36:00,320 --> 01:36:04,040
Speaker 1: debate needs to happen, and I think it will. I

1860
01:36:04,080 --> 01:36:07,360
Speaker 1: hope it will, because this virus, not just the desk,

1861
01:36:07,400 --> 01:36:10,240
Speaker 1: but the lockdowns, the school closures. It is affect that

1862
01:36:10,280 --> 01:36:12,280
Speaker 1: all of us, right, we've all been affected by it.

1863
01:36:12,280 --> 01:36:16,320
Speaker 1: It's too important not to have that examination take place.

1864
01:36:16,439 --> 01:36:19,160
Speaker 1: And so hopefully people like you and me and so

1865
01:36:19,160 --> 01:36:21,719
Speaker 1: many others who have been who've been carrying that flag,

1866
01:36:21,760 --> 01:36:24,240
Speaker 1: will well to do their part to make sure that

1867
01:36:24,280 --> 01:36:26,920
Speaker 1: we have that conversation. You've done almost two hours with

1868
01:36:27,000 --> 01:36:28,519
Speaker 1: us here. I think we could go on and on.

1869
01:36:28,600 --> 01:36:31,400
Speaker 1: It's been fascinating. I encourage everybody listening to go follow

1870
01:36:31,720 --> 01:36:35,439
Speaker 1: avic avic Ovic man. After all that time I was

1871
01:36:35,439 --> 01:36:38,040
Speaker 1: trying to get your yeah, I know it would be

1872
01:36:38,080 --> 01:36:39,599
Speaker 1: so much easier if they started to ring with an

1873
01:36:39,600 --> 01:36:42,040
Speaker 1: oh instead of an a uh at a v I

1874
01:36:42,160 --> 01:36:45,120
Speaker 1: K go follow him. He does incredible work. If you've

1875
01:36:45,200 --> 01:36:47,920
Speaker 1: enjoyed this conversation, thank him for spending the time with us.

1876
01:36:48,200 --> 01:36:50,960
Speaker 1: I'm Clay Travis. Thank you again for coming on. And

1877
01:36:51,000 --> 01:36:52,320
Speaker 1: this has been wins and losses.