Speaker 1: This is Wins and Losses with Clay Travis. Clay talks
Speaker 1: with the most entertaining people in sports, entertainment and business.
Speaker 1: Now here's Clay Travis. Welcome in Wins and Losses Podcast.
Speaker 1: I am Clay Travis, and we're about to be joined
Speaker 1: by O vic Roy, who I think you guys are
Speaker 1: really going to enjoy. He's been doing fantastic work looking
Speaker 1: at the data surrounding the coronavirus, making recommendations on so
Speaker 1: many different levels, been writing for the Wall Street Journal,
Speaker 1: among other locations. We have never actually spoken before, but
Speaker 1: I am impressed by the work that he's done. I
Speaker 1: found him on social media over the last several months,
Speaker 1: and we bring him in now. Oh vic Roy, let
Speaker 1: me go ahead and start here. How can people find
Speaker 1: you on social media? How can they read your work? Ovic?
Speaker 1: And thanks for joining us. Hey, thanks Clay, Well, thanks
Speaker 1: to my eccentric parents. My name is spelled A v
Speaker 1: i K, not Ovic, O v i K. It's a
Speaker 1: v i K and that's my Twitter handle, A v
Speaker 1: I K, just like it sounds a viasm Victor I K.
Speaker 1: All right, So your background as we get into so
Speaker 1: many different interesting topics that I want to discuss with you.
Speaker 1: But what is your educational background that led you into
Speaker 1: the profession that you have now and what do you
Speaker 1: do for a living. It's a bit of a zig
Speaker 1: zag path. My undergraduate degree was in molecular biology at
Speaker 1: M I. T. And then I went to medical school
Speaker 1: at Yale. And then instead of becoming a doctor or
Speaker 1: a scientist, I went into biotechnology investing, where I invested
Speaker 1: in companies trying to develop new treatments for diseases, vaccines,
Speaker 1: all that sort of thing. And then I got really
Speaker 1: interested in healthcare re form and that led me down
Speaker 1: the rabbit hole of healthcare policy and economic policy in general,
Speaker 1: and worked on a bunch of presidential campaigns and and
Speaker 1: now I run a think tank in Austin, Texas called
Speaker 1: the Foundation for Research on Equal Opportunity, where we come
Speaker 1: up with ideas to help more Americans climb up the
Speaker 1: economic ladder of success. All right, So I'm fascinated by
Speaker 1: several different things. You've already told this, So what is
Speaker 1: the reaction. You go to M I T. And then
Speaker 1: you go to Yale Medical School and I don't know
Speaker 1: the answer to this. I went to law school and
Speaker 1: I practiced law for a couple of years, and even
Speaker 1: not practicing law was considered to be a sort of
Speaker 1: a risky choice by many people, because you have a
Speaker 1: good profession that's out there. What percentage of your classmates
Speaker 1: or people who go to a medical school as good
Speaker 1: as Yale end up not actually practicing medicine when they graduate, Well,
Speaker 1: it's a small percent at most schools, it's probably close
Speaker 1: to zero. But Yale was a particular place where they
Speaker 1: actually encourage you to to pursue your interests outside of
Speaker 1: medical school. It was like a path Baale system and
Speaker 1: things like that. And so I'd say in a typical
Speaker 1: Yale class, which is a hundred people per class, about
Speaker 1: five to ten end up doing something that's sort of
Speaker 1: like you know, in law school, it's very typical, right,
Speaker 1: A lot of lawyers go into things of the law,
Speaker 1: and especially yeah, especially after a few years. Most people
Speaker 1: have to go in and make money initially, but then
Speaker 1: they'll start to filter out. Everybody I always say who's
Speaker 1: a lawyer has got a dream of not being a lawyer.
Speaker 1: But most people who go to medicine, That's why I
Speaker 1: was interested. Most people who go to medical school go
Speaker 1: on in practice. So is that something you came to
Speaker 1: a decision before you even started medical school, or what
Speaker 1: was it about, sort of the capitalistic economy I guess
Speaker 1: of the biotechnology universe that attracted you more than being
Speaker 1: a traditional doctor. Well, my dad was also a scientist.
Speaker 1: He was a biochemist, and so I grew up around
Speaker 1: all these incredible people who had been like the people
Speaker 1: who had characterized DNA and RNA, and we're the pioneers
Speaker 1: in this modern field of genetics and biology that we're
Speaker 1: now living in. So I always had this real excitement
Speaker 1: about it. I thought I wanted to be a scientist.
Speaker 1: And then you know what the problem is, Like, you know,
Speaker 1: at m I T a core of the faculty has
Speaker 1: Nobel Prize that I'm walking around and I'm like, there
Speaker 1: is no way I'm going to win a Nobel Prize.
Speaker 1: I'm not smart enough. So how am I going to
Speaker 1: actually do something useful to the world. I don't know.
Speaker 1: So I struggled with it for a while, and I thought,
Speaker 1: you know what, maybe I can invest in biotech companies.
Speaker 1: I can help build the latest new cure for some disease.
Speaker 1: That would be something useful I could do with my life,
Speaker 1: and that let me down that path, and here I
Speaker 1: am doing this now because obviously health care reform and
Speaker 1: economic policy in general effects so many people. A lot
Speaker 1: of people struggle to find affordable health insurance. Uh. And
Speaker 1: there are a lot of cures that we need to
Speaker 1: have for for disease that people have, and and not
Speaker 1: just in health care, a lot of oarliious higher education.
Speaker 1: How do you afford college? How do you afford to
Speaker 1: keep the lights on in your house? There are lots
Speaker 1: of things where we need new ideas, and it's been
Speaker 1: fun to work on work on trying to develop those ideas. Okay, yeah, no,
Speaker 1: it's so how do you decide? So I'm kind of
Speaker 1: fascinated by the concept of being an investor in biotechnology
Speaker 1: companies because you obviously have to be sophisticated to even
Speaker 1: understand as an investor, like for people out there who
Speaker 1: don't really think about it very much, being a quote
Speaker 1: unquote sophisticated investor is typically requires a certain net worth,
Speaker 1: but you can invest in a variety of different companies,
Speaker 1: sometimes for better or ill uh. And but biotechnology, I
Speaker 1: would imagine there's a lot of I I you tell me,
Speaker 1: but I would imagine there's a lot of snake oil
Speaker 1: cells out there who are trying to peddle things that
Speaker 1: may or may not make a lot of sense. So
Speaker 1: I would think a medical degree like yours would basically,
Speaker 1: for lack of a better way to describe it, allow
Speaker 1: you to speak the language so that you're may be
Speaker 1: better able to understand. And maybe also your dad being
Speaker 1: involved help with that as well. But what was your
Speaker 1: process like in terms of investing and finding companies that
Speaker 1: you found to be worthy of putting money behind. Yeah,
Speaker 1: it's it's so it's great and and really relevant that
Speaker 1: you're bringing this up, actually because it has a lot
Speaker 1: to do with how I ended up being a contrarian
Speaker 1: on COVID stuff. So yes, I I UH entered the
Speaker 1: workforce a finished school in two thousands, and that was
Speaker 1: right around the time that the Human Genome Project had
Speaker 1: been completed. So for those who remember those days, the
Speaker 1: Human Genome Project was at the time this gigantic UH
Speaker 1: scientific enterprise to sequence the entire human genome. Every d
Speaker 1: N a piece of DNA and you're comprise of the
Speaker 1: human genetic code from beginning to end, because that had
Speaker 1: never been done before. And that was finally finished in
Speaker 1: two thousand and one, and there was a big dot
Speaker 1: com boom in the nineties when the Internet as we
Speaker 1: know at first came into being. And right after that
Speaker 1: dot com bubble burst, there was this basically this genomics bubble.
Speaker 1: All these stocks called this genomics and that genomics were
Speaker 1: We're getting multibillion dollar market caps and nobody knew what
Speaker 1: they did and a lot of it was hyped. And
Speaker 1: so UH an investment firm I've never heard of called
Speaker 1: Bain Capital. I have to know a couple of people
Speaker 1: who worked there, and one of them reached out to
Speaker 1: me and said, hey, can you help us figure out
Speaker 1: all this genomics stuff because we're just a bunch of MBAs.
Speaker 1: We don't know anything about genomics, and we figured you
Speaker 1: can teach us. You have a degree in molecro biology,
Speaker 1: you can teach us about this stuff, and we can
Speaker 1: teach you how to read a balance sheet and then
Speaker 1: maybe you can be useful. And I'm like, wow, that's
Speaker 1: not really knowing the first thing about about how to
Speaker 1: do any of that. When I started UH that, I
Speaker 1: got recruited to to Bain Capital, moved to Boston and
Speaker 1: started investing in bi tech companies. I basically became part
Speaker 1: of this first generation of people with scientific and medical
Speaker 1: backgrounds m d s and PhD is mostly who started
Speaker 1: investing in biotech companies because it ended up it's not
Speaker 1: like a normal stock thing, where like normally, if you
Speaker 1: turn on c NBC or something, it's like, well the
Speaker 1: pe ratio is this right, or if you look at
Speaker 1: those conventional things. But biotech it's not like that at all,
Speaker 1: because the clinical trial turns out positive or negative in
Speaker 1: terms of your your latest jurif for breast cancer or whatever,
Speaker 1: and that stock goes to zero if it fails, or
Speaker 1: it triples if it succeeds. I mean, it's total volatility.
Speaker 1: It's crazy, a lot of losses, a lot of winds.
Speaker 1: It's it's kind of like baseball, where if you're batting average,
Speaker 1: it doesn't if you're batting averages below five dred you're
Speaker 1: probably not gonna survive. But if you if but you're
Speaker 1: gonna lose, you're gonna be wrong plenty. And you have
Speaker 1: to put an enormous amount of effort into statistics, right
Speaker 1: because at the end of the day, what what my
Speaker 1: job ended up becoming and a lot of people who
Speaker 1: were like me, is we ended up being incredibly intense statisticians,
Speaker 1: because what you end up doing is you're looking at
Speaker 1: a say, a breast cancer trial, new new drug for
Speaker 1: breast cancer. It's being tested in fifty women who have
Speaker 1: breast cancer. Well, fifty women is not a lot. So
Speaker 1: if there's fifty women who got the drug and fifty
Speaker 1: women who didn't, and let's say that trial got really hyped, Oh,
Speaker 1: this drug really worked in the fifty women who got
Speaker 1: the drug where they really did better, they lived longer,
Speaker 1: their breast tumors went away. But what if it's what
Speaker 1: if what happened was actually the people who were in
Speaker 1: the placebo arm of the trial were actually sicker in
Speaker 1: the beginning and that excuse the results. Or maybe they
Speaker 1: measured the tumors in the wrong way. So there's all
Speaker 1: sorts of subtleties about a way a clinical trial operates
Speaker 1: that you can then make a bet and say, Okay,
Speaker 1: is this drug overhyped or under hyped? Are people overhyping
Speaker 1: the drug as you said, like is it snake? Or
Speaker 1: there's this trial that's been published in the New England
Speaker 1: Journal of Medicine that says this this drug really works.
Speaker 1: But then in a larger trial with thousands of patients
Speaker 1: is going to fail or is it the other way around.
Speaker 1: Maybe it's a drug that didn't do that well at
Speaker 1: early state trials, but in bigger trials there is a
Speaker 1: signal there and it ends up being really successful. Those
Speaker 1: are the kinds of things that people like me we're
Speaker 1: making bets on, betting tens of in fact, sometimes hundreds
Speaker 1: of millions of dollars, trying to figure out which side
Speaker 1: was right. How do you do? I did all right?
Speaker 1: I did all right. Uh. And that's what kind of
Speaker 1: gave me the freedom to to start a nonprofit on
Speaker 1: a lark. Yeah, a financial cushion, all right. So that
Speaker 1: is all interesting and fascinating background. And you said something
Speaker 1: a couple of minutes ago. You said that your work
Speaker 1: at Bain Capital, and your work and going to m
Speaker 1: I T and also going to Yale Medical School and
Speaker 1: putting it to work molecular biology, looking at the genome projects,
Speaker 1: all of these different things obviously lead you to being
Speaker 1: able to look at data and figure out what I
Speaker 1: would call, for lack of a better way, what the
Speaker 1: signal is versus what the noise is. So there's so
Speaker 1: much noise on a day to day basis, regardless of
Speaker 1: what you do for a living, whatever people are doing
Speaker 1: for livings where they listen to us out there, most
Speaker 1: of it is external noise that isn't really getting to
Speaker 1: the essence of what you do. Figuring out the signal
Speaker 1: is essentially what you had to do for these biochemistry
Speaker 1: uh and and investigations for lack of another way of
Speaker 1: putting it. That also then corresponds in an incredibly unique
Speaker 1: way with what's happened with the coronavirus, where every day,
Speaker 1: it seems to me, we are deluged with data, with news,
Speaker 1: with viral stories meaning not necessarily virus stories, but literally
Speaker 1: stories that go viral about the virus. How do you
Speaker 1: cut through that noise and figure out what the real
Speaker 1: signal is? So let's go in. The story starts in January.
Speaker 1: When did you first become aware of the coronavirus and
Speaker 1: start to read and pay attention to it in China? Well,
Speaker 1: I started hearing the stories right away in in you know,
Speaker 1: just January February when the news started the break, But
Speaker 1: but at that time we didn't know that it was coming.
Speaker 1: I think it wasn't. One was the first case in
Speaker 1: in Washington, stated was I want to say February. It
Speaker 1: was late January, so we started to hear about this
Speaker 1: thing in Wuhan. There was a lot of suppression and uh,
Speaker 1: and then we started to see the in Washington and
Speaker 1: certainly followed that all the way through because healthcare is
Speaker 1: health care policy, and healthcare stuff is on my radar
Speaker 1: always because as part of my job. So you said
Speaker 1: you were comfortable early on with looking into the data
Speaker 1: and maybe becoming somewhat of a contrarian in terms of
Speaker 1: what your analysis has done. Such a good job of,
Speaker 1: I think is for people who don't know, and again
Speaker 1: I would encourage them to go follow you on Twitter
Speaker 1: at a v I k oh vic Roy we are
Speaker 1: talking to right now. Uh. He does incredible work looking
Speaker 1: at the data and putting it in context. And I
Speaker 1: believe the first time I started to see your data
Speaker 1: percolate on my feed and start to follow it aggressively
Speaker 1: was when you were looking at risk factors for college
Speaker 1: age kids, comparing let's say the seasonal flu and also
Speaker 1: with kids who are elementary school age and saying, hey,
Speaker 1: I understand that you know there's a lot of attention
Speaker 1: on the coronavirus right now, but the data would reflect
Speaker 1: that most young people are under greater danger from the
Speaker 1: seasonal flu or pneumonia. That's a counterintuitive data. Fect When
Speaker 1: did you start to dive into the numbers in such
Speaker 1: a way, Because this is eerily similar, I would think,
Speaker 1: and I bet you would agree with what you were
Speaker 1: doing looking at these biotech trials where the headline might
Speaker 1: be one thing, but when you actually go and look
Speaker 1: underneath the surface and start to examine it, some of
Speaker 1: the data is telling a different story maybe than the
Speaker 1: data that the media is sharing at the top line
Speaker 1: level of reporting. Yeah, that's that's exactly right, Clay. I mean,
Speaker 1: you've hit it on the head. And I would say also,
Speaker 1: by the way, I think one of the reasons when
Speaker 1: I listen to your your show and your podcast, you
Speaker 1: do an amazing job of just walking people through the
Speaker 1: real statistical situation. And I think that goes to show
Speaker 1: that there's an analogous, analogous situation of sports. Right you
Speaker 1: think about this explosion of sports analytics today and what
Speaker 1: is that all about. It's all about the fact that
Speaker 1: conventional ways of measuring performance in sports, starting with baseball,
Speaker 1: but the true in it, it's true in every sport
Speaker 1: don't necessarily accurately measure how a player or a team
Speaker 1: is performing, and so there's been this explosion of trying
Speaker 1: to be much more rigorous about how to measure that.
Speaker 1: And on Wall Street that's basically what you're doing on
Speaker 1: Wall Street two. You're you're trying to say, at least
Speaker 1: in biotech investing, and it's not just true in biotech investing,
Speaker 1: but especially true in biotech investing. What you're trying to
Speaker 1: figure out is, Okay, this stock is really hyped. What
Speaker 1: are people getting wrong? This stock is down in the dumps?
Speaker 1: What are people getting wrong? You're you're you're training. If
Speaker 1: you try to be good, try to outperform is to
Speaker 1: always try to swim against the tide and try to
Speaker 1: identify what other people aren't thinking about when they look
Speaker 1: at the data, and that habit of mind or cast
Speaker 1: of mind, if you want to call it, that's how
Speaker 1: That's what's driven all my policy work too. When I
Speaker 1: write about public policy, when I write about healthcare, when
Speaker 1: I write about COVID, it's all about, Okay, what are
Speaker 1: people missing? And the obvious thing right away was this
Speaker 1: real skew in the age distribution of who was getting
Speaker 1: really sick and who was dying from COVID. There was
Speaker 1: a huge skew towards the elderly. And I started writing
Speaker 1: about this pretty early on it. The early data out
Speaker 1: of China, the early data out of Italy, the places
Speaker 1: that really got hit a little bit before the US
Speaker 1: got hit, they all were showing the same skew where
Speaker 1: in the case of the US, literally of the people
Speaker 1: who died of COVID are over the age of sixty five.
Speaker 1: And I would start writing about this and tweeting about
Speaker 1: it and putting in our in our work at free
Speaker 1: out dot org, which is our think tank, and some
Speaker 1: people would ask me like, well, okay, that's that's interesting.
Speaker 1: People over are the ones that dying or are the
Speaker 1: ones over sixty five? Isn't that true? Most things don't,
Speaker 1: don't Most things end up, you know, basically killing the
Speaker 1: elderly more than they kill young people. So I was
Speaker 1: actually curious to say, okay, let's let's actually try to
Speaker 1: dig into that and figure out, Okay, how does the
Speaker 1: skew work for a more conventional infectious disease like influenza
Speaker 1: compared to COVID, Because if you look at the history
Speaker 1: of influenza pandemics, they actually do hurt and kill children.
Speaker 1: They do kill young people. The famous uh influenza pandemic
Speaker 1: of nineteen eighteen killed a lot of soldiers. That was,
Speaker 1: more soldiers died because of influenza than died at least
Speaker 1: American soldiers then died actually fighting in the trenches in
Speaker 1: World War One. So a lot of young people die.
Speaker 1: Then that's why we closed the schools if there's like
Speaker 1: a crazy influenza pandemic. So so I was interested in
Speaker 1: this and what I found. If you actually look at
Speaker 1: CDC data, official government data over a ten year period
Speaker 1: from two thousand seven to two thousand seventeen, which is
Speaker 1: our most recent cut of the data, and you compare
Speaker 1: the number of people who die from influenza by age
Speaker 1: bracket to those who die from COVID, you see a
Speaker 1: much worse skew for COVID. In other words, COVID is
Speaker 1: much more skewed in terms of serious illness or death
Speaker 1: towards the elderly than your typical infectious disease or any
Speaker 1: other kind of disease. So this is interesting to me
Speaker 1: too because one of the sort of aphorisms I would
Speaker 1: say about war and people who listen to this know
Speaker 1: also that I'm a history buff and I love of
Speaker 1: you know, studying American and and other history a lot.
Speaker 1: You typically end up fighting the last war, sorry, the
Speaker 1: most recent war, with the technology from the previous war, right,
Speaker 1: because everybody who has everybody who has studied all the
Speaker 1: things that have happened in warfare is going to apply
Speaker 1: the lessons that have applied from prior war. But you're then,
Speaker 1: you know, let's use the Civil War for an example,
Speaker 1: which is a particular interest of mine. You're using the
Speaker 1: Napoleon Napoleonic tactics, but now the technology, the ability of rifles, cannons,
Speaker 1: all of the rapid fire weapons have changed. So the
Speaker 1: mortality rates skyrockets, right, because you're fighting, and gradually you
Speaker 1: adjust and people learn, hey, maybe we should be fighting
Speaker 1: in a different way. The reason why I used that
Speaker 1: as an example is so many people and this frustrated me,
Speaker 1: and I bet it frustrated you when you actually looked
Speaker 1: at the data. So many people used data from the
Speaker 1: flu to justify shutting down schools based on the coronavirus.
Speaker 1: But this particular covid infection was not like the flu
Speaker 1: in that the age range of the impacted were different.
Speaker 1: So the decision to shut down schools might well have
Speaker 1: made sense in the pandemic a hundred years ago, and
Speaker 1: you study it and you say, oh, that's the lesson
Speaker 1: we should take away. But it's not the same virus.
Speaker 1: So you're fighting a new virus which has never existed
Speaker 1: before with the tactics that would have worked against the
Speaker 1: virus a hundred years ago. That's a misfit, right, But
Speaker 1: most people aren't sophisticated enough to think about that. You were,
Speaker 1: But that has to be frustrating to you from a
Speaker 1: public policy perspective to see it and not be able
Speaker 1: to cut through the noise and make people realize the
Speaker 1: data and what you've seen. Well, first of all, let
Speaker 1: me just say you're absolutely right, a thousand percent. This
Speaker 1: issue of fighting the last war is exactly what's going
Speaker 1: on here. And and so there's so many different dimensions
Speaker 1: of how that's true. Clay, I'll give you one. So
Speaker 1: you've heard people say, well, there were these plans in
Speaker 1: place from the George W. Bush administration and the Obama administration,
Speaker 1: why didn't Trump use those plans to fight the coronavirus. Well,
Speaker 1: those plans were not designed for coronavirus. They were designed
Speaker 1: for influenza. In fact, if you actually look at the
Speaker 1: cover page of the reports, they say things like our
Speaker 1: plan for dealing with a novel influenza pandemic. Now, influenza
Speaker 1: is a different virus from coronaviruses like COVID stars kobe
Speaker 1: to the virus that causes COVID nineteen. And again this
Speaker 1: is part of having that molecular biology backrouprom m I
Speaker 1: T I understand the difference between different types of viruses
Speaker 1: and how they actually infect you and how they're they're
Speaker 1: how they actually work in the body. They're very different,
Speaker 1: and so it's very important to understand that not all
Speaker 1: viruses are the same. The way they behave in your body,
Speaker 1: their lethality, their virulence can be very different. And so
Speaker 1: to your point, yes, you closing schools and a severe
Speaker 1: influenza pandemic makes sense because the young children and young
Speaker 1: adults do get killed old from really bad influenza pandemics
Speaker 1: in the in the case of nineteen eighteen especially, But
Speaker 1: this is not an influenza pandemic, and it's also true
Speaker 1: of all these sort of public health epidemiologists types. So
Speaker 1: one of the things you'll see a lot of people say,
Speaker 1: particularly on social media as well, how dare you write
Speaker 1: or tweet about COVID. You don't have a right to
Speaker 1: have an opinion because you're not an epidemiologist. Now, the
Speaker 1: problem is, first of all, epidemiology is we can get
Speaker 1: in to have a long discussion about what you actually
Speaker 1: learn in epidemiology school or epidemology grad school of public
Speaker 1: health school. But a lot of what you do, a
Speaker 1: lot of how epidemiologists or public health officials, how they
Speaker 1: cut their teeth is studying things like influenza pandemic. So
Speaker 1: a lot of the pronouncements that they're making with this
Speaker 1: incredible certainty in the sense, well you, if you don't
Speaker 1: listen to me, you're against science, are based on historical
Speaker 1: evidence of what has worked or what has happened with influenza.
Speaker 1: And this is a completely new virus that we've never
Speaker 1: seen before, and so a lot of that expertise doesn't
Speaker 1: really work because you're dealing with something completely different. And
Speaker 1: you I want to circle back right now, because you
Speaker 1: just said something, somebody says, oh, you're not an epidemiologist,
Speaker 1: you're not a virologist. I try to share intelligent people.
Speaker 1: You went to m I T. You went to Yale
Speaker 1: Medical School. I've got decent degree background as well. But ultimately,
Speaker 1: intelligent people who are contrarians or who are skeptics are
Speaker 1: very often right, and people who think that they are
Speaker 1: the quote unquote experts very often are wrong when you
Speaker 1: actually look at the data. Right. And so when you say, hey,
Speaker 1: we're only going to listen to scientists or we're only
Speaker 1: going to listen to quote unquote experts, I mean the
Speaker 1: data is telling us a story that it shouldn't matter
Speaker 1: who's telling the story, right, Like when when you are
Speaker 1: coming out and sharing your data on why schools should
Speaker 1: be back open based on looking at the direct c
Speaker 1: DC data, that's more valid than somebody who studied uh
Speaker 1: the influenza outbreak a hundred years ago, and it is
Speaker 1: trying to draw lessons from there. Yet it seems to
Speaker 1: me like in the public media sphere there's more benefit
Speaker 1: given to those epidemiologists. Maybe then would be justified based
Speaker 1: on the data. That's a kind of a long winded question,
Speaker 1: but you have to see that on a regular basis
Speaker 1: with what you've been writing and talking about. Well, I
Speaker 1: would you're I would agree with you, but I would
Speaker 1: also describe it in a different way because there is
Speaker 1: not consensus in the scientific community and and it is
Speaker 1: actually anti scientific to demand that alternative hypotheses be thrown
Speaker 1: out for no reason. There's actual evidence about what's happening,
Speaker 1: and the evidence heavily waits in one direction another. That's
Speaker 1: one thing. But in a situation where you're dealing with
Speaker 1: an unknown virus that we've never seen before and that's
Speaker 1: spreading in a way that has unique characteristics, then you
Speaker 1: as a scientist and again my dad was in scientist
Speaker 1: I group around size my whole life. Uh, as a scientist,
Speaker 1: you're obligated not to throw out any theory, any hypothesis
Speaker 1: until you can convincingly with the evidence disprove it. And
Speaker 1: in fact, there are lots of there's a lot of
Speaker 1: evidence that that plausible scientific theories are not are being suppressed.
Speaker 1: I'll give you an example. There's an epidemails or a
Speaker 1: biomathematician who does a lot of things around population health
Speaker 1: named Gabriella Gomet and she tweeted a couple of weeks
Speaker 1: ago that she actually has been trying to write some
Speaker 1: stuff about her immunity population immunity and how that population
Speaker 1: immunity may maybe closer at hands than other people think.
Speaker 1: And she can't get the work published in scientific journals
Speaker 1: because some people have shared with her. The editors of
Speaker 1: these journals say, well, if we if we publish your
Speaker 1: work and people become less scared of COVID, then maybe
Speaker 1: that will lead people to not wear masks and stuff.
Speaker 1: And we don't want that. Therefore, we've got to keep
Speaker 1: this kind of optimistic take off off the table. Now
Speaker 1: that's not science, right, It's not science when you artificially suppressed,
Speaker 1: for subjective or political reasons, alternative hypothesis of what's going on.
Speaker 1: And that's the lesson I really want to drive home
Speaker 1: here is a lot of the people who are screaming
Speaker 1: the loudest about trusting the science are not actually acting scientifically.
Speaker 1: Because if you're acting scientifically, you're looking very hard at
Speaker 1: the data. You're not ruling out any theory until you've
Speaker 1: got got convincing evidence that it's wrong. That's really well
Speaker 1: said and much better than my sort of haphazard question
Speaker 1: that I asked there. Why do you think that is? So?
Speaker 1: Why do you think it is? And that's hugely important.
Speaker 1: I think the scientific method is a rigorous adversarial system. Right.
Speaker 1: There are a lot of people out there who believe
Speaker 1: science only has one answer, right because we've proven, say,
Speaker 1: what the boiling point of water is or what the
Speaker 1: freezing point is, but that had to be tested over time, right,
Speaker 1: And when you have these rigorous battles over what might
Speaker 1: or might not be the ruth, that's how science advances.
Speaker 1: But when you don't allow that battle to me it
Speaker 1: kind of ties in with the marketplace of ideas and
Speaker 1: why I'm such a huge First Amendment absolutist. When you
Speaker 1: constrict sort of the available universe of argument or discussion,
Speaker 1: you are actually penalizing our ability to arrive at a
Speaker 1: truth or a universally ultimately recognize truth. Right. I mean
Speaker 1: the entire purpose of science is I've got this hypothesis,
Speaker 1: let me test it. When you start saying to people, oh,
Speaker 1: that hypothesis makes people uncomfortable, we can't discuss it, you're
Speaker 1: actually combating science. That's exactly right. And one one important
Speaker 1: element of this that's that's that's essential to really think
Speaker 1: about and where you can really get your spiddy sense
Speaker 1: up in a sense, is when people conflate predictions with facts.
Speaker 1: A prediction is about something that may happen in the future.
Speaker 1: And look, it may be more probable that Alabama wins
Speaker 1: the national championship then Michigan State, Uh, but it isn't
Speaker 1: guaranteed that Alabama is going to win the national championship, right,
Speaker 1: And so similarly, uh. In science, you hear a lot
Speaker 1: of people say, well, it's a fact that X will
Speaker 1: happen in the future, but we don't know because the
Speaker 1: world is a very complex place and there are a
Speaker 1: lot of variable to go into whether something happens or not,
Speaker 1: especially when you're talking about a novel virus that no
Speaker 1: one has ever seen before. And so that's where there's
Speaker 1: particularly been a poisonous climate where if you have a
Speaker 1: different view as to what may happen in the future
Speaker 1: in a situation where there's a lot of uncertainty, there's
Speaker 1: been a lot of suppression debate at that because we
Speaker 1: can't we can't, we can't give any anybody reason to
Speaker 1: be optimistic, because if you're optimistic, then maybe you know
Speaker 1: you'll you'll hang out at a bar with your friends
Speaker 1: and communicate the disease to other people. And we can't,
Speaker 1: we can't have that, And the problem is if you
Speaker 1: engage in that kind of let's call it dishonest suppression,
Speaker 1: then people don't listen to you because they don't trust you.
Speaker 1: If they don't trust they're gonna say, you know what,
Speaker 1: I don't trust that guy who's telling me not to
Speaker 1: do all this stuff because he's been wrong half the
Speaker 1: time anyway, and he's demanding that I listened to because
Speaker 1: he's a scientist, or that I'm not going to believe
Speaker 1: in science. And that's actually more dangerous for science and
Speaker 1: the scientific enterprise that people cloak themselves in the words
Speaker 1: science but they're not actually being scientific, because then people
Speaker 1: out there say, well, if that's what scientists and I'm
Speaker 1: not for it. Fox Sports Radio has the best sports
Speaker 1: talk lineup in the nation. Catch all of our shows
Speaker 1: at Fox Sports Radio dot com and within the I
Speaker 1: Heart Radio apps. Search f s R to listen live.
Speaker 1: We're talking to O vic Roy. I'm Clay Travis. This
Speaker 1: is Wins and Losses. I mean, now this obviously, this
Speaker 1: subject has utterly fascinated me on several different levels. And
Speaker 1: you know that I've spent a lot of time talking
Speaker 1: about this from the sports perspective and We're going to
Speaker 1: circle around to it on a sports perspective. But it
Speaker 1: seems to me you've talked about the analytics revolution that
Speaker 1: we've seen in sports. It seems to me that the
Speaker 1: essence of why our national conversation about the coronavirus has
Speaker 1: been so bad ultimately boils down to something you were
Speaker 1: just talking about, which is, there's a very poor understanding
Speaker 1: of statistics and probability in this country. And I'm gonna
Speaker 1: give people out there, and I want to give you
Speaker 1: a chance to tee off on this too. But and
Speaker 1: and really it kind of goes to why you've been
Speaker 1: able to be successful looking at biotech companies. I think
Speaker 1: it goes to why I've been successful in my chosen
Speaker 1: field of of life. Um, it's because I tend to
Speaker 1: be skeptical of consensus opinion and actually look at the
Speaker 1: data myself. But you talked about, you know, Alabama playing
Speaker 1: Michigan State. Sports fans are universally bad about this. In
Speaker 1: college football in particular, there's an idea that if a
Speaker 1: team plays and one team beats the other, when that
Speaker 1: means that team was quote unquote better. But the reality is,
Speaker 1: and this has been something I love thinking about you know,
Speaker 1: if you played a million minute game instead of a
Speaker 1: sixty minute game, the team that played for a million minutes,
Speaker 1: you know, is probably going to be the better team
Speaker 1: if it wins, because your data sample size is a
Speaker 1: million games. But when you play sixty minutes of a
Speaker 1: football game, any one of those sixty minutes that you
Speaker 1: pull out of the million minutes could go so many
Speaker 1: different directions. And sports fans it seems to me kind
Speaker 1: of understand this in the context of, oh, well, that's
Speaker 1: why we play a seven game series, because over the
Speaker 1: course of a seven game series, the inferior team might
Speaker 1: win by thirty one game, but they might lose the
Speaker 1: other four, right, and we don't look at the sum
Speaker 1: total of the of the games. The coronavirus, it seems
Speaker 1: to me, and the way that the media has covered it,
Speaker 1: so many people in my industry are bad at math. Right.
Speaker 1: One of the reasons why I think people become journalists
Speaker 1: is they're good at reading and writing, they're bad at math,
Speaker 1: and they're running from math and science. And I'm not
Speaker 1: great at math and science. I'm not pretending to be
Speaker 1: incredible at it, but I'm better than most people in
Speaker 1: my industry. And so the failure of understanding probability and statistics,
Speaker 1: particularly in a social media age, where you say, oh,
Speaker 1: this thirty four year old woman was completely healthy and
Speaker 1: then she died. That story goes viral all over social media.
Speaker 1: Even though it's an outlier. It's in no way representative
Speaker 1: of what happens when the average thirty four year old
Speaker 1: or twenty four year old or sixteen year old get
Speaker 1: sick with the coronavirus. Yet people believe it because it's
Speaker 1: a story that they want to believe. So I've talked
Speaker 1: a lot. They're kind of setting the table, but I
Speaker 1: want to circle back around to the original premise. How
Speaker 1: much of our national failure with the coronavirus has to
Speaker 1: do with a national failure to understand probability and statistics. Well,
Speaker 1: I would say there's no doubt that our response to
Speaker 1: the coronavirus has been utterly and badly damaged by a
Speaker 1: failure to understand statistics. By the way you're selling yourself shortly,
Speaker 1: I mean, I've looked to your show. You do an
Speaker 1: amazing job of communicating what's really going on from a
Speaker 1: quantitative standpoint to your audience, and you're doing an incredible
Speaker 1: public service, because you have such a big audience and
Speaker 1: you're sharing this data with a lot of people who
Speaker 1: otherwise wouldn't get it from anywhere else. So I want
Speaker 1: to thank you for that. I'm trying, but by the way,
Speaker 1: I get crushed for it, right, Like I get crushed
Speaker 1: because people are like, oh my god, you're a you know,
Speaker 1: sports guy who went to law school. Why in the world.
Speaker 1: And the reality is because I want sports to come back.
Speaker 1: But when I see something that I believe is factually
Speaker 1: inaccurate and being discussed poorly by media, it just draws
Speaker 1: me and I want to try to get real facts
Speaker 1: out there in a way that they're not being So
Speaker 1: I appreciate you saying that, but I'm sure you get
Speaker 1: this all the time, and I get it certainly, Like
Speaker 1: you don't care about people dying. No, I wish nobody
Speaker 1: ever died, right. I wish we were all immortal. I
Speaker 1: wish your grandma, my grandma, everybody's kids, every everybody was
Speaker 1: safe forever. That's not the reality of the world in
Speaker 1: which we live. And I am troubled by what I
Speaker 1: would say, is this very poor ability to discuss complex
Speaker 1: issues where it's like people either like Hey, we've got
Speaker 1: to completely shut down, nobody can leave their homes, or
Speaker 1: we gotta be completely wide open. And and it's like
Speaker 1: the nuances. We need to be somewhere in the middle.
Speaker 1: We need to be living our lives but not allowing
Speaker 1: the coronavirus to destroy our world. If that makes sense,
Speaker 1: well totally. And I can give you some concrete examples
Speaker 1: of how this is played out in real time. So
Speaker 1: one example is school closures. Right, we're seeing all these
Speaker 1: and let's leave let's leave colleges aside for the moment,
Speaker 1: I'm talking about pre k kindergarten, primary school, elementary school.
Speaker 1: That the overwhelming, overwhelming scientific evidence at this point. I
Speaker 1: mean basically it's anti scientific to argue that kids are
Speaker 1: at risk, uh, you know, in a meaningful way of
Speaker 1: obviously there's a handful. There's literally like thirty nine kids
Speaker 1: aged one fifteen who died of COVID in the United
Speaker 1: States out of fifty million in that repeat that again,
Speaker 1: because I think it's a big it's a big deal.
Speaker 1: And according to the most recent CDC data, kids fifteen
Speaker 1: and under thirty nine have died of the coronavirus between
Speaker 1: the ages of one and by the way that's with
Speaker 1: that's with the coronavirus, because I bet if you went
Speaker 1: into those thirty nine what you would find is they
Speaker 1: have significant health issues on top of whatever they got
Speaker 1: from the COVID impact. Right, that's right. It's people who
Speaker 1: have died who have tested positive for COVID. Whether the
Speaker 1: actual cause of death was COVID or not, we don't know.
Speaker 1: But thirty nine kids. And I guess how many kids
Speaker 1: live in the United States who are aged one, uh
Speaker 1: fourteen or one to fifteen. I mean, there's what three
Speaker 1: d and thirty ish million people in the United States.
Speaker 1: I would guess that there's got to be what fifty
Speaker 1: or sixty million kids at that age, fifty seven million,
Speaker 1: fifty seven millions, always saying thirty nine out of fifty
Speaker 1: seven million kids. And we're shutting down schools. And by
Speaker 1: the way, you know what that means. Shutting down school
Speaker 1: it's not exactly good for children, particularly low income kids
Speaker 1: who have no other alternative. If you're a single mom
Speaker 1: and you have to work, Let's say you work at
Speaker 1: a pharmacy or grocery store, what are you gonna do?
Speaker 1: Are you gonna go to work and lead just three
Speaker 1: year old at home you can't. Uh, there's forty estimated
Speaker 1: about forty thousand cases of child abuse that are going
Speaker 1: unreported in the United States because schools are closed right now,
Speaker 1: and let alone the mental healthy, educational deficits, the emotional development.
Speaker 1: It's just incredible costs. So, like we often talk about
Speaker 1: this purely in terms of what's your risk of getting COVID,
Speaker 1: what's your risk of not getting COVID, and we don't
Speaker 1: talk about the costs on the other side of the equation.
Speaker 1: The cost to a kid who doesn't get to go
Speaker 1: to school. Uh, the cost to a business that shuts
Speaker 1: down permanently. It's estimated that over a hundred thousand businesses,
Speaker 1: maybe even more have shut down permanently because they didn't
Speaker 1: have the cash cushion. Once you start losing your revenue,
Speaker 1: but you can't keep your payroll going, you can't pay
Speaker 1: the rent for your building, and you're done and you quit. Uh.
Speaker 1: That's not good for a lot of people. And people say, well,
Speaker 1: it's just about dollars. No, it's not just about dollars.
Speaker 1: Is actually a lot of evidence that shows that when
Speaker 1: you have a massive economic dislocation or a massive recession
Speaker 1: or a massive disruption that leads to shorten life expectancy
Speaker 1: as well for a lot of different reasons. Think about
Speaker 1: the opioid crisis, where is that happening economically depressed parts
Speaker 1: of the country to a significant degree. Why do you
Speaker 1: think that all of those facts which are so incredibly
Speaker 1: important are not able to cut through the noise. That's
Speaker 1: a big question that I have because it's frustrating to me.
Speaker 1: I understand the audience and we have I'm fortunate to
Speaker 1: have a substantial audience that we have built up, but
Speaker 1: I'm still a pinprick of the overall media audience. Right,
Speaker 1: Why do you think that data that you just shared
Speaker 1: and by the way, credit to the Wall Street Journal
Speaker 1: for carrying your story was on the front page. I mean,
Speaker 1: your work is getting out there, and I think you're
Speaker 1: doing an incredible job of it. But why do you
Speaker 1: think those stories, those facts are having such difficulty cutting
Speaker 1: through the noise. And there are so many people out
Speaker 1: there with kids that are terrified that their kids are
Speaker 1: going to die of COVID that would not think twice
Speaker 1: about ever pulling their kid out of school from the
Speaker 1: seasonal flu, even though the seasonal flew is far more
Speaker 1: dangerous and by the way, don't even think twice about
Speaker 1: sending their kid out to a swimming pool without parental
Speaker 1: supervision when their kid is far more likely to drown
Speaker 1: there than they ever already get COVID. It's totally right, um.
Speaker 1: And you know, just like you were saying, like, I'm lucky,
Speaker 1: I have I have a platform. I'm the policy editor
Speaker 1: at Forbes. I can put my stuff there, I can
Speaker 1: put my stuff on Twitter, I can put my stuff
Speaker 1: at the Wall Street Journal when they when they asked
Speaker 1: me to. And so I've been lucky and then that
Speaker 1: I've had those opportunities to get the word out there.
Speaker 1: But you're right, it's it's overwhelmed by the NonStop wild
Speaker 1: wall alarmism, uh, coming from the people who think everything
Speaker 1: should be shut down all the time. And there's a
Speaker 1: couple of different you know, I have a couple of
Speaker 1: different hypotheses that I think are pretty plausible as to
Speaker 1: wise it's happening. The first is the media has always
Speaker 1: been about alarmism. I mean, the thing we used to
Speaker 1: talk about if you ever took a statistics class, the
Speaker 1: thing that people used to always talking about in statistics
Speaker 1: class was well, people are often more afraid of flying
Speaker 1: that they are driving their car because every plane crash
Speaker 1: ever gets plastered all over the newspaper and plaster TV. Right,
Speaker 1: So a lot of people have this impression that's not
Speaker 1: safe to fly, when in fact, your chances of dying
Speaker 1: in a plane crash are orders of magnitude lower than
Speaker 1: your chances of dying in a car accident or even
Speaker 1: crossing the street in a busy intersection. So that's an
Speaker 1: example of where the media because it's the disaster of
Speaker 1: the plane crashes. You remember that the Malaysia when that
Speaker 1: Malaysia plane went Malaysian Airline plane went disappear and nothing
Speaker 1: else for like four days, right, for like four months, honestly,
Speaker 1: Malaysian three. And I was fascinated by that too because
Speaker 1: it felt like when that thing disappeared, uh, you know,
Speaker 1: maybe there was something other. You know, we still don't
Speaker 1: know acent it seems like the pilot was involved. But
Speaker 1: that story was such a mystery. It was not only
Speaker 1: a plane disappearing, it was not knowing why the plane disappeared,
Speaker 1: which is probably the greatest thing ever. Another example would
Speaker 1: be shark attacks. Right, every time somebody gets attacked by
Speaker 1: a shark, you hear about it. Uh, and so everybody
Speaker 1: who goes into the ocean summer vacation time right now,
Speaker 1: everybody is thinking, oh my god, I'm gonna get eaten
Speaker 1: by a shark. Yeah. The old school adage among you know,
Speaker 1: the newspaper hands is if it bleeds, it leads. Anything
Speaker 1: that's sort of catastrophic or disastrous gets that headline. And
Speaker 1: we all know that, we all we all consume the news.
Speaker 1: We we understand that that's part of it. So that's
Speaker 1: definitely it's like this is cat nipped to that kind
Speaker 1: of journalism. Right. So that's that's number one. I think
Speaker 1: number two is uh for for certain people who are
Speaker 1: more politically politically oriented. Uh, it's clearly a situation where, um,
Speaker 1: you know, if you if you're a journalist who hates
Speaker 1: Trump and you didn't you know, you were frustrated by
Speaker 1: the fact that the economy was roaring along. Record low
Speaker 1: unemployment in the winter last winter for all races, not
Speaker 1: just whites, but also blacks and Hispanics and Asians, record
Speaker 1: lownemployment for everybody, record low disparities in the in the
Speaker 1: difference between employment unemployment of blacks and whites, for example.
Speaker 1: So the economy was doing incredibly well. So people were like, well,
Speaker 1: you know, boy, that's annoying because we hate Trump and
Speaker 1: we want them to lose. And now you have a
Speaker 1: story that you can say, this is all Trump's fault.
Speaker 1: Trump is the reason why a hundred seventy thousand people
Speaker 1: have died in America. And so there's there's an enthusiasm
Speaker 1: in a sense for the negative take on the government response.
Speaker 1: And I'm not trying to say the government response doesn't
Speaker 1: have things to criticize about it, whether federal, state, or local. Uh,
Speaker 1: but but it is to say that that that has
Speaker 1: been a huge part of the story. And I'll give
Speaker 1: you some examples of why that is, or examples of
Speaker 1: how why I think that is. There if you if
Speaker 1: you ask the average person on the street, you know
Speaker 1: who who watch the CNN or read the New York Times,
Speaker 1: They'll say, you know what, why can't every governor handle
Speaker 1: COVID like Andrew Cuomo, the governor of New York handled it.
Speaker 1: He's just done such a great job. Why can't everyone
Speaker 1: feel like him? In fact, Andrew Cuomo just published a
Speaker 1: book about his triumph in conconquering COVID and wrestling it
Speaker 1: to the ground. Now, this makes absolutely no sense according
Speaker 1: to the data, because New York has been by far
Speaker 1: the worst performing state by a country mile. California, Texas,
Speaker 1: and Florida combined have had far fewer deaths from COVID
Speaker 1: nineteens than New York has, whether per capita or not.
Speaker 1: And yet somehow New York is portrayed as this success story.
Speaker 1: It's not a success story at all. It's been a
Speaker 1: complete catastrophic failure. Arguably, of any states that have done
Speaker 1: pretty well, it's been the Texas and the Florida is
Speaker 1: that never completely locked down their economy, and while they
Speaker 1: have had death from COVID, it's been at a far
Speaker 1: lower scale than New York. But you wouldn't know that
Speaker 1: from the coverage. And that goes to this point about
Speaker 1: the politics, Like if we were just looking at the data,
Speaker 1: and we would have a lot more questions, we'd be
Speaker 1: asking about build the Blasio, the mayor of New York City,
Speaker 1: and Eventrew Cuomo, the governor of New York, and on
Speaker 1: a bunch of his neighbors by the way, like Murphy
Speaker 1: and New Jersey. It's so true, and I look at
Speaker 1: the data and do you think that the media that
Speaker 1: is praising Andrew Cuomo and Murphy who is next to him,
Speaker 1: and by the way, to kind of put into context,
Speaker 1: the day to New York and New Jersey's death rate
Speaker 1: is twice the worst country in the world from COVID
Speaker 1: so far the most recent numbers that I looked at,
Speaker 1: Belgium was the worst, and New York and New Jersey
Speaker 1: work twice what Belgium was. Okay, do you and this
Speaker 1: gets into a hypothesis situation again, because we really don't know.
Speaker 1: But this is something that I just think about a
Speaker 1: great deal I can forgive people who are ignorant because
Speaker 1: they are listening to media that is telling them things
Speaker 1: that are not true. Right, Like, So, if you read
Speaker 1: the New York Times and you have convinced yourself that
Speaker 1: Andrew Cuomo and Governor Murphy did an incredible job, that's
Speaker 1: because those journalists are telling you that, right. You are
Speaker 1: being told that is not a truth. But you trust
Speaker 1: the New York Times or you trust CNN to get
Speaker 1: that right, and so you are misapprehending what the data
Speaker 1: is actually saying. I don't like I'm not as bothered
Speaker 1: by people who believe things that are untrue because they're
Speaker 1: listening to people in positions of authority. I am desperately
Speaker 1: bothered by people in positions of authority in my industry,
Speaker 1: in the media who are sharing untruths about New York
Speaker 1: and New Jersey such that people are willing to buy
Speaker 1: a book that suggests it's supposed to come out in
Speaker 1: October that Andrew Cuomo triumphed over the coronavirus when he
Speaker 1: literally did the worst job of any politician, arguably in
Speaker 1: the world. I mean, it's just such an upside down story.
Speaker 1: So do you think the journalists are not sophisticated enough
Speaker 1: to actually look at the data? Do you think they're
Speaker 1: intentionally misleading their audiences? How is it possible for something
Speaker 1: that is so untrue to become so widely believed such
Speaker 1: that I believe right now Andrew Cuomo has the highest
Speaker 1: popularity rating of almost any governor in the country, despite
Speaker 1: clear evidence that he probably did a worse job than
Speaker 1: any politician in the entire world with the coronavirus. Well,
Speaker 1: you know, it's it's it's this phol Andrew Cuomo thing
Speaker 1: is like one of the craziest aspects of this whole
Speaker 1: six months, it's just been what is going on, there's
Speaker 1: such a disconnect between what his actual performance has been
Speaker 1: and not just in terms of the numbers, in terms
Speaker 1: of COVID, in terms of his actual decisions, because a
Speaker 1: lot of his actual decisions are the distress, yes, which
Speaker 1: you know about that. I'm gonna ask you about that directly,
Speaker 1: which is because this is the other place where I
Speaker 1: really started seeing your work. We knew early on, when
Speaker 1: the infection first was recognized in a Washington nursing home,
Speaker 1: that the elderly people, when you looked at the data
Speaker 1: from Italy, that elderly people were particularly susceptible to this virus,
Speaker 1: and that therefore the most susceptible people in the entire
Speaker 1: country were people in nursing homes. And so what happened
Speaker 1: in New York although they're not sharing their honest data,
Speaker 1: and I think you've been you've been looking at this too,
Speaker 1: but you went out and looked and said, okay, where
Speaker 1: are people actually dying? And you found out that the
Speaker 1: death rate inside of nursing homes was just I mean,
Speaker 1: like I think in Canada, for instance, not just the
Speaker 1: United States. The data that I saw of all deaths
Speaker 1: in Canada have been inside nursing homes. Uh. And so
Speaker 1: New York believed these forecasts that they were gonna need
Speaker 1: a hundred and forty thousand hospital beds. They ended up peaking.
Speaker 1: And you can correct me on some of this data
Speaker 1: if I'm wrong, because I'm doing it off the top
Speaker 1: of my head. They ended up right around nineteen thousand,
Speaker 1: uh actually hospital beds. So the order of the forecast
Speaker 1: was way off. But as a result, Cuomo sent all
Speaker 1: of these infected patients back into nursing homes, which was
Speaker 1: like putting kindling, you know, right beside a forest fire
Speaker 1: and it exploded. And the same thing happened in New
Speaker 1: Jersey and in Michigan and in all these other states
Speaker 1: that had early outbreaks and followed his lead. It wasn't
Speaker 1: just that he made a poor decision. It was that
Speaker 1: all these liming governors followed his lead and end up
Speaker 1: making disastrous decisions. So how is that all not a
Speaker 1: primary point of story, because to me, it's the biggest
Speaker 1: story of the coronavirus outbreak from a death perspective. Yeah,
Speaker 1: I mean, what's really important to understanding about what you
Speaker 1: just described, Clay, is that Andrew Cuomo forced these forced
Speaker 1: these nursing homes. His Health Department issued in order forcing
Speaker 1: the nursing homes to accept COVID infected patients, and the
Speaker 1: nursing home operators screamed, bloody murder. There's a there's a
Speaker 1: great article from like the March twenty seven of the
Speaker 1: Wall Street Journal that you can find if you just
Speaker 1: google nursing home Andrew Cuomo March, you might be able
Speaker 1: to find the article. The nursing homes knew that this
Speaker 1: was a potentially fatal decision, literally fatal decisions if people
Speaker 1: were screaming bloody murder bout at the time, and he
Speaker 1: did it anyway because the experts that he was talking to,
Speaker 1: quote unquote experts told them, well, gosh, what we know
Speaker 1: from influenza, You've got to keep those hospital beds clear.
Speaker 1: We don't have to worry about the nursing homes. Well,
Speaker 1: we really have to worry about it as the hospitals,
Speaker 1: which is totally backwards, because if you actually infect everyone
Speaker 1: the nursing home, where do you think they're gonna end
Speaker 1: up in the hospital definitely sick of COVID. So that
Speaker 1: was an incredibly bad decision that was in part that
Speaker 1: was it was Andrew mcuhma's decision. But it was also
Speaker 1: a failure of experts who advised him to make that decision.
Speaker 1: And that's part of the reason why you're not seeing
Speaker 1: the accountabilit because those experts don't want to, you know,
Speaker 1: take credit for that for that advice. Be sure to
Speaker 1: catch live editions about Kicked the coverage with Clay Travis
Speaker 1: week days at six am Eastern, three am Pacific. We're
Speaker 1: talking to O vic Roy. I'm Clay Travis. This is
Speaker 1: Wins and Losses. Sorry to cut you off. Continue, Oh please,
Speaker 1: do you know I was going to bring up another
Speaker 1: element of this phenomena we were talking before about just
Speaker 1: the news coverage and and how distorted is. Let me
Speaker 1: give you an example that's that's not related to what's
Speaker 1: been happening in the US with COVID, but is related
Speaker 1: to the US media coverage of the whole thing. There
Speaker 1: was a story published on July eighteenth in the New
Speaker 1: York Times by a poor bum on Mondabilia. I think
Speaker 1: I'm pronouncing that correctly. The headline of the articles older
Speaker 1: children spread the ronavirus just as much as adults. Large
Speaker 1: study finds the study of nearly sixty five thousand people
Speaker 1: in South Korea, suggests that school reopenings will trigger more
Speaker 1: out backs, and the whole articles about the study by
Speaker 1: there the South Korean c DC that actually didn't look
Speaker 1: at sixty five thousand kids. It looked at a couple
Speaker 1: of hundred kids and found that there were some adults
Speaker 1: in those households who also had COVID. So she published
Speaker 1: this very long article. I it was probably on the
Speaker 1: front page and there are time certainly very prominent place
Speaker 1: and I read it online, so I don't know what
Speaker 1: page in the newspaper it actually appeared on. And well,
Speaker 1: what's interesting about it is that so that was a
Speaker 1: story that was being cited by everyone, Oh, you can't
Speaker 1: open schools because there's a South Korea study that shows
Speaker 1: that even young kids can infect everybody, even though in
Speaker 1: nobody in Europe has seen this effect. Nobody in the
Speaker 1: rest of the world where they've opened schools to see
Speaker 1: in this fact, in South Korea there's a study that
Speaker 1: shows that kids will affect adults, and so we got
Speaker 1: to keep the schools close. That was the takeaway. And
Speaker 1: you saw all this chatter in other news papers, other
Speaker 1: media on social media about this article of the New
Speaker 1: York Times saying this, well, fast forward a couple of
Speaker 1: weeks later, and what do we find when the full
Speaker 1: data set is actually released by the Korean CDC. It
Speaker 1: turns out forty of the forty one cases of kids
Speaker 1: and adults having COVID in the same household, they were
Speaker 1: infected simultaneously. It wasn't the kids infecting the adults. The
Speaker 1: kids and the adults in that household were simultaneously infected
Speaker 1: by somebody else. So forty or forty one cases were
Speaker 1: not actually of kids infecting adults. There kids just getting
Speaker 1: infected by other people. The one case of COVID, uh,
Speaker 1: somebody who was a child infecting someone else. A teenage
Speaker 1: girl infected her younger sister. And that's it. One case
Speaker 1: in the entire country of South Carota, of South Korea.
Speaker 1: But you're not gonna see a front page store in
Speaker 1: the New York Times saying, hey, guess what, everybody, that
Speaker 1: South Korea study that we touted a couple of weeks
Speaker 1: ago totally misconceived, totally misinterpreted and out of deal. You're
Speaker 1: not going to see that story. And that's an example
Speaker 1: of where a factual situation, just by the way it's
Speaker 1: being covered completely distorts a very very important policy question,
Speaker 1: which is do we bring sixty million kids and young
Speaker 1: adults back to school this fall? What would the data
Speaker 1: tell us we should have done? All? Right, So let's
Speaker 1: pretend that that we had all this data that we
Speaker 1: have now. And for people out there who are listening
Speaker 1: to us, I'm talking to O vic Roy Clay Travis
Speaker 1: here wins and losses, and we're talking in I think
Speaker 1: it's August twenty five days all run together. I think
Speaker 1: it's August August twenty one, whatever it is now, with
Speaker 1: the benefit of hindsight, right, everybody always likes say hindsights.
Speaker 1: With all of the data that you have out there
Speaker 1: right now, what would have been the appropriate and smartest
Speaker 1: decision in March and April in May? Because it's one
Speaker 1: thing to say, and I think you would probably agree.
Speaker 1: Maybe there's the fog of are in March when we
Speaker 1: shut down a lot of people don't know what's going on.
Speaker 1: I would argue there was enough data out there to
Speaker 1: suggest that shutting completely down wasn't the smart move. But
Speaker 1: let's pretend that you have all the data, all the
Speaker 1: time that you've spent looking at everything what is the
Speaker 1: right thing to do right now and what would have
Speaker 1: been Let's pretend that we could have been flawless and
Speaker 1: we could have executed perfectly. The appropriate response to coronavirus
Speaker 1: based on what we know now is what. I'll give
Speaker 1: you three core ideas of the core concepts or core frameworks.
Speaker 1: Number one, we should have reopened schools, particularly for kids
Speaker 1: under the age of twelve in the sprint, like Europe did,
Speaker 1: and we definitely should be opening schools for younger kids
Speaker 1: starting now. Uh, that's something that Europe did. They had
Speaker 1: enormous success with it. They had no problems of kids,
Speaker 1: no problems of kids infecting adults. And in fact, part
Speaker 1: of why Europe maybe haven't so much more success in
Speaker 1: the US in terms of the course of the pandemic
Speaker 1: is because kids went back to school. Because the kids
Speaker 1: all probably got some low level exposure, the virus developed
Speaker 1: immunity and also transmitted immunity to others. Effect in Germany,
Speaker 1: that's what they think. They think that reopening schools acted
Speaker 1: as a break on the transmission of COVID nineteen. So
Speaker 1: that's a counterintuitive. Yeah, that's a counterintuitive thought. Sorry to
Speaker 1: cut you off, but we there's a strong argument to
Speaker 1: be made that reopening schools, rather than leading to mass outbreaks,
Speaker 1: actually makes them less likely. So in addition to the
Speaker 1: fact that kids obviously benefit from being in school, there's
Speaker 1: an argument that being in school actually makes us safer
Speaker 1: as opposed to more dangerous. I just want to cut
Speaker 1: you off because that's a counterintuitive take that you would
Speaker 1: hear almost nowhere else in the media. Sorry, Okay, So
Speaker 1: that's point one. Yeah, and if you get if you
Speaker 1: dig into my Twitter feed and if you read the
Speaker 1: Wall Street Journal article, you'll you'll see the links to
Speaker 1: the German scientists in particular who have been making this argument.
Speaker 1: So that's point one. Point two. We should have done
Speaker 1: a lot more, and we should still do a lot
Speaker 1: more to protect people who live in nursing homest of
Speaker 1: all the deaths in the United States from COVID nineteen
Speaker 1: or with no COVID nineteen have taken place among residents
Speaker 1: in nursing homes and other assisted living facilities that house
Speaker 1: zero point six percent of the U S population. Now,
Speaker 1: in any normal crisis, the fact that forty five percent
Speaker 1: of the death were occurring in zero point six percent
Speaker 1: of the population that would be the headline every day.
Speaker 1: Every day we'd be seeing on CNN some anchors asking
Speaker 1: a politician, what are you doing to protect people in
Speaker 1: nursing homes today? That's what we'd be talking about every
Speaker 1: hour of every day, but we're not. Why is that?
Speaker 1: That's one of the again, the craziest things about this
Speaker 1: whole situation. So we should be doing, we should have
Speaker 1: done all along a lot more to protect people in
Speaker 1: nursing homes, and we still have a ways to go,
Speaker 1: uh to do that, to get to a point where
Speaker 1: we can really say that people in nursing homes are protected.
Speaker 1: Were made a lot of progress in a in a
Speaker 1: sense of like to you know, a little bit too late.
Speaker 1: We should have gotten there much early, or particularly at
Speaker 1: the state level, as we've talked about with Andrew Cuomo.
Speaker 1: But but that's an area where we still need to
Speaker 1: do more. So that's that would be like And by
Speaker 1: the way, that figure is probably low because the data
Speaker 1: that you have on New York, the way they classify
Speaker 1: nursing home deaths likely drastically undercut the number of people
Speaker 1: who actually died who were in nursing homes in New York. Right,
Speaker 1: it's probably the case like I mentioned earlier, candidates, it's
Speaker 1: probably over half in the United States. Right if you
Speaker 1: had the best possible statistical data of all to be
Speaker 1: able to put together, that's not a crazy hypothesis, right. No.
Speaker 1: And in fact, if if you want to dig into
Speaker 1: the data, if any of your listeners want to dig
Speaker 1: into the data, they can go to our website f
Speaker 1: R E O P P free op dot org and
Speaker 1: there's an article They're titled, uh Nursing Homes and Assisted
Speaker 1: living Facilities account for COVID nineteen deaths, and we we
Speaker 1: put all the data in there. I also have a
Speaker 1: Forbes article about it. But the free dot org articles
Speaker 1: the WOE that has the most updated information you can
Speaker 1: dig through at the state level how your state has
Speaker 1: been doing. And by the way, when we first started
Speaker 1: reporting on this, we basically compiled all the data from
Speaker 1: the state health departments and thirteen states weren't even reporting
Speaker 1: the data. This is in like June. Thirteen states weren't
Speaker 1: even reporting the data. It wasn't until I basically humiliated
Speaker 1: them by writing this article in Forbes that got like
Speaker 1: one point two million page views that all of a sudden,
Speaker 1: the state departments started to say oh yeah, actually, here's
Speaker 1: our you know, nursing home deat. So you know, it
Speaker 1: was it was it was just this crazy thing where again,
Speaker 1: forty five or half the desk maybe more are coming
Speaker 1: in in in nursing homes and assistant living facilities, and
Speaker 1: yet some states weren't even reporting the data. They didn't
Speaker 1: even know what percentage of the people in their states
Speaker 1: that were dying were in nursing homes, and some parts
Speaker 1: of the country it's even higher than fift In Minnesota,
Speaker 1: it's like it's like Canada, it's and and so that
Speaker 1: was one of the things that's like absolutely a thing
Speaker 1: that we could have done better than we certainly could
Speaker 1: be doing better now. And then the third bucket is
Speaker 1: the economic lockdown. So you'll remember when Texas and Florida,
Speaker 1: well Florida ever really completely locked down, but Texas did
Speaker 1: lockdown in May and then they reopened in June. And
Speaker 1: there were all these predictions in in national newspapers and
Speaker 1: other media organizations about how Texas, hundreds of thousands people
Speaker 1: are gonna die. I was gonna be terrible, it was
Speaker 1: gonna be apocalyptic. You know, these these rednecks from Texas
Speaker 1: didn't know what they were doing. And if you go
Speaker 1: by those predictions of tens or hundreds of thousands of
Speaker 1: people dying compared to what actually happened. Yes, there were
Speaker 1: there have been people who died of COVID in Texas.
Speaker 1: There was a rise in cases and hospitalizations and deaths
Speaker 1: in the late summer, but much much less so than
Speaker 1: it was predicted, far less though we were never in Texas.
Speaker 1: It never turned into New York, and Florida never turned
Speaker 1: into New York. These places that reopened in the Sun Belt,
Speaker 1: they never turned into New York. And yet there was
Speaker 1: this kind of almost rooting for for that failure to happen.
Speaker 1: And that's I think what that shows is that there
Speaker 1: is a balance to be struck. And there's a lot
Speaker 1: of actually academic research on this, and her really gets
Speaker 1: talked about that there's diminished returns to a total lockdown. Yes,
Speaker 1: it may or may not make sense to close bars.
Speaker 1: It certainly probably makes sense to not have large gatherings
Speaker 1: of like sporting events or conventions or things like that.
Speaker 1: We have a hundred thousand people packed into a state,
Speaker 1: and that's probably a thing you want to avoid. But uh,
Speaker 1: things like allowing restaurants to open at half capacity, allowing
Speaker 1: people to go into a shop with a mask on.
Speaker 1: That's not a big deal, right, Let the car wash
Speaker 1: it's open up. Why do it? Just because the car
Speaker 1: watch is not a quote unquote essential business doesn't mean
Speaker 1: that a car wash has to be shut down. You
Speaker 1: can drive your car for the car washing. It's basically fine.
Speaker 1: So there is a balance to be struck, and Texas
Speaker 1: and Florida clearly struck that balance in a way that
Speaker 1: the total lockdown states did not. And exhibit A by
Speaker 1: the way, on that clay, it's California. Look at California.
Speaker 1: California has had the same spike in cases and hospitalizations
Speaker 1: and deaths that Texas and Florida did. But California lockdown.
Speaker 1: California did all the things that all the people in
Speaker 1: the sort of the quote unquote at the pro quote
Speaker 1: unquote pro quote science unquote class says we should do,
Speaker 1: and yet California still had an outbreak. Why is that right?
Speaker 1: So why is it that California right now the situation
Speaker 1: is arguably worse. California had a complete breakdown of their
Speaker 1: data systems. They don't even know how many people have
Speaker 1: COVID or of tested positive because they're testing data center
Speaker 1: broke down. So all that to say that, Uh, the
Speaker 1: third bucket I'd say in terms of what we need
Speaker 1: to do better is we need to identify. We need
Speaker 1: to be very objective about what measures have worked and
Speaker 1: what measures have not worked in terms of limiting the
Speaker 1: spread flattening the curved center. It's pretty clear at this
Speaker 1: point that the Texas Florida model strikes the write balance.
Speaker 1: And by the way, as you know, Clay, I don't
Speaker 1: have to remind you. I probably don't have to remind
Speaker 1: your listeners. When we originally locked down in May, the
Speaker 1: ar gament was not that we were gonna obliterate COVID nineteen.
Speaker 1: It was that we were going to flatten the curve
Speaker 1: so that the hospitals weren't overwhelmed. Well, no hospitals are
Speaker 1: getting over filmed anywhere. Today we're talking to Ovicroy. You
Speaker 1: can follow him on Twitter at a v I K
Speaker 1: encourage you to go read all of his work. He
Speaker 1: went to m I TEO Medical School. Um, and this
Speaker 1: is wins and losses. I'm Clay Travis. Alright, so this, uh,
Speaker 1: there's a lot I could still unpack about what you
Speaker 1: just said. For your for your ideas about how we
Speaker 1: should be responding today. I love that you've got the
Speaker 1: three pronged there. How much of what we're doing is
Speaker 1: cosmetic theater And what I mean by that is in
Speaker 1: New York, if you look at the rates of infection,
Speaker 1: it seems like, based on the recent data that the
Speaker 1: Governor of Florida has shared, there are many parts of
Speaker 1: Florida with similar rates of infection to New York. It
Speaker 1: seems like there is a curve, a steep curve, and
Speaker 1: then it starts back down. In fact, the rates of
Speaker 1: infection in the Northeast, if you look at the rates
Speaker 1: per million or whatever the heck it is, it's almost
Speaker 1: identical to what we've eventually seen in the South. And
Speaker 1: while everybody was panicking on some level, isn't a virus
Speaker 1: going to be a virus no matter what we do?
Speaker 1: And that even if you shut down for a long time,
Speaker 1: eventually people are going to go back outside and the
Speaker 1: virus is going to uh to spread again. Is it
Speaker 1: that like to me? There's early on I think and look,
Speaker 1: I'm I'm far from an expert, but there's only two
Speaker 1: ways to end the virus. One is by vaccine, and
Speaker 1: I'll ask you about a vaccine in a little bit.
Speaker 1: The other is about herd immunity, and it seems to
Speaker 1: me like there is a lot of data out there
Speaker 1: now which would suggest that the herd immunity requirement is
Speaker 1: way lower than we were initially told. Initially, and you'll
Speaker 1: know better than me, but it's like, hey, you need
Speaker 1: seventy or eighty percent of the population to be exposed
Speaker 1: to it. There's no way we can actually do that,
Speaker 1: And the reality is maybe it's only ten to But
Speaker 1: if you even are willing to discuss that, it's like, oh,
Speaker 1: you don't care about somebody's grandma dying? How dare you?
Speaker 1: Which seems to circle back again around to your initial point,
Speaker 1: which is science is not science. It's like it's got
Speaker 1: to have a certain negative bit to it or else
Speaker 1: you're not allowed to to share it. So what data
Speaker 1: are you seeing about her immunity and what would you
Speaker 1: surmise based on that data as we speak in in
Speaker 1: mid to late August. Well, before I get to that,
Speaker 1: let me talk about the other piece of your question,
Speaker 1: which was, well, how how can we get through this crisis?
Speaker 1: What are the what are the ways you mentioned two
Speaker 1: of them you mentioned mentioned you herd immunity, you mentioned
Speaker 1: the vaccine. There's actually a third, which is you could
Speaker 1: have a drug that treats the disease, apply the virus
Speaker 1: in a way that that he doesn't require racing. For example,
Speaker 1: hepatitis C. There's no vaccine for hepatitis C, but in
Speaker 1: recent years there have emerged treatments that are effectively cures
Speaker 1: for hepatitis C. That still the virus still bounces around
Speaker 1: the country, but you won't Your liver will not fail.
Speaker 1: You will not need a liver transplant if you take
Speaker 1: the drugs. And the same thing would be for HIV
Speaker 1: right where we have tents, but there's no vaccine. The
Speaker 1: smartest people in the world have been working on an
Speaker 1: HIV vaccine for forty years. We still don't have an
Speaker 1: HIV vaccine, but we do have effective treatments. That means
Speaker 1: that it used to be you know, you and I
Speaker 1: know because we're of that age in the eighties, if
Speaker 1: you had HIV it was a death sentence. It's not
Speaker 1: a death sentence. To day, people are living a pretty
Speaker 1: uh long lives even if they have HIV, well controlled
Speaker 1: by these drugs that are not vaccines. So you'll remember,
Speaker 1: Clay that when we first locked down in the spring,
Speaker 1: that was the argument actually was, well, we're only going
Speaker 1: to have to lock down for a couple of weeks
Speaker 1: because there are a bunch of biotech companies that are
Speaker 1: developing these drugs, uh that are gonna end up curing
Speaker 1: the disease, and we're not gonna have to worry about it.
Speaker 1: It's only gonna be a couple of weeks. Fifteen days
Speaker 1: to slow the spread was one of the phrases, catchphrases
Speaker 1: was out there, and I was writing at the time
Speaker 1: my my original cover story in the Wall Street Journal
Speaker 1: and COVID from from April was about this fact that actually,
Speaker 1: as somebody who's invested in a lot of biotech companies,
Speaker 1: people are totally overestimating a probability of success here. Most
Speaker 1: drugs that enter clinical trials fail by far. Like so
Speaker 1: the idea that we're just gonna, you know, flip the
Speaker 1: switch or snap our fingers and we're gonna have a
Speaker 1: cure for COVID, it's not gonna necessarily work that way.
Speaker 1: It maybe months or even years before we have a
Speaker 1: drug like we do now for happetitis or HIV drugs
Speaker 1: took years, in decades to develop this idea that we're
Speaker 1: gonna wait for a cure in terms of a drug
Speaker 1: to to be on the market before we reopen the economy.
Speaker 1: We could be waiting years. We could basically destroy the
Speaker 1: economy permanently if we do that, So that that was
Speaker 1: one of my arguments early on, and not just mine,
Speaker 1: with my co authors too, which included some people who
Speaker 1: are on you know, both sides of the political aisles,
Speaker 1: so to speak. We were saying, look, you can't destroy
Speaker 1: the economy for that long because businesses will permanently close
Speaker 1: and there's no assurance that effective treatments will come along.
Speaker 1: So that was back in the spring. That theory has
Speaker 1: been proven right, right, there is still not any drug
Speaker 1: on the market that cures COVID. Obviously, their treatments that
Speaker 1: people are more hopeful about than others, but nothing is
Speaker 1: incontrovertibly a cure. So then let's talk about the vaccine.
Speaker 1: So you hear a lot of hype about vaccines. Um, well,
Speaker 1: we're gonna have a vaccine by the end of the year.
Speaker 1: Some people say, now, look, we all hope that's true,
Speaker 1: but it's very important to understand that the world record
Speaker 1: for the fastest vaccine ever developed for a novel virus,
Speaker 1: it's five years. It took five years to develop a
Speaker 1: vaccine for the Boula virus. That was the record up
Speaker 1: to now. So we're talking about having a vaccine in
Speaker 1: less than a year, which would be five x but
Speaker 1: the world record for speed the vaccine development. Now, there's
Speaker 1: a lot of advances in technology, there are a lot
Speaker 1: of people working on this. There's been a lot of
Speaker 1: money put to work, so it's possible that all that
Speaker 1: ends up working, but we we we can't be assured
Speaker 1: of that. And if the idea is that we're going
Speaker 1: to keep schools closed and businesses closed and the economy
Speaker 1: shut down until we have a vaccine, what if we
Speaker 1: are waiting two, three, four or five years for vaccine.
Speaker 1: We just can't. So we have to have a plan
Speaker 1: B in case of vaccine failed. And now that gets
Speaker 1: to the third thing that you mentioned, which is the
Speaker 1: herd immunity, your population immunity. Is it possible that, like
Speaker 1: a forest fire which eventually rages for a while but
Speaker 1: then eventually runs out of dry wood to burn, could
Speaker 1: we end up in a situation where COVID eventually flames out,
Speaker 1: and it is possible, and that is that is I
Speaker 1: think my personal view is that's what we're seeing in
Speaker 1: New York City, that's what we're seeing in Sweden. That
Speaker 1: you had high death whole high death tolls early on,
Speaker 1: but over time, the people who are susceptible succumbed tragically
Speaker 1: unfortunately to the virus, and the people who are left
Speaker 1: standing are not that susceptible. And that's why that's not
Speaker 1: not Andrew Cuomo flexing his biceps, but actually the fact
Speaker 1: that the susceptible people in New York are already dead.
Speaker 1: And do you buy into that this idea that bay
Speaker 1: on the data, you're seeing that the herd immunity threshold
Speaker 1: by which you start to see substantial protections is maybe
Speaker 1: a lot lower than what was initially told by the
Speaker 1: quote unquote experts. There's there's good evans so that we
Speaker 1: were talking earlier in the show Clay about Gabriella Gomez,
Speaker 1: a scientist who has modeled this out and can't get
Speaker 1: her research published by scientific journals because it's not alarmist enough.
Speaker 1: And there's there's been some publications and other medical and
Speaker 1: scientific journals to suggested that people don't know right that
Speaker 1: there's the The sort of more negative view is that
Speaker 1: you need of a population to be infected in order
Speaker 1: for her immunity to to take place. Now, for those
Speaker 1: who don't know what her immunity is, let me just
Speaker 1: maybe pause and explain it. So, what her immunity is
Speaker 1: is the idea that so many people have been exposed
Speaker 1: to the virus that the virus itself can't explode. Like,
Speaker 1: for the virus to really explode, infect other people. You know,
Speaker 1: you as the affected person, have to wander around and
Speaker 1: counter a bunch of other people who are not yet affected.
Speaker 1: It spread the virus to those other people. Now, if
Speaker 1: two out of three of those people are already immune
Speaker 1: because they've been infected in the past, then maybe there's
Speaker 1: a chance that that third person gets infected, but the
Speaker 1: probabilities are lower. The analogy might be trying to throw
Speaker 1: a golf ball through a chain link fence. Yeah, that
Speaker 1: the whole in the chain link fence fence is big
Speaker 1: enough for you to throw the golf ball through it.
Speaker 1: But if you ever tried to throw a golf ball
Speaker 1: through a chain link fence, there's at least a fift
Speaker 1: chance or more that you hit one of the links
Speaker 1: in the fence and the ball falls to the ground right.
Speaker 1: So similarly, here, if a bunch of people are already immune,
Speaker 1: the probability or the ability of the virus to really
Speaker 1: spread cuts down dramatically. So that's what herd immunity is.
Speaker 1: It's basically a virus basically failing to spread because enough
Speaker 1: people have already been immune. And what's very very interesting,
Speaker 1: and this is something that you see a lot in
Speaker 1: the background of the scientific literature, but it doesn't get
Speaker 1: the hype, is that there may be more herd immunity,
Speaker 1: or the threshold you need to get to her immunity
Speaker 1: for COVID is a lot lower than what what the
Speaker 1: what the maybe the more negative side thinks, And the
Speaker 1: reason for that is that the common cold is also
Speaker 1: a coronavirus, and so it may be that there are
Speaker 1: a bunch of other very mild coronaviruses that are out
Speaker 1: there that people have gotten over the last winter or
Speaker 1: longer that have given them enough immunity. Two stars Kobe
Speaker 1: to the virus that causes scovide nineteen, that herd immunity
Speaker 1: from COVID from actually being infected with stars Scobe two
Speaker 1: or the novel coronavirus, that threshold is a lot lower
Speaker 1: because there's already enough community to other coronavirus is out
Speaker 1: there in the population that that combination means that we're
Speaker 1: actually a much closer to her immunity today than than
Speaker 1: we otherwise thought. And that would be the most hopeful
Speaker 1: case that the thing runs out and uh and we
Speaker 1: before even there is a vaccine, and a vaccine that
Speaker 1: gets widely distributed, the virus has already done, as you
Speaker 1: put at, the forest fire has already burned through the
Speaker 1: dry wood. And again, that's still a tragedy. I don't
Speaker 1: mean to minimize the people who are dying from COVID nineteen.
Speaker 1: It's an incredible tragedy what's happened. But it may be
Speaker 1: that we're closer to the end than we than we
Speaker 1: might otherwise believe or be led to believe. What would
Speaker 1: you say the likelihood is of another situation like this
Speaker 1: arising during our life? You you look at you know,
Speaker 1: the the data over history history, but obviously you are
Speaker 1: at times a skeptic, at times a contrarian. Are you
Speaker 1: optimistic that in forty years, let's assume that a large
Speaker 1: percentage of our audience is still going to be alive
Speaker 1: and they'll be eighty or ninety years old or younger
Speaker 1: seventy sixty. Is this something that happens again in our
Speaker 1: lives or is this something that only comes up every
Speaker 1: hundred years. What is the likelihood that we have another
Speaker 1: situation like this anytime in the next several generations. You
Speaker 1: definitely can't rule it out. I mean, pandemics, particularly influenza
Speaker 1: influenza based pandemics, do happen from time to time. They're
Speaker 1: not usually as severe or as bad as this one,
Speaker 1: but they definitely do happen from time to time. The
Speaker 1: one thing that we can hope for is if if
Speaker 1: in say twenty fifty or we have another situation like this,
Speaker 1: that science and technology have advanced to the point where
Speaker 1: we don't have to wait a year or five years
Speaker 1: to develop a vaccine. We don't have a bureaucracy like
Speaker 1: the CDC and the FDA preventing people in February in
Speaker 1: Washington State from testing for the novel coronavirus. We can
Speaker 1: distribute those tests rapidly. We figured out how to do
Speaker 1: all that so that basically in your home, you know,
Speaker 1: you have your own little kind of lab instrument in
Speaker 1: your home, and you can just basically test for all
Speaker 1: sorts of things with that without having to actually go
Speaker 1: to a doctor or go to a CBS or what
Speaker 1: have you. So I think the more we can invest
Speaker 1: in that kind of infrastructure, the more we can have
Speaker 1: data and real time data reporting from nursing homes and
Speaker 1: other places where vulnerable populations live, we should be able
Speaker 1: to respond to a virus like this better. But you know,
Speaker 1: it's always theoretically possible that a virus comes along this
Speaker 1: even more virulent, more lethal than COVID nineteen or stars
Speaker 1: copy two, especially if you factor in the possibility of
Speaker 1: bio warfare. Right, So, I don't think we can ever
Speaker 1: rule out the possibility that something worse comes along. And
Speaker 1: that's all the more reason why we have to be
Speaker 1: so objective and so serious about the lessons we learned
Speaker 1: from this crisis, Because if the only lesson we learned
Speaker 1: is we hate Trump and we throw them out, then
Speaker 1: we haven't learned anything. You mentioned testing. There's been a
Speaker 1: massive amount of discussion about testing for the coronavirus throughout
Speaker 1: the last several months. What do we need to know
Speaker 1: what should we know about testing its viability? It's important.
Speaker 1: What would you say the essence of the takeaway about
Speaker 1: testing should be, Well, there's there's a couple of things
Speaker 1: I'd say. First, it's important for people understanding because you
Speaker 1: hear people say, well, it's a failure of the US
Speaker 1: government that there aren't more tests today. That's not actually true.
Speaker 1: The US is testing more people than any other country
Speaker 1: in the world. We're testing a lot of people. The
Speaker 1: problem is and I wrote about this free opt dot org.
Speaker 1: The original paper that we published it free opt dot org,
Speaker 1: our think tank on the pandemic. It's called a New
Speaker 1: Strategy for Reopening the Economy. During COVID nineteen, we talked,
Speaker 1: we walked through all the science of this and how
Speaker 1: the tests that we have today they're not perfect. They're
Speaker 1: not like the pregnancy tests that people are most used to,
Speaker 1: where you just kind of take it to home and
Speaker 1: you know whether you're pregnant or not with incredible accuracy.
Speaker 1: The COVID tests are not that accurate, and they take
Speaker 1: a long time to get you the results back. So
Speaker 1: if you have to wait a week to get the
Speaker 1: results from a COVID test, even if the test is available,
Speaker 1: you're not, it's not useful because then what are you
Speaker 1: gonna do for that week while you sit around wait
Speaker 1: for the test. You might have gotten positive even if
Speaker 1: even if the test is negative, you might have gotten
Speaker 1: positive in the intervening couple of days. So testing alone
Speaker 1: doesn't matter. Uh. And and testing it doesn't matter, it's
Speaker 1: not as central at this point as as people think.
Speaker 1: What really matters is some of the other things that
Speaker 1: we've talked about, herd immunity, social distancing, washing your hands,
Speaker 1: basic stuff to have hygiene. Uh. But it did matter
Speaker 1: early on. If we had enough testing early on, we
Speaker 1: could have maybe niff this thing in the bud like
Speaker 1: some countries have done up to this point, in particularly
Speaker 1: in the Pacific Rim, the New Zealand, the Taiwan. Now,
Speaker 1: how what would that have looked like? What happened wasn't
Speaker 1: There was a great story I think in the Washington
Speaker 1: Post about this several months ago where when the when
Speaker 1: the stay to Washington, when they first started seeing cases
Speaker 1: of COVID nineteen or what they saw was an unexplained
Speaker 1: pneumonia and scientists in local academic centers and other labs,
Speaker 1: we're trying to figure this out and they actually developed
Speaker 1: their own kind of homebrew test of the novel coronavirus
Speaker 1: because the Chinese actually had published online the genetic sequence
Speaker 1: of the stars Kobe to coronavirus, So if you were
Speaker 1: a scientist in Washington, you could actually take that genetic
Speaker 1: sequence and they use that to develop a test. And
Speaker 1: so they actually started doing that, and the FDA and
Speaker 1: the CDC came in. The bureaucracy came in and said, no,
Speaker 1: that's illegal, you can't do that, and basically Squashing said,
Speaker 1: not only the CDC is legally allowed to develop the test,
Speaker 1: we were basically waiting for the government, the federal government,
Speaker 1: to develop the test. And it turned out the CDC
Speaker 1: waited a month. They had delays of a month in
Speaker 1: developing a test because their lab was contaminated. So that
Speaker 1: was a really really disastrous uh results, and what we
Speaker 1: should have had and what we hopefully will have in
Speaker 1: the future as a system in which testing for novel
Speaker 1: viruses is not dependent on the government. You have private
Speaker 1: sector labs, biotech companies, university scientists, all with the ability
Speaker 1: to develop tests and and compare them against each other
Speaker 1: tests for accuracy, crowdsource that test development rather than depending
Speaker 1: on a single lab in Atlanta to do it for you.
Speaker 1: So that's a huge lesson that we should have learned earlier.
Speaker 1: But in terms of scale of testing today, testing is
Speaker 1: not going to be this panacea that everyone thinks it is.
Speaker 1: And I'll give you an example of why. You know
Speaker 1: you're a lot of people talk about, well, look at
Speaker 1: the cases, the cases are rising, but as you pointed
Speaker 1: out with Florida, right, the cases are rising in Florida,
Speaker 1: but not as many people died. Why is that? Well,
Speaker 1: A big part of the reason is that a number
Speaker 1: of those cases we're in younger people. A big part
Speaker 1: of that is that people were getting tested who had
Speaker 1: very mild symptoms, and a big part of that was
Speaker 1: that you were actually reporting all that data. I'll give
Speaker 1: you an example from Europe. In France, they had exactly
Speaker 1: the same number of cases. If you look at the
Speaker 1: chart in terms of how many cases of COVID nineteen
Speaker 1: they had in France, it looks almost exactly like the chart,
Speaker 1: and neighbor in Germany the same number of cases. But
Speaker 1: guess what in France, four times as many people died
Speaker 1: of COVID then died in Germany. Why is that? Was
Speaker 1: the virus magically four times as lethal in France as
Speaker 1: it was in Germany. No, it's that the people Germany
Speaker 1: was testing were different than the people the French were testing.
Speaker 1: You have an example Denmark. In Denmark, they basically only
Speaker 1: tested people who are hospitalized for COVID, so they basically
Speaker 1: underestimate the number of cases there were. So all that
Speaker 1: to say that testing is helpful, but it's not this panasy.
Speaker 1: We're getting better. The federal government has actually moved mountains
Speaker 1: to try to increase the supply of good test The
Speaker 1: f D is working over time. They had those initial missteps,
Speaker 1: but they're now trying to recover from that initial misstep
Speaker 1: and and do a lot better at at rapidly improving
Speaker 1: better tests. This test that you probably talked about on
Speaker 1: your show, Clay that's alive, a test that the NBA
Speaker 1: helped pioneer that could be a real improvement on what
Speaker 1: we have up to this point. All is to say
Speaker 1: testing is good, and it's good that we have more tests,
Speaker 1: but testing alone isn't going to solve the problems. What
Speaker 1: you really have to do is get her immunity or
Speaker 1: a vaccine. Those are the only reliable ways to really
Speaker 1: get this virus under control. Fox Sports Radio has the
Speaker 1: best sports talk lineup in the nation. Catch all of
Speaker 1: our shows at Fox Sports Radio dot com and within
Speaker 1: the I Heart Radio app search f s R to
Speaker 1: listen live. We're talking to O vic Roy. You can
Speaker 1: follow him on Twitter at A v I K. I'm
Speaker 1: Clay Travis. This is the Wins and Losses Podcast. Couple
Speaker 1: of final questions for you, and you've been phenomenal here.
Speaker 1: I believe you went to school. You told me off
Speaker 1: airs we were starting with Chris Webber, you were a
Speaker 1: year behind him. You grew up outside of Detroit, where
Speaker 1: my wife also grew up. She went to the University
Speaker 1: of Michigan. I believe you're a University of Michigan fan.
Speaker 1: Is that right? I am. Yeah. It's been a it's
Speaker 1: been a tough decade at least on the football side,
Speaker 1: but you know, yeah, no doubt. Okay, So when you
Speaker 1: see the Big ten and the pack well shut down
Speaker 1: fall Sports from a obviously a perspective with which you
Speaker 1: are looking at this, did they get it right or
Speaker 1: did they get it wrong? Well, first of all, if
Speaker 1: Chris Webber is is listening, I just want to tell
Speaker 1: Chris I'm so happy that you've you've come back to Michigan,
Speaker 1: the Fab five as has reunited. Uh and and and
Speaker 1: those those wounds from so long ago and heal all
Speaker 1: the love to you. And I'm so glad that that
Speaker 1: all that is is getting better now after all this time.
Speaker 1: But in terms of the Big Ten and the pack
Speaker 1: of the packs, well stuff. Uh, it's been such an
Speaker 1: interesting story to follow, especially over the last week, as
Speaker 1: as you've obviously talked about a lot on your show. Uh.
Speaker 1: Really remarkable. Um. And I think there's a couple of
Speaker 1: things to say about it. One the uh, the illogic
Speaker 1: of saying you're gonna have tens of thousands of students
Speaker 1: come to campus, but it's too dangerous for people to
Speaker 1: play football. I mean, how do you think they're actually
Speaker 1: gonna get COVID? It's from the other students, right, So
Speaker 1: like does that make any sense? Um? And obviously there
Speaker 1: are some colleges that have given up on having a season.
Speaker 1: But I actually take the opposite, uh takeaway from that,
Speaker 1: which is COVID nineteen. You know, we talked about this
Speaker 1: in some of our work at Prep. COVID nineteen in
Speaker 1: the college population is not lethal. Yes, of course there
Speaker 1: are very rare cases of serious illness or death. But
Speaker 1: it's very very rare. And you know, you've you've heard
Speaker 1: this talk. And the talk that really reportedly has scared
Speaker 1: the chancellors and presidents in the Big ten inpacts well
Speaker 1: has been uh, myocarditis inflammation of the heart muscle as
Speaker 1: a result of COVID nineteen. There have been a couple
Speaker 1: of cases of that, um, again a serious illness that
Speaker 1: we should be concerned about. But the studies that have
Speaker 1: been used to scare the presidents and chancellors are studies
Speaker 1: of fifty year olds in other countries. Um. They're not
Speaker 1: of actually college aged athletes. UM. And in fact, the
Speaker 1: likelihood that a college ash athlete gets inflammatory myocarditis from
Speaker 1: COVID nineteen, as far as we know, is extremely low.
Speaker 1: And that's not to say you shouldn't be concerned about
Speaker 1: and you shouldn't try to be careful and you shouldn't
Speaker 1: try to protect the athletes. Of course you should, uh.
Speaker 1: But there there is again a lot of alarmism here
Speaker 1: rather than rigorous scientific examination of the real risk. And
Speaker 1: football players in particular play with a lot of risks
Speaker 1: every day. You know, they're they're football players who are
Speaker 1: permanently disabled because of playing football in college. So they're
Speaker 1: they're they're aware of risk. And what's been I think
Speaker 1: particularly disappointing is that the players were not part of
Speaker 1: the conversation. Right. This was being determined by commissioners and
Speaker 1: presidents without their input in almost every case. And that's
Speaker 1: not to say that maybe if their input had been
Speaker 1: in there, the decision wouldn't have been dissimilar. Maybe the parents,
Speaker 1: there are a lot of there's a maybe there's a
Speaker 1: silent majority of parents who are concerned. I don't want
Speaker 1: their kids playing, And that's fine. If they don't want
Speaker 1: to play, they shouldn't up to But these decisions were
Speaker 1: being made behind closed doors with in some cases what
Speaker 1: appears to be sketchy scientific evidence. And I'd like to
Speaker 1: see a more open discussion where we really do go
Speaker 1: through the evidence. Why did the experts get so much wrong?
Speaker 1: I started talking about this early, using sports as a prism,
Speaker 1: talking about the difficulty and I'm sure you've dealt with
Speaker 1: this as well. Of knowing both the numerator and the denominator,
Speaker 1: in other words, projecting a death rate or an infection rate,
Speaker 1: you need to know how many infections there were, and
Speaker 1: you need to know, uh, you know, how many of
Speaker 1: those people are actually dying and because of the virus
Speaker 1: as opposed to dying with the virus. And so you
Speaker 1: know this this Imperial College forecast out of out of England,
Speaker 1: which forecast over two million people would die in the
Speaker 1: United States. It doesn't I probably surprise you that the
Speaker 1: worst case scenario forecast got way more attention in the media.
Speaker 1: But the so called experts, the epidemiologists, the virologists, their
Speaker 1: forecast were completely for the most part, worthless early on
Speaker 1: when decisions were being made here, Why do you think
Speaker 1: they got so much wrong? Wow? Let's uh, we could
Speaker 1: spend an hour on that tap at topic. Before I
Speaker 1: get to that, let me say one thing else about
Speaker 1: the Big ten. A school to keep an eye on
Speaker 1: is Perdue. Perdue is run by Mitch Daniels, who is
Speaker 1: a really smart guy who used to be the governor
Speaker 1: of Indiana, was the budget chief in the White House
Speaker 1: under George W. Bush, really really smart, talented, thoughtful guy
Speaker 1: about the stuff testified before the Senate about why he
Speaker 1: was going to reopen Perdue. And they've had they've done
Speaker 1: a lot of really interesting and sophisticated things to try
Speaker 1: to make a fall semester at Purdue work. And let
Speaker 1: me go through some of them. We don't know if
Speaker 1: it's gonna work, but he's certainly doing some really interesting
Speaker 1: things that are worth keeping an eye. And first he
Speaker 1: required that everybody test negative for COVID before coming to campus,
Speaker 1: and they sponsored the testing. They tested over thirty produced
Speaker 1: students and they had a positivity rate of less than
Speaker 1: one percent of that student body. UM, and they have
Speaker 1: some other plans in place, so like let's say, UH,
Speaker 1: you do get sick, or you do you have had COVID,
Speaker 1: so you have immunity. Let's put those Let's rum those
Speaker 1: people who are now immune with the people who might
Speaker 1: have preexisting conditions or other vulnerabilities so that they can
Speaker 1: be even more safe in those particular UH facilities. So
Speaker 1: there are a lot of things that Perdue was doing
Speaker 1: that I think are worth watching to see if you
Speaker 1: compare and contrast that to u n C, which famously
Speaker 1: shut down. UNC actually only tested a thousand of their
Speaker 1: students prior to opening up the campus. And of course,
Speaker 1: and they had a bunch of outbreaks that they breaked
Speaker 1: out about and decided to shut everything down again, which
Speaker 1: was just stupid on every level. They should have had
Speaker 1: a better plan because people are going to test positive,
Speaker 1: and having having people test positive should not be a
Speaker 1: reason to shut down the campus if you have a
Speaker 1: plan in place to handle those positive cases. So those
Speaker 1: are two schools I think that are like examples of
Speaker 1: of a good of a good way to handle the
Speaker 1: thoughtful way to handle it, and maybe a thought less
Speaker 1: way to handle it. Um and then so let me
Speaker 1: then just just go onto your your question about the
Speaker 1: math modeling. I mean, it's just been This is another
Speaker 1: part of the story that does not get enough attention
Speaker 1: today that will over time. And part of it, as
Speaker 1: you said, or maybe the majority of it, as you said,
Speaker 1: is that journalists aren't really good at math. But people
Speaker 1: really need to dig into what these models are. There's
Speaker 1: a tendency for the average person understandably say, well, that
Speaker 1: guy's got a PhD in statistics, he's got a PhD
Speaker 1: in maths, he's smarter than me. I should just defer
Speaker 1: to him, he's the expert. But if you actually dig
Speaker 1: into what these models are, they're incredibly simplistic, or they're
Speaker 1: based on incredibly flimsy uh data points about what's going
Speaker 1: to happen. It would be like saying, to use the
Speaker 1: college football analogy again, who's going to be the national
Speaker 1: champion of college football in right? Like you can make
Speaker 1: some guesses about that, but we all know the college
Speaker 1: football landscape shifts and and and flows over over a
Speaker 1: decade or two different powers emerge, So you're not gonna
Speaker 1: necessarily have any idea. And yet these models are being
Speaker 1: put forward with this level of conviction like, well, if
Speaker 1: you don't agree with me, you're against science. And of
Speaker 1: course that model from the Imperial College London that predicted
Speaker 1: two million dust turned out to be completely wrong. And
Speaker 1: a part of what's been happening here is that what
Speaker 1: people do, what a lot of the dirty secret of
Speaker 1: a lot of models, Clay, is what they'll do is
Speaker 1: they'll take a set of data, like they'll take the
Speaker 1: curve of how COVID evolved in wuha, how many cases
Speaker 1: on this day versus that day versus that day, and
Speaker 1: then they'll just find a mathematical equation that if you
Speaker 1: chart that mathematical equation looks like that curve and then say, okay,
Speaker 1: we're gonna use that equation to predict what happens in
Speaker 1: the future, Well that that doesn't make any sense. Just
Speaker 1: you know, the thing we were talking about stocks earlier,
Speaker 1: what do they say in all the stock brokerage commercially,
Speaker 1: they say past performance is not a guarantee of future results.
Speaker 1: And the same is true with viruses. Right, just because
Speaker 1: the curve has looked this way in the past doesn't
Speaker 1: mean it's going to look a certain way in the future.
Speaker 1: And yet this very simplistic way of modeling where you say, well,
Speaker 1: you know, there's this equation and it kind of looks
Speaker 1: on a graph like the way COVID nineteen evolved in Wuhan.
Speaker 1: So I'm gonna use that as my model predict theres
Speaker 1: two million dats. That's not science. That's basically fancy guesswork.
Speaker 1: And yet that fancy guesswork dramatically affected policy in Western economies.
Speaker 1: Not only that, arguably that fancy guests work literally caused
Speaker 1: a lot more deaths because that fancy guest work was
Speaker 1: the quote unquote expert forecast, which I think Andrew Cuomo,
Speaker 1: if we gave him truth serum, would say he believed,
Speaker 1: which is why he sent those patients back into nursing
Speaker 1: homes right like, he believed they were going to need
Speaker 1: a hundred and forty thousand beds because of those forecasts.
Speaker 1: Actually they only needed nineteen thousand. But if he had
Speaker 1: known they were only gonna need nineteen thousand, it's likely
Speaker 1: that tens of thousands of people might well be alive
Speaker 1: in this country today. They actually followed those forecast advice
Speaker 1: would were wildly off and as a result ended up
Speaker 1: with arguably way more people dead than would have died
Speaker 1: if they had not. And you know another example, we
Speaker 1: were talking earlier on the show Clay about about how
Speaker 1: all the public health experts they were educated and trained
Speaker 1: on influenza pandemics, and so they basically went back to
Speaker 1: that they were fighting the last war they were. They
Speaker 1: were drawing from those influenza pandemic playbooks to talk about
Speaker 1: COVID nineteen or how to deal with COVID nineteen. A
Speaker 1: great example was people don't talk about anymore, but remember
Speaker 1: how we were all terrified that we were going to
Speaker 1: run out of ventilators. They're always talking about in the spring,
Speaker 1: Oh gosh, we don't have enough ventilators. What are we
Speaker 1: gonna do? Well, it turned out in New York City
Speaker 1: of the people who were put on ventilators died because
Speaker 1: the ventilators actually made the disease worse, because it wasn't
Speaker 1: fundamentally a respiratory disease. It was fundamentally and inflammatory disease.
Speaker 1: And that's an example of where, uh, to your point,
Speaker 1: the the the expert opinion about well, this is just
Speaker 1: like influenza. We got to do what we would do
Speaker 1: in influenza. Pandemics turned out to be a fatal, fatal decision.
Speaker 1: We're talking to Vic Roy. I'm Clay Travis. This is
Speaker 1: wins and losses. What letter grade would you give the
Speaker 1: United States media for the way that they have covered
Speaker 1: this pandemic? I mean, I wish I could give him
Speaker 1: a G because it's not even enough, right because, like
Speaker 1: you know, you could graduate from high school with an
Speaker 1: F in a in a particular class. But uh, what's
Speaker 1: happened here, the the the distortion and the misrepresentation of
Speaker 1: what's been going on in Again, I don't think that
Speaker 1: distortion has always been intentional. I think some people, uh,
Speaker 1: you know, are are getting a certain weird pleasure out
Speaker 1: of out of making things look better or worse than
Speaker 1: they are. But I think a lot of people are
Speaker 1: just genuinely scared, and they're writing articles that reflect how
Speaker 1: scared they are. Uh. But that that that inability to
Speaker 1: put numbers in contains up. There was a Indiana Junior
Speaker 1: high that shut down because one person had COVID. I mean,
Speaker 1: you know, you'll see these numbers, aren't well. Texas today
Speaker 1: had five cases. What does that mean? How many people
Speaker 1: live in Texas? How many those people are getting sick,
Speaker 1: how many those people are dying? You never see that information.
Speaker 1: There's just been so much like that. I just you know,
Speaker 1: it's it's really been. It's been very, very bad. And
Speaker 1: the only saving grace at the end of the day
Speaker 1: has been, in a sense, the existence of the Internet.
Speaker 1: Because for all the things they're terrible about social media
Speaker 1: we can complain about or whatever, it has allowed people
Speaker 1: like you and and people who are these epidemiologists who
Speaker 1: are have this sort of con contrary and opinion, they're
Speaker 1: able to express themselves. They they're able to put research
Speaker 1: out there, They're able to put analyzes out there that
Speaker 1: people who want to look at the evidence can examine.
Speaker 1: And I think that has enabled, in a sense, a
Speaker 1: kind of an end around around that more traditional gatekeeping process.
Speaker 1: It has been perfect. There have been websites, uh, and
Speaker 1: big internet companies have tried to say, well, if you
Speaker 1: disagree with the World Health Organization, we're gonna shut down
Speaker 1: your account, Like the World Health Organization is kind of
Speaker 1: lot wrong. And I think some of the tech companies
Speaker 1: have realized that that was a mistake. But how that's
Speaker 1: an important thing to to to keep an eye and
Speaker 1: make sure that there's always channels for alternative views because
Speaker 1: I don't think that problem is gonna get better. We're
Speaker 1: not going to magically wake up with a different news
Speaker 1: media ecosystem which everyone's got a degree in statistics. Yeah,
Speaker 1: and this goes to my biggest issue. And I see
Speaker 1: so many connections across so many different fabrics of American
Speaker 1: society and world society today, and the one that really
Speaker 1: kind of connects it all to me is science is
Speaker 1: about combat, and we don't think enough about the combat
Speaker 1: of ideas. Right, you come up with a theory or
Speaker 1: a hypothesis, however you want to classify it, and the
Speaker 1: scientific method requires an adversarial response to that, because only
Speaker 1: by challenging hypotheses or theories can we determine what the
Speaker 1: truth really is what I see far too often in
Speaker 1: our society today. And this is why I was saying
Speaker 1: earlier I'm a first amendmad absolutist. Is it seems like
Speaker 1: we are creating arenas where only acceptable opinions are allowed
Speaker 1: to go head to head with each other. And I
Speaker 1: think we've seen that with the coronavirus, where science all
Speaker 1: of a sudden has become so political that if you
Speaker 1: have the belief, hey, we need to figure out a
Speaker 1: way to live with the coronavirus, we need to figure
Speaker 1: out a way to live with COVID, maybe all of
Speaker 1: these dire forecasts aren't true. You were considered to be
Speaker 1: contributing to and almost an accessory to death. That is
Speaker 1: scary to me, and I bet it's scary to you,
Speaker 1: as somebody who makes a living in many ways actually
Speaker 1: diving into the data. If you can't share that data
Speaker 1: and have a legitimate debate with someone without being accused
Speaker 1: of facilitating death in the country or even God forbid,
Speaker 1: rooting for it, that's an inherent flaw our debate in
Speaker 1: our national discourse. You know, I think if there's if
Speaker 1: that's probably what you're just describing, Clay is the single
Speaker 1: most important thing that our country and the world, but
Speaker 1: particularly our country has to get better at. You we
Speaker 1: have to have an ecosystem, a way of debating all ideas,
Speaker 1: but particularly scientific ideas, but all of these economic policy politics,
Speaker 1: you know, racial issues we have. We have to have
Speaker 1: a way of talking about all these issues in in
Speaker 1: a in a way that where we're competing theories, competing
Speaker 1: hypotheses can be addressed and considered and not suppressed. That's
Speaker 1: incredibly important. We've always got to make sure that that
Speaker 1: that alternative, that contrarian approached ideas, uh, is there. And
Speaker 1: by the way, like we were talking earlier about my
Speaker 1: by my time as an investor, that's the essence of
Speaker 1: every great successful investor. Is they big when everybody else
Speaker 1: is zagging? Right? You think about Billy Bean, same thing, right,
Speaker 1: the most success full Uh. You know it's true. And
Speaker 1: in football especially right, you think about the people who
Speaker 1: have come up with creative ways of new new offensive
Speaker 1: or defensive schemes. Sometimes those offensive and defensive schemes are
Speaker 1: actually were invented a hundred ten years ago, but they've
Speaker 1: just fallen out of a style, so people don't adjust
Speaker 1: to them. Right, So there's there's a real need for
Speaker 1: contrarian thinking at all times, and we've got to find
Speaker 1: a way to ensure the people who disagree with the
Speaker 1: majority point of view on a scientific issue are not
Speaker 1: characterized as anti science merely for disagreeing with one take
Speaker 1: on the evidence. Because you can have a very evidence
Speaker 1: driven view that's different from what another person who's looking
Speaker 1: to the same evidence concludes. And unless we have a
Speaker 1: system in which that's possible and that's allowed, we're not
Speaker 1: going to be truly scientific. We're not going to be
Speaker 1: pro science, and we're not going to actually do right
Speaker 1: by our country. Final question for you and I, and
Speaker 1: this is obviously predictive in nature, but I'm curious what
Speaker 1: you think to a there is a national consensus that
Speaker 1: the Vietnam War was mismanaged and that we shouldn't have
Speaker 1: been involved in Vietnam. I think there's also a consensus that,
Speaker 1: maybe among most people, that the Iraq War did not
Speaker 1: make make sense when you consider the cost in lives,
Speaker 1: in economics, all those different things, and that sometimes takes
Speaker 1: and you know this as well as I do. The
Speaker 1: retrospective arc of history, we have to go back in
Speaker 1: twenty five years from now, thirty years, fifteen years, whatever
Speaker 1: the math, maybe we have a clearer vision of what
Speaker 1: the full story was right, and that's ultimately what ends
Speaker 1: up being written. Will people look back on our response
Speaker 1: to the coronavirus in ten years and right honest portrayals
Speaker 1: of what we got right and what we got wrong?
Speaker 1: Or is the media so left wing convinced, Because what's
Speaker 1: fascinating is the left wingers ended up being right in
Speaker 1: many ways about the Iraq War and about Vietnam, right
Speaker 1: without getting into the particulars of those wars, but that
Speaker 1: made them willing to acknowledge for the public, Hey, we
Speaker 1: got this wrong. I think the left wing has gotten
Speaker 1: the coronavirus completely wrong, right. I think that has been
Speaker 1: a failure, more so by left wing media than anyone.
Speaker 1: Will we get an honest appraisal of the coronavirus and
Speaker 1: our response to it in the next ten or twenty
Speaker 1: years or is it so intensely political that no one
Speaker 1: is ever going to admit what they got wrong and
Speaker 1: what we failed from as a country. Well, Clay, it's
Speaker 1: so interesting that you bring up the Vietnam War because
Speaker 1: there was a famous book written about the Vietnam War
Speaker 1: by David Halberstam called the Best and the Brightest Yes,
Speaker 1: And it's all about how the Vietnam War was prosecuted
Speaker 1: by the greatest science oriented experts of the country at
Speaker 1: that time. They were all Harvard and Yale graduates. They
Speaker 1: all had impressive resumes, lots of degrees. The main general
Speaker 1: or the Secretary of Defense, Robert McNamara, was one of
Speaker 1: these guys. He he pioneered, you know, analytic thinking and
Speaker 1: the way the Forward Motor Company worked in the middle
Speaker 1: of the twentieth century. And that's what you know. He
Speaker 1: tried to basically apply those lessons of you know, quantitation
Speaker 1: and metrics for everything to the to the way the
Speaker 1: army operated in Vietnam. It turned out to be a
Speaker 1: complete catastrophic failure. Uh. And and so my hope is that, uh,
Speaker 1: we have some similar examination of what happened in this crisis,
Speaker 1: and we draw the lessons from this crisis that David
Speaker 1: Halbert Stam was able to pull out of the Vietnam
Speaker 1: War when he wrote that book, Because that is an
Speaker 1: incredibly important part. We have to have a lot more
Speaker 1: humility around what we call expertise, and we have to
Speaker 1: have a genuinely, genuinely sincere uh openness, particularly in this
Speaker 1: era of big data. So much information is online, so
Speaker 1: many people can take a look at that data and
Speaker 1: come up with it. We've got to have a much
Speaker 1: more open source, crowdsourced approach to thinking about evidence, and
Speaker 1: if we do that, we'll do a much better job
Speaker 1: for vulnerable populations in this country and many others. Do
Speaker 1: you want to write a book on this to help
Speaker 1: tell the story after this is all done, because at
Speaker 1: some point we're going to go back to normalcy, But
Speaker 1: if we don't learn from the errors that we have made.
Speaker 1: And I think there's a strong argument that the worst
Speaker 1: decision in my life was the war in Iraq, right,
Speaker 1: I think you can make that argument, or the twenty
Speaker 1: one century as Vietnam was before you and I. But
Speaker 1: I think for older people who are listening to that,
Speaker 1: that's probably the biggest failure in American policy. I feel
Speaker 1: like the coronavirus has is in that level. But I
Speaker 1: wonder if it's ever going to be acknowledged in the
Speaker 1: same way. Well, you know, uh, I thought about writing
Speaker 1: a book about it, and and maybe I will. I
Speaker 1: got to find a spare time because they're no kidding
Speaker 1: our think tank free up dot org. But I but
Speaker 1: maybe I will and and and you're right to that
Speaker 1: debate needs to happen, and I think it will. I
Speaker 1: hope it will, because this virus, not just the desk,
Speaker 1: but the lockdowns, the school closures. It is affect that
Speaker 1: all of us, right, we've all been affected by it.
Speaker 1: It's too important not to have that examination take place.
Speaker 1: And so hopefully people like you and me and so
Speaker 1: many others who have been who've been carrying that flag,
Speaker 1: will well to do their part to make sure that
Speaker 1: we have that conversation. You've done almost two hours with
Speaker 1: us here. I think we could go on and on.
Speaker 1: It's been fascinating. I encourage everybody listening to go follow
Speaker 1: avic avic Ovic man. After all that time I was
Speaker 1: trying to get your yeah, I know it would be
Speaker 1: so much easier if they started to ring with an
Speaker 1: oh instead of an a uh at a v I
Speaker 1: K go follow him. He does incredible work. If you've
Speaker 1: enjoyed this conversation, thank him for spending the time with us.
Speaker 1: I'm Clay Travis. Thank you again for coming on. And
Speaker 1: this has been wins and losses.