Clay Travis Sits Down With Avik Roy

Episode Transcript
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Speaker 1 (00:02):
This is Wins and Losses with Clay Travis. Clay talks
with the most entertaining people in sports, entertainment and business.
Now here's Clay Travis. Welcome in Wins and Losses Podcast.
I am Clay Travis, and we're about to be joined

(00:22):
by O vic Roy, who I think you guys are
really going to enjoy. He's been doing fantastic work looking
at the data surrounding the coronavirus, making recommendations on so
many different levels, been writing for the Wall Street Journal,
among other locations. We have never actually spoken before, but
I am impressed by the work that he's done. I
found him on social media over the last several months,

(00:44):
and we bring him in now. Oh vic Roy, let
me go ahead and start here. How can people find
you on social media? How can they read your work? Ovic?
And thanks for joining us. Hey, thanks Clay, Well, thanks
to my eccentric parents. My name is spelled A v
i K, not Ovic, O v i K. It's a
v i K and that's my Twitter handle, A v
I K, just like it sounds a viasm Victor I K.

(01:06):
All right, So your background as we get into so
many different interesting topics that I want to discuss with you.
But what is your educational background that led you into
the profession that you have now and what do you
do for a living. It's a bit of a zig
zag path. My undergraduate degree was in molecular biology at
M I. T. And then I went to medical school

(01:27):
at Yale. And then instead of becoming a doctor or
a scientist, I went into biotechnology investing, where I invested
in companies trying to develop new treatments for diseases, vaccines,
all that sort of thing. And then I got really
interested in healthcare re form and that led me down
the rabbit hole of healthcare policy and economic policy in general,
and worked on a bunch of presidential campaigns and and

(01:47):
now I run a think tank in Austin, Texas called
the Foundation for Research on Equal Opportunity, where we come
up with ideas to help more Americans climb up the
economic ladder of success. All right, So I'm fascinated by
several different things. You've already told this, So what is
the reaction. You go to M I T. And then
you go to Yale Medical School and I don't know
the answer to this. I went to law school and

(02:08):
I practiced law for a couple of years, and even
not practicing law was considered to be a sort of
a risky choice by many people, because you have a
good profession that's out there. What percentage of your classmates
or people who go to a medical school as good
as Yale end up not actually practicing medicine when they graduate, Well,

(02:29):
it's a small percent at most schools, it's probably close
to zero. But Yale was a particular place where they
actually encourage you to to pursue your interests outside of
medical school. It was like a path Baale system and
things like that. And so I'd say in a typical
Yale class, which is a hundred people per class, about
five to ten end up doing something that's sort of
like you know, in law school, it's very typical, right,

(02:50):
A lot of lawyers go into things of the law,
and especially yeah, especially after a few years. Most people
have to go in and make money initially, but then
they'll start to filter out. Everybody I always say who's
a lawyer has got a dream of not being a lawyer.
But most people who go to medicine, That's why I
was interested. Most people who go to medical school go
on in practice. So is that something you came to
a decision before you even started medical school, or what

(03:13):
was it about, sort of the capitalistic economy I guess
of the biotechnology universe that attracted you more than being
a traditional doctor. Well, my dad was also a scientist.
He was a biochemist, and so I grew up around
all these incredible people who had been like the people
who had characterized DNA and RNA, and we're the pioneers
in this modern field of genetics and biology that we're

(03:36):
now living in. So I always had this real excitement
about it. I thought I wanted to be a scientist.
And then you know what the problem is, Like, you know,
at m I T a core of the faculty has
Nobel Prize that I'm walking around and I'm like, there
is no way I'm going to win a Nobel Prize.
I'm not smart enough. So how am I going to
actually do something useful to the world. I don't know.
So I struggled with it for a while, and I thought,
you know what, maybe I can invest in biotech companies.

(03:57):
I can help build the latest new cure for some disease.
That would be something useful I could do with my life,
and that let me down that path, and here I
am doing this now because obviously health care reform and
economic policy in general effects so many people. A lot
of people struggle to find affordable health insurance. Uh. And
there are a lot of cures that we need to
have for for disease that people have, and and not
just in health care, a lot of oarliious higher education.
How do you afford college? How do you afford to

(04:19):
keep the lights on in your house? There are lots
of things where we need new ideas, and it's been
fun to work on work on trying to develop those ideas. Okay, yeah, no,
it's so how do you decide? So I'm kind of
fascinated by the concept of being an investor in biotechnology
companies because you obviously have to be sophisticated to even
understand as an investor, like for people out there who

(04:40):
don't really think about it very much, being a quote
unquote sophisticated investor is typically requires a certain net worth,
but you can invest in a variety of different companies,
sometimes for better or ill uh. And but biotechnology, I
would imagine there's a lot of I I you tell me,
but I would imagine there's a lot of snake oil
cells out there who are trying to peddle things that

(05:02):
may or may not make a lot of sense. So
I would think a medical degree like yours would basically,
for lack of a better way to describe it, allow
you to speak the language so that you're may be
better able to understand. And maybe also your dad being
involved help with that as well. But what was your
process like in terms of investing and finding companies that
you found to be worthy of putting money behind. Yeah,

(05:25):
it's it's so it's great and and really relevant that
you're bringing this up, actually because it has a lot
to do with how I ended up being a contrarian
on COVID stuff. So yes, I I UH entered the
workforce a finished school in two thousands, and that was
right around the time that the Human Genome Project had
been completed. So for those who remember those days, the

(05:47):
Human Genome Project was at the time this gigantic UH
scientific enterprise to sequence the entire human genome. Every d
N a piece of DNA and you're comprise of the
human genetic code from beginning to end, because that had
never been done before. And that was finally finished in
two thousand and one, and there was a big dot
com boom in the nineties when the Internet as we

(06:07):
know at first came into being. And right after that
dot com bubble burst, there was this basically this genomics bubble.
All these stocks called this genomics and that genomics were
We're getting multibillion dollar market caps and nobody knew what
they did and a lot of it was hyped. And
so UH an investment firm I've never heard of called
Bain Capital. I have to know a couple of people
who worked there, and one of them reached out to
me and said, hey, can you help us figure out

(06:29):
all this genomics stuff because we're just a bunch of MBAs.
We don't know anything about genomics, and we figured you
can teach us. You have a degree in molecro biology,
you can teach us about this stuff, and we can
teach you how to read a balance sheet and then
maybe you can be useful. And I'm like, wow, that's
not really knowing the first thing about about how to
do any of that. When I started UH that, I
got recruited to to Bain Capital, moved to Boston and

(06:50):
started investing in bi tech companies. I basically became part
of this first generation of people with scientific and medical
backgrounds m d s and PhD is mostly who started
investing in biotech companies because it ended up it's not
like a normal stock thing, where like normally, if you
turn on c NBC or something, it's like, well the
pe ratio is this right, or if you look at

(07:10):
those conventional things. But biotech it's not like that at all,
because the clinical trial turns out positive or negative in
terms of your your latest jurif for breast cancer or whatever,
and that stock goes to zero if it fails, or
it triples if it succeeds. I mean, it's total volatility.
It's crazy, a lot of losses, a lot of winds.
It's it's kind of like baseball, where if you're batting average,

(07:31):
it doesn't if you're batting averages below five dred you're
probably not gonna survive. But if you if but you're
gonna lose, you're gonna be wrong plenty. And you have
to put an enormous amount of effort into statistics, right
because at the end of the day, what what my
job ended up becoming and a lot of people who
were like me, is we ended up being incredibly intense statisticians,
because what you end up doing is you're looking at

(07:51):
a say, a breast cancer trial, new new drug for
breast cancer. It's being tested in fifty women who have
breast cancer. Well, fifty women is not a lot. So
if there's fifty women who got the drug and fifty
women who didn't, and let's say that trial got really hyped, Oh,
this drug really worked in the fifty women who got
the drug where they really did better, they lived longer,
their breast tumors went away. But what if it's what

(08:13):
if what happened was actually the people who were in
the placebo arm of the trial were actually sicker in
the beginning and that excuse the results. Or maybe they
measured the tumors in the wrong way. So there's all
sorts of subtleties about a way a clinical trial operates
that you can then make a bet and say, Okay,
is this drug overhyped or under hyped? Are people overhyping
the drug as you said, like is it snake? Or

(08:33):
there's this trial that's been published in the New England
Journal of Medicine that says this this drug really works.
But then in a larger trial with thousands of patients
is going to fail or is it the other way around.
Maybe it's a drug that didn't do that well at
early state trials, but in bigger trials there is a
signal there and it ends up being really successful. Those
are the kinds of things that people like me we're
making bets on, betting tens of in fact, sometimes hundreds

(08:55):
of millions of dollars, trying to figure out which side
was right. How do you do? I did all right?
I did all right. Uh. And that's what kind of
gave me the freedom to to start a nonprofit on
a lark. Yeah, a financial cushion, all right. So that
is all interesting and fascinating background. And you said something
a couple of minutes ago. You said that your work

(09:18):
at Bain Capital, and your work and going to m
I T and also going to Yale Medical School and
putting it to work molecular biology, looking at the genome projects,
all of these different things obviously lead you to being
able to look at data and figure out what I
would call, for lack of a better way, what the
signal is versus what the noise is. So there's so

(09:39):
much noise on a day to day basis, regardless of
what you do for a living, whatever people are doing
for livings where they listen to us out there, most
of it is external noise that isn't really getting to
the essence of what you do. Figuring out the signal
is essentially what you had to do for these biochemistry
uh and and investigations for lack of another way of

(10:03):
putting it. That also then corresponds in an incredibly unique
way with what's happened with the coronavirus, where every day,
it seems to me, we are deluged with data, with news,
with viral stories meaning not necessarily virus stories, but literally
stories that go viral about the virus. How do you
cut through that noise and figure out what the real

(10:25):
signal is? So let's go in. The story starts in January.
When did you first become aware of the coronavirus and
start to read and pay attention to it in China? Well,
I started hearing the stories right away in in you know,
just January February when the news started the break, But

(10:46):
but at that time we didn't know that it was coming.
I think it wasn't. One was the first case in
in Washington, stated was I want to say February. It
was late January, so we started to hear about this
thing in Wuhan. There was a lot of suppression and uh,
and then we started to see the in Washington and
certainly followed that all the way through because healthcare is
health care policy, and healthcare stuff is on my radar

(11:07):
always because as part of my job. So you said
you were comfortable early on with looking into the data
and maybe becoming somewhat of a contrarian in terms of
what your analysis has done. Such a good job of,
I think is for people who don't know, and again
I would encourage them to go follow you on Twitter
at a v I k oh vic Roy we are

(11:27):
talking to right now. Uh. He does incredible work looking
at the data and putting it in context. And I
believe the first time I started to see your data
percolate on my feed and start to follow it aggressively
was when you were looking at risk factors for college
age kids, comparing let's say the seasonal flu and also

(11:47):
with kids who are elementary school age and saying, hey,
I understand that you know there's a lot of attention
on the coronavirus right now, but the data would reflect
that most young people are under greater danger from the
seasonal flu or pneumonia. That's a counterintuitive data. Fect When
did you start to dive into the numbers in such

(12:08):
a way, Because this is eerily similar, I would think,
and I bet you would agree with what you were
doing looking at these biotech trials where the headline might
be one thing, but when you actually go and look
underneath the surface and start to examine it, some of
the data is telling a different story maybe than the
data that the media is sharing at the top line
level of reporting. Yeah, that's that's exactly right, Clay. I mean,

(12:32):
you've hit it on the head. And I would say also,
by the way, I think one of the reasons when
I listen to your your show and your podcast, you
do an amazing job of just walking people through the
real statistical situation. And I think that goes to show
that there's an analogous, analogous situation of sports. Right you
think about this explosion of sports analytics today and what
is that all about. It's all about the fact that

(12:53):
conventional ways of measuring performance in sports, starting with baseball,
but the true in it, it's true in every sport
don't necessarily accurately measure how a player or a team
is performing, and so there's been this explosion of trying
to be much more rigorous about how to measure that.
And on Wall Street that's basically what you're doing on
Wall Street two. You're you're trying to say, at least

(13:14):
in biotech investing, and it's not just true in biotech investing,
but especially true in biotech investing. What you're trying to
figure out is, Okay, this stock is really hyped. What
are people getting wrong? This stock is down in the dumps?
What are people getting wrong? You're you're you're training. If
you try to be good, try to outperform is to
always try to swim against the tide and try to
identify what other people aren't thinking about when they look

(13:37):
at the data, and that habit of mind or cast
of mind, if you want to call it, that's how
That's what's driven all my policy work too. When I
write about public policy, when I write about healthcare, when
I write about COVID, it's all about, Okay, what are
people missing? And the obvious thing right away was this
real skew in the age distribution of who was getting

(13:59):
really sick and who was dying from COVID. There was
a huge skew towards the elderly. And I started writing
about this pretty early on it. The early data out
of China, the early data out of Italy, the places
that really got hit a little bit before the US
got hit, they all were showing the same skew where
in the case of the US, literally of the people

(14:20):
who died of COVID are over the age of sixty five.
And I would start writing about this and tweeting about
it and putting in our in our work at free
out dot org, which is our think tank, and some
people would ask me like, well, okay, that's that's interesting.
People over are the ones that dying or are the
ones over sixty five? Isn't that true? Most things don't,
don't Most things end up, you know, basically killing the
elderly more than they kill young people. So I was

(14:41):
actually curious to say, okay, let's let's actually try to
dig into that and figure out, Okay, how does the
skew work for a more conventional infectious disease like influenza
compared to COVID, Because if you look at the history
of influenza pandemics, they actually do hurt and kill children.
They do kill young people. The famous uh influenza pandemic
of nineteen eighteen killed a lot of soldiers. That was,

(15:04):
more soldiers died because of influenza than died at least
American soldiers then died actually fighting in the trenches in
World War One. So a lot of young people die.
Then that's why we closed the schools if there's like
a crazy influenza pandemic. So so I was interested in
this and what I found. If you actually look at
CDC data, official government data over a ten year period

(15:26):
from two thousand seven to two thousand seventeen, which is
our most recent cut of the data, and you compare
the number of people who die from influenza by age
bracket to those who die from COVID, you see a
much worse skew for COVID. In other words, COVID is
much more skewed in terms of serious illness or death
towards the elderly than your typical infectious disease or any

(15:46):
other kind of disease. So this is interesting to me
too because one of the sort of aphorisms I would
say about war and people who listen to this know
also that I'm a history buff and I love of
you know, studying American and and other history a lot.
You typically end up fighting the last war, sorry, the

(16:10):
most recent war, with the technology from the previous war, right,
because everybody who has everybody who has studied all the
things that have happened in warfare is going to apply
the lessons that have applied from prior war. But you're then,
you know, let's use the Civil War for an example,
which is a particular interest of mine. You're using the
Napoleon Napoleonic tactics, but now the technology, the ability of rifles, cannons,

(16:36):
all of the rapid fire weapons have changed. So the
mortality rates skyrockets, right, because you're fighting, and gradually you
adjust and people learn, hey, maybe we should be fighting
in a different way. The reason why I used that
as an example is so many people and this frustrated me,
and I bet it frustrated you when you actually looked
at the data. So many people used data from the

(16:57):
flu to justify shutting down schools based on the coronavirus.
But this particular covid infection was not like the flu
in that the age range of the impacted were different.
So the decision to shut down schools might well have
made sense in the pandemic a hundred years ago, and
you study it and you say, oh, that's the lesson

(17:18):
we should take away. But it's not the same virus.
So you're fighting a new virus which has never existed
before with the tactics that would have worked against the
virus a hundred years ago. That's a misfit, right, But
most people aren't sophisticated enough to think about that. You were,
But that has to be frustrating to you from a
public policy perspective to see it and not be able

(17:41):
to cut through the noise and make people realize the
data and what you've seen. Well, first of all, let
me just say you're absolutely right, a thousand percent. This
issue of fighting the last war is exactly what's going
on here. And and so there's so many different dimensions
of how that's true. Clay, I'll give you one. So
you've heard people say, well, there were these plans in

(18:01):
place from the George W. Bush administration and the Obama administration,
why didn't Trump use those plans to fight the coronavirus. Well,
those plans were not designed for coronavirus. They were designed
for influenza. In fact, if you actually look at the
cover page of the reports, they say things like our
plan for dealing with a novel influenza pandemic. Now, influenza

(18:24):
is a different virus from coronaviruses like COVID stars kobe
to the virus that causes COVID nineteen. And again this
is part of having that molecular biology backrouprom m I
T I understand the difference between different types of viruses
and how they actually infect you and how they're they're
how they actually work in the body. They're very different,
and so it's very important to understand that not all

(18:45):
viruses are the same. The way they behave in your body,
their lethality, their virulence can be very different. And so
to your point, yes, you closing schools and a severe
influenza pandemic makes sense because the young children and young
adults do get killed old from really bad influenza pandemics
in the in the case of nineteen eighteen especially, But
this is not an influenza pandemic, and it's also true

(19:08):
of all these sort of public health epidemiologists types. So
one of the things you'll see a lot of people say,
particularly on social media as well, how dare you write
or tweet about COVID. You don't have a right to
have an opinion because you're not an epidemiologist. Now, the
problem is, first of all, epidemiology is we can get
in to have a long discussion about what you actually
learn in epidemiology school or epidemology grad school of public

(19:31):
health school. But a lot of what you do, a
lot of how epidemiologists or public health officials, how they
cut their teeth is studying things like influenza pandemic. So
a lot of the pronouncements that they're making with this
incredible certainty in the sense, well you, if you don't
listen to me, you're against science, are based on historical
evidence of what has worked or what has happened with influenza.

(19:53):
And this is a completely new virus that we've never
seen before, and so a lot of that expertise doesn't
really work because you're dealing with something completely different. And
you I want to circle back right now, because you
just said something, somebody says, oh, you're not an epidemiologist,
you're not a virologist. I try to share intelligent people.
You went to m I T. You went to Yale

(20:14):
Medical School. I've got decent degree background as well. But ultimately,
intelligent people who are contrarians or who are skeptics are
very often right, and people who think that they are
the quote unquote experts very often are wrong when you
actually look at the data. Right. And so when you say, hey,

(20:36):
we're only going to listen to scientists or we're only
going to listen to quote unquote experts, I mean the
data is telling us a story that it shouldn't matter
who's telling the story, right, Like when when you are
coming out and sharing your data on why schools should
be back open based on looking at the direct c
DC data, that's more valid than somebody who studied uh

(21:00):
the influenza outbreak a hundred years ago, and it is
trying to draw lessons from there. Yet it seems to
me like in the public media sphere there's more benefit
given to those epidemiologists. Maybe then would be justified based
on the data. That's a kind of a long winded question,
but you have to see that on a regular basis
with what you've been writing and talking about. Well, I

(21:23):
would you're I would agree with you, but I would
also describe it in a different way because there is
not consensus in the scientific community and and it is
actually anti scientific to demand that alternative hypotheses be thrown
out for no reason. There's actual evidence about what's happening,
and the evidence heavily waits in one direction another. That's

(21:45):
one thing. But in a situation where you're dealing with
an unknown virus that we've never seen before and that's
spreading in a way that has unique characteristics, then you
as a scientist and again my dad was in scientist
I group around size my whole life. Uh, as a scientist,
you're obligated not to throw out any theory, any hypothesis
until you can convincingly with the evidence disprove it. And

(22:08):
in fact, there are lots of there's a lot of
evidence that that plausible scientific theories are not are being suppressed.
I'll give you an example. There's an epidemails or a
biomathematician who does a lot of things around population health
named Gabriella Gomet and she tweeted a couple of weeks
ago that she actually has been trying to write some
stuff about her immunity population immunity and how that population

(22:31):
immunity may maybe closer at hands than other people think.
And she can't get the work published in scientific journals
because some people have shared with her. The editors of
these journals say, well, if we if we publish your
work and people become less scared of COVID, then maybe
that will lead people to not wear masks and stuff.
And we don't want that. Therefore, we've got to keep

(22:52):
this kind of optimistic take off off the table. Now
that's not science, right, It's not science when you artificially suppressed,
for subjective or political reasons, alternative hypothesis of what's going on.
And that's the lesson I really want to drive home
here is a lot of the people who are screaming
the loudest about trusting the science are not actually acting scientifically.

(23:15):
Because if you're acting scientifically, you're looking very hard at
the data. You're not ruling out any theory until you've
got got convincing evidence that it's wrong. That's really well
said and much better than my sort of haphazard question
that I asked there. Why do you think that is? So?
Why do you think it is? And that's hugely important.

(23:37):
I think the scientific method is a rigorous adversarial system. Right.
There are a lot of people out there who believe
science only has one answer, right because we've proven, say,
what the boiling point of water is or what the
freezing point is, but that had to be tested over time, right,
And when you have these rigorous battles over what might

(23:58):
or might not be the ruth, that's how science advances.
But when you don't allow that battle to me it
kind of ties in with the marketplace of ideas and
why I'm such a huge First Amendment absolutist. When you
constrict sort of the available universe of argument or discussion,
you are actually penalizing our ability to arrive at a

(24:19):
truth or a universally ultimately recognize truth. Right. I mean
the entire purpose of science is I've got this hypothesis,
let me test it. When you start saying to people, oh,
that hypothesis makes people uncomfortable, we can't discuss it, you're
actually combating science. That's exactly right. And one one important

(24:40):
element of this that's that's that's essential to really think
about and where you can really get your spiddy sense
up in a sense, is when people conflate predictions with facts.
A prediction is about something that may happen in the future.
And look, it may be more probable that Alabama wins
the national championship then Michigan State, Uh, but it isn't

(25:04):
guaranteed that Alabama is going to win the national championship, right,
And so similarly, uh. In science, you hear a lot
of people say, well, it's a fact that X will
happen in the future, but we don't know because the
world is a very complex place and there are a
lot of variable to go into whether something happens or not,
especially when you're talking about a novel virus that no

(25:25):
one has ever seen before. And so that's where there's
particularly been a poisonous climate where if you have a
different view as to what may happen in the future
in a situation where there's a lot of uncertainty, there's
been a lot of suppression debate at that because we
can't we can't, we can't give any anybody reason to
be optimistic, because if you're optimistic, then maybe you know

(25:46):
you'll you'll hang out at a bar with your friends
and communicate the disease to other people. And we can't,
we can't have that, And the problem is if you
engage in that kind of let's call it dishonest suppression,
then people don't listen to you because they don't trust you.
If they don't trust they're gonna say, you know what,
I don't trust that guy who's telling me not to
do all this stuff because he's been wrong half the
time anyway, and he's demanding that I listened to because

(26:07):
he's a scientist, or that I'm not going to believe
in science. And that's actually more dangerous for science and
the scientific enterprise that people cloak themselves in the words
science but they're not actually being scientific, because then people
out there say, well, if that's what scientists and I'm
not for it. Fox Sports Radio has the best sports
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(26:29):
Heart Radio apps. Search f s R to listen live.
We're talking to O vic Roy. I'm Clay Travis. This
is Wins and Losses. I mean, now this obviously, this
subject has utterly fascinated me on several different levels. And
you know that I've spent a lot of time talking
about this from the sports perspective and We're going to
circle around to it on a sports perspective. But it

(26:50):
seems to me you've talked about the analytics revolution that
we've seen in sports. It seems to me that the
essence of why our national conversation about the coronavirus has
been so bad ultimately boils down to something you were
just talking about, which is, there's a very poor understanding
of statistics and probability in this country. And I'm gonna
give people out there, and I want to give you

(27:11):
a chance to tee off on this too. But and
and really it kind of goes to why you've been
able to be successful looking at biotech companies. I think
it goes to why I've been successful in my chosen
field of of life. Um, it's because I tend to
be skeptical of consensus opinion and actually look at the
data myself. But you talked about, you know, Alabama playing

(27:32):
Michigan State. Sports fans are universally bad about this. In
college football in particular, there's an idea that if a
team plays and one team beats the other, when that
means that team was quote unquote better. But the reality is,
and this has been something I love thinking about you know,
if you played a million minute game instead of a

(27:53):
sixty minute game, the team that played for a million minutes,
you know, is probably going to be the better team
if it wins, because your data sample size is a
million games. But when you play sixty minutes of a
football game, any one of those sixty minutes that you
pull out of the million minutes could go so many
different directions. And sports fans it seems to me kind

(28:17):
of understand this in the context of, oh, well, that's
why we play a seven game series, because over the
course of a seven game series, the inferior team might
win by thirty one game, but they might lose the
other four, right, and we don't look at the sum
total of the of the games. The coronavirus, it seems
to me, and the way that the media has covered it,
so many people in my industry are bad at math. Right.

(28:38):
One of the reasons why I think people become journalists
is they're good at reading and writing, they're bad at math,
and they're running from math and science. And I'm not
great at math and science. I'm not pretending to be
incredible at it, but I'm better than most people in
my industry. And so the failure of understanding probability and statistics,
particularly in a social media age, where you say, oh,

(29:00):
this thirty four year old woman was completely healthy and
then she died. That story goes viral all over social media.
Even though it's an outlier. It's in no way representative
of what happens when the average thirty four year old
or twenty four year old or sixteen year old get
sick with the coronavirus. Yet people believe it because it's
a story that they want to believe. So I've talked

(29:23):
a lot. They're kind of setting the table, but I
want to circle back around to the original premise. How
much of our national failure with the coronavirus has to
do with a national failure to understand probability and statistics. Well,
I would say there's no doubt that our response to
the coronavirus has been utterly and badly damaged by a

(29:45):
failure to understand statistics. By the way you're selling yourself shortly,
I mean, I've looked to your show. You do an
amazing job of communicating what's really going on from a
quantitative standpoint to your audience, and you're doing an incredible
public service, because you have such a big audience and
you're sharing this data with a lot of people who
otherwise wouldn't get it from anywhere else. So I want
to thank you for that. I'm trying, but by the way,

(30:06):
I get crushed for it, right, Like I get crushed
because people are like, oh my god, you're a you know,
sports guy who went to law school. Why in the world.
And the reality is because I want sports to come back.
But when I see something that I believe is factually
inaccurate and being discussed poorly by media, it just draws
me and I want to try to get real facts
out there in a way that they're not being So

(30:28):
I appreciate you saying that, but I'm sure you get
this all the time, and I get it certainly, Like
you don't care about people dying. No, I wish nobody
ever died, right. I wish we were all immortal. I
wish your grandma, my grandma, everybody's kids, every everybody was
safe forever. That's not the reality of the world in
which we live. And I am troubled by what I
would say, is this very poor ability to discuss complex

(30:52):
issues where it's like people either like Hey, we've got
to completely shut down, nobody can leave their homes, or
we gotta be completely wide open. And and it's like
the nuances. We need to be somewhere in the middle.
We need to be living our lives but not allowing
the coronavirus to destroy our world. If that makes sense,
well totally. And I can give you some concrete examples

(31:12):
of how this is played out in real time. So
one example is school closures. Right, we're seeing all these
and let's leave let's leave colleges aside for the moment,
I'm talking about pre k kindergarten, primary school, elementary school.
That the overwhelming, overwhelming scientific evidence at this point. I
mean basically it's anti scientific to argue that kids are

(31:33):
at risk, uh, you know, in a meaningful way of
obviously there's a handful. There's literally like thirty nine kids
aged one fifteen who died of COVID in the United
States out of fifty million in that repeat that again,
because I think it's a big it's a big deal.
And according to the most recent CDC data, kids fifteen
and under thirty nine have died of the coronavirus between

(31:55):
the ages of one and by the way that's with
that's with the coronavirus, because I bet if you went
into those thirty nine what you would find is they
have significant health issues on top of whatever they got
from the COVID impact. Right, that's right. It's people who
have died who have tested positive for COVID. Whether the
actual cause of death was COVID or not, we don't know.

(32:18):
But thirty nine kids. And I guess how many kids
live in the United States who are aged one, uh
fourteen or one to fifteen. I mean, there's what three
d and thirty ish million people in the United States.
I would guess that there's got to be what fifty
or sixty million kids at that age, fifty seven million,
fifty seven millions, always saying thirty nine out of fifty

(32:42):
seven million kids. And we're shutting down schools. And by
the way, you know what that means. Shutting down school
it's not exactly good for children, particularly low income kids
who have no other alternative. If you're a single mom
and you have to work, Let's say you work at
a pharmacy or grocery store, what are you gonna do?
Are you gonna go to work and lead just three
year old at home you can't. Uh, there's forty estimated

(33:04):
about forty thousand cases of child abuse that are going
unreported in the United States because schools are closed right now,
and let alone the mental healthy, educational deficits, the emotional development.
It's just incredible costs. So, like we often talk about
this purely in terms of what's your risk of getting COVID,
what's your risk of not getting COVID, and we don't
talk about the costs on the other side of the equation.

(33:27):
The cost to a kid who doesn't get to go
to school. Uh, the cost to a business that shuts
down permanently. It's estimated that over a hundred thousand businesses,
maybe even more have shut down permanently because they didn't
have the cash cushion. Once you start losing your revenue,
but you can't keep your payroll going, you can't pay
the rent for your building, and you're done and you quit. Uh.

(33:48):
That's not good for a lot of people. And people say, well,
it's just about dollars. No, it's not just about dollars.
Is actually a lot of evidence that shows that when
you have a massive economic dislocation or a massive recession
or a massive disruption that leads to shorten life expectancy
as well for a lot of different reasons. Think about
the opioid crisis, where is that happening economically depressed parts
of the country to a significant degree. Why do you

(34:10):
think that all of those facts which are so incredibly
important are not able to cut through the noise. That's
a big question that I have because it's frustrating to me.
I understand the audience and we have I'm fortunate to
have a substantial audience that we have built up, but
I'm still a pinprick of the overall media audience. Right,
Why do you think that data that you just shared

(34:32):
and by the way, credit to the Wall Street Journal
for carrying your story was on the front page. I mean,
your work is getting out there, and I think you're
doing an incredible job of it. But why do you
think those stories, those facts are having such difficulty cutting
through the noise. And there are so many people out
there with kids that are terrified that their kids are

(34:53):
going to die of COVID that would not think twice
about ever pulling their kid out of school from the
seasonal flu, even though the seasonal flew is far more
dangerous and by the way, don't even think twice about
sending their kid out to a swimming pool without parental
supervision when their kid is far more likely to drown
there than they ever already get COVID. It's totally right, um.

(35:16):
And you know, just like you were saying, like, I'm lucky,
I have I have a platform. I'm the policy editor
at Forbes. I can put my stuff there, I can
put my stuff on Twitter, I can put my stuff
at the Wall Street Journal when they when they asked
me to. And so I've been lucky and then that
I've had those opportunities to get the word out there.
But you're right, it's it's overwhelmed by the NonStop wild
wall alarmism, uh, coming from the people who think everything

(35:38):
should be shut down all the time. And there's a
couple of different you know, I have a couple of
different hypotheses that I think are pretty plausible as to
wise it's happening. The first is the media has always
been about alarmism. I mean, the thing we used to
talk about if you ever took a statistics class, the
thing that people used to always talking about in statistics
class was well, people are often more afraid of flying

(36:00):
that they are driving their car because every plane crash
ever gets plastered all over the newspaper and plaster TV. Right,
So a lot of people have this impression that's not
safe to fly, when in fact, your chances of dying
in a plane crash are orders of magnitude lower than
your chances of dying in a car accident or even
crossing the street in a busy intersection. So that's an

(36:22):
example of where the media because it's the disaster of
the plane crashes. You remember that the Malaysia when that
Malaysia plane went Malaysian Airline plane went disappear and nothing
else for like four days, right, for like four months, honestly,
Malaysian three. And I was fascinated by that too because
it felt like when that thing disappeared, uh, you know,
maybe there was something other. You know, we still don't

(36:44):
know acent it seems like the pilot was involved. But
that story was such a mystery. It was not only
a plane disappearing, it was not knowing why the plane disappeared,
which is probably the greatest thing ever. Another example would
be shark attacks. Right, every time somebody gets attacked by
a shark, you hear about it. Uh, and so everybody
who goes into the ocean summer vacation time right now,

(37:04):
everybody is thinking, oh my god, I'm gonna get eaten
by a shark. Yeah. The old school adage among you know,
the newspaper hands is if it bleeds, it leads. Anything
that's sort of catastrophic or disastrous gets that headline. And
we all know that, we all we all consume the news.
We we understand that that's part of it. So that's
definitely it's like this is cat nipped to that kind

(37:25):
of journalism. Right. So that's that's number one. I think
number two is uh for for certain people who are
more politically politically oriented. Uh, it's clearly a situation where, um,
you know, if you if you're a journalist who hates
Trump and you didn't you know, you were frustrated by
the fact that the economy was roaring along. Record low

(37:46):
unemployment in the winter last winter for all races, not
just whites, but also blacks and Hispanics and Asians, record
lownemployment for everybody, record low disparities in the in the
difference between employment unemployment of blacks and whites, for example.
So the economy was doing incredibly well. So people were like, well,
you know, boy, that's annoying because we hate Trump and
we want them to lose. And now you have a

(38:06):
story that you can say, this is all Trump's fault.
Trump is the reason why a hundred seventy thousand people
have died in America. And so there's there's an enthusiasm
in a sense for the negative take on the government response.
And I'm not trying to say the government response doesn't
have things to criticize about it, whether federal, state, or local. Uh,
but but it is to say that that that has

(38:27):
been a huge part of the story. And I'll give
you some examples of why that is, or examples of
how why I think that is. There if you if
you ask the average person on the street, you know
who who watch the CNN or read the New York Times,
They'll say, you know what, why can't every governor handle
COVID like Andrew Cuomo, the governor of New York handled it.
He's just done such a great job. Why can't everyone

(38:47):
feel like him? In fact, Andrew Cuomo just published a
book about his triumph in conconquering COVID and wrestling it
to the ground. Now, this makes absolutely no sense according
to the data, because New York has been by far
the worst performing state by a country mile. California, Texas,
and Florida combined have had far fewer deaths from COVID

(39:10):
nineteens than New York has, whether per capita or not.
And yet somehow New York is portrayed as this success story.
It's not a success story at all. It's been a
complete catastrophic failure. Arguably, of any states that have done
pretty well, it's been the Texas and the Florida is
that never completely locked down their economy, and while they
have had death from COVID, it's been at a far
lower scale than New York. But you wouldn't know that

(39:32):
from the coverage. And that goes to this point about
the politics, Like if we were just looking at the data,
and we would have a lot more questions, we'd be
asking about build the Blasio, the mayor of New York City,
and Eventrew Cuomo, the governor of New York, and on
a bunch of his neighbors by the way, like Murphy
and New Jersey. It's so true, and I look at
the data and do you think that the media that

(39:53):
is praising Andrew Cuomo and Murphy who is next to him,
and by the way, to kind of put into context,
the day to New York and New Jersey's death rate
is twice the worst country in the world from COVID
so far the most recent numbers that I looked at,
Belgium was the worst, and New York and New Jersey

(40:13):
work twice what Belgium was. Okay, do you and this
gets into a hypothesis situation again, because we really don't know.
But this is something that I just think about a
great deal I can forgive people who are ignorant because
they are listening to media that is telling them things
that are not true. Right, Like, So, if you read
the New York Times and you have convinced yourself that

(40:34):
Andrew Cuomo and Governor Murphy did an incredible job, that's
because those journalists are telling you that, right. You are
being told that is not a truth. But you trust
the New York Times or you trust CNN to get
that right, and so you are misapprehending what the data
is actually saying. I don't like I'm not as bothered
by people who believe things that are untrue because they're

(40:57):
listening to people in positions of authority. I am desperately
bothered by people in positions of authority in my industry,
in the media who are sharing untruths about New York
and New Jersey such that people are willing to buy
a book that suggests it's supposed to come out in
October that Andrew Cuomo triumphed over the coronavirus when he

(41:19):
literally did the worst job of any politician, arguably in
the world. I mean, it's just such an upside down story.
So do you think the journalists are not sophisticated enough
to actually look at the data? Do you think they're
intentionally misleading their audiences? How is it possible for something
that is so untrue to become so widely believed such

(41:43):
that I believe right now Andrew Cuomo has the highest
popularity rating of almost any governor in the country, despite
clear evidence that he probably did a worse job than
any politician in the entire world with the coronavirus. Well,
you know, it's it's it's this phol Andrew Cuomo thing
is like one of the craziest aspects of this whole

(42:05):
six months, it's just been what is going on, there's
such a disconnect between what his actual performance has been
and not just in terms of the numbers, in terms
of COVID, in terms of his actual decisions, because a
lot of his actual decisions are the distress, yes, which
you know about that. I'm gonna ask you about that directly,
which is because this is the other place where I

(42:26):
really started seeing your work. We knew early on, when
the infection first was recognized in a Washington nursing home,
that the elderly people, when you looked at the data
from Italy, that elderly people were particularly susceptible to this virus,
and that therefore the most susceptible people in the entire

(42:46):
country were people in nursing homes. And so what happened
in New York although they're not sharing their honest data,
and I think you've been you've been looking at this too,
but you went out and looked and said, okay, where
are people actually dying? And you found out that the
death rate inside of nursing homes was just I mean,
like I think in Canada, for instance, not just the

(43:07):
United States. The data that I saw of all deaths
in Canada have been inside nursing homes. Uh. And so
New York believed these forecasts that they were gonna need
a hundred and forty thousand hospital beds. They ended up peaking.
And you can correct me on some of this data
if I'm wrong, because I'm doing it off the top
of my head. They ended up right around nineteen thousand,
uh actually hospital beds. So the order of the forecast

(43:30):
was way off. But as a result, Cuomo sent all
of these infected patients back into nursing homes, which was
like putting kindling, you know, right beside a forest fire
and it exploded. And the same thing happened in New
Jersey and in Michigan and in all these other states
that had early outbreaks and followed his lead. It wasn't

(43:51):
just that he made a poor decision. It was that
all these liming governors followed his lead and end up
making disastrous decisions. So how is that all not a
primary point of story, because to me, it's the biggest
story of the coronavirus outbreak from a death perspective. Yeah,
I mean, what's really important to understanding about what you
just described, Clay, is that Andrew Cuomo forced these forced

(44:13):
these nursing homes. His Health Department issued in order forcing
the nursing homes to accept COVID infected patients, and the
nursing home operators screamed, bloody murder. There's a there's a
great article from like the March twenty seven of the
Wall Street Journal that you can find if you just
google nursing home Andrew Cuomo March, you might be able
to find the article. The nursing homes knew that this

(44:35):
was a potentially fatal decision, literally fatal decisions if people
were screaming bloody murder bout at the time, and he
did it anyway because the experts that he was talking to,
quote unquote experts told them, well, gosh, what we know
from influenza, You've got to keep those hospital beds clear.
We don't have to worry about the nursing homes. Well,
we really have to worry about it as the hospitals,

(44:55):
which is totally backwards, because if you actually infect everyone
the nursing home, where do you think they're gonna end
up in the hospital definitely sick of COVID. So that
was an incredibly bad decision that was in part that
was it was Andrew mcuhma's decision. But it was also
a failure of experts who advised him to make that decision.

(45:15):
And that's part of the reason why you're not seeing
the accountabilit because those experts don't want to, you know,
take credit for that for that advice. Be sure to
catch live editions about Kicked the coverage with Clay Travis
week days at six am Eastern, three am Pacific. We're
talking to O vic Roy. I'm Clay Travis. This is
Wins and Losses. Sorry to cut you off. Continue, Oh please,
do you know I was going to bring up another

(45:37):
element of this phenomena we were talking before about just
the news coverage and and how distorted is. Let me
give you an example that's that's not related to what's
been happening in the US with COVID, but is related
to the US media coverage of the whole thing. There
was a story published on July eighteenth in the New
York Times by a poor bum on Mondabilia. I think
I'm pronouncing that correctly. The headline of the articles older

(45:59):
children spread the ronavirus just as much as adults. Large
study finds the study of nearly sixty five thousand people
in South Korea, suggests that school reopenings will trigger more
out backs, and the whole articles about the study by
there the South Korean c DC that actually didn't look
at sixty five thousand kids. It looked at a couple

(46:19):
of hundred kids and found that there were some adults
in those households who also had COVID. So she published
this very long article. I it was probably on the
front page and there are time certainly very prominent place
and I read it online, so I don't know what
page in the newspaper it actually appeared on. And well,
what's interesting about it is that so that was a
story that was being cited by everyone, Oh, you can't
open schools because there's a South Korea study that shows

(46:42):
that even young kids can infect everybody, even though in
nobody in Europe has seen this effect. Nobody in the
rest of the world where they've opened schools to see
in this fact, in South Korea there's a study that
shows that kids will affect adults, and so we got
to keep the schools close. That was the takeaway. And
you saw all this chatter in other news papers, other
media on social media about this article of the New

(47:02):
York Times saying this, well, fast forward a couple of
weeks later, and what do we find when the full
data set is actually released by the Korean CDC. It
turns out forty of the forty one cases of kids
and adults having COVID in the same household, they were
infected simultaneously. It wasn't the kids infecting the adults. The
kids and the adults in that household were simultaneously infected

(47:26):
by somebody else. So forty or forty one cases were
not actually of kids infecting adults. There kids just getting
infected by other people. The one case of COVID, uh,
somebody who was a child infecting someone else. A teenage
girl infected her younger sister. And that's it. One case
in the entire country of South Carota, of South Korea.

(47:49):
But you're not gonna see a front page store in
the New York Times saying, hey, guess what, everybody, that
South Korea study that we touted a couple of weeks
ago totally misconceived, totally misinterpreted and out of deal. You're
not going to see that story. And that's an example
of where a factual situation, just by the way it's
being covered completely distorts a very very important policy question,

(48:12):
which is do we bring sixty million kids and young
adults back to school this fall? What would the data
tell us we should have done? All? Right, So let's
pretend that that we had all this data that we
have now. And for people out there who are listening
to us, I'm talking to O vic Roy Clay Travis
here wins and losses, and we're talking in I think

(48:34):
it's August twenty five days all run together. I think
it's August August twenty one, whatever it is now, with
the benefit of hindsight, right, everybody always likes say hindsights.
With all of the data that you have out there
right now, what would have been the appropriate and smartest
decision in March and April in May? Because it's one

(48:56):
thing to say, and I think you would probably agree.
Maybe there's the fog of are in March when we
shut down a lot of people don't know what's going on.
I would argue there was enough data out there to
suggest that shutting completely down wasn't the smart move. But
let's pretend that you have all the data, all the
time that you've spent looking at everything what is the
right thing to do right now and what would have

(49:20):
been Let's pretend that we could have been flawless and
we could have executed perfectly. The appropriate response to coronavirus
based on what we know now is what. I'll give
you three core ideas of the core concepts or core frameworks.
Number one, we should have reopened schools, particularly for kids

(49:42):
under the age of twelve in the sprint, like Europe did,
and we definitely should be opening schools for younger kids
starting now. Uh, that's something that Europe did. They had
enormous success with it. They had no problems of kids,
no problems of kids infecting adults. And in fact, part
of why Europe maybe haven't so much more success in
the US in terms of the course of the pandemic

(50:04):
is because kids went back to school. Because the kids
all probably got some low level exposure, the virus developed
immunity and also transmitted immunity to others. Effect in Germany,
that's what they think. They think that reopening schools acted
as a break on the transmission of COVID nineteen. So
that's a counterintuitive. Yeah, that's a counterintuitive thought. Sorry to
cut you off, but we there's a strong argument to

(50:26):
be made that reopening schools, rather than leading to mass outbreaks,
actually makes them less likely. So in addition to the
fact that kids obviously benefit from being in school, there's
an argument that being in school actually makes us safer
as opposed to more dangerous. I just want to cut
you off because that's a counterintuitive take that you would
hear almost nowhere else in the media. Sorry, Okay, So

(50:46):
that's point one. Yeah, and if you get if you
dig into my Twitter feed and if you read the
Wall Street Journal article, you'll you'll see the links to
the German scientists in particular who have been making this argument.
So that's point one. Point two. We should have done
a lot more, and we should still do a lot
more to protect people who live in nursing homest of

(51:07):
all the deaths in the United States from COVID nineteen
or with no COVID nineteen have taken place among residents
in nursing homes and other assisted living facilities that house
zero point six percent of the U S population. Now,
in any normal crisis, the fact that forty five percent
of the death were occurring in zero point six percent
of the population that would be the headline every day.

(51:29):
Every day we'd be seeing on CNN some anchors asking
a politician, what are you doing to protect people in
nursing homes today? That's what we'd be talking about every
hour of every day, but we're not. Why is that?
That's one of the again, the craziest things about this
whole situation. So we should be doing, we should have
done all along a lot more to protect people in
nursing homes, and we still have a ways to go,

(51:50):
uh to do that, to get to a point where
we can really say that people in nursing homes are protected.
Were made a lot of progress in a in a
sense of like to you know, a little bit too late.
We should have gotten there much early, or particularly at
the state level, as we've talked about with Andrew Cuomo.
But but that's an area where we still need to
do more. So that's that would be like And by
the way, that figure is probably low because the data

(52:10):
that you have on New York, the way they classify
nursing home deaths likely drastically undercut the number of people
who actually died who were in nursing homes in New York. Right,
it's probably the case like I mentioned earlier, candidates, it's
probably over half in the United States. Right if you
had the best possible statistical data of all to be

(52:31):
able to put together, that's not a crazy hypothesis, right. No.
And in fact, if if you want to dig into
the data, if any of your listeners want to dig
into the data, they can go to our website f
R E O P P free op dot org and
there's an article They're titled, uh Nursing Homes and Assisted
living Facilities account for COVID nineteen deaths, and we we
put all the data in there. I also have a

(52:52):
Forbes article about it. But the free dot org articles
the WOE that has the most updated information you can
dig through at the state level how your state has
been doing. And by the way, when we first started
reporting on this, we basically compiled all the data from
the state health departments and thirteen states weren't even reporting
the data. This is in like June. Thirteen states weren't
even reporting the data. It wasn't until I basically humiliated

(53:14):
them by writing this article in Forbes that got like
one point two million page views that all of a sudden,
the state departments started to say oh yeah, actually, here's
our you know, nursing home deat. So you know, it
was it was it was just this crazy thing where again,
forty five or half the desk maybe more are coming
in in in nursing homes and assistant living facilities, and
yet some states weren't even reporting the data. They didn't
even know what percentage of the people in their states

(53:37):
that were dying were in nursing homes, and some parts
of the country it's even higher than fift In Minnesota,
it's like it's like Canada, it's and and so that
was one of the things that's like absolutely a thing
that we could have done better than we certainly could
be doing better now. And then the third bucket is
the economic lockdown. So you'll remember when Texas and Florida,

(53:59):
well Florida ever really completely locked down, but Texas did
lockdown in May and then they reopened in June. And
there were all these predictions in in national newspapers and
other media organizations about how Texas, hundreds of thousands people
are gonna die. I was gonna be terrible, it was
gonna be apocalyptic. You know, these these rednecks from Texas
didn't know what they were doing. And if you go
by those predictions of tens or hundreds of thousands of

(54:21):
people dying compared to what actually happened. Yes, there were
there have been people who died of COVID in Texas.
There was a rise in cases and hospitalizations and deaths
in the late summer, but much much less so than
it was predicted, far less though we were never in Texas.
It never turned into New York, and Florida never turned
into New York. These places that reopened in the Sun Belt,

(54:43):
they never turned into New York. And yet there was
this kind of almost rooting for for that failure to happen.
And that's I think what that shows is that there
is a balance to be struck. And there's a lot
of actually academic research on this, and her really gets
talked about that there's diminished returns to a total lockdown. Yes,
it may or may not make sense to close bars.

(55:05):
It certainly probably makes sense to not have large gatherings
of like sporting events or conventions or things like that.
We have a hundred thousand people packed into a state,
and that's probably a thing you want to avoid. But uh,
things like allowing restaurants to open at half capacity, allowing
people to go into a shop with a mask on.
That's not a big deal, right, Let the car wash

(55:26):
it's open up. Why do it? Just because the car
watch is not a quote unquote essential business doesn't mean
that a car wash has to be shut down. You
can drive your car for the car washing. It's basically fine.
So there is a balance to be struck, and Texas
and Florida clearly struck that balance in a way that
the total lockdown states did not. And exhibit A by
the way, on that clay, it's California. Look at California.

(55:46):
California has had the same spike in cases and hospitalizations
and deaths that Texas and Florida did. But California lockdown.
California did all the things that all the people in
the sort of the quote unquote at the pro quote
unquote pro quote science unquote class says we should do,
and yet California still had an outbreak. Why is that right?

(56:08):
So why is it that California right now the situation
is arguably worse. California had a complete breakdown of their
data systems. They don't even know how many people have
COVID or of tested positive because they're testing data center
broke down. So all that to say that, Uh, the
third bucket I'd say in terms of what we need
to do better is we need to identify. We need

(56:29):
to be very objective about what measures have worked and
what measures have not worked in terms of limiting the
spread flattening the curved center. It's pretty clear at this
point that the Texas Florida model strikes the write balance.
And by the way, as you know, Clay, I don't
have to remind you. I probably don't have to remind
your listeners. When we originally locked down in May, the

(56:49):
ar gament was not that we were gonna obliterate COVID nineteen.
It was that we were going to flatten the curve
so that the hospitals weren't overwhelmed. Well, no hospitals are
getting over filmed anywhere. Today we're talking to Ovicroy. You
can follow him on Twitter at a v I K
encourage you to go read all of his work. He
went to m I TEO Medical School. Um, and this

(57:12):
is wins and losses. I'm Clay Travis. Alright, so this, uh,
there's a lot I could still unpack about what you
just said. For your for your ideas about how we
should be responding today. I love that you've got the
three pronged there. How much of what we're doing is
cosmetic theater And what I mean by that is in

(57:32):
New York, if you look at the rates of infection,
it seems like, based on the recent data that the
Governor of Florida has shared, there are many parts of
Florida with similar rates of infection to New York. It
seems like there is a curve, a steep curve, and
then it starts back down. In fact, the rates of
infection in the Northeast, if you look at the rates

(57:53):
per million or whatever the heck it is, it's almost
identical to what we've eventually seen in the South. And
while everybody was panicking on some level, isn't a virus
going to be a virus no matter what we do?
And that even if you shut down for a long time,
eventually people are going to go back outside and the

(58:14):
virus is going to uh to spread again. Is it
that like to me? There's early on I think and look,
I'm I'm far from an expert, but there's only two
ways to end the virus. One is by vaccine, and
I'll ask you about a vaccine in a little bit.
The other is about herd immunity, and it seems to
me like there is a lot of data out there
now which would suggest that the herd immunity requirement is

(58:37):
way lower than we were initially told. Initially, and you'll
know better than me, but it's like, hey, you need
seventy or eighty percent of the population to be exposed
to it. There's no way we can actually do that,
And the reality is maybe it's only ten to But
if you even are willing to discuss that, it's like, oh,
you don't care about somebody's grandma dying? How dare you?

(58:58):
Which seems to circle back again around to your initial point,
which is science is not science. It's like it's got
to have a certain negative bit to it or else
you're not allowed to to share it. So what data
are you seeing about her immunity and what would you
surmise based on that data as we speak in in
mid to late August. Well, before I get to that,

(59:22):
let me talk about the other piece of your question,
which was, well, how how can we get through this crisis?
What are the what are the ways you mentioned two
of them you mentioned mentioned you herd immunity, you mentioned
the vaccine. There's actually a third, which is you could
have a drug that treats the disease, apply the virus
in a way that that he doesn't require racing. For example,
hepatitis C. There's no vaccine for hepatitis C, but in

(59:45):
recent years there have emerged treatments that are effectively cures
for hepatitis C. That still the virus still bounces around
the country, but you won't Your liver will not fail.
You will not need a liver transplant if you take
the drugs. And the same thing would be for HIV
right where we have tents, but there's no vaccine. The
smartest people in the world have been working on an

(01:00:05):
HIV vaccine for forty years. We still don't have an
HIV vaccine, but we do have effective treatments. That means
that it used to be you know, you and I
know because we're of that age in the eighties, if
you had HIV it was a death sentence. It's not
a death sentence. To day, people are living a pretty
uh long lives even if they have HIV, well controlled
by these drugs that are not vaccines. So you'll remember,

(01:00:27):
Clay that when we first locked down in the spring,
that was the argument actually was, well, we're only going
to have to lock down for a couple of weeks
because there are a bunch of biotech companies that are
developing these drugs, uh that are gonna end up curing
the disease, and we're not gonna have to worry about it.
It's only gonna be a couple of weeks. Fifteen days
to slow the spread was one of the phrases, catchphrases
was out there, and I was writing at the time

(01:00:49):
my my original cover story in the Wall Street Journal
and COVID from from April was about this fact that actually,
as somebody who's invested in a lot of biotech companies,
people are totally overestimating a probability of success here. Most
drugs that enter clinical trials fail by far. Like so
the idea that we're just gonna, you know, flip the
switch or snap our fingers and we're gonna have a

(01:01:11):
cure for COVID, it's not gonna necessarily work that way.
It maybe months or even years before we have a
drug like we do now for happetitis or HIV drugs
took years, in decades to develop this idea that we're
gonna wait for a cure in terms of a drug
to to be on the market before we reopen the economy.
We could be waiting years. We could basically destroy the

(01:01:34):
economy permanently if we do that, So that that was
one of my arguments early on, and not just mine,
with my co authors too, which included some people who
are on you know, both sides of the political aisles,
so to speak. We were saying, look, you can't destroy
the economy for that long because businesses will permanently close
and there's no assurance that effective treatments will come along.
So that was back in the spring. That theory has

(01:01:56):
been proven right, right, there is still not any drug
on the market that cures COVID. Obviously, their treatments that
people are more hopeful about than others, but nothing is
incontrovertibly a cure. So then let's talk about the vaccine.
So you hear a lot of hype about vaccines. Um, well,
we're gonna have a vaccine by the end of the year.
Some people say, now, look, we all hope that's true,

(01:02:16):
but it's very important to understand that the world record
for the fastest vaccine ever developed for a novel virus,
it's five years. It took five years to develop a
vaccine for the Boula virus. That was the record up
to now. So we're talking about having a vaccine in
less than a year, which would be five x but

(01:02:38):
the world record for speed the vaccine development. Now, there's
a lot of advances in technology, there are a lot
of people working on this. There's been a lot of
money put to work, so it's possible that all that
ends up working, but we we we can't be assured
of that. And if the idea is that we're going
to keep schools closed and businesses closed and the economy
shut down until we have a vaccine, what if we

(01:03:00):
are waiting two, three, four or five years for vaccine.
We just can't. So we have to have a plan
B in case of vaccine failed. And now that gets
to the third thing that you mentioned, which is the
herd immunity, your population immunity. Is it possible that, like
a forest fire which eventually rages for a while but
then eventually runs out of dry wood to burn, could

(01:03:21):
we end up in a situation where COVID eventually flames out,
and it is possible, and that is that is I
think my personal view is that's what we're seeing in
New York City, that's what we're seeing in Sweden. That
you had high death whole high death tolls early on,
but over time, the people who are susceptible succumbed tragically

(01:03:42):
unfortunately to the virus, and the people who are left
standing are not that susceptible. And that's why that's not
not Andrew Cuomo flexing his biceps, but actually the fact
that the susceptible people in New York are already dead.
And do you buy into that this idea that bay
on the data, you're seeing that the herd immunity threshold

(01:04:03):
by which you start to see substantial protections is maybe
a lot lower than what was initially told by the
quote unquote experts. There's there's good evans so that we
were talking earlier in the show Clay about Gabriella Gomez,
a scientist who has modeled this out and can't get
her research published by scientific journals because it's not alarmist enough.

(01:04:25):
And there's there's been some publications and other medical and
scientific journals to suggested that people don't know right that
there's the The sort of more negative view is that
you need of a population to be infected in order
for her immunity to to take place. Now, for those
who don't know what her immunity is, let me just
maybe pause and explain it. So, what her immunity is

(01:04:48):
is the idea that so many people have been exposed
to the virus that the virus itself can't explode. Like,
for the virus to really explode, infect other people. You know,
you as the affected person, have to wander around and
counter a bunch of other people who are not yet affected.
It spread the virus to those other people. Now, if
two out of three of those people are already immune

(01:05:11):
because they've been infected in the past, then maybe there's
a chance that that third person gets infected, but the
probabilities are lower. The analogy might be trying to throw
a golf ball through a chain link fence. Yeah, that
the whole in the chain link fence fence is big
enough for you to throw the golf ball through it.
But if you ever tried to throw a golf ball
through a chain link fence, there's at least a fift

(01:05:33):
chance or more that you hit one of the links
in the fence and the ball falls to the ground right.
So similarly, here, if a bunch of people are already immune,
the probability or the ability of the virus to really
spread cuts down dramatically. So that's what herd immunity is.
It's basically a virus basically failing to spread because enough

(01:05:53):
people have already been immune. And what's very very interesting,
and this is something that you see a lot in
the background of the scientific literature, but it doesn't get
the hype, is that there may be more herd immunity,
or the threshold you need to get to her immunity
for COVID is a lot lower than what what the
what the maybe the more negative side thinks, And the

(01:06:16):
reason for that is that the common cold is also
a coronavirus, and so it may be that there are
a bunch of other very mild coronaviruses that are out
there that people have gotten over the last winter or
longer that have given them enough immunity. Two stars Kobe
to the virus that causes scovide nineteen, that herd immunity

(01:06:38):
from COVID from actually being infected with stars Scobe two
or the novel coronavirus, that threshold is a lot lower
because there's already enough community to other coronavirus is out
there in the population that that combination means that we're
actually a much closer to her immunity today than than
we otherwise thought. And that would be the most hopeful

(01:06:58):
case that the thing runs out and uh and we
before even there is a vaccine, and a vaccine that
gets widely distributed, the virus has already done, as you
put at, the forest fire has already burned through the
dry wood. And again, that's still a tragedy. I don't
mean to minimize the people who are dying from COVID nineteen.
It's an incredible tragedy what's happened. But it may be

(01:07:20):
that we're closer to the end than we than we
might otherwise believe or be led to believe. What would
you say the likelihood is of another situation like this
arising during our life? You you look at you know,
the the data over history history, but obviously you are

(01:07:42):
at times a skeptic, at times a contrarian. Are you
optimistic that in forty years, let's assume that a large
percentage of our audience is still going to be alive
and they'll be eighty or ninety years old or younger
seventy sixty. Is this something that happens again in our
lives or is this something that only comes up every
hundred years. What is the likelihood that we have another

(01:08:04):
situation like this anytime in the next several generations. You
definitely can't rule it out. I mean, pandemics, particularly influenza
influenza based pandemics, do happen from time to time. They're
not usually as severe or as bad as this one,
but they definitely do happen from time to time. The
one thing that we can hope for is if if
in say twenty fifty or we have another situation like this,

(01:08:29):
that science and technology have advanced to the point where
we don't have to wait a year or five years
to develop a vaccine. We don't have a bureaucracy like
the CDC and the FDA preventing people in February in
Washington State from testing for the novel coronavirus. We can
distribute those tests rapidly. We figured out how to do

(01:08:49):
all that so that basically in your home, you know,
you have your own little kind of lab instrument in
your home, and you can just basically test for all
sorts of things with that without having to actually go
to a doctor or go to a CBS or what
have you. So I think the more we can invest
in that kind of infrastructure, the more we can have
data and real time data reporting from nursing homes and

(01:09:11):
other places where vulnerable populations live, we should be able
to respond to a virus like this better. But you know,
it's always theoretically possible that a virus comes along this
even more virulent, more lethal than COVID nineteen or stars
copy two, especially if you factor in the possibility of
bio warfare. Right, So, I don't think we can ever

(01:09:32):
rule out the possibility that something worse comes along. And
that's all the more reason why we have to be
so objective and so serious about the lessons we learned
from this crisis, Because if the only lesson we learned
is we hate Trump and we throw them out, then
we haven't learned anything. You mentioned testing. There's been a
massive amount of discussion about testing for the coronavirus throughout

(01:09:56):
the last several months. What do we need to know
what should we know about testing its viability? It's important.
What would you say the essence of the takeaway about
testing should be, Well, there's there's a couple of things
I'd say. First, it's important for people understanding because you
hear people say, well, it's a failure of the US
government that there aren't more tests today. That's not actually true.

(01:10:19):
The US is testing more people than any other country
in the world. We're testing a lot of people. The
problem is and I wrote about this free opt dot org.
The original paper that we published it free opt dot org,
our think tank on the pandemic. It's called a New
Strategy for Reopening the Economy. During COVID nineteen, we talked,
we walked through all the science of this and how

(01:10:41):
the tests that we have today they're not perfect. They're
not like the pregnancy tests that people are most used to,
where you just kind of take it to home and
you know whether you're pregnant or not with incredible accuracy.
The COVID tests are not that accurate, and they take
a long time to get you the results back. So
if you have to wait a week to get the
results from a COVID test, even if the test is available,

(01:11:01):
you're not, it's not useful because then what are you
gonna do for that week while you sit around wait
for the test. You might have gotten positive even if
even if the test is negative, you might have gotten
positive in the intervening couple of days. So testing alone
doesn't matter. Uh. And and testing it doesn't matter, it's
not as central at this point as as people think.
What really matters is some of the other things that

(01:11:22):
we've talked about, herd immunity, social distancing, washing your hands,
basic stuff to have hygiene. Uh. But it did matter
early on. If we had enough testing early on, we
could have maybe niff this thing in the bud like
some countries have done up to this point, in particularly
in the Pacific Rim, the New Zealand, the Taiwan. Now,

(01:11:44):
how what would that have looked like? What happened wasn't
There was a great story I think in the Washington
Post about this several months ago where when the when
the stay to Washington, when they first started seeing cases
of COVID nineteen or what they saw was an unexplained
pneumonia and scientists in local academic centers and other labs,
we're trying to figure this out and they actually developed

(01:12:05):
their own kind of homebrew test of the novel coronavirus
because the Chinese actually had published online the genetic sequence
of the stars Kobe to coronavirus, So if you were
a scientist in Washington, you could actually take that genetic
sequence and they use that to develop a test. And
so they actually started doing that, and the FDA and

(01:12:26):
the CDC came in. The bureaucracy came in and said, no,
that's illegal, you can't do that, and basically Squashing said,
not only the CDC is legally allowed to develop the test,
we were basically waiting for the government, the federal government,
to develop the test. And it turned out the CDC
waited a month. They had delays of a month in
developing a test because their lab was contaminated. So that

(01:12:47):
was a really really disastrous uh results, and what we
should have had and what we hopefully will have in
the future as a system in which testing for novel
viruses is not dependent on the government. You have private
sector labs, biotech companies, university scientists, all with the ability
to develop tests and and compare them against each other

(01:13:09):
tests for accuracy, crowdsource that test development rather than depending
on a single lab in Atlanta to do it for you.
So that's a huge lesson that we should have learned earlier.
But in terms of scale of testing today, testing is
not going to be this panacea that everyone thinks it is.
And I'll give you an example of why. You know
you're a lot of people talk about, well, look at
the cases, the cases are rising, but as you pointed

(01:13:30):
out with Florida, right, the cases are rising in Florida,
but not as many people died. Why is that? Well,
A big part of the reason is that a number
of those cases we're in younger people. A big part
of that is that people were getting tested who had
very mild symptoms, and a big part of that was
that you were actually reporting all that data. I'll give
you an example from Europe. In France, they had exactly

(01:13:52):
the same number of cases. If you look at the
chart in terms of how many cases of COVID nineteen
they had in France, it looks almost exactly like the chart,
and neighbor in Germany the same number of cases. But
guess what in France, four times as many people died
of COVID then died in Germany. Why is that? Was
the virus magically four times as lethal in France as

(01:14:12):
it was in Germany. No, it's that the people Germany
was testing were different than the people the French were testing.
You have an example Denmark. In Denmark, they basically only
tested people who are hospitalized for COVID, so they basically
underestimate the number of cases there were. So all that
to say that testing is helpful, but it's not this panasy.

(01:14:33):
We're getting better. The federal government has actually moved mountains
to try to increase the supply of good test The
f D is working over time. They had those initial missteps,
but they're now trying to recover from that initial misstep
and and do a lot better at at rapidly improving
better tests. This test that you probably talked about on
your show, Clay that's alive, a test that the NBA

(01:14:54):
helped pioneer that could be a real improvement on what
we have up to this point. All is to say
testing is good, and it's good that we have more tests,
but testing alone isn't going to solve the problems. What
you really have to do is get her immunity or
a vaccine. Those are the only reliable ways to really
get this virus under control. Fox Sports Radio has the
best sports talk lineup in the nation. Catch all of

(01:15:17):
our shows at Fox Sports Radio dot com and within
the I Heart Radio app search f s R to
listen live. We're talking to O vic Roy. You can
follow him on Twitter at A v I K. I'm
Clay Travis. This is the Wins and Losses Podcast. Couple
of final questions for you, and you've been phenomenal here.
I believe you went to school. You told me off
airs we were starting with Chris Webber, you were a

(01:15:40):
year behind him. You grew up outside of Detroit, where
my wife also grew up. She went to the University
of Michigan. I believe you're a University of Michigan fan.
Is that right? I am. Yeah. It's been a it's
been a tough decade at least on the football side,
but you know, yeah, no doubt. Okay, So when you
see the Big ten and the pack well shut down

(01:16:01):
fall Sports from a obviously a perspective with which you
are looking at this, did they get it right or
did they get it wrong? Well, first of all, if
Chris Webber is is listening, I just want to tell
Chris I'm so happy that you've you've come back to Michigan,
the Fab five as has reunited. Uh and and and

(01:16:22):
those those wounds from so long ago and heal all
the love to you. And I'm so glad that that
all that is is getting better now after all this time.
But in terms of the Big Ten and the pack
of the packs, well stuff. Uh, it's been such an
interesting story to follow, especially over the last week, as
as you've obviously talked about a lot on your show. Uh.

(01:16:43):
Really remarkable. Um. And I think there's a couple of
things to say about it. One the uh, the illogic
of saying you're gonna have tens of thousands of students
come to campus, but it's too dangerous for people to
play football. I mean, how do you think they're actually
gonna get COVID? It's from the other students, right, So

(01:17:05):
like does that make any sense? Um? And obviously there
are some colleges that have given up on having a season.
But I actually take the opposite, uh takeaway from that,
which is COVID nineteen. You know, we talked about this
in some of our work at Prep. COVID nineteen in
the college population is not lethal. Yes, of course there
are very rare cases of serious illness or death. But

(01:17:28):
it's very very rare. And you know, you've you've heard
this talk. And the talk that really reportedly has scared
the chancellors and presidents in the Big ten inpacts well
has been uh, myocarditis inflammation of the heart muscle as
a result of COVID nineteen. There have been a couple
of cases of that, um, again a serious illness that
we should be concerned about. But the studies that have

(01:17:50):
been used to scare the presidents and chancellors are studies
of fifty year olds in other countries. Um. They're not
of actually college aged athletes. UM. And in fact, the
likelihood that a college ash athlete gets inflammatory myocarditis from
COVID nineteen, as far as we know, is extremely low.

(01:18:10):
And that's not to say you shouldn't be concerned about
and you shouldn't try to be careful and you shouldn't
try to protect the athletes. Of course you should, uh.
But there there is again a lot of alarmism here
rather than rigorous scientific examination of the real risk. And
football players in particular play with a lot of risks
every day. You know, they're they're football players who are

(01:18:31):
permanently disabled because of playing football in college. So they're
they're they're aware of risk. And what's been I think
particularly disappointing is that the players were not part of
the conversation. Right. This was being determined by commissioners and
presidents without their input in almost every case. And that's
not to say that maybe if their input had been
in there, the decision wouldn't have been dissimilar. Maybe the parents,

(01:18:53):
there are a lot of there's a maybe there's a
silent majority of parents who are concerned. I don't want
their kids playing, And that's fine. If they don't want
to play, they shouldn't up to But these decisions were
being made behind closed doors with in some cases what
appears to be sketchy scientific evidence. And I'd like to
see a more open discussion where we really do go
through the evidence. Why did the experts get so much wrong?

(01:19:15):
I started talking about this early, using sports as a prism,
talking about the difficulty and I'm sure you've dealt with
this as well. Of knowing both the numerator and the denominator,
in other words, projecting a death rate or an infection rate,
you need to know how many infections there were, and
you need to know, uh, you know, how many of
those people are actually dying and because of the virus

(01:19:38):
as opposed to dying with the virus. And so you
know this this Imperial College forecast out of out of England,
which forecast over two million people would die in the
United States. It doesn't I probably surprise you that the
worst case scenario forecast got way more attention in the media.
But the so called experts, the epidemiologists, the virologists, their

(01:20:00):
forecast were completely for the most part, worthless early on
when decisions were being made here, Why do you think
they got so much wrong? Wow? Let's uh, we could
spend an hour on that tap at topic. Before I
get to that, let me say one thing else about
the Big ten. A school to keep an eye on
is Perdue. Perdue is run by Mitch Daniels, who is

(01:20:20):
a really smart guy who used to be the governor
of Indiana, was the budget chief in the White House
under George W. Bush, really really smart, talented, thoughtful guy
about the stuff testified before the Senate about why he
was going to reopen Perdue. And they've had they've done
a lot of really interesting and sophisticated things to try
to make a fall semester at Purdue work. And let

(01:20:42):
me go through some of them. We don't know if
it's gonna work, but he's certainly doing some really interesting
things that are worth keeping an eye. And first he
required that everybody test negative for COVID before coming to campus,
and they sponsored the testing. They tested over thirty produced
students and they had a positivity rate of less than
one percent of that student body. UM, and they have

(01:21:03):
some other plans in place, so like let's say, UH,
you do get sick, or you do you have had COVID,
so you have immunity. Let's put those Let's rum those
people who are now immune with the people who might
have preexisting conditions or other vulnerabilities so that they can
be even more safe in those particular UH facilities. So
there are a lot of things that Perdue was doing

(01:21:23):
that I think are worth watching to see if you
compare and contrast that to u n C, which famously
shut down. UNC actually only tested a thousand of their
students prior to opening up the campus. And of course,
and they had a bunch of outbreaks that they breaked
out about and decided to shut everything down again, which
was just stupid on every level. They should have had
a better plan because people are going to test positive,
and having having people test positive should not be a

(01:21:44):
reason to shut down the campus if you have a
plan in place to handle those positive cases. So those
are two schools I think that are like examples of
of a good of a good way to handle the
thoughtful way to handle it, and maybe a thought less
way to handle it. Um and then so let me
then just just go onto your your question about the
math modeling. I mean, it's just been This is another
part of the story that does not get enough attention

(01:22:06):
today that will over time. And part of it, as
you said, or maybe the majority of it, as you said,
is that journalists aren't really good at math. But people
really need to dig into what these models are. There's
a tendency for the average person understandably say, well, that
guy's got a PhD in statistics, he's got a PhD
in maths, he's smarter than me. I should just defer
to him, he's the expert. But if you actually dig

(01:22:27):
into what these models are, they're incredibly simplistic, or they're
based on incredibly flimsy uh data points about what's going
to happen. It would be like saying, to use the
college football analogy again, who's going to be the national
champion of college football in right? Like you can make
some guesses about that, but we all know the college

(01:22:49):
football landscape shifts and and and flows over over a
decade or two different powers emerge, So you're not gonna
necessarily have any idea. And yet these models are being
put forward with this level of conviction like, well, if
you don't agree with me, you're against science. And of
course that model from the Imperial College London that predicted
two million dust turned out to be completely wrong. And

(01:23:12):
a part of what's been happening here is that what
people do, what a lot of the dirty secret of
a lot of models, Clay, is what they'll do is
they'll take a set of data, like they'll take the
curve of how COVID evolved in wuha, how many cases
on this day versus that day versus that day, and
then they'll just find a mathematical equation that if you
chart that mathematical equation looks like that curve and then say, okay,

(01:23:35):
we're gonna use that equation to predict what happens in
the future, Well that that doesn't make any sense. Just
you know, the thing we were talking about stocks earlier,
what do they say in all the stock brokerage commercially,
they say past performance is not a guarantee of future results.
And the same is true with viruses. Right, just because
the curve has looked this way in the past doesn't
mean it's going to look a certain way in the future.

(01:23:57):
And yet this very simplistic way of modeling where you say, well,
you know, there's this equation and it kind of looks
on a graph like the way COVID nineteen evolved in Wuhan.
So I'm gonna use that as my model predict theres
two million dats. That's not science. That's basically fancy guesswork.
And yet that fancy guesswork dramatically affected policy in Western economies.

(01:24:22):
Not only that, arguably that fancy guests work literally caused
a lot more deaths because that fancy guest work was
the quote unquote expert forecast, which I think Andrew Cuomo,
if we gave him truth serum, would say he believed,
which is why he sent those patients back into nursing
homes right like, he believed they were going to need

(01:24:42):
a hundred and forty thousand beds because of those forecasts.
Actually they only needed nineteen thousand. But if he had
known they were only gonna need nineteen thousand, it's likely
that tens of thousands of people might well be alive
in this country today. They actually followed those forecast advice
would were wildly off and as a result ended up

(01:25:03):
with arguably way more people dead than would have died
if they had not. And you know another example, we
were talking earlier on the show Clay about about how
all the public health experts they were educated and trained
on influenza pandemics, and so they basically went back to
that they were fighting the last war they were. They
were drawing from those influenza pandemic playbooks to talk about

(01:25:27):
COVID nineteen or how to deal with COVID nineteen. A
great example was people don't talk about anymore, but remember
how we were all terrified that we were going to
run out of ventilators. They're always talking about in the spring,
Oh gosh, we don't have enough ventilators. What are we
gonna do? Well, it turned out in New York City
of the people who were put on ventilators died because
the ventilators actually made the disease worse, because it wasn't

(01:25:49):
fundamentally a respiratory disease. It was fundamentally and inflammatory disease.
And that's an example of where, uh, to your point,
the the the expert opinion about well, this is just
like influenza. We got to do what we would do
in influenza. Pandemics turned out to be a fatal, fatal decision.
We're talking to Vic Roy. I'm Clay Travis. This is
wins and losses. What letter grade would you give the

(01:26:13):
United States media for the way that they have covered
this pandemic? I mean, I wish I could give him
a G because it's not even enough, right because, like
you know, you could graduate from high school with an
F in a in a particular class. But uh, what's

(01:26:34):
happened here, the the the distortion and the misrepresentation of
what's been going on in Again, I don't think that
distortion has always been intentional. I think some people, uh,
you know, are are getting a certain weird pleasure out
of out of making things look better or worse than
they are. But I think a lot of people are
just genuinely scared, and they're writing articles that reflect how

(01:26:54):
scared they are. Uh. But that that that inability to
put numbers in contains up. There was a Indiana Junior
high that shut down because one person had COVID. I mean,
you know, you'll see these numbers, aren't well. Texas today
had five cases. What does that mean? How many people
live in Texas? How many those people are getting sick,
how many those people are dying? You never see that information.

(01:27:17):
There's just been so much like that. I just you know,
it's it's really been. It's been very, very bad. And
the only saving grace at the end of the day
has been, in a sense, the existence of the Internet.
Because for all the things they're terrible about social media
we can complain about or whatever, it has allowed people
like you and and people who are these epidemiologists who

(01:27:39):
are have this sort of con contrary and opinion, they're
able to express themselves. They they're able to put research
out there, They're able to put analyzes out there that
people who want to look at the evidence can examine.
And I think that has enabled, in a sense, a
kind of an end around around that more traditional gatekeeping process.
It has been perfect. There have been websites, uh, and

(01:27:59):
big internet companies have tried to say, well, if you
disagree with the World Health Organization, we're gonna shut down
your account, Like the World Health Organization is kind of
lot wrong. And I think some of the tech companies
have realized that that was a mistake. But how that's
an important thing to to to keep an eye and
make sure that there's always channels for alternative views because
I don't think that problem is gonna get better. We're

(01:28:19):
not going to magically wake up with a different news
media ecosystem which everyone's got a degree in statistics. Yeah,
and this goes to my biggest issue. And I see
so many connections across so many different fabrics of American
society and world society today, and the one that really
kind of connects it all to me is science is
about combat, and we don't think enough about the combat

(01:28:42):
of ideas. Right, you come up with a theory or
a hypothesis, however you want to classify it, and the
scientific method requires an adversarial response to that, because only
by challenging hypotheses or theories can we determine what the
truth really is what I see far too often in

(01:29:03):
our society today. And this is why I was saying
earlier I'm a first amendmad absolutist. Is it seems like
we are creating arenas where only acceptable opinions are allowed
to go head to head with each other. And I
think we've seen that with the coronavirus, where science all
of a sudden has become so political that if you

(01:29:24):
have the belief, hey, we need to figure out a
way to live with the coronavirus, we need to figure
out a way to live with COVID, maybe all of
these dire forecasts aren't true. You were considered to be
contributing to and almost an accessory to death. That is
scary to me, and I bet it's scary to you,
as somebody who makes a living in many ways actually

(01:29:47):
diving into the data. If you can't share that data
and have a legitimate debate with someone without being accused
of facilitating death in the country or even God forbid,
rooting for it, that's an inherent flaw our debate in
our national discourse. You know, I think if there's if
that's probably what you're just describing, Clay is the single

(01:30:10):
most important thing that our country and the world, but
particularly our country has to get better at. You we
have to have an ecosystem, a way of debating all ideas,
but particularly scientific ideas, but all of these economic policy politics,
you know, racial issues we have. We have to have
a way of talking about all these issues in in

(01:30:30):
a in a way that where we're competing theories, competing
hypotheses can be addressed and considered and not suppressed. That's
incredibly important. We've always got to make sure that that
that alternative, that contrarian approached ideas, uh, is there. And
by the way, like we were talking earlier about my
by my time as an investor, that's the essence of

(01:30:51):
every great successful investor. Is they big when everybody else
is zagging? Right? You think about Billy Bean, same thing, right,
the most success full Uh. You know it's true. And
in football especially right, you think about the people who
have come up with creative ways of new new offensive
or defensive schemes. Sometimes those offensive and defensive schemes are
actually were invented a hundred ten years ago, but they've

(01:31:13):
just fallen out of a style, so people don't adjust
to them. Right, So there's there's a real need for
contrarian thinking at all times, and we've got to find
a way to ensure the people who disagree with the
majority point of view on a scientific issue are not
characterized as anti science merely for disagreeing with one take

(01:31:34):
on the evidence. Because you can have a very evidence
driven view that's different from what another person who's looking
to the same evidence concludes. And unless we have a
system in which that's possible and that's allowed, we're not
going to be truly scientific. We're not going to be
pro science, and we're not going to actually do right
by our country. Final question for you and I, and

(01:31:56):
this is obviously predictive in nature, but I'm curious what
you think to a there is a national consensus that
the Vietnam War was mismanaged and that we shouldn't have
been involved in Vietnam. I think there's also a consensus that,
maybe among most people, that the Iraq War did not
make make sense when you consider the cost in lives,

(01:32:16):
in economics, all those different things, and that sometimes takes
and you know this as well as I do. The
retrospective arc of history, we have to go back in
twenty five years from now, thirty years, fifteen years, whatever
the math, maybe we have a clearer vision of what
the full story was right, and that's ultimately what ends

(01:32:37):
up being written. Will people look back on our response
to the coronavirus in ten years and right honest portrayals
of what we got right and what we got wrong?
Or is the media so left wing convinced, Because what's
fascinating is the left wingers ended up being right in

(01:32:57):
many ways about the Iraq War and about Vietnam, right
without getting into the particulars of those wars, but that
made them willing to acknowledge for the public, Hey, we
got this wrong. I think the left wing has gotten
the coronavirus completely wrong, right. I think that has been
a failure, more so by left wing media than anyone.

(01:33:18):
Will we get an honest appraisal of the coronavirus and
our response to it in the next ten or twenty
years or is it so intensely political that no one
is ever going to admit what they got wrong and
what we failed from as a country. Well, Clay, it's
so interesting that you bring up the Vietnam War because

(01:33:40):
there was a famous book written about the Vietnam War
by David Halberstam called the Best and the Brightest Yes,
And it's all about how the Vietnam War was prosecuted
by the greatest science oriented experts of the country at
that time. They were all Harvard and Yale graduates. They
all had impressive resumes, lots of degrees. The main general

(01:34:05):
or the Secretary of Defense, Robert McNamara, was one of
these guys. He he pioneered, you know, analytic thinking and
the way the Forward Motor Company worked in the middle
of the twentieth century. And that's what you know. He
tried to basically apply those lessons of you know, quantitation
and metrics for everything to the to the way the
army operated in Vietnam. It turned out to be a

(01:34:25):
complete catastrophic failure. Uh. And and so my hope is that, uh,
we have some similar examination of what happened in this crisis,
and we draw the lessons from this crisis that David
Halbert Stam was able to pull out of the Vietnam
War when he wrote that book, Because that is an
incredibly important part. We have to have a lot more

(01:34:47):
humility around what we call expertise, and we have to
have a genuinely, genuinely sincere uh openness, particularly in this
era of big data. So much information is online, so
many people can take a look at that data and
come up with it. We've got to have a much
more open source, crowdsourced approach to thinking about evidence, and

(01:35:07):
if we do that, we'll do a much better job
for vulnerable populations in this country and many others. Do
you want to write a book on this to help
tell the story after this is all done, because at
some point we're going to go back to normalcy, But
if we don't learn from the errors that we have made.
And I think there's a strong argument that the worst
decision in my life was the war in Iraq, right,

(01:35:28):
I think you can make that argument, or the twenty
one century as Vietnam was before you and I. But
I think for older people who are listening to that,
that's probably the biggest failure in American policy. I feel
like the coronavirus has is in that level. But I
wonder if it's ever going to be acknowledged in the
same way. Well, you know, uh, I thought about writing

(01:35:50):
a book about it, and and maybe I will. I
got to find a spare time because they're no kidding
our think tank free up dot org. But I but
maybe I will and and and you're right to that
debate needs to happen, and I think it will. I
hope it will, because this virus, not just the desk,
but the lockdowns, the school closures. It is affect that
all of us, right, we've all been affected by it.

(01:36:12):
It's too important not to have that examination take place.
And so hopefully people like you and me and so
many others who have been who've been carrying that flag,
will well to do their part to make sure that
we have that conversation. You've done almost two hours with
us here. I think we could go on and on.
It's been fascinating. I encourage everybody listening to go follow
avic avic Ovic man. After all that time I was

(01:36:35):
trying to get your yeah, I know it would be
so much easier if they started to ring with an
oh instead of an a uh at a v I
K go follow him. He does incredible work. If you've
enjoyed this conversation, thank him for spending the time with us.
I'm Clay Travis. Thank you again for coming on. And
this has been wins and losses.

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