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Speaker 1: This is Masters in Business with Very Results on Bluebird Radio.

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Speaker 1: This week on the podcast, I have an extra special guest,

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Speaker 1: and strap yourself in because this is going to be fascinating.

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Speaker 1: Charity Dean is the protagonist of Michael Lewis's new book,

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Speaker 1: The Premonition. She's also I'm going to give her a

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Speaker 1: promotion the head of public health of the California Public

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Speaker 1: Health Department as well as the co founder of the

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Speaker 1: Public Health Company. And if you are at all interested

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Speaker 1: in how infectious disease spreads, what we're doing right and

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Speaker 1: wrong with vaccinations and containing COVID, and what the risks

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Speaker 1: are that we're looking at from our current circumstances, including

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Speaker 1: the very dangerous delta variant, you're going to find this

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Speaker 1: to be an absolutely fascinating conversation. So, with no further ado,

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Speaker 1: my conversation with Charity team, this is Mesters in Business

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Speaker 1: with Very Results on Bloomberg Radio. My special guest this

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Speaker 1: week is Dr Charity Dean. She was the deputy public

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Speaker 1: Health officer for the Santa Barbara County Public Health Department

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Speaker 1: before becoming assistant director at the California Department of Public Health.

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Speaker 1: In Michael Lewis called her one of the few people

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Speaker 1: who saw the real danger of the COVID virus before

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Speaker 1: the rest of the country did, and she was featured

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Speaker 1: as one of the main characters in his book The Premonition,

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Speaker 1: a Pandemic Story. She is co founder of the public

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Speaker 1: health company Charity Dean. Welcome to Bloomberg. It's a pleasure

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Speaker 1: to be with you. I'm really looking forward to this conversation.

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Speaker 1: I loved the book. I mean, I love everything Michael

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Speaker 1: Lewis writes, but the book was especially interesting. But I

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Speaker 1: want to roll back and start um with your back round.

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Speaker 1: Tell us what a public health officer is and how

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Speaker 1: did you become one. Well, a public health officer is

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Speaker 1: the local official for a county or a local jurisdiction

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Speaker 1: who is responsible for ensuring that the public is safe

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Speaker 1: against a host of different types of threats. MY favorite threat,

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Speaker 1: of course, is communicable disease control. But there's a number

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Speaker 1: of things they do. They oversee restaurant inspections or water safety.

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Speaker 1: I think the thing they're best known for, though, really

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Speaker 1: is disease threats. Because in the US, public health officers

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Speaker 1: sprung up across the US and responds to local needs.

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Speaker 1: You know, if we rewind a hundred years that might

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Speaker 1: have been tuberculosis or smallpox or cholera, polio, etcetera. So

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Speaker 1: today the local public health officers are largely unknown to

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Speaker 1: the general public, but their role is really important. And

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Speaker 1: in the book The Premonition, we learned that the local

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Speaker 1: public health officers have a lot of authority. They can

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Speaker 1: shut down doctors if they're a source of um malpractice

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Speaker 1: or a source of infection or in fact, there's some

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Speaker 1: really fascinating stories. People kind of push back and say,

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Speaker 1: you can't do that, and the answer seems to be

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Speaker 1: watch me well. I learned that on the fly when

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Speaker 1: I became the health officer for Santa Barbara County. The

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Speaker 1: thing that intrigued me the most is called layer jurisdictional authority,

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Speaker 1: and what that means is the police powers to enforce

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Speaker 1: communicable disease control in California set with the local health officer.

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Speaker 1: In some states they sit with the state health officer,

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Speaker 1: but in California it's with the local county public health officer,

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Speaker 1: and the law actually says that they can take whatever

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Speaker 1: measures are necessary to control the spread of disease. And

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Speaker 1: that's an incredible amount of police power. Authority, and it's

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Speaker 1: a huge responsibility. So really it's the job of the

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Speaker 1: local public health officer across the United States to protect

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Speaker 1: national security, health security, and now after COVID, we know

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Speaker 1: even the outcome of the local economy is impacted by

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Speaker 1: the decisions they make. Quite quite interesting. What was your

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Speaker 1: early experiences with infectious diseases? How did you gravitate in

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Speaker 1: that direction? You know, I was always interested in outbreaks,

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Speaker 1: even as a child. I was interested in pandemics um

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Speaker 1: as a child. As a child, I was interested in

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Speaker 1: the really awful gnarly hemorrhagic viruses that we see in

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Speaker 1: in Africa and interested in how they spread silently. And

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Speaker 1: I love bubonic plague. And I read about the Spanish

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Speaker 1: flu of nineteen eighteen, and cholera outbreaks, and just a

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Speaker 1: host of different diseases that have swept through society. So

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Speaker 1: when I went to college at age seventeen, I was

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Speaker 1: premed and I majored in microbiology, because that's where you

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Speaker 1: learn about all those horrific diseases. I don't think I've

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Speaker 1: ever heard the sentence quote I love bubonic plague unquote

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Speaker 1: ever ever stated before on at least not on this

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Speaker 1: radio station. Well, it's fascinating, right, I mean, public health

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Speaker 1: disease control has shaped societies, Its shaped the world, and

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Speaker 1: if you look at you know what's impacted societies and economies,

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Speaker 1: it's it's been disease. And what's what's the biggest intervention

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Speaker 1: public health has had has been vaccines, um and today

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Speaker 1: people tend to think it's something that is uh the

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Speaker 1: underbelly of society that they don't have to worry about.

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Speaker 1: Government's got that if that risk ever arises, government will

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Speaker 1: come and save us. But as local health officer, I

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Speaker 1: figured out really quickly, Uh, no one's coming to save you.

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Speaker 1: And yet you have this enormous responsibility. And if you're

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Speaker 1: really good at your job, nobody knows because that means

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Speaker 1: you've prevented it. You've stopped the outbreak. Right, they find

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Speaker 1: out when you mess up, and giant thing of merca

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Speaker 1: spreads all over the county. So how did you work

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Speaker 1: your way from Santa Barbara to State of California. Well,

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Speaker 1: I was the local health officer in Santa Barbara over

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Speaker 1: a number of different outbreaks disasters. I was the health

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Speaker 1: officer there during the Thomas Fire, which at the time

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Speaker 1: was the largest fire in the history of California. It's

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Speaker 1: a strange time of year. It was in December and

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Speaker 1: it was followed by a really powerful rainstorm that led

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Speaker 1: to the Mona Cito mud slides. And as I was

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Speaker 1: managing a number of different health risks around the mud slides,

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Speaker 1: not just communicable disease, but a host of other things,

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Speaker 1: I was in close, close communication with a state health officer,

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Speaker 1: Karen Smith, who was my person to call. You know,

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Speaker 1: when you're a local health officer and you need backup,

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Speaker 1: you need a thought partner, you call the state. So

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Speaker 1: Karen and I spent a lot of time on the phone,

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Speaker 1: and shortly after that, she called me one day and said,

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Speaker 1: what would you think about moving up to Sacramento to

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Speaker 1: be my number two? And I said yes, and uh,

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Speaker 1: that's how the adventure started. And I had always I

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Speaker 1: thought that that's where I wanted to end up, was

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Speaker 1: being state health officer, because I thought I could have,

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Speaker 1: you know, an incredible amount of impact there at that level.

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Speaker 1: But California has forty million people, and California is really

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Speaker 1: like its own nation. It's so diverse. So it made

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Speaker 1: me nervous moving from making risk based differential diagnoses on

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Speaker 1: behalf of four people at the county to doing that

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Speaker 1: at the state on behalf of forty million people. So

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Speaker 1: it's a different comfort level, right, stakes are higher, stakes

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Speaker 1: are higher, and mistakes are are more costly. And but

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Speaker 1: the same problem. If you succeed, nobody knows. It's always

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Speaker 1: the challenge of public health because we succeed and then

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Speaker 1: it's silent and you can't see that succeed in the

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Speaker 1: data and statistics if you're not tracking outcomes. And traditionally

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Speaker 1: public health disease control does not have a good way

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Speaker 1: to show return on investment other than nothing bad has happened.

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Speaker 1: Money must be well spent. But that's it's hard to

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Speaker 1: prove a negative, isn't isn't it? It is hard. We

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Speaker 1: can get to that later because I'll tell you why

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Speaker 1: I'm so passionate about the private sector need for this

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Speaker 1: and the mandate to show return on investment. So before

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Speaker 1: we get to the private sector, and I have lots

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Speaker 1: of questions for you about that. You were also the

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Speaker 1: co chair of the California Testing Task Force for COVID

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Speaker 1: nineteen and you ramp that up to over a hundred

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Speaker 1: thousand tests a day in a very short period of time.

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Speaker 1: Tell us about that. That sounds like a giant logistical undertaking.

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Speaker 1: It was. It was the greatest experience in rapid project

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Speaker 1: management I've ever had. And I would be remiss if

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Speaker 1: I didn't mention my amazing co lead, which is Paul Markovitch.

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Speaker 1: He's the CEO and president of Lucille, California. And what

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Speaker 1: happened is there was no testing in California or anywhere else.

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Speaker 1: The state was only able to do about two thousand

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Speaker 1: PCR today. And the backstory, as you know, is the

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Speaker 1: CDC and f d A had said nobody could test.

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Speaker 1: Wait for the CDCs test, so we waited. They rolled

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Speaker 1: it out, it didn't work. They said, wait, we'll fix it,

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Speaker 1: So we waited. They rolled it out again, it didn't work,

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Speaker 1: and so the United States missed that critical two month

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Speaker 1: window of containment. Without testing, there's no way you can

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Speaker 1: contain a threat because you can't find it. So by

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Speaker 1: the time they opened the floodgates that any microbiology lab

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Speaker 1: could test, the problem was that the labs weren't doing it,

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Speaker 1: and we had supply chain issues where everyone needed the

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Speaker 1: same testing supplies, and so we were asked, Paul and

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Speaker 1: I were asked to found and stand up the Testing

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Speaker 1: Task Force to fix testing for California and tell us

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Speaker 1: what you did, because California, at least in the beginning

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Speaker 1: of the crisis, seemed to have done a much better

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Speaker 1: job than a lot of other states, especially when you

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Speaker 1: consider the outbreak was in Washington, not that far away

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Speaker 1: from Cali. For it from Seattle, Washington. Yeah, well, I

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Speaker 1: would say the outbreaks were invisible. Communities in California were

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Speaker 1: burning up with COVID. It's just math and microbiology. I

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Speaker 1: was whiteboarding it out, you know, at home on my

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Speaker 1: white board. I knew exactly how many cases I thought

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Speaker 1: there were. The problem is we couldn't detect community spread

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Speaker 1: without testing, and so Paul and I put together a plan. Essentially,

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Speaker 1: I'd break it down in terms of supply and demand. Uh,

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Speaker 1: we were doing two thousand tests today. Our objective was

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Speaker 1: to get to a hundred thousand tests today. We ended

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Speaker 1: up blowing through that. We got to about a hundred

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Speaker 1: and forty thousand test today, much sooner than we thought

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Speaker 1: we would. And so the supply part is you need

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Speaker 1: to actually have the testing, you need to have testing

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Speaker 1: locations throughout the state. And the demand part is you

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Speaker 1: need people to show up and get tested, and so

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Speaker 1: we broke it down into project management. We had task

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Speaker 1: forces and work streams, and we had daily stand up

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Speaker 1: meetings twice a day. Was eighteen hours a day sprint,

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Speaker 1: you know, for about twelve weeks, and the way that

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Speaker 1: we managed it was just getting really tactical and operational.

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Speaker 1: Um At one point, I rewrote California's guidance for who

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Speaker 1: could be tested on a Friday night and we published

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Speaker 1: it on the website on a Sunday night, and it

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Speaker 1: was far more aggressive than the CDC's guidance. But we

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Speaker 1: needed people to show up to get tested, and the

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Speaker 1: CDC guidance was so restrictive nobody could get tested. What

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Speaker 1: was the CDC guidance? Because you would think, if you're

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Speaker 1: trying to stop an epidemic that could spread to anybody

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Speaker 1: with a respiratory system, you would want to say, that's

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Speaker 1: our our guideline. If you breathe there, you're eligible for testing.

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Speaker 1: That's that's right, That's what it should have been. The

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Speaker 1: CDC's guidance was very restrictive at that time. You had

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Speaker 1: to have symptoms, you had to be in certain risk

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Speaker 1: categories exactly and so so you know, from a practical

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Speaker 1: public health standpoint, the CDC's guidance was literally counter productive

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Speaker 1: to public health. And so looking at what we need

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Speaker 1: to do, we have to test essential workers. Obviously they're

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Speaker 1: on the front lines, and so putting it in buckets

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Speaker 1: that made more practical sense. Yeah, prioritizing them is really

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Speaker 1: what drove people into the testing centers. And so in

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Speaker 1: in parallel to that, we set up over the course

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Speaker 1: of two weeks, we stood up eighty testing centers across

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Speaker 1: the state of California, which is a big state, and

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Speaker 1: then of course revised the guidance for folks to get tested,

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Speaker 1: you know, started driving in people to those locations, and

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Speaker 1: so that's how we scaled it up quickly. Yeah, I

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Speaker 1: was kind of shocked in the early days of testing

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Speaker 1: that if you're an NBA player you can get tested,

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Speaker 1: but if you were a grocery worker deliveries into people's houses,

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Speaker 1: that's right, if you were literally going from place to

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Speaker 1: place as a supercarrier, you couldn't get tested. It made

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Speaker 1: no sense early days, that's right. It was bonkers, and

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Speaker 1: that's why the United States failed at containment. You know,

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Speaker 1: once you once you lose your chance to contain the threat.

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Speaker 1: Then you get to choose between something horrible and something

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Speaker 1: even more horrible, and that choyce of you know, massive

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Speaker 1: outbreaks everywhere and bad health outcomes and the economy. I

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Speaker 1: believe it's a false choice. If you put all your

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Speaker 1: energy at containing the threat in the beginning, and without

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Speaker 1: testing and without an operational infrastructure to contain COVID, the

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Speaker 1: US had no shot. There was no effort to contain

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Speaker 1: the pathogen in the beginning, which is why we're in

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Speaker 1: the circumstance we are. Now, let's talk a little bit

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Speaker 1: about where we are today. You've seen the best and

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Speaker 1: worst parts of the nation's medical system. What's your takeaway

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Speaker 1: about how well we did, how well should we have done?

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Speaker 1: And where did we go wrong? Sure, I would start

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Speaker 1: off by saying the COVID nineteen containment effort did not happen.

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Speaker 1: So that was a massive failure. But it didn't happen

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Speaker 1: because the United States of America does not have a

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Speaker 1: system's capability to contain a fast moving, novel pathogen that

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Speaker 1: emerges simultaneously at multiple locations in the US. Isn't that

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Speaker 1: in the modern world? How? I mean, we've seen a

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Speaker 1: Bola in Africa where it's restricted, but there's no NASS

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Speaker 1: transit aircraft. People hopping on a plane in one continent

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Speaker 1: and eight hours later on a different continent. That's a

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Speaker 1: very different world and economy. But in a modern interconnected economy,

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Speaker 1: isn't that how things are gonna pop up? There's going

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Speaker 1: to be the first super spreader can seede a million

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Speaker 1: people pretty quickly. Yeah, you're exactly right. You know, my

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Speaker 1: master's degree is in tropical medicine, which really is an

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Speaker 1: antiquated term. The diseases that we formerly thought were tropical, now,

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Speaker 1: thanks to globalization and and to some degree global warming,

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Speaker 1: can move much faster. And so what we're seeing is

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Speaker 1: that the pandemic pathogen spread has sped up, the risk

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Speaker 1: is higher, and those events are happen more and more frequently.

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Speaker 1: All right, So now we know we didn't do a

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Speaker 1: great job with containment. We did no job with containment. Um,

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Speaker 1: what about creating the vaccines? We seem to have done

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Speaker 1: a pretty good job with that. We did. The vaccines

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Speaker 1: were a shining success. You know, I think what Operation

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Speaker 1: Warp speed accomplished and full disclosure. Um, those guys are

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Speaker 1: my friends, Uh, they're Wolverines. And so you know from

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Speaker 1: the book from the Bush administration, the group who essentially

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Speaker 1: created the pandemic response, someone else in the White House

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Speaker 1: named them the Wolverines. I don't remember why. I think

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Speaker 1: there's some It was after a silly nineteen eighties movie

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Speaker 1: with Patrick Swayze called Red Dawn. Yeah, yeah, so I

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Speaker 1: think it was Richard Hatchett that nicknamed them the Wolverines

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Speaker 1: after this Red Dawn movie, and that was the name

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Speaker 1: of our email chain. But listen, even if they're not

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Speaker 1: your friends, even the most anti Trump hater in the

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Speaker 1: world has to admit that was the most successful thing

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Speaker 1: of the entire administration of vaccine was not only was

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Speaker 1: it created, it was safe, it was effective, and they

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Speaker 1: made sure that there would be sufficient doses at least

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Speaker 1: three quarters of the way. Make sure they were a

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Speaker 1: ton of doses in the you know, early days of

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Speaker 1: the vaccination process. That's right, Operation warp speed. To develop

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Speaker 1: a vaccine as fast as they did is a shining

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Speaker 1: success and there are many lessons learned from that where

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Speaker 1: we can get even better and faster. I think the

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Speaker 1: other success is the private sector. What we saw during

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Speaker 1: COVID and what I certainly saw a leading California in

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Speaker 1: the testing task force is the private sector ran to

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Speaker 1: the fight. They said, our resources are at your fingertips.

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Speaker 1: How can we help you. You know, you look at

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Speaker 1: different companies, whether it's you know, the pharmaceutical companies that

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Speaker 1: developed the vaccine or the testing companies that quickly developed

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Speaker 1: rapid tests. The private sector played an incredible role. I

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Speaker 1: think what's frustrating to me, you know, when I was

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Speaker 1: in government was that there was really a struggle to

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Speaker 1: leverage the private sector in the response. Meaning up to

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Speaker 1: this point, the response for public health was seen as

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Speaker 1: government's role alone. That didn't work. It has to be

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Speaker 1: a whole of society response. We have to have a

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Speaker 1: place for individual citizen volunteers, for the private sectors that

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Speaker 1: the whole society responds together instead of government trying to

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Speaker 1: peace mill it on their own. So let's stick with

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Speaker 1: the vaccinations. The numbers we're going up enormously in different

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Speaker 1: parts of the country, and then everything seems to have plateaued.

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Speaker 1: We we were going three million a day, two million

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Speaker 1: a day. We seem to be getting close to national

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Speaker 1: herd immunity. But when you break it down on a

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Speaker 1: state and county basis there are that whole supercluster between

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Speaker 1: Georgia and Texas. Is you know, vaccination rates you look

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Speaker 1: at the Northeast, you look at parts of the West,

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Speaker 1: and and the industrial Midwest are running well over fifty

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Speaker 1: percent in some places. Um, how do you evaluate the

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Speaker 1: state of our our vaccinated nation or not? You know,

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Speaker 1: it's a shame that vaccinations have become so politicized because

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Speaker 1: it's really just science. It's microbiology and math. The equation

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Speaker 1: for her immunity is one minus one over are not,

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Speaker 1: and that are not is how many people will one

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Speaker 1: contagious person infect? So there are not of the original

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Speaker 1: covid virus was somewhere between two point five and three

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Speaker 1: that are not. Of the delta variant is somewhere between

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Speaker 1: five and eight. That means one contagious person infects five

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Speaker 1: or eight other people. So if the equation for her

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Speaker 1: immunity is one minus one of her are not, what

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Speaker 1: that means exponential? That's right. It means we actually now

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Speaker 1: need a higher percentage of people who are immune in

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Speaker 1: order to stop the spread of the delta variant. So

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Speaker 1: how big a threat are these reluctant vaxers or anti

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Speaker 1: vaxers in that you know, Texas to Georgia, it's about

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Speaker 1: eights to parts of eight states. Um, that really seemed

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Speaker 1: to be when you look at a map of the country,

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Speaker 1: that's the reddest both politically ends in terms of the

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Speaker 1: lowest vaccination rate. Not a coincidence, but will hold that

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Speaker 1: argument aside till later. How dangerous are these groups of

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Speaker 1: unvaccinated people to themselves, to their friends and family, and

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Speaker 1: to the rest of the country. Well, I think you know.

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Speaker 1: And I get asked this question a lot around vaccine hesitancy,

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Speaker 1: and it's one of the oldest issues in public health,

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Speaker 1: and actually because back to Jacobson v. Massachusetts in nineteen

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Speaker 1: o five, the right of the individual to decline vaccination

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Speaker 1: versus the protection of the herd, the good of the whole.

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Speaker 1: And we've dealt with this a lot in California, and

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Speaker 1: I really understand vaccine hesitancy. And what I've told my

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Speaker 1: friends and family who are hesitant to get the vaccine

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Speaker 1: is stay down to my house. No, you don't just

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Speaker 1: get vaccinated to protect yourself. You get vaccinated for the

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Speaker 1: people that you love and the people that they love,

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Speaker 1: and the people that they love. Because the situation. The

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Speaker 1: reality is this, people have two choices. They can get

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Speaker 1: vaccinated or they can get COVID with adulta variant that

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Speaker 1: has an are not a five to eight. Those are

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Speaker 1: their two choices. You get vaccinated and protected, or you

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Speaker 1: are going to get COVID because this is spreading so rapidly.

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Speaker 1: M that's quite horrifying. So what do you do with

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Speaker 1: a person who basically says this whole thing is a hoax.

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Speaker 1: Six thousand Americans didn't die, it's just like the flu.

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Speaker 1: The President said that it will be gone like that.

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Speaker 1: How do you respond to somebody who's been propagandized to

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Speaker 1: that level. Yeah, I think it's a real challenge where

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Speaker 1: politics and misinformation has, unfortunately um found its way into

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Speaker 1: science messaging the way. You know, I struggle with it

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Speaker 1: when I have those conversations with people. What we know

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Speaker 1: from public health vaccine efforts in the past is that

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Speaker 1: coming down with a hammer doesn't work. UM. It tends

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Speaker 1: to uh, unless you're more literal than with an actual hammer.

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Speaker 1: Coming out with the metaphorical hammer, yeah, it doesn't work.

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Speaker 1: I think the caret works better, which is explaining to

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Speaker 1: people that protect Grandma, that's right, protect Grandma. That's worked

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Speaker 1: incredibly well in Mexico, which is a different set of

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Speaker 1: cultural values than we have where the message of protect

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Speaker 1: grandma really really works in the United States. What we

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Speaker 1: know works more is protect the vulnerable children around you,

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Speaker 1: protect the vulnerable people around you. So I don't know.

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Speaker 1: I mean, it's a hard conversation with people that are

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Speaker 1: vaccine hesitant today, but they need to understand that delta

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Speaker 1: variant is spreading so quickly, and it's just the variant

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Speaker 1: of the day. There will be a aditional variants that emerged.

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Speaker 1: It's only the fourth letter. There's more common exactly. You

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Speaker 1: know this, Uh. I trust the behavior of this virus

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Speaker 1: far more than I trust the predictive behavior of people.

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Speaker 1: Why pathogens do exactly what we know they'll do. I

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Speaker 1: love studying pathage, I love outbreaks, I love winning competition.

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Speaker 1: And they're going to get better and better at infecting

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Speaker 1: their hosts because if they don't do that, there's a

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Speaker 1: reason the delta variant is dominant. It's pushed aside the

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Speaker 1: previous variants. That's right. The virus is going to mutate

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Speaker 1: and it's going to spread, and it's going to mutate

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Speaker 1: to spread. So of course we see a strain of

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Speaker 1: the virus that spreads easier. It won't be the last strain.

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Speaker 1: There will there will be more. It will continue to mutate.

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Speaker 1: That's quite amazing. And what about the chip that Bill

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Speaker 1: Gates is putting in the vaccine to track me and

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Speaker 1: my iPhone? Which I always find hilarious. They're concerned about

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Speaker 1: being tracked, but this literally tracks everywhere you've been. How

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Speaker 1: do you respond to that sort of stuff? You know,

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Speaker 1: my my fourteen year old and twelve year old. Every

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Speaker 1: now and then we'll we'll send me some videos that

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Speaker 1: they see circulating online that have conspiracy theories, and I

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Speaker 1: watch them because I want to understand why people are

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Speaker 1: scared and hesitant, and I think it's important not to

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Speaker 1: discount their fears. At this point in the pandemic, people

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Speaker 1: don't know who to trust, and instead of discounting them,

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Speaker 1: the more interesting conversation to me is how might we,

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Speaker 1: as one united country build the kind of system solution

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Speaker 1: and leadership that a whole of society would trust, even

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Speaker 1: if they have different political beliefs, you know, red and blue,

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Speaker 1: extreme sides of the aisle. So I think it's really

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Speaker 1: a systems failure of government institutions in this pandemic response,

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Speaker 1: that we're at the point where people are so politicized

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Speaker 1: that getting a vaccine or not, which is just pure science,

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Speaker 1: is now made out to be full of conspiracy theories. Right.

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Speaker 1: I saw a hilarious cartoon this weekend. If we would

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Speaker 1: have had Fox News in the fifties, Um, we'd still

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Speaker 1: be fighting polio, which is kind of kind of amusing. Um,

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Speaker 1: let's move past vaccine hesitancy and talk a little bit

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Speaker 1: about where we are today about whatever the next pandemic

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Speaker 1: is going to be. Are we better equipped now than

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Speaker 1: we were in to fight an upcoming pandemic or do

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Speaker 1: we still have the same systemic problems that were revealed

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Speaker 1: last year? We still have the same systemic problems. Look,

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Speaker 1: the United States and companies and government have essentially duct

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Speaker 1: taped together a response to COVID with individual vigilance and

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Speaker 1: individual heroic efforts, but neither of those are an effective

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Speaker 1: strategy in the long term for managing what we now

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Speaker 1: know as an existential risk to the U S economy.

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Speaker 1: And so if if a different pathogen threat were to

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Speaker 1: occur today, we are no better are prepared. And that's

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Speaker 1: what I'm really focused on again, not just for private sector,

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Speaker 1: for government, but as a whole of society response. What

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Speaker 1: are the systems solutions for the country to contain a

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Speaker 1: fast moving, novel pathogen that occurs simultaneously across the US,

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Speaker 1: and and so what can we do? What should we

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Speaker 1: be doing? Well, there's a number of approaches UM. They

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Speaker 1: certainly include UM intelligence, leveraging everything. All the technology that

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Speaker 1: we have in Silicon Valley has not hit the public

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Speaker 1: health system, and in disease control, largely the decisions and

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Speaker 1: the operations are done manually or by individuals. I was

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Speaker 1: the individual doing that, and so there's an incredible opportunity

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Speaker 1: with Silicon Valley innovative technology to apply it to disease control.

429
00:25:50,960 --> 00:25:54,320
Speaker 1: There's also the operational aspect of it, meaning looking at

430
00:25:54,520 --> 00:25:58,720
Speaker 1: local public health health departments. They are the frontline generals.

431
00:25:58,720 --> 00:26:01,040
Speaker 1: They're on the ground. You know, my job, is Michael

432
00:26:01,040 --> 00:26:03,480
Speaker 1: describes in the book, was to control disease by all

433
00:26:03,560 --> 00:26:07,800
Speaker 1: means necessary. So operationally as a country, I really think

434
00:26:07,800 --> 00:26:11,800
Speaker 1: it's important that we rethink who's doing the frontline operations,

435
00:26:11,840 --> 00:26:14,520
Speaker 1: do they have enough resources, and do we have a

436
00:26:14,560 --> 00:26:18,480
Speaker 1: way to quickly get centralized intelligence out to all those people?

437
00:26:19,119 --> 00:26:21,640
Speaker 1: Right now, the way they share information is fax machines

438
00:26:21,680 --> 00:26:25,080
Speaker 1: and emails and phone calls, and that cannot move as

439
00:26:25,119 --> 00:26:28,160
Speaker 1: fast as the pathogen. Well, I guess you can fight

440
00:26:28,800 --> 00:26:33,560
Speaker 1: pandemics in the century with late twenty century technologies. That's right.

441
00:26:33,560 --> 00:26:36,080
Speaker 1: The pathogens are moving faster, and so we have to

442
00:26:36,119 --> 00:26:39,560
Speaker 1: develop systems that can move even faster than the pathogens.

443
00:26:39,920 --> 00:26:42,320
Speaker 1: And you know what I was inspired by was watching

444
00:26:42,359 --> 00:26:46,600
Speaker 1: Twitter and how fast rumors can spread on Twitter. Uh

445
00:26:46,640 --> 00:26:49,399
Speaker 1: in January, That's where I was going to look for

446
00:26:49,440 --> 00:26:53,480
Speaker 1: intelligence from. Yeah, I was watching you know, video feeds

447
00:26:53,480 --> 00:26:57,680
Speaker 1: and information on social media, and I realized Twitter moves

448
00:26:57,720 --> 00:27:01,439
Speaker 1: faster than the actual virus moves. Things go viral on

449
00:27:01,480 --> 00:27:04,959
Speaker 1: Twitter faster than the virus goes viral. I'm gonna confirm

450
00:27:05,000 --> 00:27:07,800
Speaker 1: what you said because I have a vivid recollection of

451
00:27:08,440 --> 00:27:11,800
Speaker 1: when Osama bin Laden was killed by the U S

452
00:27:11,800 --> 00:27:16,000
Speaker 1: Special Forces. That first was revealed to the public by

453
00:27:16,160 --> 00:27:20,959
Speaker 1: some local who was describing what was happening on go

454
00:27:21,040 --> 00:27:24,560
Speaker 1: back and recall that was the first confirmation about helicopters,

455
00:27:24,560 --> 00:27:27,880
Speaker 1: about the soul going on. And then subsequently we we

456
00:27:28,440 --> 00:27:31,320
Speaker 1: you know, everybody else put the pieces together. But yeah,

457
00:27:31,320 --> 00:27:34,119
Speaker 1: the speed of Twitter Twitter is the new when they

458
00:27:34,240 --> 00:27:36,280
Speaker 1: used to talk about in the stock market the tape.

459
00:27:36,680 --> 00:27:40,640
Speaker 1: Twitter's new tape across is Twitter before crosses anything else. Yeah,

460
00:27:40,920 --> 00:27:45,399
Speaker 1: you know, and even though it's oftentimes spreading misinformation, especially

461
00:27:45,400 --> 00:27:48,439
Speaker 1: around things like vaccinations, um and and that's you know,

462
00:27:48,520 --> 00:27:51,560
Speaker 1: something that we really need to address. The truth is

463
00:27:51,760 --> 00:27:55,560
Speaker 1: the speed at which information goes viral on Twitter is

464
00:27:55,600 --> 00:28:00,960
Speaker 1: what gave me hope that Silicon Valley technology can revolutionize

465
00:28:01,000 --> 00:28:04,560
Speaker 1: disease control to move faster than the pathogen, because that

466
00:28:04,640 --> 00:28:07,639
Speaker 1: kind of technology exists. So let's talk a little bit

467
00:28:07,680 --> 00:28:12,440
Speaker 1: about your new venture, the Public Health Company. The mission

468
00:28:12,560 --> 00:28:17,520
Speaker 1: is to protect business and communities from infectious disease. That's

469
00:28:17,640 --> 00:28:21,560
Speaker 1: quite That's quite an aggressive mission, isn't it. Absolutely? And

470
00:28:22,000 --> 00:28:27,480
Speaker 1: what inspired me leading the COVID response with Amazing Humans

471
00:28:27,480 --> 00:28:32,359
Speaker 1: in California was that the private sector was experiencing what

472
00:28:32,560 --> 00:28:35,240
Speaker 1: I had experienced as local health officer and a state

473
00:28:35,280 --> 00:28:37,840
Speaker 1: health officer, and that is, no one's coming to save you.

474
00:28:38,000 --> 00:28:43,240
Speaker 1: When there's a disease outbreak. You have limited intelligence, sketchy

475
00:28:43,280 --> 00:28:46,520
Speaker 1: information from scattered sources, but you have to make an

476
00:28:46,560 --> 00:28:51,160
Speaker 1: operational plan. And not making a decision is a decision.

477
00:28:51,720 --> 00:28:54,120
Speaker 1: In other words, you're damned if you do and damned

478
00:28:54,120 --> 00:28:57,440
Speaker 1: if you don't, and you've got to pick. And I

479
00:28:57,520 --> 00:29:00,680
Speaker 1: was amazed how the private sector business is ran to

480
00:29:00,720 --> 00:29:03,080
Speaker 1: the fight. They wanted to do everything they could to help,

481
00:29:03,080 --> 00:29:06,080
Speaker 1: but they did not have the tools. They didn't have

482
00:29:06,160 --> 00:29:08,520
Speaker 1: a public health department inside of them. Some of them

483
00:29:08,520 --> 00:29:13,680
Speaker 1: had duct taped one together. And we're really left having

484
00:29:13,760 --> 00:29:17,920
Speaker 1: transferred the risk management of infectious disease to local and

485
00:29:18,000 --> 00:29:23,080
Speaker 1: state government who were overwhelmed by the pandemic. And so

486
00:29:24,440 --> 00:29:28,680
Speaker 1: by may or June of I couldn't stand it anymore.

487
00:29:28,760 --> 00:29:31,480
Speaker 1: I knew what needed to happen. I knew that Silicon

488
00:29:31,560 --> 00:29:36,720
Speaker 1: Valley technology could revolutionize infectious disease risk management, and that

489
00:29:36,800 --> 00:29:40,200
Speaker 1: the tools, the way that I thought about risk, and

490
00:29:40,280 --> 00:29:43,400
Speaker 1: the differential diagnosis and the operational plan, all of that

491
00:29:43,840 --> 00:29:46,840
Speaker 1: could be revolutionized by tech. And why weren't we putting

492
00:29:46,880 --> 00:29:50,240
Speaker 1: that into scalable software? And honestly, there was nothing else

493
00:29:50,280 --> 00:29:52,360
Speaker 1: I could do with my life, you know, by by

494
00:29:52,520 --> 00:29:56,320
Speaker 1: July August than to launch that effort. And so really

495
00:29:56,320 --> 00:29:58,920
Speaker 1: our mission at the Public Health Company is we are

496
00:29:58,960 --> 00:30:03,960
Speaker 1: building those solution in technology to democratize access to risk

497
00:30:04,040 --> 00:30:08,720
Speaker 1: management of infectious disease, so that private sector businesses, globally

498
00:30:08,760 --> 00:30:12,360
Speaker 1: distributed enterprises that deal with infectious disease all the time,

499
00:30:12,960 --> 00:30:15,360
Speaker 1: that they have that kind of expertise at their fingertips.

500
00:30:15,720 --> 00:30:17,440
Speaker 1: So how is this set up? Is this going to

501
00:30:17,560 --> 00:30:21,280
Speaker 1: be like a dot org or and a philanthropic organization.

502
00:30:21,560 --> 00:30:24,360
Speaker 1: Are you setting it up like a consulting firm or

503
00:30:24,400 --> 00:30:27,640
Speaker 1: do you want this to become a scalable technology and

504
00:30:27,760 --> 00:30:30,880
Speaker 1: app that people can buy and used to manage their

505
00:30:30,880 --> 00:30:37,080
Speaker 1: own um prevention of infectious diseases. We are developing software

506
00:30:37,320 --> 00:30:41,120
Speaker 1: and as I looked at the very early applications we

507
00:30:41,160 --> 00:30:45,600
Speaker 1: had developed, I got really excited because it had to

508
00:30:45,600 --> 00:30:49,520
Speaker 1: do with genomic variants and how the virus mutates. And

509
00:30:49,560 --> 00:30:54,000
Speaker 1: it's a concept called genomic epidemiology or I affectionately also

510
00:30:54,080 --> 00:30:56,320
Speaker 1: call it genomic weather mapping. You know how you can

511
00:30:56,320 --> 00:30:57,800
Speaker 1: look at a weather map and you can see the

512
00:30:57,800 --> 00:31:01,040
Speaker 1: weather forecast and what's coming by region. So we can

513
00:31:01,080 --> 00:31:04,680
Speaker 1: actually do that for variance of the virus and say,

514
00:31:05,160 --> 00:31:08,160
Speaker 1: how is the delta variant different from the alpha variant

515
00:31:08,200 --> 00:31:10,880
Speaker 1: and what does that mean for the state of Georgia

516
00:31:11,120 --> 00:31:14,680
Speaker 1: or for a specific county. We can predict, you know,

517
00:31:15,240 --> 00:31:18,600
Speaker 1: in a time stamped way, the likelihood that cases are

518
00:31:18,600 --> 00:31:20,719
Speaker 1: going to go up or down. And not just for COVID,

519
00:31:20,800 --> 00:31:23,400
Speaker 1: but these are the kinds of models that every local

520
00:31:23,440 --> 00:31:26,840
Speaker 1: disease controller has to do in their head figuring out

521
00:31:27,080 --> 00:31:29,600
Speaker 1: what's the direction of the mannincha cockle outbreak, for example.

522
00:31:29,640 --> 00:31:32,800
Speaker 1: Michael Lewis tells that story in the book. So math

523
00:31:32,880 --> 00:31:37,080
Speaker 1: and microbiology really is, you know, very simple data science modeling,

524
00:31:37,760 --> 00:31:42,320
Speaker 1: codifying that into software and giving disease forecasting and then

525
00:31:42,440 --> 00:31:45,320
Speaker 1: the answer to the question what do I do now

526
00:31:45,400 --> 00:31:48,080
Speaker 1: based on it? Handing that to someone in a way

527
00:31:48,120 --> 00:31:52,920
Speaker 1: that is simple, accessible at their fingertips is an incredible

528
00:31:53,040 --> 00:31:56,200
Speaker 1: need and it doesn't exist. And it's for the private sector,

529
00:31:56,280 --> 00:31:58,920
Speaker 1: it's for the public sector, it's for healthcare systems. We

530
00:31:59,000 --> 00:32:02,920
Speaker 1: are creating some new not just with the technology platform,

531
00:32:03,000 --> 00:32:05,680
Speaker 1: but a new sector of someone that you can pick

532
00:32:05,760 --> 00:32:08,880
Speaker 1: up the phone and call when when there's an infectious

533
00:32:08,880 --> 00:32:11,640
Speaker 1: disease threat. So what's the company going to look like

534
00:32:11,800 --> 00:32:14,560
Speaker 1: in terms of a product. Will I be able to

535
00:32:14,600 --> 00:32:18,080
Speaker 1: get an app on my phone and get alerts that hey,

536
00:32:18,160 --> 00:32:21,440
Speaker 1: the region where you are is now showing an uptick

537
00:32:21,680 --> 00:32:25,840
Speaker 1: of infections and take steps to protect yourself or is

538
00:32:25,840 --> 00:32:28,360
Speaker 1: it going to be more for the private corporate sector.

539
00:32:28,480 --> 00:32:31,640
Speaker 1: Tell us what the product is going to look like. Sure, well,

540
00:32:31,680 --> 00:32:33,880
Speaker 1: I think the best way to explain it would be

541
00:32:34,160 --> 00:32:37,360
Speaker 1: the analogy of cybersecurity. You know, there were some really

542
00:32:37,440 --> 00:32:40,520
Speaker 1: high profile cyber attacks in two thousand ten, two thousand

543
00:32:40,520 --> 00:32:46,160
Speaker 1: and eleven, and after that private enterprise realized, oh, this

544
00:32:46,240 --> 00:32:48,880
Speaker 1: is a risk that we own. We own this potentially

545
00:32:48,920 --> 00:32:52,600
Speaker 1: catastrophic risk to our enterprise, and so we have to

546
00:32:52,640 --> 00:32:56,920
Speaker 1: have that capability running in the background, monitoring the risk

547
00:32:57,120 --> 00:33:00,360
Speaker 1: that can ramp up or ramp down. And so the

548
00:33:00,480 --> 00:33:04,600
Speaker 1: quietly humming machine in the background of cybersecurity is a

549
00:33:04,680 --> 00:33:07,840
Speaker 1: perfect analogy to what we're creating because really, as a

550
00:33:07,840 --> 00:33:11,520
Speaker 1: as a tech enabled service, we are scalable software, but

551
00:33:11,600 --> 00:33:14,280
Speaker 1: we are humans behind it. I would argue the best

552
00:33:14,520 --> 00:33:17,040
Speaker 1: humans in the world, you know um Carter, Measher and

553
00:33:17,120 --> 00:33:19,520
Speaker 1: Jodorici from the book or advisers to the company. I

554
00:33:19,560 --> 00:33:23,120
Speaker 1: talked to those guys every day. We're codifying that thinking

555
00:33:23,160 --> 00:33:27,160
Speaker 1: in those tools into scalable software in a way that

556
00:33:27,200 --> 00:33:30,800
Speaker 1: we can help a business in an ongoing way manage

557
00:33:30,840 --> 00:33:34,840
Speaker 1: the risk the potential threats in all geographic regions. But

558
00:33:34,880 --> 00:33:37,120
Speaker 1: then help them know how do what do we do

559
00:33:37,240 --> 00:33:43,240
Speaker 1: practically tactically operationally for our business? Quite intriguing. So you're,

560
00:33:43,600 --> 00:33:46,200
Speaker 1: for lack of a bit of where your logo is

561
00:33:46,440 --> 00:33:53,600
Speaker 1: prevent detects contain explain what those three words mean in

562
00:33:53,760 --> 00:33:58,840
Speaker 1: terms of providing a shield for the private sector or

563
00:33:58,920 --> 00:34:03,760
Speaker 1: the public sect against an infectious outbreak. Sure, So I

564
00:34:03,760 --> 00:34:09,560
Speaker 1: would start with explaining it by the most important skill

565
00:34:10,480 --> 00:34:13,560
Speaker 1: is index of suspicion. So for any doctor or for

566
00:34:13,640 --> 00:34:18,400
Speaker 1: any frontline public health officer, having an index of suspicion

567
00:34:19,320 --> 00:34:23,680
Speaker 1: means you've gathered data from all the sketchy sources, maybe

568
00:34:23,680 --> 00:34:27,520
Speaker 1: which are somewhat unreliable, but taken together, it gives you

569
00:34:27,560 --> 00:34:30,839
Speaker 1: a sense of the direction things are headed. So that's

570
00:34:30,960 --> 00:34:35,320
Speaker 1: infectious disease modeling and certainly can be codified into software.

571
00:34:35,640 --> 00:34:39,360
Speaker 1: So being able to look at disease forecasting and predict

572
00:34:39,440 --> 00:34:42,880
Speaker 1: what the potential threats are, having a plan of action

573
00:34:43,040 --> 00:34:46,360
Speaker 1: of how you're going to prevent that from happening, and

574
00:34:46,400 --> 00:34:50,000
Speaker 1: then being able to detect it. You know, testing, looking

575
00:34:50,040 --> 00:34:52,759
Speaker 1: at the different capabilities we have from what we've seen

576
00:34:52,800 --> 00:34:56,920
Speaker 1: in COVID is vast development of rapid tests. The rapid

577
00:34:56,920 --> 00:34:58,759
Speaker 1: tests that have been developed by the private sector are

578
00:34:58,800 --> 00:35:02,719
Speaker 1: incredible and are very very good. And then containment, you know,

579
00:35:02,760 --> 00:35:07,280
Speaker 1: like I mentioned before, containment is where all the effort

580
00:35:07,320 --> 00:35:09,920
Speaker 1: has to be for the government because the risk of

581
00:35:09,960 --> 00:35:14,200
Speaker 1: failing at containment, as the United States did in means

582
00:35:14,239 --> 00:35:17,080
Speaker 1: that then we have to make these awful choices, you

583
00:35:17,080 --> 00:35:20,400
Speaker 1: know that impact the economy and impact health, and so

584
00:35:20,920 --> 00:35:25,640
Speaker 1: for our clients, both government and private businesses. Really the

585
00:35:26,120 --> 00:35:30,560
Speaker 1: value proposition is the ability to have disease forecasting, take

586
00:35:30,600 --> 00:35:34,040
Speaker 1: action based on it, decrease your risk, and find that

587
00:35:34,120 --> 00:35:38,120
Speaker 1: balance between continuity of operation and the health of your employees,

588
00:35:38,120 --> 00:35:40,800
Speaker 1: the health of the community, and knowing that that brand

589
00:35:41,200 --> 00:35:44,279
Speaker 1: has a safe environment. And from what I've read, this

590
00:35:44,360 --> 00:35:47,840
Speaker 1: is more than just the germ of a startup idea.

591
00:35:48,160 --> 00:35:51,160
Speaker 1: This is a real company that's funded, that has clients.

592
00:35:51,400 --> 00:35:54,200
Speaker 1: Tell us a little bit about where you are in

593
00:35:54,239 --> 00:35:58,040
Speaker 1: the growth curve of the public health company. Sure, well,

594
00:35:58,800 --> 00:36:01,319
Speaker 1: my archetype is a public health official, right, That's what

595
00:36:01,400 --> 00:36:04,200
Speaker 1: I had been my whole career, And I jumped from

596
00:36:04,239 --> 00:36:07,880
Speaker 1: being a health official of a state the large estate

597
00:36:08,440 --> 00:36:13,440
Speaker 1: UH to learning Silicon Valley in business and understanding the

598
00:36:13,480 --> 00:36:17,440
Speaker 1: pain points of the private sector. And of governments and

599
00:36:17,520 --> 00:36:22,200
Speaker 1: of healthcare systems and architecting a technology based solution. It

600
00:36:22,320 --> 00:36:25,640
Speaker 1: has been an adventure. And what has happened is our

601
00:36:25,640 --> 00:36:28,759
Speaker 1: partners in Silicon Valley have rallied behind the public health

602
00:36:28,800 --> 00:36:33,200
Speaker 1: company and our seed funding was in the spring. I'm

603
00:36:33,239 --> 00:36:36,520
Speaker 1: thrilled to have partnered with ven Rock. I was introduced

604
00:36:36,520 --> 00:36:38,719
Speaker 1: to them by Todd Park, who's on our board of

605
00:36:38,760 --> 00:36:41,960
Speaker 1: directors and was really a thought partner to me as

606
00:36:42,000 --> 00:36:45,000
Speaker 1: I founded this company, and he introduced me to ven

607
00:36:45,080 --> 00:36:49,120
Speaker 1: Rock because they are very thoughtful at the kind of

608
00:36:49,120 --> 00:36:53,520
Speaker 1: partnerships that they have around tech enabled services and healthcare

609
00:36:53,680 --> 00:36:56,960
Speaker 1: I T companies, and so it's been an incredible growth

610
00:36:57,000 --> 00:37:00,919
Speaker 1: curve for me and learning curve. And we have about

611
00:37:00,920 --> 00:37:05,040
Speaker 1: twenty employees right now at this point, actually as of recently,

612
00:37:05,120 --> 00:37:07,560
Speaker 1: I believe the numbers about up to twenty five. We

613
00:37:07,600 --> 00:37:11,840
Speaker 1: are growing and scaling fast to build out these capabilities

614
00:37:11,840 --> 00:37:15,000
Speaker 1: in software and if memory serves. Vent Rock has been

615
00:37:15,000 --> 00:37:17,439
Speaker 1: around for quite a while, haven't they, I believe, since

616
00:37:17,480 --> 00:37:21,520
Speaker 1: the nineteen sixties. One of the older silicon companies. Yeah,

617
00:37:21,560 --> 00:37:25,200
Speaker 1: they were one of the first, and you know, partnering

618
00:37:25,200 --> 00:37:27,920
Speaker 1: with them has been incredible for me as a first

619
00:37:27,920 --> 00:37:31,480
Speaker 1: time founder and first time CEO. I have a ton

620
00:37:31,600 --> 00:37:35,200
Speaker 1: to learn, and doing that in partnership with someone who

621
00:37:35,640 --> 00:37:37,399
Speaker 1: is so well versed in this and has a lot

622
00:37:37,400 --> 00:37:40,920
Speaker 1: of experience has been incredible. And tell us a little bit.

623
00:37:41,320 --> 00:37:47,120
Speaker 1: What is an index of suspicion? It sounds so so clandestine.

624
00:37:47,320 --> 00:37:51,320
Speaker 1: What is an index of suspicion? An index of suspicion

625
00:37:51,440 --> 00:37:54,160
Speaker 1: is if you told me I have a nineteen year

626
00:37:54,200 --> 00:37:57,400
Speaker 1: old college kid that has Ninja cockle disease and lives

627
00:37:57,400 --> 00:38:01,960
Speaker 1: in a fraternity, My next thought as well, I generally

628
00:38:02,000 --> 00:38:07,120
Speaker 1: know how fraternity social behavior goes, and I know there's

629
00:38:07,160 --> 00:38:11,040
Speaker 1: probably a lot of cross pollination, so I suspect there

630
00:38:11,080 --> 00:38:15,880
Speaker 1: are grab a whiteboard and a pen twelve other cases

631
00:38:16,000 --> 00:38:20,440
Speaker 1: currently that are undetected, and that immediately informs the action

632
00:38:20,480 --> 00:38:24,160
Speaker 1: that I take. So an index of suspicion is having

633
00:38:24,200 --> 00:38:29,840
Speaker 1: a strong situational awareness of the environment, knowing the pathogen

634
00:38:29,920 --> 00:38:32,520
Speaker 1: and how it behaves, and then doing the math and

635
00:38:32,600 --> 00:38:36,320
Speaker 1: microbiology to figure out very quickly what is the risk

636
00:38:36,680 --> 00:38:39,200
Speaker 1: this is about to get really big, how contagious is

637
00:38:39,239 --> 00:38:41,640
Speaker 1: this and how bad is the outcome for that disease?

638
00:38:41,760 --> 00:38:45,319
Speaker 1: That's right, quite quite interesting, Uh, There's a quote from

639
00:38:45,320 --> 00:38:47,520
Speaker 1: the book that I grabbed that I really liked, which

640
00:38:47,719 --> 00:38:50,720
Speaker 1: was quote, what scares me the most is our ability

641
00:38:50,719 --> 00:38:54,160
Speaker 1: to respond to a new pathogen, maybe one we've never

642
00:38:54,160 --> 00:38:58,680
Speaker 1: seen before, or an old pathogen like influenza that's mutated.

643
00:38:59,120 --> 00:39:02,160
Speaker 1: We know we have to be prepared for that. Are

644
00:39:02,239 --> 00:39:05,399
Speaker 1: we prepared for that? I do not believe that we are,

645
00:39:05,560 --> 00:39:09,720
Speaker 1: And that's what motivated me to launch the Public Health Company,

646
00:39:10,000 --> 00:39:14,680
Speaker 1: because unless we have a technology platform that can move

647
00:39:14,840 --> 00:39:20,000
Speaker 1: faster than the pathogen, we will never be prepared. Quite

648
00:39:20,120 --> 00:39:25,480
Speaker 1: interesting and and frightening. So when you got the finished book,

649
00:39:25,800 --> 00:39:27,799
Speaker 1: and then I want to circle back to how you

650
00:39:27,840 --> 00:39:30,680
Speaker 1: met Michael. But when you got the finished book, what

651
00:39:30,760 --> 00:39:36,440
Speaker 1: was it like reading about yourself? How surreal was that experience?

652
00:39:37,000 --> 00:39:42,000
Speaker 1: You know, it was really painful because I'm a fairly

653
00:39:42,560 --> 00:39:45,960
Speaker 1: private person. I've been a government official my whole career,

654
00:39:46,800 --> 00:39:50,319
Speaker 1: and to see my life story in print in a

655
00:39:50,440 --> 00:39:55,439
Speaker 1: book was shocking. And it's also really hard to read

656
00:39:55,480 --> 00:39:59,400
Speaker 1: about yourself in print. That's kind of interesting. So let's

657
00:40:00,120 --> 00:40:04,680
Speaker 1: circle back to the beginning. How did Michael Lewis find you?

658
00:40:04,680 --> 00:40:07,440
Speaker 1: You know, he texted me randomly out of the blue,

659
00:40:07,800 --> 00:40:11,200
Speaker 1: and I had never read a Michael Lewis book. I

660
00:40:11,239 --> 00:40:13,280
Speaker 1: recognized his name. I was going to say, I assume

661
00:40:13,360 --> 00:40:16,040
Speaker 1: you kind of know who he is, kind of. I

662
00:40:16,080 --> 00:40:18,760
Speaker 1: recognized his name. I mainly knew who he was because

663
00:40:18,800 --> 00:40:21,560
Speaker 1: my friend DJ Pittill had told me about the Fifth

664
00:40:21,680 --> 00:40:23,879
Speaker 1: Risk and that process of being in the Fifth risk.

665
00:40:24,040 --> 00:40:26,120
Speaker 1: And so wait, wait, let me just stop you here.

666
00:40:26,760 --> 00:40:31,160
Speaker 1: Money Ball, the movies, money Ball, The blind Side, the

667
00:40:31,200 --> 00:40:35,000
Speaker 1: Big Short, these were all pretty big movies. Certainly, The

668
00:40:35,040 --> 00:40:37,920
Speaker 1: Big Short was a giant movie when it came out. No,

669
00:40:38,120 --> 00:40:40,319
Speaker 1: not your cup didn't didn't catch you? Well, the Big

670
00:40:40,320 --> 00:40:44,880
Speaker 1: Short was not a giant movie for internal medicine microbiology docs.

671
00:40:45,800 --> 00:40:48,600
Speaker 1: So you know, let's just say I had seen The

672
00:40:48,640 --> 00:40:52,120
Speaker 1: blind Side and I loved that movie, but certainly didn't

673
00:40:52,160 --> 00:40:55,000
Speaker 1: remember who wrote the book. Should I be saying this publicly?

674
00:40:55,400 --> 00:40:57,280
Speaker 1: You could say that I had never read a Michael

675
00:40:57,320 --> 00:41:00,759
Speaker 1: Lewis book. I recognized his name, I mean because DJ

676
00:41:00,840 --> 00:41:03,160
Speaker 1: Patil had told me about his experience being in the

677
00:41:03,200 --> 00:41:06,839
Speaker 1: Fifth Risk. But you know, Michael texted me, and on

678
00:41:06,880 --> 00:41:09,000
Speaker 1: that first phone call, I said, I'm happy to speak

679
00:41:09,000 --> 00:41:11,879
Speaker 1: with you. I googled him. On that first phone call,

680
00:41:11,960 --> 00:41:14,840
Speaker 1: he said who are you? And I said, what do

681
00:41:14,880 --> 00:41:17,440
Speaker 1: you what do you mean? And he said, well, you

682
00:41:17,440 --> 00:41:20,480
Speaker 1: know the Wolverines and read Dawn said to speak to

683
00:41:20,560 --> 00:41:24,520
Speaker 1: Charity Dean and um, he'd contacted DJ and Todd Park

684
00:41:24,600 --> 00:41:26,319
Speaker 1: and they said, talked to Charity Dean and then Joe

685
00:41:26,360 --> 00:41:29,080
Speaker 1: Deesi at bio hub and said, he said, I've never

686
00:41:29,120 --> 00:41:33,640
Speaker 1: heard of you, and I thought, well, okay, yeah, okay,

687
00:41:33,800 --> 00:41:37,640
Speaker 1: that's fine. But it was a funny first conversation. In fact,

688
00:41:38,320 --> 00:41:40,880
Speaker 1: it started off the first two minutes. I said, Michael,

689
00:41:41,640 --> 00:41:43,080
Speaker 1: I know I don't know you, but I've got to

690
00:41:43,080 --> 00:41:45,359
Speaker 1: make a fifty one forty nine decision. Have you ever

691
00:41:45,360 --> 00:41:48,040
Speaker 1: had to make one of those? And he said, well,

692
00:41:48,080 --> 00:41:50,279
Speaker 1: I'm making one right now. I'm deciding whether or not

693
00:41:50,360 --> 00:41:53,960
Speaker 1: I'm going to write a book. And so we talked

694
00:41:54,040 --> 00:41:58,320
Speaker 1: through the meaning you have a certainty and how brave

695
00:41:58,320 --> 00:42:00,879
Speaker 1: are you going to be? Are you gonna pull the clip? Essentially, yeah,

696
00:42:00,920 --> 00:42:03,200
Speaker 1: So we talked through the decision I was making and

697
00:42:03,200 --> 00:42:06,399
Speaker 1: the decision he was making, And of course what I

698
00:42:06,480 --> 00:42:09,000
Speaker 1: didn't know at the time is I think by the

699
00:42:09,080 --> 00:42:11,279
Speaker 1: end of the conversation he had decided to write a

700
00:42:11,280 --> 00:42:13,320
Speaker 1: book and I was going to be in it, and

701
00:42:13,600 --> 00:42:17,919
Speaker 1: I had no clue, no clue about that, So what

702
00:42:17,960 --> 00:42:20,200
Speaker 1: was the next step? So you you finished the conversation,

703
00:42:20,320 --> 00:42:23,120
Speaker 1: you leave thinking, oh, so that seemed like an interesting guy.

704
00:42:23,480 --> 00:42:26,840
Speaker 1: He leaves saying, Oh, I'm writing this book now. Meanwhile,

705
00:42:26,960 --> 00:42:30,080
Speaker 1: the way he operates, he's already been assembling, he's been

706
00:42:30,080 --> 00:42:34,440
Speaker 1: collecting characters, he's been lighting, writing vignettes all year. He

707
00:42:34,480 --> 00:42:37,279
Speaker 1: sees a thread that connects all of these together, and

708
00:42:37,360 --> 00:42:41,160
Speaker 1: suddenly there's a full narrative. What was the next step

709
00:42:41,200 --> 00:42:43,040
Speaker 1: with him? How did he how did he proceed from

710
00:42:43,040 --> 00:42:46,200
Speaker 1: there with you and and the rest of the characters

711
00:42:46,200 --> 00:42:48,800
Speaker 1: in the book. Well, what I didn't know at that time,

712
00:42:48,880 --> 00:42:53,000
Speaker 1: and he told me later, is before he even called me,

713
00:42:53,520 --> 00:42:55,920
Speaker 1: he had an entire file with my name on it,

714
00:42:56,520 --> 00:42:59,120
Speaker 1: everything I'd ever done, anything that was out there publicly

715
00:42:59,120 --> 00:43:03,120
Speaker 1: about me. And it makes a ton of sense because

716
00:43:03,160 --> 00:43:06,839
Speaker 1: as he asked me questions and you know, would come

717
00:43:06,880 --> 00:43:09,520
Speaker 1: interview me and talk through how the public health system

718
00:43:09,520 --> 00:43:13,040
Speaker 1: works from microbiology or my own story, Um, he already

719
00:43:13,080 --> 00:43:15,239
Speaker 1: knew the answer to some of that about me, And

720
00:43:15,320 --> 00:43:18,160
Speaker 1: so I think I think he was getting to know

721
00:43:18,200 --> 00:43:22,120
Speaker 1: a potential character. I was clueless when he asked if

722
00:43:22,160 --> 00:43:26,080
Speaker 1: I would be willing to be a character in the book. Um,

723
00:43:26,160 --> 00:43:28,880
Speaker 1: I thought I was one of twenty. Maybe there'd be

724
00:43:28,920 --> 00:43:33,080
Speaker 1: one or two lines from me. I never imagined I

725
00:43:33,120 --> 00:43:36,080
Speaker 1: would be the protagonist. Not only are you the one

726
00:43:36,080 --> 00:43:38,800
Speaker 1: of the key protagonists in the book, but the book

727
00:43:38,920 --> 00:43:44,000
Speaker 1: begins with your story. That That was the surprise that

728
00:43:44,160 --> 00:43:47,279
Speaker 1: you're the opening scene. It was a total surprise. I

729
00:43:47,360 --> 00:43:49,799
Speaker 1: had no idea until I read the book, which was

730
00:43:49,880 --> 00:43:53,040
Speaker 1: the day before I sat down with sixty Minutes. So

731
00:43:53,560 --> 00:43:58,080
Speaker 1: I had no clue. And and yet I had made

732
00:43:58,080 --> 00:44:01,920
Speaker 1: a decision in July of when he asked me to

733
00:44:01,960 --> 00:44:04,120
Speaker 1: be a character in the book. I was either going

734
00:44:04,200 --> 00:44:07,839
Speaker 1: to let him into my life or not. And I

735
00:44:07,880 --> 00:44:10,000
Speaker 1: had been such a private person up to that point.

736
00:44:10,120 --> 00:44:12,960
Speaker 1: But I don't have any secrets. It's a fairly vanilla

737
00:44:13,040 --> 00:44:16,440
Speaker 1: life of a mom and a public servant. I'm gonna

738
00:44:16,480 --> 00:44:18,960
Speaker 1: throw a yellow card on that one, but we'll circle

739
00:44:19,000 --> 00:44:22,880
Speaker 1: back to it, because it clearly is not a vanilla life.

740
00:44:23,400 --> 00:44:26,440
Speaker 1: You're basically calling governors and saying, hey, idiot, if you

741
00:44:26,440 --> 00:44:28,360
Speaker 1: don't do this, you're gonna kill a lot of people.

742
00:44:28,840 --> 00:44:30,680
Speaker 1: You need to you need to step up your game.

743
00:44:30,760 --> 00:44:34,840
Speaker 1: That's not exactly dropping the kids off at soccer. Yeah. Well,

744
00:44:34,920 --> 00:44:37,480
Speaker 1: to me, you know, I took my oath of office

745
00:44:37,880 --> 00:44:42,000
Speaker 1: very seriously to protect and defend the United States against

746
00:44:42,080 --> 00:44:45,600
Speaker 1: all threats for and in domestic and that includes pathogens.

747
00:44:45,719 --> 00:44:48,560
Speaker 1: And so for me, it was always just fulfilling the

748
00:44:48,600 --> 00:44:52,440
Speaker 1: oath that I took to protect communities by all means necessary. Well,

749
00:44:52,480 --> 00:44:54,680
Speaker 1: it's good to see that some people still honor their

750
00:44:54,760 --> 00:44:58,000
Speaker 1: oath of office. But we won't digress into that direction.

751
00:44:58,080 --> 00:45:00,600
Speaker 1: Let's stick with the book, which by the way, is

752
00:45:00,640 --> 00:45:05,120
Speaker 1: sitting on the credenza of my house where by the

753
00:45:05,160 --> 00:45:07,000
Speaker 1: front door, where I was going to bring it with

754
00:45:07,040 --> 00:45:10,440
Speaker 1: me to have you signed. Well, that means that we

755
00:45:10,480 --> 00:45:12,040
Speaker 1: have to see each other again. I'll come back and

756
00:45:12,040 --> 00:45:15,080
Speaker 1: sign it. I was gonna say, after we open up again,

757
00:45:15,120 --> 00:45:18,400
Speaker 1: but we're kind of open up again. This just to

758
00:45:18,520 --> 00:45:20,959
Speaker 1: digress a little bit. Not only is this the first

759
00:45:21,000 --> 00:45:23,319
Speaker 1: time I'm in the Bloomberg building in a year and

760
00:45:23,840 --> 00:45:26,240
Speaker 1: almost a year and a half, today was my first

761
00:45:26,800 --> 00:45:30,719
Speaker 1: uh COVID test, right, And how was it? I know

762
00:45:30,800 --> 00:45:32,920
Speaker 1: I'm here talking, so I assumed they wouldn't let me

763
00:45:32,960 --> 00:45:35,920
Speaker 1: in the building without it. So you're having this conversation

764
00:45:35,960 --> 00:45:39,040
Speaker 1: back and forth with him, not only are are you

765
00:45:39,120 --> 00:45:41,320
Speaker 1: unaware of the fact that you're becoming the lead character

766
00:45:41,400 --> 00:45:45,640
Speaker 1: in the book, but you're educating him on infectious disease.

767
00:45:46,360 --> 00:45:49,480
Speaker 1: I know he's a quick learner and an in depth learner,

768
00:45:49,840 --> 00:45:52,040
Speaker 1: but tell us a little bit about what that back

769
00:45:52,080 --> 00:45:54,120
Speaker 1: and forth was like, you know it was. It was

770
00:45:54,440 --> 00:45:59,080
Speaker 1: really painful at times. Um Dice basically spent a year

771
00:45:59,160 --> 00:46:05,440
Speaker 1: teaching Michael is microbiology and public health disease control. And

772
00:46:06,640 --> 00:46:08,919
Speaker 1: I took him on a tour of the places where

773
00:46:08,960 --> 00:46:11,239
Speaker 1: some of those stories took place in Santa Barbara. So

774
00:46:11,280 --> 00:46:14,840
Speaker 1: I took him to the homeless shelter, the coroner's office

775
00:46:14,960 --> 00:46:18,400
Speaker 1: where the autopsy was done, which, by the way, again,

776
00:46:18,920 --> 00:46:22,040
Speaker 1: that's an insane I hope that makes it into the

777
00:46:22,040 --> 00:46:25,480
Speaker 1: movie if they make a movie. That's an insane scene

778
00:46:25,520 --> 00:46:27,880
Speaker 1: in the book about you, Like, all right, you two

779
00:46:27,960 --> 00:46:31,680
Speaker 1: idiot stopped falling around and outcomes the knife and you

780
00:46:31,719 --> 00:46:35,560
Speaker 1: crack open the sternum and start pulling out internal organs,

781
00:46:35,600 --> 00:46:39,640
Speaker 1: and the corner and his assistant stood there horrified. In

782
00:46:39,719 --> 00:46:42,480
Speaker 1: the book, was that an accurate depiction of what was

783
00:46:42,520 --> 00:46:45,480
Speaker 1: going on the way that Michael wrote it in the

784
00:46:45,520 --> 00:46:49,680
Speaker 1: book was accurate? And what was funny about that scenario

785
00:46:49,880 --> 00:46:54,120
Speaker 1: is I knew how risk averse and hesitant. Uh, the

786
00:46:54,320 --> 00:46:58,839
Speaker 1: pathologist was to cut open her chest because of the

787
00:46:58,920 --> 00:47:03,200
Speaker 1: fear that using a bone saw could theoretically there was

788
00:47:03,239 --> 00:47:07,560
Speaker 1: one report in the literature spread aerisolized the tuberculosis bacteria,

789
00:47:08,280 --> 00:47:10,759
Speaker 1: and so I understood the fear. But by the way,

790
00:47:10,840 --> 00:47:14,959
Speaker 1: she's masked and has a full um face shield. Right

791
00:47:15,200 --> 00:47:19,120
Speaker 1: when I showed up, everyone was wearing full ppe But

792
00:47:19,200 --> 00:47:21,920
Speaker 1: I was the tuberculosis controller. I was. I had literally

793
00:47:21,960 --> 00:47:26,480
Speaker 1: just been elected president of the California Tuberculosis Controllers Association.

794
00:47:26,640 --> 00:47:28,160
Speaker 1: You never knew it existed. I was going to say,

795
00:47:28,160 --> 00:47:30,640
Speaker 1: who even I was the president? That must be Those

796
00:47:30,719 --> 00:47:33,440
Speaker 1: must be wild parties. They are super fun because we

797
00:47:33,480 --> 00:47:37,640
Speaker 1: talk about things like drug resistant tuberculosis treatment. Sounds like fun.

798
00:47:37,719 --> 00:47:40,200
Speaker 1: Does sound like a So TV doesn't scare me, you know.

799
00:47:40,360 --> 00:47:43,239
Speaker 1: It's so you show up and you're like, I'll crack

800
00:47:43,280 --> 00:47:45,439
Speaker 1: her open. I don't care. Like they thought they were

801
00:47:45,520 --> 00:47:48,479
Speaker 1: intimidating you by saying you want those organs, you break

802
00:47:48,480 --> 00:47:52,560
Speaker 1: her open. I think that they thought, and I should

803
00:47:52,560 --> 00:47:54,920
Speaker 1: be careful here projecting what I think they thought. But

804
00:47:55,000 --> 00:47:57,960
Speaker 1: I think my impression was if it was if they

805
00:47:58,200 --> 00:48:00,560
Speaker 1: if I was going to issue a health offs or order,

806
00:48:00,600 --> 00:48:03,080
Speaker 1: which I had, then it would have to be done

807
00:48:03,120 --> 00:48:07,000
Speaker 1: under their terms, which was outside and that I couldn't

808
00:48:07,680 --> 00:48:10,040
Speaker 1: be absent and not only had to be present, but

809
00:48:10,080 --> 00:48:14,080
Speaker 1: had to be a very active participant. And what they

810
00:48:14,080 --> 00:48:16,720
Speaker 1: didn't know is I had started out as a surgeon.

811
00:48:16,880 --> 00:48:20,240
Speaker 1: I've done surgery for you know, some time in Africa,

812
00:48:20,280 --> 00:48:22,720
Speaker 1: and I had been a general surgery resident at Cottage.

813
00:48:22,760 --> 00:48:25,560
Speaker 1: I was super comfortable in the trauma bay cracking a

814
00:48:25,640 --> 00:48:28,399
Speaker 1: chest open or you know, making fast decisions. And so

815
00:48:29,200 --> 00:48:32,640
Speaker 1: what might have scared another doctor h to me was

816
00:48:32,719 --> 00:48:34,880
Speaker 1: just Okay, so this is what we're doing today, Alright,

817
00:48:35,800 --> 00:48:39,680
Speaker 1: let's go. It's really an amazing part of the book,

818
00:48:39,719 --> 00:48:43,160
Speaker 1: and it sets the tone for who you are. It's like,

819
00:48:43,400 --> 00:48:46,040
Speaker 1: you're not gonna you boys are not gonna look down

820
00:48:46,040 --> 00:48:48,399
Speaker 1: at me and intimidate me because I know you show

821
00:48:48,520 --> 00:48:51,560
Speaker 1: up your five foot nothing blond and these guys think

822
00:48:51,560 --> 00:48:54,000
Speaker 1: they're going to run rough shot over you. Is that

823
00:48:54,120 --> 00:48:58,080
Speaker 1: is that your life experience? I should be careful what

824
00:48:58,160 --> 00:49:00,200
Speaker 1: I say here. No, you could say whatever you want

825
00:49:00,239 --> 00:49:03,080
Speaker 1: to tell you. Listen, your life has already spread out

826
00:49:03,120 --> 00:49:06,960
Speaker 1: in Michael Lewis's book, and he implied, you know, I'm

827
00:49:07,000 --> 00:49:12,880
Speaker 1: following his lead. He implied as much that historically you

828
00:49:13,040 --> 00:49:17,399
Speaker 1: were very much underestimated by the Boys Club and this

829
00:49:17,560 --> 00:49:20,879
Speaker 1: was the perfect manifestation of that and the opportunity to say,

830
00:49:21,600 --> 00:49:24,760
Speaker 1: my beer, watch this. You know, it's funny. So, uh,

831
00:49:24,800 --> 00:49:26,440
Speaker 1: for those of us not here in this studio, I'm

832
00:49:26,480 --> 00:49:30,840
Speaker 1: five ft four, I'm a solid hundred and fifteen pounds, solid, solid,

833
00:49:31,200 --> 00:49:33,880
Speaker 1: long blonde hair. I always wear a suit and heels,

834
00:49:34,120 --> 00:49:37,480
Speaker 1: and so I get that the way that I look

835
00:49:37,719 --> 00:49:42,080
Speaker 1: projects a certain impression. And all my life, all my career,

836
00:49:42,200 --> 00:49:44,560
Speaker 1: I've been in the house of medicine. The house of

837
00:49:44,600 --> 00:49:47,839
Speaker 1: medicine feels like a fraternity at times. The boys Club

838
00:49:47,960 --> 00:49:53,040
Speaker 1: evenlike finance, which you know, so even in medical school,

839
00:49:53,120 --> 00:49:55,040
Speaker 1: you know, I went to med school at Tulane in

840
00:49:55,080 --> 00:49:57,680
Speaker 1: New Orleans, and um, you know, the halls are lined

841
00:49:57,760 --> 00:50:01,719
Speaker 1: with with photos and portraits of of men, and so

842
00:50:01,880 --> 00:50:04,680
Speaker 1: as a woman, it feels like a very different game.

843
00:50:04,880 --> 00:50:08,160
Speaker 1: And I figured out early on that in general, I

844
00:50:08,200 --> 00:50:11,440
Speaker 1: was going to be assumed to be one type of person.

845
00:50:11,680 --> 00:50:14,640
Speaker 1: And that's why I love trauma surgery. It's fast decisions,

846
00:50:14,719 --> 00:50:18,120
Speaker 1: it's blood and guts, it's taking action. You're judged on

847
00:50:18,160 --> 00:50:21,640
Speaker 1: what you actually do, and so I love that. So, yeah,

848
00:50:21,719 --> 00:50:25,960
Speaker 1: Michael definitely locked onto that story as kind of an example,

849
00:50:26,040 --> 00:50:30,200
Speaker 1: an archetypal example of what I had experienced throughout my career.

850
00:50:30,600 --> 00:50:34,960
Speaker 1: And he wasn't wrong to say the very least. So

851
00:50:34,960 --> 00:50:38,719
Speaker 1: so let's talk about a couple of things um from

852
00:50:38,719 --> 00:50:41,400
Speaker 1: the book that I liked, that I thought was really interesting,

853
00:50:41,440 --> 00:50:44,799
Speaker 1: and see if we can extrapolate forward to today. So

854
00:50:44,960 --> 00:50:49,440
Speaker 1: I like the concept that measuring hospitalizations and deaths are

855
00:50:49,480 --> 00:50:52,560
Speaker 1: a bit like starlight. You're dealing with the phenomena that

856
00:50:52,600 --> 00:50:56,799
Speaker 1: has taken place long before. Everything that you're seeing in

857
00:50:56,920 --> 00:50:59,920
Speaker 1: terms of data is ancient, because if someone's in the

858
00:51:00,040 --> 00:51:04,280
Speaker 1: hospital today, it means they were infected ten, four, twenty

859
00:51:04,360 --> 00:51:08,680
Speaker 1: one days ago or longer. What does this mean for

860
00:51:09,840 --> 00:51:15,279
Speaker 1: detecting and containing infectious outbreaks? And secondly, what does it

861
00:51:15,320 --> 00:51:17,200
Speaker 1: mean when you have an entity like the c d

862
00:51:17,320 --> 00:51:20,520
Speaker 1: C that all they want is more data and the

863
00:51:20,560 --> 00:51:23,680
Speaker 1: more data you get, the later you are in that

864
00:51:23,760 --> 00:51:27,680
Speaker 1: containment curve. That's right, It's such a good point, which

865
00:51:27,760 --> 00:51:32,680
Speaker 1: is basically, by design, a system or a thinker a

866
00:51:32,800 --> 00:51:38,000
Speaker 1: human that can contain a fast moving novel pathogen by

867
00:51:38,040 --> 00:51:42,359
Speaker 1: design has to operate in the setting of uncertainty. And

868
00:51:42,400 --> 00:51:46,640
Speaker 1: the challenge with the CDC, and I include myself in

869
00:51:46,640 --> 00:51:51,560
Speaker 1: this local and state public health departments, is that they've

870
00:51:51,600 --> 00:51:57,239
Speaker 1: been or they they were created under an architecture to

871
00:51:57,400 --> 00:52:02,520
Speaker 1: reward being risk averse to only act under certainty, to

872
00:52:02,600 --> 00:52:06,000
Speaker 1: have a lot of data. The problem is, in an

873
00:52:06,080 --> 00:52:11,000
Speaker 1: uncertain circumstance, not taking action is action. It means you

874
00:52:11,040 --> 00:52:13,759
Speaker 1: lose your chance at containment and a worse outcome if

875
00:52:13,800 --> 00:52:16,480
Speaker 1: if the numbers chake out that way, that's right. And

876
00:52:16,520 --> 00:52:19,479
Speaker 1: so if you wait until you're certain, you've waited too long,

877
00:52:19,560 --> 00:52:21,800
Speaker 1: you've lost your chance. And that's what we saw happen

878
00:52:21,880 --> 00:52:26,680
Speaker 1: with COVID. There was no effort by CDC states I

879
00:52:26,680 --> 00:52:28,839
Speaker 1: would say locals, but there really was an effort by

880
00:52:28,880 --> 00:52:32,480
Speaker 1: some locals to contain this when when we had the chance.

881
00:52:32,520 --> 00:52:37,880
Speaker 1: And the reason is that an institution that needs certainty,

882
00:52:37,880 --> 00:52:41,040
Speaker 1: by definition, it will be too late, too late to act.

883
00:52:41,400 --> 00:52:44,640
Speaker 1: So so let's talk about states. What states got it right,

884
00:52:45,200 --> 00:52:47,279
Speaker 1: what states got it wrong, or if you want to

885
00:52:47,320 --> 00:52:51,560
Speaker 1: be a little more circumspect, what were the actions taken

886
00:52:51,680 --> 00:52:53,920
Speaker 1: that you thought were right and what were some of

887
00:52:53,920 --> 00:52:58,879
Speaker 1: the actions taken that had adverse outcomes. I don't even

888
00:52:58,920 --> 00:53:01,960
Speaker 1: know how to answer that, because we all got it wrong.

889
00:53:03,120 --> 00:53:05,160
Speaker 1: Who got it less wrong? Who got it more wrong?

890
00:53:06,360 --> 00:53:11,360
Speaker 1: I have tremendous respect for Governor Newsom in California. Every

891
00:53:11,400 --> 00:53:15,120
Speaker 1: decision I saw him make was right. He wanted to

892
00:53:15,200 --> 00:53:19,720
Speaker 1: understand the science, he wanted to understand the modeling, and

893
00:53:20,040 --> 00:53:22,919
Speaker 1: he was brave and bold and leaned forward and led

894
00:53:22,960 --> 00:53:25,399
Speaker 1: the country when he issued the stay at home order.

895
00:53:26,480 --> 00:53:29,440
Speaker 1: What was hard about that is governors had a horrible

896
00:53:29,560 --> 00:53:33,200
Speaker 1: choice where suddenly they were not being led at the

897
00:53:33,239 --> 00:53:36,520
Speaker 1: federal level. They were not given any tools to test.

898
00:53:37,200 --> 00:53:39,960
Speaker 1: They were basically told, you're on your own, no one's

899
00:53:40,000 --> 00:53:43,239
Speaker 1: coming to save you. Does that sound familiar? Um? And

900
00:53:43,320 --> 00:53:47,000
Speaker 1: so you know, the way that California led the country

901
00:53:47,040 --> 00:53:50,480
Speaker 1: I absolutely believe was right. But I don't want to

902
00:53:50,480 --> 00:53:54,960
Speaker 1: give anyone anymore than a D minus in this response.

903
00:53:55,520 --> 00:53:58,000
Speaker 1: And that is a system's failure. In other words, like

904
00:53:58,400 --> 00:54:01,239
Speaker 1: the humans got it right, the humans did exactly what

905
00:54:01,280 --> 00:54:05,560
Speaker 1: they should have done with heroic efforts, but the system failed.

906
00:54:05,560 --> 00:54:08,200
Speaker 1: The whole system failed. And so no matter how good

907
00:54:08,280 --> 00:54:11,759
Speaker 1: a governor's leadership was or individual vigilance. It was it

908
00:54:11,800 --> 00:54:14,319
Speaker 1: was going to be no match for this pathogen. What

909
00:54:14,400 --> 00:54:19,680
Speaker 1: are your thoughts on school closing lockdowns? Because we from

910
00:54:19,680 --> 00:54:25,799
Speaker 1: what we've learned about pandemic, that was very effective. When

911
00:54:25,800 --> 00:54:30,000
Speaker 1: you look at Pennsylvania in Philadelphia versus Um, was it

912
00:54:30,120 --> 00:54:35,640
Speaker 1: Cincinnati was the comparable state where city St. Louis versus Philadelphia.

913
00:54:35,840 --> 00:54:40,040
Speaker 1: That was really an interesting comparison. And when you actually

914
00:54:40,080 --> 00:54:45,399
Speaker 1: run the numbers, the faster UM lockdown was much more

915
00:54:45,440 --> 00:54:48,560
Speaker 1: effective at containment. That's right. And the reason why it

916
00:54:48,600 --> 00:54:51,160
Speaker 1: was more effective is what you had pointed out earlier

917
00:54:51,200 --> 00:54:54,839
Speaker 1: that if you wait until you have deaths and hospitalizations,

918
00:54:55,200 --> 00:54:57,839
Speaker 1: then you're too far behind. You haven't acted fast enough.

919
00:54:57,920 --> 00:55:00,319
Speaker 1: So with the very first death, at the very first case,

920
00:55:00,360 --> 00:55:02,400
Speaker 1: you have to act quickly. So you asked what I

921
00:55:02,400 --> 00:55:05,759
Speaker 1: think about schools? Well, what I love about having met

922
00:55:05,800 --> 00:55:08,839
Speaker 1: Carter Mesher and Richard Hatchett is they did not tell

923
00:55:08,880 --> 00:55:11,080
Speaker 1: me through the whole time we're on phone calls, as

924
00:55:11,200 --> 00:55:13,440
Speaker 1: as Wolverine's on the Red Don calls, they did not

925
00:55:13,560 --> 00:55:16,480
Speaker 1: tell me that they had written the pandemic plan for

926
00:55:16,520 --> 00:55:21,319
Speaker 1: the CDC under the Bush administration. They're so humble, but

927
00:55:21,440 --> 00:55:24,000
Speaker 1: the premise of that is, they had gone back and

928
00:55:24,080 --> 00:55:27,680
Speaker 1: re examined nineteen eighteen and discovered that the difference between St.

929
00:55:27,680 --> 00:55:31,440
Speaker 1: Louis and Philadelphia was St. Louis lockdown sooner and prevented

930
00:55:31,640 --> 00:55:35,360
Speaker 1: a number of deaths and bad outcomes. And so schools

931
00:55:35,360 --> 00:55:37,880
Speaker 1: play a huge role. You know, with COVID, children are

932
00:55:37,960 --> 00:55:40,799
Speaker 1: less likely to show symptoms. This is opposite than a

933
00:55:40,880 --> 00:55:45,160
Speaker 1: century You go, right, that was a well, So the

934
00:55:45,200 --> 00:55:48,920
Speaker 1: pathogen is different in that the Spanish flu of nineteen eighteen.

935
00:55:49,000 --> 00:55:51,280
Speaker 1: Not to get too wonky here, but the death curve

936
00:55:51,320 --> 00:55:54,759
Speaker 1: looked like a wright so very young were very fay girl,

937
00:55:55,360 --> 00:55:58,600
Speaker 1: very young, but also twenty year old, so high spike

938
00:55:58,640 --> 00:56:01,440
Speaker 1: and the children high spike year olds, and then high

939
00:56:01,440 --> 00:56:05,120
Speaker 1: spike and the elderly. The difference with COVID is children

940
00:56:05,120 --> 00:56:09,400
Speaker 1: are largely asymptomatic, but they absolutely still spread it. And

941
00:56:09,480 --> 00:56:13,000
Speaker 1: so that was the logic behind closing down schools is

942
00:56:13,520 --> 00:56:16,759
Speaker 1: children can spread it to each other. Uh, we did

943
00:56:16,800 --> 00:56:19,879
Speaker 1: not have ample testing to detect that, and children can't

944
00:56:19,880 --> 00:56:23,360
Speaker 1: be vaccinated. Right now, I think it's really important. You know,

945
00:56:23,360 --> 00:56:25,440
Speaker 1: I have three kids and I homeschooled them for a

946
00:56:25,520 --> 00:56:29,080
Speaker 1: year during COVID while running the COVID response, It was hard.

947
00:56:29,440 --> 00:56:32,360
Speaker 1: I think getting kids back to school is critical. Kids

948
00:56:32,440 --> 00:56:34,640
Speaker 1: need to be back to school. What does that mean

949
00:56:34,920 --> 00:56:38,160
Speaker 1: all of us adults need to be vaccinated and tools

950
00:56:38,200 --> 00:56:41,640
Speaker 1: like rapid tests and masking and decreasing the risk where

951
00:56:41,640 --> 00:56:43,839
Speaker 1: we can, we need to implement that to make sure

952
00:56:43,840 --> 00:56:45,680
Speaker 1: our kiddos can get back to school in the fall.

953
00:56:46,320 --> 00:56:50,800
Speaker 1: Makes a lot of sense. Uh, what happened in cities

954
00:56:50,880 --> 00:56:54,360
Speaker 1: where they cave to pressure from either business interests or

955
00:56:54,400 --> 00:56:58,320
Speaker 1: political interests about reopening more quickly. What with the results

956
00:56:58,320 --> 00:57:02,279
Speaker 1: of that? You mean now we're in nine team both. Well,

957
00:57:02,320 --> 00:57:06,440
Speaker 1: that's the great St. Louis versus Philadelphia comparison from nineteen eighteen.

958
00:57:06,480 --> 00:57:09,759
Speaker 1: And Philadelphia they move forward and had their parade as

959
00:57:09,800 --> 00:57:13,200
Speaker 1: planned with thousands and thousands of people breathing and coughing

960
00:57:13,239 --> 00:57:16,160
Speaker 1: on each other, and you know, a few weeks later

961
00:57:16,200 --> 00:57:20,400
Speaker 1: it was a massacre of deaths in Philadelphia. And what

962
00:57:20,440 --> 00:57:23,240
Speaker 1: we're about to see in the United States is what

963
00:57:23,400 --> 00:57:26,400
Speaker 1: Carter Mesher calls a tale of two America's and I agree,

964
00:57:26,960 --> 00:57:30,800
Speaker 1: meaning in communities that are under vaccinated, the delta variant

965
00:57:30,920 --> 00:57:33,680
Speaker 1: is going to move very fast. Remember, restrictions have been

966
00:57:33,720 --> 00:57:36,960
Speaker 1: lifted a lot of people are not masking. Congregate gatherings

967
00:57:37,000 --> 00:57:40,560
Speaker 1: are taking place, and so we're about to see two America's,

968
00:57:40,640 --> 00:57:45,680
Speaker 1: vaccinated America versus unvaccinated America. And I'm very, very concerned

969
00:57:45,800 --> 00:57:50,720
Speaker 1: about the delta variant causing wreaking havoc in unvaccinated regions.

970
00:57:51,920 --> 00:57:56,160
Speaker 1: Quite quite shocking. So so, like so many other of

971
00:57:56,200 --> 00:57:59,720
Speaker 1: Michael Lewis's book, this book like looks like it's heading

972
00:58:00,520 --> 00:58:03,880
Speaker 1: uh to be made as a major motion picture. If

973
00:58:03,920 --> 00:58:07,600
Speaker 1: you could pick somebody to play you in the film,

974
00:58:07,760 --> 00:58:10,440
Speaker 1: assuming they make a film, who would you pick? Because

975
00:58:10,480 --> 00:58:13,080
Speaker 1: I have a couple of actresses I would I would

976
00:58:13,320 --> 00:58:16,800
Speaker 1: think about like Natalie Portman or Claire Danes or there's

977
00:58:16,800 --> 00:58:20,840
Speaker 1: a handful of people I think could take your role. Um,

978
00:58:20,960 --> 00:58:23,880
Speaker 1: Julia Roberts already did Aaron Rockovic, so she's off the table.

979
00:58:24,120 --> 00:58:27,360
Speaker 1: Who would you pick to play yourself? I am neutral.

980
00:58:27,640 --> 00:58:30,960
Speaker 1: I don't have an opinion. Reese Witherspoon, that could be

981
00:58:31,000 --> 00:58:34,840
Speaker 1: a good actress. What I told Universal Studios is I

982
00:58:35,000 --> 00:58:37,480
Speaker 1: offer my support and help if I can be helpful

983
00:58:37,480 --> 00:58:40,880
Speaker 1: to you anyway, and I I want to just be supportive.

984
00:58:41,040 --> 00:58:43,200
Speaker 1: They are the artists. This is what they do. They're

985
00:58:43,240 --> 00:58:46,440
Speaker 1: really good at it. I'm a nerdy public health doctor,

986
00:58:46,560 --> 00:58:48,360
Speaker 1: so I think I'll let Hollywood do what it does.

987
00:58:48,440 --> 00:58:50,800
Speaker 1: Come on, you know, there has to be someone you

988
00:58:50,840 --> 00:58:53,920
Speaker 1: think would do a good job playing you. I'm neutral, alright,

989
00:58:54,200 --> 00:58:57,840
Speaker 1: let me so I'm gonna throw a different curve ball

990
00:58:58,120 --> 00:59:01,440
Speaker 1: at you. That's movie related. Let's talk a little bit

991
00:59:01,440 --> 00:59:07,520
Speaker 1: about Star Wars. Oh, let's talk about stars looking for So.

992
00:59:07,720 --> 00:59:10,439
Speaker 1: I have to assume you've been watching The Mandalorian, right

993
00:59:10,720 --> 00:59:14,000
Speaker 1: I did. Have you watched any of the other animated

994
00:59:14,040 --> 00:59:17,600
Speaker 1: features on Disney, like Rebels, Oh, I can't bring myself

995
00:59:17,640 --> 00:59:19,400
Speaker 1: to do it, because some of them are really good.

996
00:59:19,560 --> 00:59:22,160
Speaker 1: I'm a Star Wars purist. So I watched The Mandalorian

997
00:59:22,200 --> 00:59:24,240
Speaker 1: because my my I have three boys, they're ten, twelve,

998
00:59:24,240 --> 00:59:26,280
Speaker 1: and fourteen, and they begged me and convinced me to

999
00:59:26,280 --> 00:59:29,600
Speaker 1: watch the man. It was great. It was good, but

1000
00:59:29,760 --> 00:59:32,240
Speaker 1: it's not a Star Wars. So the reason I love

1001
00:59:32,280 --> 00:59:34,840
Speaker 1: Star Wars is I first got interested in The Hero's

1002
00:59:34,920 --> 00:59:39,200
Speaker 1: Journey by Joseph Campbell, Man of a Thousand Faces, And

1003
00:59:39,240 --> 00:59:41,960
Speaker 1: when I discovered that Joseph Campbell had been a thought

1004
00:59:42,000 --> 00:59:45,920
Speaker 1: partner in designing Star Wars to be the classic heroes Journey.

1005
00:59:45,960 --> 00:59:47,520
Speaker 1: That's why I fell in love with Star Wars. So

1006
00:59:47,520 --> 00:59:50,720
Speaker 1: I haven't been able to watch any of the spinoffs. Yeah,

1007
00:59:50,720 --> 00:59:55,920
Speaker 1: but you watched all right, so if um you watched,

1008
00:59:56,560 --> 00:59:59,280
Speaker 1: obviously we all started with Star Wars and then Empire

1009
00:59:59,360 --> 01:00:03,000
Speaker 1: and then Jef Die. But then you know the episode

1010
01:00:03,040 --> 01:00:06,840
Speaker 1: the first couple sort of went off the rails after that,

1011
01:00:07,080 --> 01:00:10,440
Speaker 1: and it was only the most recent You want to

1012
01:00:10,440 --> 01:00:13,840
Speaker 1: talk about jaw Banks, that's scandalous. I so I love

1013
01:00:13,920 --> 01:00:16,760
Speaker 1: the pod race scene in episode one with Anakin as

1014
01:00:16,800 --> 01:00:19,040
Speaker 1: a little kiddo. That is one of my all time

1015
01:00:19,040 --> 01:00:23,520
Speaker 1: favorite stars. That's a good scene in a not great particular.

1016
01:00:23,960 --> 01:00:27,240
Speaker 1: I love that movie. The most recent few have been

1017
01:00:27,360 --> 01:00:31,640
Speaker 1: a huge improvement over the previous view. They're fantastic. And

1018
01:00:31,680 --> 01:00:34,920
Speaker 1: what I love about um, you know the first three.

1019
01:00:35,360 --> 01:00:38,600
Speaker 1: So if you look at Anakin Skywalker, Luke Skywalker, and

1020
01:00:38,680 --> 01:00:41,520
Speaker 1: certainly Ray, all of them are orphans in the desert

1021
01:00:41,800 --> 01:00:44,120
Speaker 1: who don't know who their family of origin is and

1022
01:00:44,120 --> 01:00:46,320
Speaker 1: they don't know what they're calling is. And I think

1023
01:00:46,360 --> 01:00:49,120
Speaker 1: that resonates with every human, you know, groping our way

1024
01:00:49,120 --> 01:00:50,840
Speaker 1: in the darkness to figure out who am I and

1025
01:00:50,880 --> 01:00:52,880
Speaker 1: what am I supposed to do? On Earth. I love

1026
01:00:52,920 --> 01:00:55,320
Speaker 1: how animated this this makes you. I can't get you

1027
01:00:55,360 --> 01:00:58,960
Speaker 1: off the fence on who should play you, Reese Witherspoon,

1028
01:00:59,000 --> 01:01:01,280
Speaker 1: give me a call, but Star Wars and you're just

1029
01:01:01,360 --> 01:01:04,960
Speaker 1: suddenly so I only have you for a few more minutes.

1030
01:01:05,080 --> 01:01:08,480
Speaker 1: Let me jump to my favorite questions that we ask

1031
01:01:08,560 --> 01:01:12,280
Speaker 1: all of our guests, and speaking of of Star Wars,

1032
01:01:12,320 --> 01:01:14,960
Speaker 1: tell us what you're streaming? What do you What are

1033
01:01:14,960 --> 01:01:18,360
Speaker 1: you when the boys watching during lockdown on Netflix or

1034
01:01:18,400 --> 01:01:29,280
Speaker 1: Amazon Prime besides the Mandalorian, Oh, I nothing, nothing. Um,

1035
01:01:29,400 --> 01:01:31,439
Speaker 1: that's not the answer you want. No, I don't. I'm

1036
01:01:31,480 --> 01:01:36,400
Speaker 1: just curious. It's answer. Yeah, it's listen. It's always a

1037
01:01:36,480 --> 01:01:41,040
Speaker 1: curiosity when you find out that someone who studies infectious

1038
01:01:41,080 --> 01:01:44,080
Speaker 1: disease love Star Wars. That sort of stuff is so

1039
01:01:44,360 --> 01:01:49,000
Speaker 1: honestly during lockdown, like your boys are not watching The Crown, No,

1040
01:01:49,080 --> 01:01:52,080
Speaker 1: they're not watching The Crown. The question over dinner is

1041
01:01:52,160 --> 01:01:54,480
Speaker 1: which Star Wars episode are you gonna watch? But you

1042
01:01:54,480 --> 01:01:56,840
Speaker 1: don't watch. It's been a year. You're not watching a

1043
01:01:56,880 --> 01:02:00,360
Speaker 1: different Star Wars every night, Episode seven about fifty six times,

1044
01:02:00,400 --> 01:02:03,160
Speaker 1: to the point that my kids say, over dinner, Mom,

1045
01:02:03,160 --> 01:02:05,960
Speaker 1: it's not episode seven. We're not watching that again, and

1046
01:02:06,040 --> 01:02:11,080
Speaker 1: so we we do watch Star Wars a lot. That's frightening. Yeah, um,

1047
01:02:11,120 --> 01:02:15,520
Speaker 1: all right, so let me let me go on, but

1048
01:02:15,560 --> 01:02:19,680
Speaker 1: it's Star Wars. Well, we watched The Mandaloriana. We watched

1049
01:02:19,720 --> 01:02:23,840
Speaker 1: Stranger Things. We repeated season one of Stranger Things because

1050
01:02:23,840 --> 01:02:27,080
Speaker 1: it's so good. We love Stranger Things. Um. I think

1051
01:02:27,120 --> 01:02:29,520
Speaker 1: that's it that we pretty much were pretty we stick

1052
01:02:29,560 --> 01:02:33,960
Speaker 1: to the same stuff. Yeah, um this, I'm gonna make

1053
01:02:34,000 --> 01:02:36,280
Speaker 1: a recommendation for you. But I'm drawing a blank on

1054
01:02:36,320 --> 01:02:39,800
Speaker 1: the name now, which is pretty awful, and I'll circle back.

1055
01:02:39,880 --> 01:02:42,880
Speaker 1: I also make them watch any movies about carbon. Did

1056
01:02:42,880 --> 01:02:47,120
Speaker 1: you see Altered Carbon? Altered Carbon? Now? It could be

1057
01:02:47,200 --> 01:02:51,840
Speaker 1: the best sci fi series I've seen this year. And

1058
01:02:51,920 --> 01:02:54,760
Speaker 1: that includes Well, The Boys might be a little too

1059
01:02:54,760 --> 01:02:57,280
Speaker 1: gross and violent for you. I don't know if that. Oh,

1060
01:02:57,280 --> 01:03:00,000
Speaker 1: we don't mind gross and violence. I generally I generally

1061
01:03:00,040 --> 01:03:03,200
Speaker 1: avoid you know, certain other categories. But gross and violent

1062
01:03:03,320 --> 01:03:05,560
Speaker 1: is Finally, there's some sexual stuff in it, but it's

1063
01:03:05,600 --> 01:03:13,560
Speaker 1: mostly just what happens if superheroes are corrupt corporate players.

1064
01:03:14,080 --> 01:03:16,880
Speaker 1: Is the theme on that. It's from a graphic novel,

1065
01:03:17,160 --> 01:03:20,080
Speaker 1: and if if If The Boys is a little too

1066
01:03:20,120 --> 01:03:23,920
Speaker 1: grizzly for you. Umbrella Academy was really interesting in the

1067
01:03:23,960 --> 01:03:27,640
Speaker 1: same orphan theme runs through that, as as Star Wars

1068
01:03:27,640 --> 01:03:30,360
Speaker 1: such is kind of interesting. There's a ton of really

1069
01:03:30,400 --> 01:03:33,240
Speaker 1: interesting sci fi that have come out but altered carbon.

1070
01:03:33,600 --> 01:03:38,680
Speaker 1: It's two seasons and it's just everybody I've recommended that too,

1071
01:03:38,720 --> 01:03:42,480
Speaker 1: has come back and said five stars. It's just so good. Okay, So,

1072
01:03:42,600 --> 01:03:44,760
Speaker 1: and I'm a Star Wars so I'm gonna tell you

1073
01:03:44,800 --> 01:03:47,320
Speaker 1: if I'm gonna make one wreck, that that's the one.

1074
01:03:47,480 --> 01:03:50,080
Speaker 1: Let's talk about your career a little bit. Who were

1075
01:03:50,160 --> 01:03:53,360
Speaker 1: some of your mentors that helped shape and guide your career.

1076
01:03:56,000 --> 01:03:59,280
Speaker 1: When I was at Tulane, I decided I wanted to

1077
01:03:59,360 --> 01:04:02,480
Speaker 1: do trauma surgery because I was working with Norm McSwain

1078
01:04:02,880 --> 01:04:05,800
Speaker 1: and Dr Norm McSwain was a famous trauma surgeon at

1079
01:04:05,840 --> 01:04:09,840
Speaker 1: Tulane and I absolutely adored him. And even though my

1080
01:04:09,880 --> 01:04:13,840
Speaker 1: path ended up twisting and turning into public health disease control,

1081
01:04:14,280 --> 01:04:17,960
Speaker 1: the way that I think about taking fast action, you know,

1082
01:04:18,120 --> 01:04:21,320
Speaker 1: cut the patient open, a lot of it goes back

1083
01:04:21,560 --> 01:04:26,120
Speaker 1: to Norm McSwain. The other mentor was doctor Steve Jose,

1084
01:04:26,360 --> 01:04:28,919
Speaker 1: who was an infectious disease doctor that Michael Lewis talked

1085
01:04:28,920 --> 01:04:31,440
Speaker 1: about in the book, and he trained me how to

1086
01:04:31,520 --> 01:04:35,280
Speaker 1: think about infectious disease. And so it's really the combination of,

1087
01:04:36,080 --> 01:04:38,960
Speaker 1: you know, the fast scalpel of a trauma surgeon with

1088
01:04:39,040 --> 01:04:42,480
Speaker 1: the critical risk management of an infectious disease doctor. That

1089
01:04:42,560 --> 01:04:47,560
Speaker 1: shaped how I manage outbreaks. Very interesting. Let's talk about books.

1090
01:04:47,600 --> 01:04:49,440
Speaker 1: What are some of your favorite books and what are

1091
01:04:49,440 --> 01:04:52,120
Speaker 1: you reading right now? Sure? Well, I'm reading a book

1092
01:04:52,120 --> 01:04:54,600
Speaker 1: by Ben Horrow. It's called The Hard Thing About Hard Things,

1093
01:04:55,360 --> 01:04:57,480
Speaker 1: and I absolutely love it. You know, I'm a new

1094
01:04:57,560 --> 01:05:01,080
Speaker 1: CEO and I'm learning and I'm learning fast, and so

1095
01:05:01,560 --> 01:05:05,000
Speaker 1: that book has really influenced my thinking around making hard

1096
01:05:05,040 --> 01:05:09,800
Speaker 1: decisions and doing them sooner. Uh. And I let's see

1097
01:05:09,840 --> 01:05:13,240
Speaker 1: what podcasts am I listening? Oh, the podcast I'm listening

1098
01:05:13,240 --> 01:05:16,320
Speaker 1: to is by Reid Hoffman, Masters of Scale. He interviews

1099
01:05:16,400 --> 01:05:19,560
Speaker 1: really interesting business leaders and actually met him last week

1100
01:05:19,600 --> 01:05:23,040
Speaker 1: for the first time, and so the Reid Hoffman podcast

1101
01:05:23,080 --> 01:05:25,280
Speaker 1: is really helpful to me to hear from other startup

1102
01:05:25,360 --> 01:05:27,800
Speaker 1: CEOs who ended up being very successful. What were their

1103
01:05:27,880 --> 01:05:30,640
Speaker 1: challenges and strategies in the very beginning. I'm trying to

1104
01:05:30,640 --> 01:05:36,280
Speaker 1: remember the name of Ben Horowitz is colleague Scott Poor,

1105
01:05:36,760 --> 01:05:40,640
Speaker 1: whose general counsel at UM Entrees and Horowitz, wrote a

1106
01:05:40,680 --> 01:05:43,840
Speaker 1: book on the Secret to Silicon Value of the Secret

1107
01:05:44,280 --> 01:05:48,680
Speaker 1: of Venture Capital, and it's really just a very smart,

1108
01:05:48,800 --> 01:05:51,000
Speaker 1: basic how to. Here's what you should know if you're

1109
01:05:51,000 --> 01:05:53,640
Speaker 1: setting up a company, if you're taking outside financing, and

1110
01:05:53,680 --> 01:05:57,160
Speaker 1: it's just written from someone from the inside. So if

1111
01:05:57,160 --> 01:06:00,360
Speaker 1: you like Ben's book, his colleague Scott's book is really

1112
01:06:01,920 --> 01:06:05,040
Speaker 1: what's interesting. I've kind of worked my way through UM

1113
01:06:05,160 --> 01:06:10,720
Speaker 1: half the A sixteen Z crew. They're they're really interesting guys. UM.

1114
01:06:10,920 --> 01:06:14,840
Speaker 1: Next question, what sort of advice would you give to

1115
01:06:14,960 --> 01:06:18,320
Speaker 1: a recent college grad who was interested in a career

1116
01:06:18,840 --> 01:06:25,800
Speaker 1: in either public health or infectious disease. I would ask

1117
01:06:25,840 --> 01:06:29,040
Speaker 1: them what they really want to do. Because of what

1118
01:06:29,080 --> 01:06:32,880
Speaker 1: they really want to do is change the world or

1119
01:06:33,120 --> 01:06:37,120
Speaker 1: protect communities, then they need to think hard about what

1120
01:06:37,160 --> 01:06:41,480
Speaker 1: their career path is because becoming a medical doctor, you know,

1121
01:06:41,560 --> 01:06:44,360
Speaker 1: my path was I went to medical school at the

1122
01:06:44,400 --> 01:06:47,160
Speaker 1: same time in parallel, doing a master's in public health

1123
01:06:47,160 --> 01:06:51,080
Speaker 1: and tropical medicine, then did general surgery, then did internal medicine.

1124
01:06:51,120 --> 01:06:53,600
Speaker 1: It is many, many years, and I would tell them

1125
01:06:53,640 --> 01:06:56,160
Speaker 1: to think long and hard about what's the endgame, what

1126
01:06:56,200 --> 01:06:59,800
Speaker 1: are they hoping to do? Because public health and medicine

1127
01:06:59,840 --> 01:07:03,800
Speaker 1: is not the only path to accomplishing what they want

1128
01:07:03,840 --> 01:07:06,920
Speaker 1: to I think people tend to think, oh, I need

1129
01:07:06,960 --> 01:07:09,960
Speaker 1: to become a doctor and or get a PhD in

1130
01:07:10,040 --> 01:07:12,680
Speaker 1: public health to make a difference, But there's lots of

1131
01:07:12,720 --> 01:07:15,880
Speaker 1: ways to make a difference that are not as long

1132
01:07:15,880 --> 01:07:20,160
Speaker 1: of a path. Interesting and our final question, what do

1133
01:07:20,200 --> 01:07:22,960
Speaker 1: you know about the world of public health today that

1134
01:07:23,040 --> 01:07:25,960
Speaker 1: you wish you knew twenty five or so years ago

1135
01:07:26,000 --> 01:07:29,040
Speaker 1: when you were first getting started? Sure, well, I'm forty

1136
01:07:29,080 --> 01:07:32,360
Speaker 1: three years old, so twenty five or so years ago

1137
01:07:32,480 --> 01:07:36,400
Speaker 1: would put me at what call years ago? Though? So

1138
01:07:36,440 --> 01:07:39,320
Speaker 1: when you were first thinking about public health and and

1139
01:07:39,880 --> 01:07:44,840
Speaker 1: what what insight do you have today might have helped you? Um,

1140
01:07:44,920 --> 01:07:49,960
Speaker 1: when you were just getting out of medical school. I

1141
01:07:50,000 --> 01:07:52,400
Speaker 1: wish I had known that I was just as smart

1142
01:07:53,320 --> 01:07:57,240
Speaker 1: that a poor kid from rural California, I'm sorry, a

1143
01:07:57,280 --> 01:08:00,880
Speaker 1: poor kid from rural Oregon that grew up on government

1144
01:08:00,960 --> 01:08:06,040
Speaker 1: assistance with parents that didn't have college degrees, could not

1145
01:08:06,160 --> 01:08:11,360
Speaker 1: only be just as successful, but could absolutely lead a

1146
01:08:11,400 --> 01:08:14,400
Speaker 1: public health department or lead an entire state. I didn't

1147
01:08:14,440 --> 01:08:17,680
Speaker 1: know that then. I was very intimidated by my classmates

1148
01:08:17,680 --> 01:08:21,479
Speaker 1: who came from wealthy families and ivy league colleges and

1149
01:08:21,560 --> 01:08:26,400
Speaker 1: I didn't. And that impostor syndrome has plagued me my

1150
01:08:26,479 --> 01:08:30,879
Speaker 1: whole career, and I still struggle with it. And today,

1151
01:08:31,040 --> 01:08:34,200
Speaker 1: you know, covid response to such a great example where

1152
01:08:34,240 --> 01:08:37,479
Speaker 1: what really matters is how brave someone is and how

1153
01:08:37,600 --> 01:08:42,120
Speaker 1: committed they are, and how much they persevere, not necessarily

1154
01:08:43,000 --> 01:08:46,439
Speaker 1: the privilege or their family of origin. But when you're

1155
01:08:46,479 --> 01:08:49,679
Speaker 1: twenty three years old you don't know that yet. That's

1156
01:08:49,760 --> 01:08:53,600
Speaker 1: quite interesting. Thank you, Charity for being so generous with

1157
01:08:53,680 --> 01:08:57,240
Speaker 1: your time. We have been speaking with Charity Dean. She

1158
01:08:57,640 --> 01:09:01,200
Speaker 1: is the co founder of the Public Health Company, as

1159
01:09:01,240 --> 01:09:05,480
Speaker 1: well as the protagonist of Michael Lewis's new book, The Premonition.

1160
01:09:05,880 --> 01:09:08,439
Speaker 1: If you enjoy this conversation, well, be sure and check

1161
01:09:08,479 --> 01:09:11,920
Speaker 1: out any of our previous UH three and fifty or

1162
01:09:11,960 --> 01:09:15,679
Speaker 1: so such podcasts that we've done over the past seven years.

1163
01:09:16,040 --> 01:09:20,479
Speaker 1: You can find those at iTunes, Spotify, wherever finer podcasts

1164
01:09:20,520 --> 01:09:24,439
Speaker 1: are found. We love your comments, feedback and suggestions right

1165
01:09:24,520 --> 01:09:27,840
Speaker 1: to us at m IB podcast at Bloomberg dot net.

1166
01:09:28,360 --> 01:09:31,320
Speaker 1: You can sign up from my Daily Reads at Ridolts

1167
01:09:31,400 --> 01:09:35,080
Speaker 1: dot com. Check out my weekly column on Bloomberg dot

1168
01:09:35,120 --> 01:09:39,360
Speaker 1: com slash Opinion. Follow me on Twitter at rit Halts.

1169
01:09:39,560 --> 01:09:41,320
Speaker 1: I would be remiss if I did not thank the

1170
01:09:41,360 --> 01:09:44,880
Speaker 1: crack staff that helps put this conversation together each week.

1171
01:09:45,320 --> 01:09:49,480
Speaker 1: Paris Wald is my producer. Charlie Wouldner is my audio engineer.

1172
01:09:49,760 --> 01:09:53,439
Speaker 1: A Tika val Bron is our project manager. Michael Batnick

1173
01:09:53,640 --> 01:09:57,640
Speaker 1: is my researcher. I'm Barry Hults. You've been listening to

1174
01:09:57,760 --> 01:10:01,200
Speaker 1: b STOOD Business on Bloomberg Radio.