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Speaker 1: Hi everyone, Sophia Bush here, Welcome to Work in Progress,

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Speaker 1: where I talk to people who inspire me about how

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Speaker 1: they got to where they are and where they think

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Speaker 1: they're still going. Hi everyone, welcome to another special episode

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Speaker 1: of Work in Progress, where we'll be continuing our new

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Speaker 1: video series Need to Know as I sit down each

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Speaker 1: week with experts to get the answers to your most

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Speaker 1: pressing questions about life right now. I am so excited

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Speaker 1: about my guest today brings with her so much incredible

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Speaker 1: insight and information about our current health crisis and more

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Speaker 1: that you need to know. The brilliant Jessica Mulatty Rivera.

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Speaker 1: Jessica is a microbiologist, epidemiologist, and science communicator that has

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Speaker 1: dedicated the last fifty years of her career to infectious

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Speaker 1: disease epidemiology and public health policy and vaccine advocacy. So yes,

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Speaker 1: this is going to be a really good conversation to

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Speaker 1: share with anyone in your life who might be suspicious

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Speaker 1: of vaccines or who's read very dangerous disinformation about them.

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Speaker 1: She's currently serving as the science communication lead for the

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Speaker 1: COVID Tracking Project at the Atlantic, in addition to remaining

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Speaker 1: an active member of the COVID nineteen dispersed volunteer Research

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Speaker 1: Network and an expert contributor for NBC in the Bay

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Speaker 1: Area and CNN. Jessica has a special gift for being

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Speaker 1: able to translate complex scientific concepts into impactful, effective, and

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Speaker 1: judgment free information. She makes really complex topics accessible to

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Speaker 1: a wide audience. And in addition to the work that

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Speaker 1: she does professionally, Jessica is a white if Anna mom

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Speaker 1: raising two young children under the age of five, so

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Speaker 1: she gets the science of COVID and of the life

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Speaker 1: complexities that it can cause. In my conversation with Jessica,

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Speaker 1: we discuss how she became interested in epidemiology, her life

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Speaker 1: changing internship with a human rights firm, and how that

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Speaker 1: led her to be even more committed to science, racial

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Speaker 1: disparities in public health, the current pandemic, and how her

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Speaker 1: and her family are coping with it, plus the danger

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Speaker 1: of misinformation, how science actually disproves conspiracies, and so much more.

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Speaker 1: Enjoy Hi, my smartest friend. Hi. That is a high

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Speaker 1: compliment because we we weirdly have a pretty brilliant friend group.

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Speaker 1: But I'm so excited to have you on the podcast

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Speaker 1: today because you are hands down one of the most

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Speaker 1: brilliant women that I know, and I also feel unbearably fortunate,

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Speaker 1: in the midst of a global pandemic to be able

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Speaker 1: to text a scientists in either a panic or for

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Speaker 1: clarity at any time of day or night. You are

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Speaker 1: a saintly human because you always respond to me, and

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Speaker 1: I can't wait to ask you all of the questions

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Speaker 1: that I ask you in private in this forum so

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Speaker 1: that everyone else can benefit from your expertise as well.

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Speaker 1: Oh thank you, You're so sweet. Um, this is my joy.

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Speaker 1: I really love doing this and I honestly couldn't have

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Speaker 1: ever predicted that this is the kind of way that

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Speaker 1: I can utilize my education, but I'm happy to do

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Speaker 1: it well, and your education I'm going to run through

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Speaker 1: and perhaps make you blush a bit. But here we go.

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Speaker 1: For all of our listeners today, I'm interviewing Jess A

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Speaker 1: because she is a wonderful friend and b because she

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Speaker 1: is a brilliant scientist. I really loved actually having to

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Speaker 1: prep a podcast with you, because I was like, this,

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Speaker 1: is this weird that I'm researching my own friend, Um,

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Speaker 1: But it it feels like such an important moment to

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Speaker 1: let people know many of your arenas of expertise. You

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Speaker 1: started actually at USC, at the University of Southern California,

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Speaker 1: where you earned a Bachelor of Science. Your field of

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Speaker 1: study was health promotion and disease prevention studies, and you

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Speaker 1: got dual minors because of course you did in natural

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Speaker 1: science and Spanish. You graduated magna cum laude, no worries,

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Speaker 1: you were on the Dean's list. You directed research at

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Speaker 1: the QECH School of Medicine, and then you went onto

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Speaker 1: Georgetown you got a Master of Science in bio Hazardous

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Speaker 1: threat Agents and Emerging infectious Diseases, and you graduated sumac

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Speaker 1: mative because of course you did so. When when COVID

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Speaker 1: was first making its point of entry into the country

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Speaker 1: tree and we were watching what was happening overseas. Uh,

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Speaker 1: you and I, well, you really were so kind to

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Speaker 1: grace me with your presence on multiple Instagram lives. We

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Speaker 1: were answering people's questions and those were still very early days.

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Speaker 1: And so it feels like now six plus months into

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Speaker 1: a global pandemic making itself very at home in the

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Speaker 1: United States and unfortunately US doing the worst really of

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Speaker 1: any country on Earth. Um, it feels like there's a

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Speaker 1: lot of questions that we need to get to and

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Speaker 1: a lot of information that you, as one of the

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Speaker 1: leading scientists of the COVID tracking project, can offer to

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Speaker 1: everyone who's listening to this episode. And I want to

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Speaker 1: ask you all of those questions, but before we get

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Speaker 1: into pandemics and what they mean, and if we're going

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Speaker 1: to be wearing masks forever, and will anything ever feel

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Speaker 1: normal again? And what was normal? Anyway? I want to

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Speaker 1: go backwards, and I like to do this with everyone

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Speaker 1: who comes on the show. You are such impressive human

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Speaker 1: and I want to know how you ended up being

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Speaker 1: like this. Were you? Were you just a wildly smart

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Speaker 1: and inquisitive little girl. Were you always into science in school? Like?

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Speaker 1: Who was Jessica at eight or ten? Where did you

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Speaker 1: grow up? What was your family? Like? Give us, give

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Speaker 1: us the lay of the land. Yeah, so you know,

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Speaker 1: I grew up in an immigrant house. My parents moved

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Speaker 1: here from Egypt and I was born a year later.

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Speaker 1: And my mom stands out is probably my biggest inspiration

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Speaker 1: for my curious mind. She is an agricultural engineer by

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Speaker 1: training UM and she actually put aside her career to

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Speaker 1: become a mom, which is a sacrifice that now as

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Speaker 1: I'm a mom, I you know, deeply respect and know

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Speaker 1: how difficult it was for her to make that choice. Um.

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Speaker 1: But for as long as I can remember, my mom

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Speaker 1: always was sitting next to me while I did homework,

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Speaker 1: and and she enjoyed it. She enjoyed explaining things to me.

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Speaker 1: She you know, I remember the like very silly songs

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Speaker 1: she made to help me memorize the state capitals and

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Speaker 1: multiplication tables and like, you know, even kind of mixing

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Speaker 1: the type of way she learned math and science and

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Speaker 1: Arabic and teaching me kind of another way to reinforce

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Speaker 1: the message in English. Um. Yeah, it was just a

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Speaker 1: really special kind of experience as a as a learner

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Speaker 1: growing up in this country because my mom was so

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Speaker 1: educated from Egypt and she came in and it was

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Speaker 1: just like I'm gonna learn how we do it here

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Speaker 1: and I'm going to like reinforce it at home. So

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Speaker 1: homework was always a big priority, and it was the

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Speaker 1: first thing I did when I went home. And you know,

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Speaker 1: I very much grew up in that stereotypical kind of

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Speaker 1: first generation American home where academics were the number one thing,

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Speaker 1: and I am grateful for it. I benefited from it. Um.

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Speaker 1: I remember one time doing like a really strange, you know,

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Speaker 1: self motivated experiment on our fish and our fish tank.

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Speaker 1: Can you know it just was endlessly curious, endlessly wanting

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Speaker 1: to know how things work and why, and um, you

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Speaker 1: know very much looked to my parents as an inspiration

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Speaker 1: for that. And you know, they said I wanted to

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Speaker 1: be a doctor from very young and always loved math

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Speaker 1: and science, always excelled in math and science, and um,

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Speaker 1: you know, it took a while for me to realize

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Speaker 1: like what that actually meant, because you know, I went

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Speaker 1: to USC and I intended on being premed I studied

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Speaker 1: HP health Promotion and disease prevention. It was in the

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Speaker 1: School of Medicine. It had a science and public health emphasis,

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Speaker 1: and I loved it. But it also had this really

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Speaker 1: wonderful intersection of other disciplines like human rights and sociology

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Speaker 1: and anthropology, and I remember it distinctly. I was preparing

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Speaker 1: to study for the MCATs in my senior year and

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Speaker 1: I took a class called health and Human Rights and

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Speaker 1: it wrecked me. I remember thinking like, oh my gosh,

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Speaker 1: I don't know if I can commit to the next

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Speaker 1: you know, ten to fifteen years and five to seven

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Speaker 1: dred thousand dollars, you know, to become an m D.

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Speaker 1: I I feel like I want to explore this more,

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Speaker 1: this like social connection to medicine and to my parents

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Speaker 1: extreme dismay. I decided not to apply to med school

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Speaker 1: and instead I took an internship in Washington, d C.

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Speaker 1: Unpaid and even writes firm. My parents were devastated, as

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Speaker 1: you can imagine, I had, you know, everything was. I

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Speaker 1: went to school at USC because I grew up in

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Speaker 1: l A. That was my option to be in l A.

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Speaker 1: And so the first chance I could leave, I took it,

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Speaker 1: and I moved to d C. And I really didn't

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Speaker 1: look back. It was intended to be a four month internship.

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Speaker 1: That internship turned into a job, and I had this

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Speaker 1: unique job of using my science background to help. I

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Speaker 1: traveled to fifteen countries in like three years, basically training

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Speaker 1: our offices on things like public health and water sanitation

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Speaker 1: and you know, how to take care in like emergency

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Speaker 1: situations and natural disasters, and an experience that no person

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Speaker 1: right out of college would expect. But it was just

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Speaker 1: so formative for me, and I was I did that

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Speaker 1: for a few years and a good friend of mine

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Speaker 1: who was working at Georgetown recruited me to be part

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Speaker 1: of this really neat project called Project Argus. And this

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Speaker 1: is kind of like where the rest of my life

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Speaker 1: can kind of point back to um. Project Argus was

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Speaker 1: a It was in the Division of Integrated Biodefense and

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Speaker 1: they essentially, we're a team of analysts that tracked indicators

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Speaker 1: and warnings of emerging threats and that were you know,

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Speaker 1: biological chemical related to animals, plants, and humans. And they

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Speaker 1: needed people who spoke different languages to essentially read news

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Speaker 1: from all over the world, translate it and create these

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Speaker 1: like you know, situation reports that were then fed to

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Speaker 1: the US government, and the US government used this data

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Speaker 1: to help predict emerging threats, emerging pandemics, and you know

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Speaker 1: it's you know now looking back and I see all

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Speaker 1: these articles like, oh, we should have a weather channel

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Speaker 1: for emerging diseases. We had one. We had one. This

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Speaker 1: is like one of the main things that kills me

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Speaker 1: about what's going on right now is this, you know,

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Speaker 1: the fallout from devaluing and defunding public health. We had

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Speaker 1: an amazing system and we had about forty something languages

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Speaker 1: spoken in our analyst floor. Our team was one of

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Speaker 1: the first people to identify the emergence of the two

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Speaker 1: nine one and one pandemic in Mexico. UM it was incredible.

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Speaker 1: I loved that work. And while I was there, I

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Speaker 1: studied at Georgetown School of Medicine and that's what I

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Speaker 1: got my degree in emerging infectious diseases. So can I

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Speaker 1: ask you a clarification question, because one of the things

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Speaker 1: that I've learned a lot about from you is the

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Speaker 1: value of science, communication and translation, and for better or worse,

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Speaker 1: my natural inquisitive mind and refusal to give up on

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Speaker 1: something until I understand it means that I often wind

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Speaker 1: up translating pretty complex data for my audiences. And when

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Speaker 1: you're talking about this network, when you're talking about Project Arguess,

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Speaker 1: what I'm hearing you say is that you worked on

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Speaker 1: a huge floor of analysts. Over forty languages were spoken.

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Speaker 1: And what I'm hearing is that the reason you were

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Speaker 1: reading news from around the world was let's say that

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Speaker 1: this team had still existed as it should have at

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Speaker 1: the end of twenty nineteen, someone on that team who

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Speaker 1: spoke Chinese would have known what was happening in Wuhan.

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Speaker 1: Much earlier, and we would have been able to mobilize

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Speaker 1: the pandemic response network at the CDC and across all

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Speaker 1: the levels of the United States government and public health.

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Speaker 1: But because okay, so that's really interesting to me that

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Speaker 1: it required both language expertise and scientific expertise on a

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Speaker 1: team to look at what was happening around the world.

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Speaker 1: And when you talk about the era in which you

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Speaker 1: were working at that project UM in the last administration,

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Speaker 1: and you talk about your team seeing H one N

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Speaker 1: one hit Mexico and knowing it was there before it

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Speaker 1: was on the news in the US, what did that mean?

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Speaker 1: Can you walk us through what the action items are

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Speaker 1: when you see an emerging pandemic as health response network worker?

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Speaker 1: What does the network then do? What does the ARGUS

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Speaker 1: team do in two thousand nine when you I D

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Speaker 1: H one N one, Because I think it would be

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Speaker 1: helpful to know what we're supposed to do or what

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Speaker 1: we used to do, so that we can understand the

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Speaker 1: current failures and the opportunities that were missed to corral

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Speaker 1: this and to control it. Yeah, that's a great question.

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Speaker 1: So we were divided into teams based on our language

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Speaker 1: expertise and our Latin America team was the one that

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Speaker 1: found it. And what was essentially happening is and we

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Speaker 1: were looking for things that you may not even expect.

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Speaker 1: We were looking for, you know, uh, school closures or

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Speaker 1: hospital hospitals being full, or an uptick and demand in ventilators,

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Speaker 1: or an uptick in the diagnosis of respiratory illnesses. And

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Speaker 1: those respiratory illnesses could be like undifferentiated, they could be

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Speaker 1: like flu like or just pneumonia. All of these things

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Speaker 1: were pieces of a puzzle that as they grew in

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Speaker 1: size and speed, we were able to then kind of

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Speaker 1: categorize what was happening in stages and we would say

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Speaker 1: this is a stage one event, and it would kind

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Speaker 1: of have increasingly stages of severity. And then when we

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Speaker 1: saw something alarming where we were seeing like an uptick

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Speaker 1: and at least a few of these things, we would

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Speaker 1: flag it. We would write a report and we would

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Speaker 1: say there's an increase in cases of respiratory illness and

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Speaker 1: increased demand and ventilator schools are shutting down, infrastructure collapse,

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Speaker 1: infrastructure strain, whatever it was, and we would basically sound

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Speaker 1: the alarms. So that's really interesting because it's important for

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Speaker 1: us I think as lay people myself included those of

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Speaker 1: us who don't have scientific degrees, but I am a

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Speaker 1: little bit of a nerd for science, so I like,

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Speaker 1: I like to get in there. It's important for us

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Speaker 1: to understand that what you were identifying in two thousand

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Speaker 1: nine in Mexico, for example, wasn't we have a pandemic

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Speaker 1: hitting You were identifying the markers, the red flags that

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Speaker 1: signal the outbreak of something potentially devastating. Yes. And it's

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Speaker 1: interesting too, is that, like there was a connection to

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Speaker 1: a farm, right, So we were following things in animal

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Speaker 1: populations and among farmers. If farmers are starting to report

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Speaker 1: a number of their chickens dying, that's a red flag

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Speaker 1: that maybe H five and one Avian influenza is happening.

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Speaker 1: If there are animals that are you know, pigs for instance,

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Speaker 1: and then it was called swine flu because we were

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Speaker 1: noticing a trend in pigs at a pig farm. Those

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Speaker 1: are all connected and so they're following to make sure

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Speaker 1: that they're seeing, Oh are these two things correlated? And

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Speaker 1: something that's really important when you're talking about following the

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Speaker 1: connection as a scientist and as a science team, not

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Speaker 1: only with what's happening to a human population nation but

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Speaker 1: an animal one. We're discussing the outbreak of zoonotic viruses, coronaviruses,

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Speaker 1: of which COVID nineteen is one and an incredibly scary one.

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Speaker 1: Are that type of virus? Correct? Can can you walk

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Speaker 1: people through what that means? Yeah? So, I mean I

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Speaker 1: would say I think now the number is over half

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Speaker 1: of the diseases that humans experience have some sort of

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Speaker 1: zootic origin, meaning it came from an animal, which is

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Speaker 1: not something that should terrify people. It's kind of what

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Speaker 1: happens as different ecosystems interact with each other. But it's

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Speaker 1: increasing in speed and it's increasing in severity because we're

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Speaker 1: interacting with more ecosystems at a rate that we shouldn't

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Speaker 1: be and that has everything to do with the amount

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Speaker 1: of travel that we do, with the amount of deforestation

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Speaker 1: that's happening, the influence that humans have on our climate.

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Speaker 1: That's causing different ecosystems to essentially crash into each other

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Speaker 1: instead of kind of friendly engagement from a distance. Um,

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Speaker 1: can you can you explain a little bit more about that?

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Speaker 1: Because I would wager that there's folks listening who are

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Speaker 1: going Wait a minute, I didn't realize that zoonotic viruses

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Speaker 1: had to do with climate change in deforestation. Can can

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Speaker 1: you walk us through what you're talking about? Yeah, totally. So.

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Speaker 1: You know, one of my favorite subjects that we studied

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Speaker 1: in graduate school was something called the one health paradigm,

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Speaker 1: and that is essentially the relationship that humans and our

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Speaker 1: environment and our plant and plants and animals have with

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Speaker 1: each other when with regard to one health, and that's

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Speaker 1: the health of the planet, that's the health of animal systems,

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Speaker 1: that the health of human systems, and when those systems

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Speaker 1: are in in conflict with each other or just crashing

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Speaker 1: into each other, Like I said previously, there are their

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Speaker 1: spillover risk. There is a risk of things that weren't

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Speaker 1: previously in our community that cross over into ours. And

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Speaker 1: that happens from my mutations over time. Um, and it's

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Speaker 1: you know, the origin of things like ebola, it's the

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Speaker 1: origin of things like avian influenza and a lot of coronaviruses.

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Speaker 1: And there are a certain species like That's for instance,

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Speaker 1: that are perfect reservoirs for certain viruses. But because of

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Speaker 1: how we are interacting with them through deforestation, through burning,

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Speaker 1: down of forests. Um, you know, from for reasons that

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Speaker 1: are inexplicable and for reasons that are sometimes necessary. Um.

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Speaker 1: You know, you are seeing and encountering animal species that

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Speaker 1: have viruses that we have no natural immunity too, So

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Speaker 1: it's it creates that vulnerability for us. You know, there

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Speaker 1: are a number of things that you can see right

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Speaker 1: now happening in our climate that are linked to our health.

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Speaker 1: Beyond things like smoke because of fires, we can say

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Speaker 1: that there is no question that the the uptick in

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Speaker 1: respiratory illnesses that have an animal origin are because of

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Speaker 1: how we are treating our planet. Wow, the uptick in

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Speaker 1: respiratory illnesses that have an animal origin is because of

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Speaker 1: how we're treating the planet. Yeah, it feels like an

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Speaker 1: important thing to sit with because, in my opinion anyway,

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Speaker 1: for far too long there has been an un natural

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Speaker 1: disconnection between us and the planet. And when you talk

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Speaker 1: about the one health model, we we are one ecosystem.

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Speaker 1: And I think, especially for a lot of folks who

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Speaker 1: live in cities, it can feel really easy to be

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Speaker 1: disconnected from the natural world. But if the planet is sick,

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Speaker 1: we're sick. You know, you and I are having this conversation.

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Speaker 1: And I'm sure my my folks out there at home

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Speaker 1: who listen to a lot of these episodes are wondering

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Speaker 1: if I'm sick. I'm not actually sick. I'm not suffering

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Speaker 1: from a cold, but I sound like this and I'm

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Speaker 1: congested like this because of the intensity of the smoke

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Speaker 1: in California. The entire West Coast is burning, and all

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Speaker 1: of us are having respiratory problems and allergies, and we

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Speaker 1: can't go outside of our homes. Neither can you. Up

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Speaker 1: in San Francisco. We literally can't be outdoors because the

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Speaker 1: air quality is so bad. And there's no part of

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Speaker 1: me that thinks any of this is a good thing.

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Speaker 1: But the thing I'm hoping is that seeing the devastation

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Speaker 1: through all of California, through Oregon, through Washington, I'm hoping

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Speaker 1: that that this can be a rude awakening for a

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Speaker 1: lot of people who have been tricked into thinking that

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Speaker 1: science is partisan. Science doesn't care how you vote. The

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Speaker 1: climate doesn't care who you support or what your personal

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Speaker 1: politics are. And I am, for one, furious and disheartened

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Speaker 1: by how trivials true science has become, by by how

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Speaker 1: some folks have tried to make everything from climate change

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Speaker 1: response to the coronavirus response political when this is about

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Speaker 1: humanity and the planet, and by the way, not just

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Speaker 1: our country. This is a global crisis. We're seeing the

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Speaker 1: climate crisis affect everyone in Brazil. We're seeing what has

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Speaker 1: happened to folks with coronavirus everywhere from the US to

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Speaker 1: South America to Europe to China. People are suffering. And

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Speaker 1: and my hope is that listeners and by extension, whomever

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Speaker 1: they speak to in their families or or circles, will

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Speaker 1: be able to take some true science data, some some

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Speaker 1: clear communication into their social circles and and really like

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Speaker 1: ring the alarm, like we we've got to We've got

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Speaker 1: to come back to a moral core here. And it's

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Speaker 1: part of the reason that I'm so excited that we're

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Speaker 1: having this conversation, because facts should not be up for debate,

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Speaker 1: and human health and public health and the health of

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Speaker 1: the planet shouldn't be a casualty of anyone's sort of

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Speaker 1: political ambition. Yeah. Absolutely, Yeah, I mean, I would argue

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Speaker 1: that pandemics are inherently political in that they are global, right,

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Speaker 1: and they affect different countries, and the diseases themselves don't

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Speaker 1: care about national borders, they don't care about state borders,

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Speaker 1: and that creates a connection between us and every other

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Speaker 1: human here. We are all vulnerable because we're all human

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Speaker 1: and we live in a very heterogeneous world. No two

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Speaker 1: bodies are the same, no two immune systems are the same,

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Speaker 1: and that should create a sense of empathy for one another.

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Speaker 1: That should create a sense of altruism that we do

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Speaker 1: things for the sake of others and ourselves because we're

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Speaker 1: all connected and we all have the same vulnerabilities. So

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Speaker 1: because of that science and you know, I would say

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Speaker 1: the accelerated politicization of science because of this pandemic has

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Speaker 1: been destructive. It does require our governments and global leaders

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Speaker 1: to be thinking right about science. It requires them to

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Speaker 1: be listening to science and to make informed decisions with

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Speaker 1: people who are experts in this field with their counsel,

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Speaker 1: and not based on opinions or gut feelings or just

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Speaker 1: a lack of interest. Um. You know, it's an incredibly

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Speaker 1: frustrating time as a scientist and a science communicator, and

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Speaker 1: as somebody who worked with the government to help prepare

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Speaker 1: us for things like this, to be seeing this devaluation

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Speaker 1: of public health and to make it into something that

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Speaker 1: is a talking point that is read or a talking

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Speaker 1: point that is blue. It's it's truly assinine. It must

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Speaker 1: be so frustrating. I remember really feeling for you reading

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Speaker 1: an article recently where you were talking about what it's

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Speaker 1: like to have studied and become an expert on emerging

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Speaker 1: infectious diseases and then actually live through a global pandemic.

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Speaker 1: How how surreal it feels for you. Do you think

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Speaker 1: part of the reason that it feels so surreal is

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Speaker 1: because those precautions, that respect for science at a government level,

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Speaker 1: the actual pandemic Response Network was disbanded. Does it just

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Speaker 1: does it make you feel crazy? Because to me, when

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Speaker 1: I think about that series of events, it feels nuts

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Speaker 1: to me. Oh yeah, I mean it's it's in a

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Speaker 1: way kind of gas lighting, right because we you know,

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Speaker 1: I've reconnected with a lot of my former colleagues back

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Speaker 1: at Georgetown and we you should see your text threads.

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Speaker 1: I mean, it's just like all caps like this could

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Speaker 1: have been prevented. We could have done something, you know,

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Speaker 1: that didn't have to be like this. And it's so

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Speaker 1: hard because you know, I remember ten twelve years ago

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Speaker 1: talking to my friends and they would say like, you know,

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Speaker 1: what are you most afraid of? And I would say

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Speaker 1: respiratory illness of pandemic proportions every time without fail. I

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Speaker 1: remember specific moments having those conversations and people being like,

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Speaker 1: it's fine, it's fine, and here we are, and I'm

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Speaker 1: like hello, Like it's he said this was going to happen.

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Speaker 1: It was just a matter of time. And I think

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Speaker 1: we also need to be careful too, because I think

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Speaker 1: people here, oh, we said this was going to happen,

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Speaker 1: or this was bound to happen as some sort of

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Speaker 1: like plot that to make it happen. This is an

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Speaker 1: unfortunate byproduct of what we're doing to our planet. It

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Speaker 1: is not some synthetic man made in a lab situation

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Speaker 1: to cause you know that that conspiracy theory is so

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Speaker 1: laughably wrong, especially because so that's what I want to

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Speaker 1: I want to get into with you, the conspiracy theories,

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Speaker 1: because when you say that ten or twelve years ago,

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Speaker 1: as a scientist, you were able to look at global

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Speaker 1: trends of deforestation, of human populations crashing into animal populations,

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Speaker 1: of what was happening with other respiratory illnesses that were

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Speaker 1: essentially laying the train track for a respiratory pandemic. You're

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Speaker 1: talking about reasonable scientific predictions. Some people hear that to

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Speaker 1: your point as someone tried to make this. I've seen

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Speaker 1: the conspiracy theories. I've got people trying to get in

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Speaker 1: my d M saying that Bill Gates made this virus,

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Speaker 1: that he owns a patent on the coronavirus, that this

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Speaker 1: was all this was created in a lab, that this

433
00:26:41,320 --> 00:26:46,679
Speaker 1: is biological warfare. And the thing is, the conspiracy theories

434
00:26:46,720 --> 00:26:51,520
Speaker 1: are taking hold. They've been dubbed a domestic terror threat

435
00:26:51,560 --> 00:26:55,280
Speaker 1: by our own FBI. Can you help me walk through

436
00:26:55,359 --> 00:26:58,720
Speaker 1: some of them and why to you as a scientist,

437
00:26:58,760 --> 00:27:03,360
Speaker 1: they're laughable and all so just factually untrue. Can you

438
00:27:03,400 --> 00:27:06,040
Speaker 1: give us the goods there so that everyone who's listening

439
00:27:06,040 --> 00:27:08,560
Speaker 1: can take some notes and then have the receipts when

440
00:27:08,560 --> 00:27:10,679
Speaker 1: they have to argue with their uncle who's like on

441
00:27:10,720 --> 00:27:14,120
Speaker 1: a conspiracy thread, which I'm not going to mention by name,

442
00:27:14,119 --> 00:27:16,000
Speaker 1: but y'all know what I'm talking about. I'm not giving

443
00:27:16,040 --> 00:27:19,520
Speaker 1: them any of my airtime. How can we take some

444
00:27:19,560 --> 00:27:23,359
Speaker 1: of these things apart? You know, it's it's amazing because

445
00:27:24,119 --> 00:27:29,239
Speaker 1: nobody cares about war and potential biological threat agents and

446
00:27:29,400 --> 00:27:33,000
Speaker 1: chemical and nuclear threat agents more than our government. If

447
00:27:33,000 --> 00:27:36,200
Speaker 1: this was anything close to that, we would have jumped

448
00:27:36,240 --> 00:27:39,199
Speaker 1: on it in a very particular way. But because the

449
00:27:39,280 --> 00:27:43,399
Speaker 1: science doesn't defend that, because science has presented the receipts,

450
00:27:43,600 --> 00:27:48,280
Speaker 1: it's very laughable that people continue to perpetuate this conspiracy theory.

451
00:27:48,560 --> 00:27:51,000
Speaker 1: And what I mean by the receipts is when a

452
00:27:51,119 --> 00:27:54,399
Speaker 1: virus is infected. If person is infected with a virus,

453
00:27:55,640 --> 00:27:59,600
Speaker 1: we can determine the genetic sequence of that virus and

454
00:27:59,760 --> 00:28:02,760
Speaker 1: it's ventially it's kind of like a family treat. You

455
00:28:02,800 --> 00:28:05,600
Speaker 1: can trace it back to its origin because very early

456
00:28:05,600 --> 00:28:07,760
Speaker 1: on into the pandemic, we got the full genome for

457
00:28:07,920 --> 00:28:10,480
Speaker 1: stars Kobe two, which is the virus that causes COVID nineteen,

458
00:28:10,920 --> 00:28:14,760
Speaker 1: and we can see what kind of difference is very slight.

459
00:28:14,960 --> 00:28:17,159
Speaker 1: I don't want to feed into the other conspiracy that

460
00:28:17,160 --> 00:28:19,679
Speaker 1: it's mutating out of control of the mutations are extremely

461
00:28:19,720 --> 00:28:23,960
Speaker 1: slow and very benign. But you can see which virus

462
00:28:24,000 --> 00:28:26,239
Speaker 1: you know this person had, and which virus that person had,

463
00:28:26,240 --> 00:28:28,399
Speaker 1: and you can kind of trace it back to its origin.

464
00:28:28,920 --> 00:28:31,600
Speaker 1: Because we can do that, we can trace it back

465
00:28:31,640 --> 00:28:34,760
Speaker 1: to the origin of the virus, which is exactly what

466
00:28:34,800 --> 00:28:38,440
Speaker 1: we determined or the Chinese government determined back in Luhan.

467
00:28:39,240 --> 00:28:45,080
Speaker 1: So there's no defensible explanation for it emerging from a

468
00:28:45,200 --> 00:28:48,520
Speaker 1: lab because we can trace it back to patient one.

469
00:28:48,760 --> 00:28:51,240
Speaker 1: You know what it looked like, and it was outside

470
00:28:51,280 --> 00:28:52,720
Speaker 1: of the lab, you know it was there was just

471
00:28:52,800 --> 00:28:59,080
Speaker 1: no lab involved. Um. So it's the thing about infectious

472
00:28:59,080 --> 00:29:02,280
Speaker 1: diseases is that one of the most horrible byproducts of

473
00:29:02,280 --> 00:29:06,120
Speaker 1: an outbreak or any epidemic is the infodemic that follows.

474
00:29:06,520 --> 00:29:09,120
Speaker 1: It's just hand in hand. I mean, it's been happening

475
00:29:09,160 --> 00:29:14,640
Speaker 1: since smallpox. When the smallpox UH disease emerged and the

476
00:29:15,000 --> 00:29:18,720
Speaker 1: vaccine was being developed, there were people that were spreading

477
00:29:18,800 --> 00:29:21,880
Speaker 1: rumors that if you've got the smallpox vaccine, you would

478
00:29:21,920 --> 00:29:25,640
Speaker 1: turn into a cow because the small pox vaccine was

479
00:29:25,760 --> 00:29:30,200
Speaker 1: derived from cowpox I mean vaccines. Actually, the etymology of

480
00:29:30,200 --> 00:29:32,760
Speaker 1: the word vodka is how we have the word of

481
00:29:32,840 --> 00:29:36,920
Speaker 1: vaccine because of the discovery of the smallpox vaccine, and

482
00:29:36,960 --> 00:29:40,880
Speaker 1: people thought that the inoculation of a very similar virus,

483
00:29:40,880 --> 00:29:44,120
Speaker 1: which was a genius idea, the cow pox vaccine, to

484
00:29:44,280 --> 00:29:47,560
Speaker 1: protect you into a from a similar disease, the smallpox

485
00:29:47,600 --> 00:29:52,400
Speaker 1: disease would somehow mutate you, and that continues to turn

486
00:29:52,440 --> 00:29:55,600
Speaker 1: into something as people completely misunderstand what we're talking about

487
00:29:55,600 --> 00:29:59,560
Speaker 1: when it comes to genetic codes and nucleic acid and

488
00:30:00,040 --> 00:30:02,520
Speaker 1: the all frightening m R and a vaccine. I mean,

489
00:30:02,600 --> 00:30:05,720
Speaker 1: people are so convinced that there is some way that

490
00:30:06,360 --> 00:30:10,480
Speaker 1: some you know, plans to alter humans, that they're creating

491
00:30:10,520 --> 00:30:13,680
Speaker 1: sci fi stories out of the headlines. And I would

492
00:30:13,720 --> 00:30:15,480
Speaker 1: say that there are a few problems here that are

493
00:30:15,480 --> 00:30:19,480
Speaker 1: perpetuating this. Number one the natural byproduct of infandemics, and

494
00:30:19,600 --> 00:30:22,200
Speaker 1: number two, I really think the media is blowing it

495
00:30:22,240 --> 00:30:26,480
Speaker 1: and making our jobs harder. The media is misrepresenting headlines.

496
00:30:26,520 --> 00:30:29,920
Speaker 1: They are jumping the gun on preprint papers. They are

497
00:30:30,000 --> 00:30:33,120
Speaker 1: jumping the gun on press releases. I mean, preprints are

498
00:30:33,160 --> 00:30:36,080
Speaker 1: something that really only people in the science community we're reading.

499
00:30:36,520 --> 00:30:39,520
Speaker 1: Can you tell us what that means, because there's certainly

500
00:30:39,560 --> 00:30:42,800
Speaker 1: some people going, what's she talking about? What's a preprint? Yeah?

501
00:30:43,280 --> 00:30:46,000
Speaker 1: So it's a preprint. Is part of the process in

502
00:30:46,520 --> 00:30:51,440
Speaker 1: publicating publications in science journalism, science writing, and academia that

503
00:30:51,760 --> 00:30:56,160
Speaker 1: before a journal actually publishes something, scientists can submit their

504
00:30:56,200 --> 00:31:00,920
Speaker 1: paper to a preprint server and basically it's like an

505
00:31:00,920 --> 00:31:03,840
Speaker 1: open call for other scientists to read it, tear it apart,

506
00:31:03,920 --> 00:31:09,320
Speaker 1: analyze it, provide feedback before it is peer reviewed. So

507
00:31:09,400 --> 00:31:13,360
Speaker 1: it's it's essentially step one of a peer review. Yeah,

508
00:31:13,440 --> 00:31:15,719
Speaker 1: but it's not part of the official peer review. It's

509
00:31:15,800 --> 00:31:18,440
Speaker 1: kind of a public opportunity for review. That's why it's

510
00:31:18,440 --> 00:31:22,080
Speaker 1: on a public service. And the preprint servers have very

511
00:31:22,240 --> 00:31:26,120
Speaker 1: you know, decently large disclaimers on top this is not published,

512
00:31:26,200 --> 00:31:29,920
Speaker 1: this is in pre print. Um. It was very niche.

513
00:31:30,000 --> 00:31:31,920
Speaker 1: It was intended for the science community to kind of

514
00:31:31,920 --> 00:31:34,600
Speaker 1: see like what's in the pipeline, what papers are actually

515
00:31:34,800 --> 00:31:38,000
Speaker 1: might be coming, because a preprint paper may not actually

516
00:31:38,040 --> 00:31:42,640
Speaker 1: be published if it's bad. But that kind of discernment

517
00:31:42,720 --> 00:31:47,040
Speaker 1: is lacking, That kind of science literacy is lacking. And

518
00:31:47,120 --> 00:31:50,800
Speaker 1: so between the media and between you know, lay people

519
00:31:50,920 --> 00:31:53,880
Speaker 1: just jumping on these links and sharing them and only

520
00:31:53,920 --> 00:31:59,120
Speaker 1: reading headlines, we're getting inundated with news that is wildly misinterpreted,

521
00:31:59,120 --> 00:32:04,000
Speaker 1: wildly miss misrepresented. Well, for example, when the data came

522
00:32:04,040 --> 00:32:06,760
Speaker 1: out from the c d C on all of the

523
00:32:06,800 --> 00:32:10,760
Speaker 1: COVID deaths in America and they stated that between six

524
00:32:10,800 --> 00:32:13,240
Speaker 1: and nine percent of people who died of COVID had

525
00:32:13,320 --> 00:32:17,800
Speaker 1: no no co morbidities. You saw a whole bunch of

526
00:32:17,880 --> 00:32:22,000
Speaker 1: infodemic nonsense happened where people said only six to nine

527
00:32:22,360 --> 00:32:26,000
Speaker 1: of two hundred thousand deaths were caused by COVID, and

528
00:32:26,880 --> 00:32:31,480
Speaker 1: that's not true. As a lay person who has the

529
00:32:31,560 --> 00:32:36,959
Speaker 1: luxury of calling a scientist, let me explain this in

530
00:32:36,960 --> 00:32:39,480
Speaker 1: in lay language, and then you tell me if I'm

531
00:32:39,480 --> 00:32:42,280
Speaker 1: doing it right. All right, a little experiment for the listeners.

532
00:32:42,280 --> 00:32:46,280
Speaker 1: So when that report comes out and says six to

533
00:32:46,960 --> 00:32:49,200
Speaker 1: two hundred thousand deaths have been caused by COVID with

534
00:32:49,360 --> 00:32:53,600
Speaker 1: no co morbidity, that means COVID is so fatal that

535
00:32:53,720 --> 00:32:57,280
Speaker 1: even if you are one of the wildly healthy people

536
00:32:57,280 --> 00:32:59,080
Speaker 1: in the United States who does not have a co

537
00:32:59,200 --> 00:33:04,840
Speaker 1: morbidity like hypertension, high blood pressure, asthma, allergies, anything which

538
00:33:04,880 --> 00:33:08,239
Speaker 1: is pretty rare, COVID will still kill you. And what

539
00:33:08,280 --> 00:33:13,800
Speaker 1: it means for the other two ton of deaths, it

540
00:33:13,880 --> 00:33:16,880
Speaker 1: means that a person with asthma, which is perfectly managed

541
00:33:17,560 --> 00:33:20,560
Speaker 1: and which would never have been fatal because they got COVID,

542
00:33:20,880 --> 00:33:24,040
Speaker 1: they died. It means that a person who had their

543
00:33:24,080 --> 00:33:27,720
Speaker 1: diabetes under control got COVID, and COVID killed them, and

544
00:33:27,760 --> 00:33:31,000
Speaker 1: they died. It means that someone who had a heart

545
00:33:31,040 --> 00:33:33,960
Speaker 1: arrhythmia which was controlled with medication and got COVID and

546
00:33:34,040 --> 00:33:38,680
Speaker 1: COVID killed them. Covid is the killer. COVID is the

547
00:33:38,680 --> 00:33:44,120
Speaker 1: fatal disease. Here. What what that information about come morbidities

548
00:33:44,240 --> 00:33:47,840
Speaker 1: means is not for us to try to detract from

549
00:33:47,880 --> 00:33:51,440
Speaker 1: COVID deaths. It's for doctors to understand that even if

550
00:33:51,480 --> 00:33:55,760
Speaker 1: their patients are perfectly under control with any other health

551
00:33:55,800 --> 00:33:58,959
Speaker 1: issue they have, COVID can take that health issue and

552
00:33:59,040 --> 00:34:04,680
Speaker 1: make it natal. Yeah. Is that accurate? It is? I mean,

553
00:34:04,800 --> 00:34:08,560
Speaker 1: I think one of the most frustrating misinterpretations of this.

554
00:34:08,640 --> 00:34:11,919
Speaker 1: I mean, that's a great summary. Sophia looked like really great. Um.

555
00:34:12,400 --> 00:34:17,560
Speaker 1: I think that what's missing is even just the history

556
00:34:17,600 --> 00:34:19,799
Speaker 1: of when COVID first emerged. What was one of the

557
00:34:19,800 --> 00:34:23,359
Speaker 1: first things scientists were saying, you're at high risk if

558
00:34:23,400 --> 00:34:26,480
Speaker 1: you have co morbidities, high risk of what high risk

559
00:34:26,560 --> 00:34:30,960
Speaker 1: of acute illness and possibly death. Now, back then, people

560
00:34:31,000 --> 00:34:33,160
Speaker 1: weren't saying, oh, well, it's not going to be the

561
00:34:33,160 --> 00:34:35,120
Speaker 1: COVID that kills you, is gonna be those other things.

562
00:34:35,120 --> 00:34:37,400
Speaker 1: Back then they were saying, oh, let's just shelter the

563
00:34:37,440 --> 00:34:40,600
Speaker 1: sick and shelter the vulnerable and shelter the old because

564
00:34:40,600 --> 00:34:42,879
Speaker 1: they knew that the outcomes where those people would be bad,

565
00:34:43,200 --> 00:34:46,440
Speaker 1: which is, you know, very contradictory because now we're seeing

566
00:34:47,520 --> 00:34:52,360
Speaker 1: those deaths. Yes, are definitely the disproportionate majority, but also

567
00:34:52,800 --> 00:34:56,680
Speaker 1: look at the six percent that should be very troubling data. Now,

568
00:34:56,840 --> 00:35:00,640
Speaker 1: the other part of this, like lack of science literacy

569
00:35:00,640 --> 00:35:04,520
Speaker 1: and even just like lack of internet literacy, is amazing

570
00:35:04,560 --> 00:35:06,719
Speaker 1: to me because so many times are trying to say, oh,

571
00:35:06,719 --> 00:35:09,760
Speaker 1: the CDC like secretly snuck it in while you were sleeping,

572
00:35:10,719 --> 00:35:13,799
Speaker 1: when like just some basic slew thing could determine that

573
00:35:13,800 --> 00:35:16,600
Speaker 1: that's not true. You know, just a simple website archive

574
00:35:17,280 --> 00:35:20,319
Speaker 1: look up can show you that language has been on

575
00:35:20,360 --> 00:35:24,560
Speaker 1: the website four months and that language, while at you know,

576
00:35:25,040 --> 00:35:29,960
Speaker 1: first glance may seem confusing, the nuance and the science

577
00:35:30,000 --> 00:35:31,880
Speaker 1: communication that could have been a little bit better on

578
00:35:31,880 --> 00:35:36,399
Speaker 1: the website was missing. And really, if you're if you're

579
00:35:36,440 --> 00:35:39,920
Speaker 1: being like literal, what this is telling you is one

580
00:35:40,719 --> 00:35:43,680
Speaker 1: of these deaths are caused by COVID. Only six percent

581
00:35:43,719 --> 00:35:46,120
Speaker 1: of them had one thing list on the death certificate.

582
00:35:46,239 --> 00:35:48,279
Speaker 1: And I've talked to a few doctors who have said

583
00:35:48,400 --> 00:35:52,200
Speaker 1: in their lifetime of practice, very rarely do you just

584
00:35:52,239 --> 00:35:55,280
Speaker 1: have one thing on a death certificate, because very rarely

585
00:35:55,400 --> 00:35:58,000
Speaker 1: somebody just has one thing going on, I mean, And

586
00:35:58,000 --> 00:36:00,080
Speaker 1: those other things don't even have to be acute, like

587
00:36:00,120 --> 00:36:02,560
Speaker 1: you said. They could be managed diabetes, it could be

588
00:36:02,640 --> 00:36:05,640
Speaker 1: managed hypertension, it could be out of control diabetes and

589
00:36:05,680 --> 00:36:09,360
Speaker 1: out of controlled hypertension. Either way, if the person didn't

590
00:36:09,360 --> 00:36:12,840
Speaker 1: have COVID, they likely would be alive. And that's the story.

591
00:36:13,840 --> 00:36:21,279
Speaker 1: Mm hmm. It feels so so important. Something I would

592
00:36:21,360 --> 00:36:24,600
Speaker 1: like to ask you about in this conversation, because we

593
00:36:24,640 --> 00:36:27,359
Speaker 1: did talk about it in our very early Instagram live

594
00:36:27,400 --> 00:36:34,359
Speaker 1: back in March, is what does a coronavirus really mean?

595
00:36:35,320 --> 00:36:41,560
Speaker 1: Because stars COVID two causes COVID nineteen, COVID nineteen is

596
00:36:41,719 --> 00:36:47,399
Speaker 1: a coronavirus, we have encountered other coronaviruses. And because we've

597
00:36:47,480 --> 00:36:50,640
Speaker 1: encountered other coronaviruses, and again, these are some receipts to

598
00:36:50,680 --> 00:36:54,319
Speaker 1: fight some of the misinformation. That's why coronaviruses are being

599
00:36:54,360 --> 00:36:58,040
Speaker 1: studied at labs all around the world, because scientists have

600
00:36:58,200 --> 00:37:03,160
Speaker 1: seen the explosion and inversions of coronaviruses and people in

601
00:37:03,280 --> 00:37:06,319
Speaker 1: your science community have known that these were going to

602
00:37:06,360 --> 00:37:09,440
Speaker 1: pose a major threat to humans, So they've been being

603
00:37:09,480 --> 00:37:13,680
Speaker 1: studied for years. But when we hear in the news

604
00:37:13,880 --> 00:37:17,920
Speaker 1: COVID nineteen is a novel virus, that means it's a

605
00:37:18,120 --> 00:37:23,759
Speaker 1: brand new type of a coronavirus. And that's why it's

606
00:37:23,800 --> 00:37:26,480
Speaker 1: so dangerous because a novel virus is something we don't

607
00:37:26,560 --> 00:37:30,120
Speaker 1: have knowledge of, our expertise around. We have to learn

608
00:37:30,160 --> 00:37:34,000
Speaker 1: in real time. But can you kind of walk us

609
00:37:34,040 --> 00:37:38,480
Speaker 1: through what's been being studied in that family of viruses?

610
00:37:39,239 --> 00:37:41,080
Speaker 1: I think a lot of people are like, is it new?

611
00:37:41,200 --> 00:37:43,200
Speaker 1: Is it old? What does it mean? I don't understand

612
00:37:43,600 --> 00:37:48,000
Speaker 1: how could we be, you know, researching for vaccines, but

613
00:37:48,080 --> 00:37:52,240
Speaker 1: then it's brand new and that's leading to vaccine misinformation.

614
00:37:52,440 --> 00:37:55,200
Speaker 1: So can we just kind of clear up um almost

615
00:37:55,239 --> 00:37:59,400
Speaker 1: from that inception point, what's going on with this virus

616
00:37:59,440 --> 00:38:02,759
Speaker 1: and in this family and viruses. Yeah, So to clarify,

617
00:38:03,000 --> 00:38:06,160
Speaker 1: stars Kobe two is the virus that causes the disease

618
00:38:06,280 --> 00:38:09,640
Speaker 1: COVID nineteen and stars Kobe two is a novel virus,

619
00:38:09,719 --> 00:38:12,520
Speaker 1: and novel means we don't have natural immunity to it.

620
00:38:12,640 --> 00:38:16,400
Speaker 1: Our bodies have not encountered this virus before, so nobody

621
00:38:16,719 --> 00:38:20,440
Speaker 1: in our population has any protection to it against it,

622
00:38:20,640 --> 00:38:22,960
Speaker 1: and everybody is susceptible. That's what I meant by like

623
00:38:23,000 --> 00:38:26,960
Speaker 1: a shared vulnerability because nobody's had it before. Now the

624
00:38:27,120 --> 00:38:31,640
Speaker 1: two tells us that it's genetically similar to stars kvie one,

625
00:38:31,800 --> 00:38:34,279
Speaker 1: which is the virus that caused STARS the outbreak that

626
00:38:34,360 --> 00:38:38,760
Speaker 1: happened in East Asia. And they're genetically similar, but they're different.

627
00:38:39,280 --> 00:38:43,480
Speaker 1: And the coronavirus family, you know, is something that we

628
00:38:43,719 --> 00:38:46,240
Speaker 1: are actually very familiar with it. In fact, every person

629
00:38:46,320 --> 00:38:49,920
Speaker 1: has experienced to coronavirus because coronavirus, at least four of

630
00:38:49,960 --> 00:38:53,080
Speaker 1: them are the cause of the common cold. You know.

631
00:38:53,160 --> 00:38:57,279
Speaker 1: Between those four coronaviruses that are very frequently circulating during

632
00:38:57,320 --> 00:39:01,080
Speaker 1: cold and flu season, UM, and even rhinoviruses, which is

633
00:39:01,120 --> 00:39:04,880
Speaker 1: the common cause for common cold. Uh. You know, everybody

634
00:39:04,920 --> 00:39:08,239
Speaker 1: has had a coronavirus in the past, and kids are

635
00:39:08,400 --> 00:39:11,840
Speaker 1: especially you know, prolific it spreading those you know, common

636
00:39:11,840 --> 00:39:16,080
Speaker 1: cold coronaviruses. UM. But we those viruses are different, and

637
00:39:16,120 --> 00:39:18,719
Speaker 1: the immunity for those last very short periods of time

638
00:39:19,440 --> 00:39:22,520
Speaker 1: and they're not acute. The three that have been acute

639
00:39:22,560 --> 00:39:27,680
Speaker 1: have been stars Immerce and now COVID nineteen. And since

640
00:39:27,760 --> 00:39:31,880
Speaker 1: the emergence of those two other severe coronaviruses, we have

641
00:39:32,000 --> 00:39:35,640
Speaker 1: had researchers at NIH and several labs across the country

642
00:39:35,719 --> 00:39:39,600
Speaker 1: focused on the family of coronavirus. Is because we have

643
00:39:39,760 --> 00:39:43,560
Speaker 1: noticed that, like I mentioned earlier, bats are a very

644
00:39:43,640 --> 00:39:47,479
Speaker 1: perfect reservoir for that family of viruses, and bats don't

645
00:39:47,480 --> 00:39:50,160
Speaker 1: even succumb to the disease. They just carry it and

646
00:39:50,200 --> 00:39:53,399
Speaker 1: because they fly, they spread it even more so they're

647
00:39:53,600 --> 00:39:56,520
Speaker 1: you know, the likelihood of this happening was pretty high

648
00:39:56,520 --> 00:40:00,920
Speaker 1: based on the data, like mosquitoes do with malaria. Right,

649
00:40:01,080 --> 00:40:03,879
Speaker 1: So mosquitoes do it because they're a vector and they're

650
00:40:03,920 --> 00:40:07,000
Speaker 1: spreading it to you with the bite. Right. The way

651
00:40:07,000 --> 00:40:10,319
Speaker 1: that uh, that could transfer the virus could be a

652
00:40:10,360 --> 00:40:12,360
Speaker 1: few ways. It could be from touching a person. It

653
00:40:12,360 --> 00:40:14,920
Speaker 1: could be from their fecal droppings. It could be if

654
00:40:14,960 --> 00:40:19,600
Speaker 1: somebody you know, was handling them in a in a market. Um.

655
00:40:19,640 --> 00:40:23,279
Speaker 1: You know, I also want to just dispel that myth

656
00:40:23,320 --> 00:40:26,520
Speaker 1: that somebody ate a bat as the origin of COVID nineteen.

657
00:40:26,560 --> 00:40:30,480
Speaker 1: That was not true. That was a terribly racist and xenophobic,

658
00:40:30,880 --> 00:40:34,120
Speaker 1: uh fake story that came out. Um, nobody was eating

659
00:40:34,120 --> 00:40:38,280
Speaker 1: a bat, you know. So you know that said coronaviruses

660
00:40:38,320 --> 00:40:42,920
Speaker 1: have had extensive research and they started vaccine research after

661
00:40:43,360 --> 00:40:47,120
Speaker 1: Stars and merce um, and that research was eventually stopped

662
00:40:47,160 --> 00:40:50,400
Speaker 1: because those two diseases eventually faded out. They didn't actually

663
00:40:50,440 --> 00:40:56,600
Speaker 1: have a very likely chance of being a repeated threat.

664
00:40:56,880 --> 00:41:00,760
Speaker 1: Both both diseases actually died out. That didn't stop the research,

665
00:41:00,840 --> 00:41:03,360
Speaker 1: but it slowed down quite a bit. But because the

666
00:41:03,440 --> 00:41:06,439
Speaker 1: research started, it also meant that we're not starting from

667
00:41:06,719 --> 00:41:10,120
Speaker 1: scratch in our COVID nineteen research. We had a really

668
00:41:10,360 --> 00:41:13,799
Speaker 1: pretty good runway to where we are right now, and

669
00:41:13,840 --> 00:41:17,640
Speaker 1: now we're able to do different technology research, different types

670
00:41:17,680 --> 00:41:21,600
Speaker 1: of technology in vaccine research like the m RNA vaccine

671
00:41:22,200 --> 00:41:25,560
Speaker 1: um to see how we can potentially protect against COVID

672
00:41:25,600 --> 00:41:29,200
Speaker 1: nineteen and maybe even other coronaviruses. Can you talk to

673
00:41:29,280 --> 00:41:33,239
Speaker 1: us a little bit about mRNA because well, actually, let

674
00:41:33,239 --> 00:41:35,839
Speaker 1: me go back. It's really helpful to have a clarification.

675
00:41:35,920 --> 00:41:38,960
Speaker 1: First of all, as you said, nobody ate a bat.

676
00:41:39,080 --> 00:41:44,359
Speaker 1: That's an incredibly inappropriate racist trope that deserves no circulation

677
00:41:44,400 --> 00:41:46,520
Speaker 1: time on the internet. So there's a receipt on that

678
00:41:46,600 --> 00:41:52,040
Speaker 1: everybody Also, it's really helpful to understand that these are

679
00:41:52,160 --> 00:41:55,680
Speaker 1: similar families of viruses in the way that you're explaining

680
00:41:55,719 --> 00:41:58,400
Speaker 1: them when you talk about the kind of influences that

681
00:41:58,520 --> 00:42:03,320
Speaker 1: kids spread versus something like a Stars, Mirrors or COVID nineteen.

682
00:42:03,760 --> 00:42:05,919
Speaker 1: In my mind, and again, tell me if I'm thinking

683
00:42:05,960 --> 00:42:09,439
Speaker 1: about this correctly, we're almost talking about a spectrum and

684
00:42:09,440 --> 00:42:11,919
Speaker 1: and you know, the common cold sort of sits over

685
00:42:12,000 --> 00:42:14,800
Speaker 1: here and it is not really a big deal. And

686
00:42:15,120 --> 00:42:18,200
Speaker 1: then we're talking about these viruses that are super on

687
00:42:18,239 --> 00:42:22,960
Speaker 1: the other end of incredibly serious, dangerous, and COVID nineteen

688
00:42:23,080 --> 00:42:26,480
Speaker 1: clearly goes so much farther even than a Stars or

689
00:42:26,520 --> 00:42:30,759
Speaker 1: a Mirs because it's not dying out. I heard a

690
00:42:30,800 --> 00:42:34,080
Speaker 1: scientist on an on an MPR podcast recently referred to

691
00:42:34,120 --> 00:42:36,759
Speaker 1: it as a very smart virus because it figured out

692
00:42:36,800 --> 00:42:42,120
Speaker 1: how to be so easily transmissible being airborne, uh, working

693
00:42:42,120 --> 00:42:44,040
Speaker 1: in the way that it does. And that feels like

694
00:42:44,040 --> 00:42:47,160
Speaker 1: a moment where I should draw a little asterisk um

695
00:42:47,200 --> 00:42:50,359
Speaker 1: if we were to transcribe this and say, we now

696
00:42:50,400 --> 00:42:54,680
Speaker 1: have the tapes of President Trump and his administration by

697
00:42:55,080 --> 00:42:59,799
Speaker 1: extension knowing that this was a fully airborne disease in February,

698
00:43:00,000 --> 00:43:03,520
Speaker 1: while misleading the American public um and while continuing to

699
00:43:03,560 --> 00:43:06,719
Speaker 1: not fund a pandemic response. So I would just like

700
00:43:06,800 --> 00:43:10,160
Speaker 1: to be very clear that they knew that this would

701
00:43:10,200 --> 00:43:13,400
Speaker 1: be deadly. Uh. He's also on on the Woodword tapes

702
00:43:13,880 --> 00:43:17,080
Speaker 1: saying how deadly this is, that if you're the wrong person,

703
00:43:17,120 --> 00:43:20,200
Speaker 1: you're a goner, and that this is essentially like the plague.

704
00:43:20,400 --> 00:43:25,840
Speaker 1: So that feels like incredibly important information because misleading the

705
00:43:25,840 --> 00:43:28,239
Speaker 1: American public with what has proven to be by far,

706
00:43:28,400 --> 00:43:33,359
Speaker 1: the most deadly outbreak of a respiratory pandemic that we've

707
00:43:33,360 --> 00:43:37,840
Speaker 1: seen in our lifetimes, UM, to me is beyond the pale.

708
00:43:38,400 --> 00:43:42,719
Speaker 1: And I want us to be really and I don't

709
00:43:42,719 --> 00:43:44,640
Speaker 1: mean us just as you and I just I mean

710
00:43:44,760 --> 00:43:46,920
Speaker 1: I mean us as everyone who's with us for this

711
00:43:47,000 --> 00:43:50,400
Speaker 1: conversation today. I want us to not downplay the reality

712
00:43:50,640 --> 00:43:54,160
Speaker 1: of of what that means of every person who has

713
00:43:54,200 --> 00:43:57,880
Speaker 1: died of this disease, who didn't have to. And I

714
00:43:57,920 --> 00:44:01,480
Speaker 1: don't want to gloss over that human toll. And I

715
00:44:02,320 --> 00:44:04,520
Speaker 1: want to make sure that we have moments to really

716
00:44:04,560 --> 00:44:07,600
Speaker 1: honor um the folks we've lost, you know, our our

717
00:44:07,640 --> 00:44:10,880
Speaker 1: first responders and doctors and E. M. T. S and

718
00:44:11,520 --> 00:44:14,920
Speaker 1: um family members, and you know, it's it's a really

719
00:44:14,960 --> 00:44:19,759
Speaker 1: devastating thing that's happened. And more than ever, I'm so

720
00:44:19,840 --> 00:44:22,600
Speaker 1: grateful to you and everyone in the science community who

721
00:44:22,640 --> 00:44:27,440
Speaker 1: has not been bullied into silence, and each of you

722
00:44:27,480 --> 00:44:30,320
Speaker 1: who are choosing to be so clear about what a

723
00:44:30,400 --> 00:44:32,920
Speaker 1: threat this is and and to continue to do the work,

724
00:44:33,360 --> 00:44:36,440
Speaker 1: to do the work trying to find us a vaccine,

725
00:44:36,520 --> 00:44:39,600
Speaker 1: to to use the research that was done on the

726
00:44:39,640 --> 00:44:45,799
Speaker 1: first two iterations of this coronavirus and to apply it

727
00:44:45,880 --> 00:44:49,200
Speaker 1: to the novel COVID nineteen and and to try to

728
00:44:49,200 --> 00:44:50,960
Speaker 1: get us out of this. You know, when you refer

729
00:44:51,080 --> 00:44:55,560
Speaker 1: to and mRNA vaccine, when you refer to all of this,

730
00:44:55,840 --> 00:44:59,600
Speaker 1: all of this lab research, how are we looking forward?

731
00:44:59,800 --> 00:45:03,120
Speaker 1: And and another asterisk? Can you talk to us about

732
00:45:03,200 --> 00:45:05,719
Speaker 1: what mr NA is, because I think a lot of

733
00:45:05,760 --> 00:45:07,879
Speaker 1: us are hearing the term and we don't really know.

734
00:45:08,480 --> 00:45:12,239
Speaker 1: But I'm curious, you know, how hopeful with your expertise

735
00:45:12,280 --> 00:45:16,800
Speaker 1: you're feeling. Yeah, you know, I always try to remind

736
00:45:16,840 --> 00:45:20,920
Speaker 1: people that everything we do today affects the data tomorrow

737
00:45:21,000 --> 00:45:23,680
Speaker 1: and affects the data in the weeks to come. And

738
00:45:23,719 --> 00:45:25,720
Speaker 1: I know a lot of us have seen a number

739
00:45:25,760 --> 00:45:29,319
Speaker 1: of epidemiological models that have predictions about how many people

740
00:45:29,320 --> 00:45:32,879
Speaker 1: are going to die. And you know, I don't feel

741
00:45:32,920 --> 00:45:35,239
Speaker 1: comfortable saying, you know, I believe it's going to be

742
00:45:35,440 --> 00:45:38,040
Speaker 1: x number of deaths because I try to remind people

743
00:45:38,160 --> 00:45:41,840
Speaker 1: like epidemiology is you know, one of the most common

744
00:45:41,880 --> 00:45:45,000
Speaker 1: phrases that we say is it depends, and it depends

745
00:45:45,080 --> 00:45:48,279
Speaker 1: because so much of this is about human behavior. So

746
00:45:48,320 --> 00:45:50,960
Speaker 1: if you say a model is going to say that

747
00:45:51,200 --> 00:45:53,480
Speaker 1: X number of people are going to die, but you

748
00:45:53,520 --> 00:45:56,640
Speaker 1: have ways to mitigate that, you would assume, well, what's

749
00:45:56,680 --> 00:46:00,000
Speaker 1: assumed there is that human behavior could essentially alter that prediction.

750
00:46:00,400 --> 00:46:03,960
Speaker 1: So knowing that that any kind of model is wrong

751
00:46:04,200 --> 00:46:07,919
Speaker 1: but is a helpful possibility and not a prediction, can

752
00:46:07,960 --> 00:46:11,719
Speaker 1: help us calibrate our behavior. Right, if we see that

753
00:46:11,760 --> 00:46:14,280
Speaker 1: this is looking like it's going to be really bad,

754
00:46:14,560 --> 00:46:18,200
Speaker 1: but we have the tools in our toolkits who lessen

755
00:46:18,320 --> 00:46:21,600
Speaker 1: that problem, we should do that, right, We should try

756
00:46:21,640 --> 00:46:24,840
Speaker 1: to mitigate the predicted outcomes and say, Okay, we're not

757
00:46:24,880 --> 00:46:28,759
Speaker 1: gonna let it get that bad. So, and a lot

758
00:46:28,800 --> 00:46:33,560
Speaker 1: of this is dependent on this perfect cocktail of mitigation efforts.

759
00:46:33,920 --> 00:46:35,399
Speaker 1: You know, I think a lot of people are looking

760
00:46:35,400 --> 00:46:36,840
Speaker 1: at the vaccine as though it's going to be a

761
00:46:36,840 --> 00:46:39,040
Speaker 1: silver bullet, and it's just not going to be. It's

762
00:46:39,040 --> 00:46:41,200
Speaker 1: gonna be one of the many tools in our tool

763
00:46:41,320 --> 00:46:45,040
Speaker 1: kit to help mitigate this disease, and we really need

764
00:46:45,080 --> 00:46:48,160
Speaker 1: to rein it in before we even get there, because

765
00:46:48,200 --> 00:46:50,400
Speaker 1: if the disease is out of control, the pandemic is

766
00:46:50,440 --> 00:46:53,920
Speaker 1: continuing to grow at you know, very rapid rates in

767
00:46:54,000 --> 00:46:57,440
Speaker 1: hotspots all over the country, a vaccine campaign is going

768
00:46:57,480 --> 00:47:00,759
Speaker 1: to be very altered by that distribute in the vaccine

769
00:47:00,760 --> 00:47:03,040
Speaker 1: and even getting the majority of people to get vaccinated,

770
00:47:03,080 --> 00:47:05,680
Speaker 1: it's going to be really difficult, period. But if it's

771
00:47:05,800 --> 00:47:08,920
Speaker 1: if that's on top of an unchecked, out of control pandemic,

772
00:47:09,239 --> 00:47:11,719
Speaker 1: that's a really big problem, which is why we need

773
00:47:11,800 --> 00:47:15,600
Speaker 1: to be lettering up to it. Mask wearing, physical distancing,

774
00:47:15,760 --> 00:47:20,520
Speaker 1: avoiding crowds, avoiding indoor gatherings. Doing all those things consistently

775
00:47:20,760 --> 00:47:25,000
Speaker 1: has actually been modeled to reduce depths by a significant number.

776
00:47:25,239 --> 00:47:28,400
Speaker 1: To eliminate the duration or at least shorten the duration

777
00:47:28,480 --> 00:47:31,640
Speaker 1: of this whole thing. People are desperate for me to say,

778
00:47:31,719 --> 00:47:34,200
Speaker 1: when is it going to end? How long we'll be

779
00:47:34,280 --> 00:47:38,120
Speaker 1: wearing masks? It all depends on everybody's choice today, It

780
00:47:38,200 --> 00:47:41,839
Speaker 1: all depends on everybody's behavior next week, and it really

781
00:47:41,880 --> 00:47:44,680
Speaker 1: all depends on the data for the vaccine, you know,

782
00:47:44,920 --> 00:47:48,360
Speaker 1: the vaccine. If we see a safe and effective vaccine,

783
00:47:48,400 --> 00:47:50,600
Speaker 1: and what I mean by that is it's safe and

784
00:47:50,640 --> 00:47:53,560
Speaker 1: that it doesn't cause harm, and it's effective and that

785
00:47:53,640 --> 00:47:57,719
Speaker 1: it actually reduces risk of getting the disease, and that

786
00:47:57,920 --> 00:48:01,480
Speaker 1: is at least fifty per the FDA guidelines for approval,

787
00:48:02,000 --> 00:48:05,520
Speaker 1: then we need to get at least at the very

788
00:48:05,600 --> 00:48:09,200
Speaker 1: least of the population vaccinated, hopefully the majority of the

789
00:48:09,200 --> 00:48:13,680
Speaker 1: population vaccinated to get anywhere near herd immunity. So I'm

790
00:48:13,719 --> 00:48:18,080
Speaker 1: really curious you referred to the misinformation that came out

791
00:48:19,280 --> 00:48:23,319
Speaker 1: when smallpox was a thing, which thankfully now is not

792
00:48:23,520 --> 00:48:26,800
Speaker 1: because of vaccines. When we think about things that vaccines

793
00:48:26,840 --> 00:48:30,399
Speaker 1: have beaten, polio, the measles, you know, all of these

794
00:48:30,400 --> 00:48:37,480
Speaker 1: preventable illnesses, why do you think from those early days

795
00:48:37,520 --> 00:48:41,320
Speaker 1: where people thought the smallpox vaccine would turn humans into cows.

796
00:48:42,960 --> 00:48:44,879
Speaker 1: I can't, I can't even say it with a straight face,

797
00:48:46,680 --> 00:48:53,480
Speaker 1: Like what where where does that distrust of science come from?

798
00:48:53,520 --> 00:48:57,360
Speaker 1: And why do you think we've seen a frightening percentage

799
00:48:57,360 --> 00:49:02,840
Speaker 1: of Americans backlash against hervaccines, say that vaccines aren't safe.

800
00:49:03,600 --> 00:49:06,560
Speaker 1: Where where does that come from? And what would you

801
00:49:06,680 --> 00:49:10,840
Speaker 1: say to any parent out there who's you know, read

802
00:49:10,920 --> 00:49:15,040
Speaker 1: some blog where some woman says, I don't vaccinate my children.

803
00:49:15,800 --> 00:49:20,759
Speaker 1: How do we beat the misinformation there? Because it's exactly

804
00:49:20,800 --> 00:49:25,239
Speaker 1: that kind of medical innovation that means kids get to

805
00:49:25,440 --> 00:49:27,880
Speaker 1: live healthy lives and grow up and be healthy adults.

806
00:49:28,239 --> 00:49:31,160
Speaker 1: Why don't we trust it? Yeah? You know, I think

807
00:49:31,160 --> 00:49:33,040
Speaker 1: there are a couple of things that are happening here.

808
00:49:33,360 --> 00:49:40,440
Speaker 1: One is, Americans are a very individualistic type of person, right,

809
00:49:40,520 --> 00:49:44,520
Speaker 1: you know, Individualism is a theme here, and a lot

810
00:49:44,600 --> 00:49:48,200
Speaker 1: of Americans have for the you know, excuse the generalization,

811
00:49:48,320 --> 00:49:52,560
Speaker 1: but um have a visceral reaction to being told what

812
00:49:52,640 --> 00:49:55,280
Speaker 1: to do with their bodies, when to do it, and why.

813
00:49:55,440 --> 00:49:58,880
Speaker 1: And I think that what's lacking is kind of what

814
00:49:58,920 --> 00:50:02,239
Speaker 1: I mentioned earlier, that altruism. The looking at vaccines is

815
00:50:02,280 --> 00:50:04,480
Speaker 1: not something that's just done for you as an individual,

816
00:50:04,760 --> 00:50:06,680
Speaker 1: but it's something that you do for your neighbors. It's

817
00:50:06,680 --> 00:50:08,799
Speaker 1: something you do for your communities that are filled with

818
00:50:08,840 --> 00:50:12,719
Speaker 1: people who cannot get vaccinated themselves. And I'm talking and

819
00:50:12,840 --> 00:50:16,240
Speaker 1: you know, compromise kids, kids who are born prematurely, people

820
00:50:16,280 --> 00:50:19,280
Speaker 1: with a weakened immune systems and chronic illnesses and cancer

821
00:50:19,440 --> 00:50:21,879
Speaker 1: going through chemotherapy. I mean, there's so many people around

822
00:50:22,000 --> 00:50:25,560
Speaker 1: us that rely on others doing the right thing, and

823
00:50:25,600 --> 00:50:28,640
Speaker 1: that is lacking in our society. On top of the

824
00:50:28,640 --> 00:50:31,400
Speaker 1: fact that I do think that are the way that

825
00:50:31,480 --> 00:50:36,760
Speaker 1: our country has you know, created a you know, unmanaged

826
00:50:36,840 --> 00:50:42,440
Speaker 1: profit situation for pharmaceutical companies. Makes people really angry, and

827
00:50:42,520 --> 00:50:45,400
Speaker 1: I can I can sympathize with that. I can sympathize

828
00:50:45,480 --> 00:50:48,400
Speaker 1: with what seems to be an out of control, growing,

829
00:50:48,880 --> 00:50:53,360
Speaker 1: you know, financially motivated system of pharmaceutical companies and people

830
00:50:53,880 --> 00:50:57,399
Speaker 1: who are very disconnected to that just being told what

831
00:50:57,480 --> 00:50:59,880
Speaker 1: to do and when to do it. But there's also

832
00:51:00,120 --> 00:51:05,520
Speaker 1: data to connect that, right, there's data, and there's so many, um,

833
00:51:05,560 --> 00:51:08,120
Speaker 1: you know, groups of people that are that are just

834
00:51:08,200 --> 00:51:12,239
Speaker 1: as interested in protecting their children too, who have done

835
00:51:12,280 --> 00:51:16,400
Speaker 1: extensive research to prove the safety and efficacy of vaccines.

836
00:51:16,719 --> 00:51:18,840
Speaker 1: I think one thing that I always try to remind

837
00:51:18,880 --> 00:51:20,880
Speaker 1: people is that we all want the same thing. We

838
00:51:20,960 --> 00:51:23,239
Speaker 1: all want to protect our children, to do the best

839
00:51:23,280 --> 00:51:26,080
Speaker 1: for our children, make the most informed decisions for our children,

840
00:51:26,520 --> 00:51:29,160
Speaker 1: and nobody here um and when I say here, in

841
00:51:29,160 --> 00:51:33,239
Speaker 1: the science community is intending to harm as far as

842
00:51:33,640 --> 00:51:37,319
Speaker 1: it goes with vaccines. But we live in an era

843
00:51:37,560 --> 00:51:42,560
Speaker 1: where regardless of the disease, regardless of the politics, there's

844
00:51:42,600 --> 00:51:45,920
Speaker 1: always going to be a snake oil salesperson slinging some

845
00:51:46,040 --> 00:51:52,000
Speaker 1: crazy remedy or some crazy alternative reality. Um about what

846
00:51:52,200 --> 00:51:55,799
Speaker 1: can create false hope and what can actually you know,

847
00:51:57,640 --> 00:52:01,279
Speaker 1: essentially create more fear and harm. Um. You know, And

848
00:52:01,320 --> 00:52:04,040
Speaker 1: we have systems in place that enable that behavior. And

849
00:52:04,080 --> 00:52:07,600
Speaker 1: that system, I think right now is social media. Our

850
00:52:07,680 --> 00:52:13,239
Speaker 1: social media platforms go virtually unchecked and basically allow for

851
00:52:13,280 --> 00:52:19,120
Speaker 1: a propagation of not just misinformation, but disinformation, and disinformation

852
00:52:19,200 --> 00:52:24,360
Speaker 1: is unique. Disinformation intends to deceive, intends to cause, you know,

853
00:52:24,480 --> 00:52:28,480
Speaker 1: discord in society. We saw that in our political intervention

854
00:52:28,560 --> 00:52:31,319
Speaker 1: that happened or you know, manipulation that happened, and we're

855
00:52:31,320 --> 00:52:34,760
Speaker 1: also seeing it in science. And that kind of stirring

856
00:52:34,800 --> 00:52:39,640
Speaker 1: the pot, creating distrust and fear and paranoia has created

857
00:52:39,680 --> 00:52:43,800
Speaker 1: a subculture of people who have become so anti science.

858
00:52:44,160 --> 00:52:47,719
Speaker 1: It's bigger than just anti vaccine. It's anti science that

859
00:52:48,719 --> 00:52:52,120
Speaker 1: engaging with that type of group is extremely difficult because

860
00:52:52,160 --> 00:52:55,440
Speaker 1: it gets very personal very quickly. You're not dealing with

861
00:52:55,480 --> 00:52:58,000
Speaker 1: the like earnest parents who wants to make a really

862
00:52:58,040 --> 00:53:00,200
Speaker 1: good choice for their kids. You're dealing with somebody who

863
00:53:00,000 --> 00:53:03,080
Speaker 1: who is threatening and who wants to cause, you know,

864
00:53:03,239 --> 00:53:07,680
Speaker 1: incite violence, and who wants to intimidate public health officials.

865
00:53:07,719 --> 00:53:09,919
Speaker 1: I mean, I've lost count with how many public health

866
00:53:09,920 --> 00:53:13,800
Speaker 1: officials have had to resign or even get personal security

867
00:53:13,840 --> 00:53:17,520
Speaker 1: because people are so anti science. I mean, it's it's

868
00:53:17,560 --> 00:53:21,560
Speaker 1: it's a trend that is mind boggling um. And I

869
00:53:21,600 --> 00:53:25,160
Speaker 1: think that for as long as we have you know,

870
00:53:25,239 --> 00:53:28,680
Speaker 1: algorithms in place that thrive off of viral content, and

871
00:53:28,680 --> 00:53:31,640
Speaker 1: this stuff is so viral, it's going to be really,

872
00:53:31,680 --> 00:53:34,879
Speaker 1: really difficult to get this under control. When we think

873
00:53:34,920 --> 00:53:38,960
Speaker 1: about the spreading of disinformation and how actually dangerous that is,

874
00:53:39,360 --> 00:53:43,759
Speaker 1: how dangerous that is for all of us, for everyone's kids,

875
00:53:43,800 --> 00:53:48,479
Speaker 1: for everyone's future. Where do you think compassion comes in?

876
00:53:48,880 --> 00:53:52,279
Speaker 1: Because you've talked a lot about how there needs to

877
00:53:52,320 --> 00:53:56,880
Speaker 1: be a place for compassion and science, and and it

878
00:53:56,960 --> 00:54:01,120
Speaker 1: strikes me as we talk about how anti science a

879
00:54:01,120 --> 00:54:04,480
Speaker 1: lot of people have become, is that people need to

880
00:54:04,520 --> 00:54:10,200
Speaker 1: have compassion for scientists, and also a reality check on

881
00:54:10,320 --> 00:54:18,200
Speaker 1: the fact that human history exists, innovation exists, the globe,

882
00:54:19,360 --> 00:54:24,600
Speaker 1: the global economy, travel, every single thing that we participate

883
00:54:24,640 --> 00:54:29,040
Speaker 1: in and enjoy about our lives exists because of science,

884
00:54:29,800 --> 00:54:37,600
Speaker 1: because of innovation, because of progress, and to discredit it,

885
00:54:37,640 --> 00:54:43,319
Speaker 1: to me feels like discrediting humanity. How do we how

886
00:54:43,360 --> 00:54:45,800
Speaker 1: do we bring compassion in and what kind of compassion

887
00:54:45,840 --> 00:54:48,840
Speaker 1: do you think we need? Yeah, I mean, I believe

888
00:54:49,040 --> 00:54:52,080
Speaker 1: with my whole heart that science communication without compassion and

889
00:54:52,120 --> 00:54:57,120
Speaker 1: empathy is empty. It just doesn't. It doesn't change anything.

890
00:54:57,280 --> 00:55:00,279
Speaker 1: It's just, you know, you can't outscience somebody, you can't

891
00:55:00,320 --> 00:55:03,000
Speaker 1: aut fact somebody who was a conspiracy theorist. There's just

892
00:55:03,040 --> 00:55:06,279
Speaker 1: I mean, there's data to even prove that too. Um.

893
00:55:06,360 --> 00:55:09,759
Speaker 1: And so what's been encouraging is seeing what you know,

894
00:55:09,840 --> 00:55:13,480
Speaker 1: COVID nineteen has created this, you know, unprecedented hunger for

895
00:55:13,520 --> 00:55:17,319
Speaker 1: science and science understanding. And that's the opportunity that I'm

896
00:55:17,360 --> 00:55:20,279
Speaker 1: seizing right now. That's when I'm doing the breakdown to

897
00:55:20,320 --> 00:55:22,359
Speaker 1: things that while it may seem simple to me because

898
00:55:22,360 --> 00:55:24,920
Speaker 1: I've studied it for fifteen years, a lot of people

899
00:55:25,000 --> 00:55:28,200
Speaker 1: didn't and I can't presume my expertise or even kind

900
00:55:28,200 --> 00:55:31,520
Speaker 1: of you know, simple understanding of things on the public.

901
00:55:31,680 --> 00:55:34,719
Speaker 1: I can't laugh at the questions that I get. I

902
00:55:34,760 --> 00:55:36,600
Speaker 1: have to look at it and like, wow, this is

903
00:55:36,640 --> 00:55:40,040
Speaker 1: not common knowledge and break it down to judgment free,

904
00:55:40,200 --> 00:55:44,560
Speaker 1: accessible information so that when people hear it, they feel

905
00:55:44,600 --> 00:55:48,160
Speaker 1: empowered and not intimidating, and they feel like they can

906
00:55:48,239 --> 00:55:51,120
Speaker 1: make choices and not like want to hide in fear.

907
00:55:51,239 --> 00:55:54,200
Speaker 1: And I'm seeing those results happening on my Instagram and

908
00:55:54,239 --> 00:55:57,759
Speaker 1: it's blowing my mind. I am seeing people have those

909
00:55:57,840 --> 00:56:01,520
Speaker 1: light bulb moments of I always thought this, and now

910
00:56:01,560 --> 00:56:04,640
Speaker 1: I'm understanding that it's actually that. And you know, to

911
00:56:04,760 --> 00:56:08,440
Speaker 1: see these small changes, even if it's just in a

912
00:56:08,440 --> 00:56:12,560
Speaker 1: few individuals, with people who are seeking understanding and who

913
00:56:12,560 --> 00:56:16,799
Speaker 1: have been led astray by sensationalist and emotionally manipulative propaganda,

914
00:56:17,440 --> 00:56:20,920
Speaker 1: it's really incredible. And I think that you know, to

915
00:56:21,080 --> 00:56:24,520
Speaker 1: get on a you know, social platform and just scream

916
00:56:24,560 --> 00:56:28,960
Speaker 1: at people and and and you know, name call and

917
00:56:29,000 --> 00:56:31,960
Speaker 1: be judgmental is not going to win anybody anything. It's

918
00:56:32,000 --> 00:56:34,680
Speaker 1: just going to create more division in an already divided world.

919
00:56:35,320 --> 00:56:39,959
Speaker 1: Mm hmmmm. I really respect that, and I think You're

920
00:56:40,080 --> 00:56:43,640
Speaker 1: using your expertise to welcome people to the table, to

921
00:56:43,880 --> 00:56:47,200
Speaker 1: begin to understand how to translate science to to be

922
00:56:47,360 --> 00:56:53,480
Speaker 1: their science communicator. Is so just incredible of you. It's

923
00:56:53,480 --> 00:56:56,960
Speaker 1: it's something that really deserves to be lauded. And as

924
00:56:57,000 --> 00:57:01,440
Speaker 1: one of the people who's following along, I'm really grateful you.

925
00:57:01,440 --> 00:57:05,359
Speaker 1: You talk about welcoming people to the table and how

926
00:57:05,400 --> 00:57:08,840
Speaker 1: everyone wants the same thing really at the end of

927
00:57:08,840 --> 00:57:12,800
Speaker 1: the day, which is a healthy future for their children,

928
00:57:12,960 --> 00:57:17,360
Speaker 1: their friends children. You are a parent to two young children.

929
00:57:19,360 --> 00:57:24,920
Speaker 1: How as a mom are you balancing everything right now?

930
00:57:25,000 --> 00:57:29,919
Speaker 1: You know? How are you leading for other moms? And

931
00:57:29,960 --> 00:57:33,600
Speaker 1: how are you managing to do all of this scientific

932
00:57:33,640 --> 00:57:37,520
Speaker 1: work while we're all working from home. What what's the

933
00:57:37,560 --> 00:57:41,600
Speaker 1: experience like for you? You know, it's crazy, There's no

934
00:57:41,720 --> 00:57:45,720
Speaker 1: other way to describate. It's crazy. I have my daughter

935
00:57:45,840 --> 00:57:48,720
Speaker 1: is almost four and my son just turned to so

936
00:57:48,880 --> 00:57:52,000
Speaker 1: we have our handsful. Um. I'm very, very fortunate to

937
00:57:52,040 --> 00:57:55,320
Speaker 1: have an amazing partner in Joshua. We we take team,

938
00:57:55,480 --> 00:57:57,520
Speaker 1: we take we tag team our schedules and we make

939
00:57:57,560 --> 00:57:59,400
Speaker 1: sure that we can kind of make you know, the

940
00:57:59,480 --> 00:58:03,280
Speaker 1: day as manageable as possible for the both of us.

941
00:58:03,320 --> 00:58:07,600
Speaker 1: But you know, uh, to have an extremely precocious and

942
00:58:07,680 --> 00:58:12,360
Speaker 1: curious almost four year old is wonderful and exhausting because

943
00:58:12,360 --> 00:58:16,040
Speaker 1: she wants to talk and ask questions and she's extremely curious.

944
00:58:16,040 --> 00:58:18,280
Speaker 1: And you know, we made the really difficult decision not

945
00:58:18,320 --> 00:58:20,960
Speaker 1: to send our kids to preschool this year. They were

946
00:58:21,080 --> 00:58:24,000
Speaker 1: enrolled and they had spot. We were, you know, preparing

947
00:58:24,120 --> 00:58:26,000
Speaker 1: to send them back this fall, and you know, just

948
00:58:26,040 --> 00:58:28,240
Speaker 1: after a lot of careful decision, we decided not to,

949
00:58:28,800 --> 00:58:31,400
Speaker 1: which doesn't make our life any easier. It makes it

950
00:58:31,560 --> 00:58:33,960
Speaker 1: that much more difficult because my work is definitely not

951
00:58:34,080 --> 00:58:37,240
Speaker 1: slowing down, and their needs are growing. But I think

952
00:58:37,320 --> 00:58:39,240
Speaker 1: one thing that we have tried to do as a

953
00:58:39,280 --> 00:58:44,600
Speaker 1: family is to make choices to stop working, to make

954
00:58:44,680 --> 00:58:47,960
Speaker 1: choices to be device free. And for me, that's really difficult,

955
00:58:48,000 --> 00:58:50,400
Speaker 1: because you know, my job is to doom scroll. My

956
00:58:50,520 --> 00:58:54,120
Speaker 1: job is to get all the bad news and synthesize

957
00:58:54,120 --> 00:58:56,560
Speaker 1: it and analyze it and create charts and figure out

958
00:58:56,560 --> 00:58:59,560
Speaker 1: how what the trends are. And but I also know

959
00:58:59,600 --> 00:59:01,640
Speaker 1: that if I continue to do that, I am missing

960
00:59:01,640 --> 00:59:03,920
Speaker 1: out on joy, I am missing out on time with

961
00:59:03,920 --> 00:59:07,640
Speaker 1: my children, whose lives are so wonderfully simple that it's enviable,

962
00:59:08,120 --> 00:59:10,560
Speaker 1: you know, Like I look at my kids who are

963
00:59:10,600 --> 00:59:14,000
Speaker 1: delighting in magnet tiles, and I'm like, all I want

964
00:59:14,040 --> 00:59:17,080
Speaker 1: to think about is making a castle right now with

965
00:59:17,120 --> 00:59:20,560
Speaker 1: my daughter, and like the extent of my stress would be,

966
00:59:20,560 --> 00:59:22,760
Speaker 1: how do we make sure that it's ten feet tall?

967
00:59:22,800 --> 00:59:27,400
Speaker 1: You know. So it's it's choices of making boundaries, it's

968
00:59:27,520 --> 00:59:30,600
Speaker 1: choices of you know, delighting in the simplicity of our

969
00:59:30,680 --> 00:59:35,160
Speaker 1: kids lives. Um, it's also engaging our kids a little

970
00:59:35,160 --> 00:59:37,200
Speaker 1: bit on the topic. You know, my my daughter is

971
00:59:37,200 --> 00:59:39,160
Speaker 1: still too young to really understand the concept of a

972
00:59:39,160 --> 00:59:42,200
Speaker 1: global pandemic, but she knows things that like, you know,

973
00:59:42,200 --> 00:59:44,640
Speaker 1: we're wearing masks to care for our neighbors, and that

974
00:59:44,720 --> 00:59:48,000
Speaker 1: we are not going to playgrounds and going to restaurants

975
00:59:48,000 --> 00:59:49,960
Speaker 1: because there's some people that are sick outside and we

976
00:59:50,000 --> 00:59:53,160
Speaker 1: want to make sure that we're protecting everybody. And those

977
00:59:53,200 --> 00:59:56,720
Speaker 1: are ways that I think you can implant empathy in

978
00:59:56,840 --> 01:00:00,479
Speaker 1: young minds, and we're trying really, really hard to do that.

979
01:00:00,480 --> 01:00:06,080
Speaker 1: That's really amazing when you think about how you talk

980
01:00:06,160 --> 01:00:08,960
Speaker 1: to your kids about this and and how you make

981
01:00:09,040 --> 01:00:13,800
Speaker 1: determinations for them. I'd love to ask some questions that

982
01:00:13,840 --> 01:00:17,040
Speaker 1: I think a lot of parents out there have because

983
01:00:17,400 --> 01:00:20,080
Speaker 1: people are wondering, can my kids go to school or not?

984
01:00:20,320 --> 01:00:25,320
Speaker 1: What's safe? You know, you've said that each family needs

985
01:00:25,360 --> 01:00:28,800
Speaker 1: to determine what risks they're comfortable with and come up

986
01:00:28,840 --> 01:00:32,640
Speaker 1: with a plan that works for their household. And I'm

987
01:00:32,680 --> 01:00:37,240
Speaker 1: really struck by that advice because again, it seems to

988
01:00:37,280 --> 01:00:39,360
Speaker 1: harken back to what you were saying about science, which

989
01:00:39,400 --> 01:00:42,440
Speaker 1: is it depends, It depends on the circumstance, It depends

990
01:00:42,480 --> 01:00:44,880
Speaker 1: on where you are. I know that some friends of

991
01:00:44,920 --> 01:00:47,640
Speaker 1: mine live in a very small town in Tennessee and

992
01:00:47,680 --> 01:00:50,000
Speaker 1: are not experiencing an outbreak, and so their kids just

993
01:00:50,040 --> 01:00:53,280
Speaker 1: went back to school. They're the teachers, are wearing masks,

994
01:00:53,280 --> 01:00:56,919
Speaker 1: everyone gets their temperatures taken every day. You know, there

995
01:00:56,920 --> 01:01:00,320
Speaker 1: are precautions in place there. That's so far for the

996
01:01:00,400 --> 01:01:03,320
Speaker 1: last five weeks have been working for them. But they're

997
01:01:03,320 --> 01:01:06,160
Speaker 1: not working in you know, a high school in Georgia,

998
01:01:06,280 --> 01:01:09,000
Speaker 1: and they're not working in so many cities around the

999
01:01:09,040 --> 01:01:13,040
Speaker 1: country that are still experiencing outbreaks. Policy isn't working across

1000
01:01:13,080 --> 01:01:17,200
Speaker 1: the board when we're experiencing a nine eleven every forty

1001
01:01:17,240 --> 01:01:20,920
Speaker 1: eight hours in the US. So when you talk about

1002
01:01:20,920 --> 01:01:25,080
Speaker 1: making those determinations. Is there a list of questions that

1003
01:01:25,280 --> 01:01:28,720
Speaker 1: come to mind right away that you would say households

1004
01:01:28,720 --> 01:01:31,840
Speaker 1: need to sit down and ask and answer for themselves

1005
01:01:32,040 --> 01:01:36,200
Speaker 1: and and in whatever specific way, in whatever specific community

1006
01:01:36,240 --> 01:01:39,880
Speaker 1: they're in, they can determine based on those questions and answers,

1007
01:01:40,680 --> 01:01:43,760
Speaker 1: what to do going forward. Yeah, I mean, I think

1008
01:01:43,840 --> 01:01:46,440
Speaker 1: one thing too, I always try to remind people is

1009
01:01:46,480 --> 01:01:50,160
Speaker 1: that there's you know, no wrong answer for a family

1010
01:01:50,320 --> 01:01:52,800
Speaker 1: if they've done, you know, the due diligence that they've done,

1011
01:01:52,800 --> 01:01:55,040
Speaker 1: and determine what works for them. You know, we are

1012
01:01:55,080 --> 01:02:00,760
Speaker 1: dealing with a another confounding factor of equity here. No

1013
01:02:00,760 --> 01:02:04,280
Speaker 1: nobody was really prepared for a pandemic, but some people

1014
01:02:04,320 --> 01:02:07,520
Speaker 1: are better able to respond because of the financial means.

1015
01:02:07,920 --> 01:02:09,720
Speaker 1: And so I think there's a lot of judgment on

1016
01:02:09,720 --> 01:02:12,800
Speaker 1: the table here with what people can afford when it

1017
01:02:12,800 --> 01:02:15,720
Speaker 1: comes to help and what people are forced to rely on,

1018
01:02:15,840 --> 01:02:18,720
Speaker 1: like other people's help and sending their kids back because

1019
01:02:18,760 --> 01:02:20,680
Speaker 1: they have to be outside at the home to work.

1020
01:02:21,320 --> 01:02:27,160
Speaker 1: The privilege of working from home should not be unmentioned. Uh,

1021
01:02:27,200 --> 01:02:29,080
Speaker 1: you know, we, my husband and I are both very

1022
01:02:29,120 --> 01:02:31,680
Speaker 1: fortunate to have jobs. You know, we are actually going

1023
01:02:31,760 --> 01:02:35,080
Speaker 1: to relocate for the next year to be closer to family,

1024
01:02:35,440 --> 01:02:37,720
Speaker 1: and my husband's job was like, yeah, come back whenever

1025
01:02:37,760 --> 01:02:40,560
Speaker 1: you feel like it's safe. And that is a privilege.

1026
01:02:40,840 --> 01:02:43,600
Speaker 1: And I think that, you know, I never want to

1027
01:02:43,600 --> 01:02:46,760
Speaker 1: speak with the assumption that everybody has that UM and

1028
01:02:46,800 --> 01:02:50,520
Speaker 1: I'm very very aware of that um. You know. There

1029
01:02:50,560 --> 01:02:53,640
Speaker 1: are some questions though, that you know, just for basic

1030
01:02:53,760 --> 01:02:56,000
Speaker 1: risk mitigation, I like to encourage people to think about,

1031
01:02:56,120 --> 01:03:00,760
Speaker 1: and that is, you know, can you avoid circumstances on

1032
01:03:00,800 --> 01:03:03,080
Speaker 1: the table, And if that circumstances sending the kids to school,

1033
01:03:03,400 --> 01:03:05,200
Speaker 1: can you do it? If you can, that's going to

1034
01:03:05,280 --> 01:03:09,120
Speaker 1: be your safest option if you can't. I'm looking for

1035
01:03:09,200 --> 01:03:12,600
Speaker 1: answers to questions like what is the mass protocol at

1036
01:03:12,600 --> 01:03:16,120
Speaker 1: the school? Is it masks for teachers, masks for students

1037
01:03:16,200 --> 01:03:19,640
Speaker 1: for both? Are people substituting with faith shields, because that's

1038
01:03:19,680 --> 01:03:23,520
Speaker 1: happening in some places. Are they physically distancing tables? Are

1039
01:03:23,560 --> 01:03:26,560
Speaker 1: they podding in classes? Are how bigger classrooms? I mean,

1040
01:03:26,600 --> 01:03:28,880
Speaker 1: those are all the questions that I want to see

1041
01:03:28,880 --> 01:03:33,200
Speaker 1: specific answers too. On top of the fact that schools

1042
01:03:33,200 --> 01:03:35,760
Speaker 1: don't exist in a bubble or in a vacuum, rather

1043
01:03:36,320 --> 01:03:38,400
Speaker 1: they are part of our community. And there are people

1044
01:03:38,440 --> 01:03:41,360
Speaker 1: that are in and out of schools, whether it's teachers, administrators,

1045
01:03:41,400 --> 01:03:47,680
Speaker 1: students that live and experience multigenerational communities, and they have

1046
01:03:47,800 --> 01:03:50,920
Speaker 1: multigenerational homes, and they have other jobs, and they have

1047
01:03:51,000 --> 01:03:54,640
Speaker 1: different places that they visit for social reasons or medical reasons,

1048
01:03:54,640 --> 01:04:00,080
Speaker 1: and so there cannot exist a impenetrable bubble when it

1049
01:04:00,120 --> 01:04:02,959
Speaker 1: comes to sending kids back to school. Uh And and

1050
01:04:03,200 --> 01:04:07,920
Speaker 1: that's why it needs to involve very regular checkens about

1051
01:04:08,000 --> 01:04:11,160
Speaker 1: the circumstance because if it starts to break, if the

1052
01:04:11,160 --> 01:04:13,440
Speaker 1: systems are starting to break, if the bubbles are starting

1053
01:04:13,440 --> 01:04:17,720
Speaker 1: to pop and things are happening, stop if you can stop.

1054
01:04:18,120 --> 01:04:19,920
Speaker 1: And that's kind of like the advice that our pediatrian

1055
01:04:19,960 --> 01:04:22,240
Speaker 1: gave us, Like, at any point if you feel uncomfortable,

1056
01:04:22,400 --> 01:04:24,480
Speaker 1: pull the kids out of school. If at any point

1057
01:04:24,520 --> 01:04:27,840
Speaker 1: it's not sustainable, transition something. And that's kind of like

1058
01:04:28,440 --> 01:04:31,280
Speaker 1: what we're trying to encourage people to understand that the

1059
01:04:31,320 --> 01:04:35,120
Speaker 1: fall in winter is a big question mark. Between the

1060
01:04:35,160 --> 01:04:38,080
Speaker 1: combination of COVID nineteen and flu season and then all

1061
01:04:38,200 --> 01:04:41,560
Speaker 1: these other group activities that are happening between sports and school,

1062
01:04:42,320 --> 01:04:45,280
Speaker 1: there are a lot of unknowns, and so we need

1063
01:04:45,360 --> 01:04:47,400
Speaker 1: to kind of take it day by day, week by

1064
01:04:47,480 --> 01:04:50,520
Speaker 1: week before we can make these conclusions that back to

1065
01:04:50,600 --> 01:04:54,040
Speaker 1: school is safe or you know, back to work is safe.

1066
01:04:55,000 --> 01:04:59,439
Speaker 1: When we talk about flu season coming, how strongly would

1067
01:04:59,440 --> 01:05:01,400
Speaker 1: you recommend the everyone get a flu shot this year?

1068
01:05:02,560 --> 01:05:05,720
Speaker 1: Very strongly recommend that everybody get a flu shot. And

1069
01:05:05,800 --> 01:05:09,320
Speaker 1: there's a very specific reason. If you get the flu,

1070
01:05:10,560 --> 01:05:14,120
Speaker 1: you are immunocompromised. You are more vulnerable to getting COVID

1071
01:05:14,200 --> 01:05:17,840
Speaker 1: nineteen and that would be terrible. A flu vaccine is

1072
01:05:17,880 --> 01:05:20,680
Speaker 1: not going to make you more at risk for flu,

1073
01:05:20,760 --> 01:05:22,800
Speaker 1: and a flu vaccine is not going to give you

1074
01:05:22,840 --> 01:05:25,240
Speaker 1: the flu. Just to kind of get some misconceptions out

1075
01:05:25,280 --> 01:05:28,000
Speaker 1: of the way, but if flu vaccine can help protect

1076
01:05:28,040 --> 01:05:32,440
Speaker 1: you against another competing respiratory illness that is circulating in

1077
01:05:32,480 --> 01:05:35,080
Speaker 1: our community. It is not a perfect vaccine and that

1078
01:05:35,160 --> 01:05:39,880
Speaker 1: it doesn't have effective protection, but if you can reduce

1079
01:05:39,920 --> 01:05:42,800
Speaker 1: your chance by anything, it's a good idea. On top

1080
01:05:42,840 --> 01:05:44,560
Speaker 1: of the fact that even if you get the flu,

1081
01:05:44,960 --> 01:05:48,760
Speaker 1: having been vaccinated with the flu vaccine, you can reduce

1082
01:05:48,840 --> 01:05:53,480
Speaker 1: the severity of the illness. And that's huge and that's

1083
01:05:53,480 --> 01:05:55,800
Speaker 1: really helpful to know too, because I've I've definitely heard

1084
01:05:55,840 --> 01:05:57,520
Speaker 1: some people say, well, you know, I got a flu

1085
01:05:57,560 --> 01:05:59,760
Speaker 1: shot and then I got the flu anyway, and so

1086
01:05:59,800 --> 01:06:03,080
Speaker 1: it's it's a really important reminder that if you are

1087
01:06:03,160 --> 01:06:06,080
Speaker 1: one of those people who gets a flu vaccine and

1088
01:06:06,120 --> 01:06:09,040
Speaker 1: then gets the flu anyway, it will be much less severe.

1089
01:06:09,400 --> 01:06:13,840
Speaker 1: That's that's helpful to know. Thank you for that now.

1090
01:06:16,720 --> 01:06:19,080
Speaker 1: I know it's hard to talk about timelines because they

1091
01:06:19,120 --> 01:06:22,080
Speaker 1: depend they depend on how well we respond to this.

1092
01:06:22,200 --> 01:06:27,000
Speaker 1: They depend on us taking measures to stop the spread

1093
01:06:27,000 --> 01:06:29,360
Speaker 1: of the virus, which are hard because we don't have

1094
01:06:29,400 --> 01:06:33,400
Speaker 1: federal leadership on this. We don't have federal mandates, so

1095
01:06:33,480 --> 01:06:36,520
Speaker 1: we don't have a nationwide plan, which I imagine as

1096
01:06:36,520 --> 01:06:39,360
Speaker 1: a scientist who used to work on national planning, is

1097
01:06:39,400 --> 01:06:44,640
Speaker 1: pretty frustrating. What are the things if you could pick

1098
01:06:44,760 --> 01:06:47,400
Speaker 1: three things that right now you think people need to know,

1099
01:06:48,400 --> 01:06:50,680
Speaker 1: three things you would like to ask people to do

1100
01:06:50,880 --> 01:06:53,720
Speaker 1: to double down on, what would they be? I'd go

1101
01:06:53,760 --> 01:06:55,920
Speaker 1: back to what I said earlier and say that pandemics

1102
01:06:55,960 --> 01:07:00,360
Speaker 1: are political, and that the politicians and the politics we

1103
01:07:01,560 --> 01:07:05,400
Speaker 1: choose can directly impact how we prepare and respond and

1104
01:07:05,480 --> 01:07:09,400
Speaker 1: prevent future public health emergencies, and we need to vote.

1105
01:07:09,880 --> 01:07:11,640
Speaker 1: We need to vote in November in a way that

1106
01:07:11,800 --> 01:07:14,280
Speaker 1: is pro science. We need to vote in a way

1107
01:07:14,360 --> 01:07:16,760
Speaker 1: that is thinking about the future of our planet and

1108
01:07:16,800 --> 01:07:18,640
Speaker 1: the future of our health and the future of our

1109
01:07:18,720 --> 01:07:24,560
Speaker 1: kids health because right now, working a government that works

1110
01:07:24,600 --> 01:07:28,880
Speaker 1: against the science community is just not productive. It's counterproductive,

1111
01:07:28,920 --> 01:07:31,600
Speaker 1: and it is dangerous, and it is in our country's

1112
01:07:31,640 --> 01:07:34,920
Speaker 1: best interest to prioritize health promotion and disease prevention. And

1113
01:07:34,960 --> 01:07:38,880
Speaker 1: it feels almost silly to say something as obvious as that. Um.

1114
01:07:38,920 --> 01:07:42,760
Speaker 1: You know, in many ways, the normal that we had

1115
01:07:42,840 --> 01:07:45,920
Speaker 1: pre COVID is how we got here because we defunded

1116
01:07:45,960 --> 01:07:48,560
Speaker 1: public health, we devalued it, and we made health care

1117
01:07:48,880 --> 01:07:52,200
Speaker 1: access to health care really difficult and dependent on income

1118
01:07:52,200 --> 01:07:56,480
Speaker 1: and employment. So we need some fundamental changes to our

1119
01:07:56,600 --> 01:08:01,040
Speaker 1: normal to move forward. So that's one UM. Number two,

1120
01:08:01,680 --> 01:08:04,680
Speaker 1: to check your sources, to be really to be an

1121
01:08:04,680 --> 01:08:07,840
Speaker 1: informed individual, and to be your own scientists. Do the

1122
01:08:07,960 --> 01:08:12,160
Speaker 1: extra search, pause before re sharing, and think critically before

1123
01:08:12,200 --> 01:08:18,639
Speaker 1: you do UM. And Third, you know, empathy. We all

1124
01:08:18,680 --> 01:08:21,960
Speaker 1: need empathy right now. Think about the implications of what

1125
01:08:22,040 --> 01:08:24,920
Speaker 1: you talk about, especially when it comes to COVID nineteen

1126
01:08:25,000 --> 01:08:27,400
Speaker 1: comparing it to each one and one for instance, and

1127
01:08:27,439 --> 01:08:30,760
Speaker 1: remember that there are two hundred thousand families right now

1128
01:08:31,120 --> 01:08:34,479
Speaker 1: who are devastated, two thousand families that likely had to

1129
01:08:34,560 --> 01:08:37,320
Speaker 1: lose their families over the phone because they weren't allowed

1130
01:08:37,360 --> 01:08:39,360
Speaker 1: to be next to them in the hospital as they died.

1131
01:08:40,240 --> 01:08:43,120
Speaker 1: So let's think about the heartbreak here, Let's think about

1132
01:08:43,160 --> 01:08:46,400
Speaker 1: the compassion that we can be having towards others, and

1133
01:08:46,479 --> 01:08:51,080
Speaker 1: let's act accordingly. Right, And it's incredibly important when you

1134
01:08:51,200 --> 01:08:58,200
Speaker 1: talk about what was normal before YEAH got us here,

1135
01:08:58,960 --> 01:09:03,559
Speaker 1: When we talk about the two people we've lost, those

1136
01:09:03,560 --> 01:09:09,120
Speaker 1: deaths were preventable. We we knew how to stop an

1137
01:09:09,120 --> 01:09:11,479
Speaker 1: outbreak like this. We did it with H one N one,

1138
01:09:11,520 --> 01:09:15,080
Speaker 1: We did it with a bowla we we have stopped

1139
01:09:15,600 --> 01:09:23,000
Speaker 1: these things from coming and harming our communities. And when

1140
01:09:23,000 --> 01:09:26,599
Speaker 1: we think about how to move forward, I really think

1141
01:09:26,600 --> 01:09:32,000
Speaker 1: it's important to remember the three things you've just highlighted,

1142
01:09:32,040 --> 01:09:35,760
Speaker 1: and also to understand that we have to look at

1143
01:09:35,800 --> 01:09:41,200
Speaker 1: the disparities that this pandemic has highlighted for us. Something

1144
01:09:41,439 --> 01:09:44,920
Speaker 1: I don't want to miss discussing with you is the

1145
01:09:45,040 --> 01:09:48,280
Speaker 1: disparities that have happened for people of color with COVID

1146
01:09:49,040 --> 01:09:52,320
Speaker 1: because you and the scientists at the COVID Tracking Project.

1147
01:09:53,000 --> 01:09:55,760
Speaker 1: For everyone who's listening from home, when we talk about

1148
01:09:55,840 --> 01:10:00,400
Speaker 1: checking sources, follow the COVID Tracking Project from the Alantic.

1149
01:10:00,520 --> 01:10:04,720
Speaker 1: It is an incredible resource and Jessica is one of

1150
01:10:04,720 --> 01:10:07,200
Speaker 1: the scientists working on it and communicating all of this

1151
01:10:07,280 --> 01:10:09,960
Speaker 1: information to us. One of the things that you all

1152
01:10:10,040 --> 01:10:13,080
Speaker 1: have done through the COVID Tracking Project is published the

1153
01:10:13,240 --> 01:10:19,920
Speaker 1: data on the racial disparities of COVID, and data has

1154
01:10:20,000 --> 01:10:24,360
Speaker 1: shown that COVID nineteen is affecting black, Indigenous and Latin

1155
01:10:24,520 --> 01:10:28,360
Speaker 1: X people and other people of color. Most nationwide, black

1156
01:10:28,360 --> 01:10:30,160
Speaker 1: people are dying at two and a half times the

1157
01:10:30,240 --> 01:10:34,440
Speaker 1: rate that white people are dying, which is a devastating

1158
01:10:34,920 --> 01:10:40,680
Speaker 1: statistic and and as you said, yet not entirely surprising

1159
01:10:40,840 --> 01:10:44,280
Speaker 1: considering the cycles of systemic racism that have led to

1160
01:10:44,320 --> 01:10:48,840
Speaker 1: bipop communities facing worse health outcomes. And when I when

1161
01:10:48,880 --> 01:10:54,920
Speaker 1: I read that, I was really shaken by your words,

1162
01:10:55,080 --> 01:11:00,839
Speaker 1: because whether we're looking at economic justice, environmental justice, gender

1163
01:11:00,880 --> 01:11:06,799
Speaker 1: based justice, we have seen disparities that put these oppressed

1164
01:11:06,840 --> 01:11:14,200
Speaker 1: communities at risk in all of those verticals. And I've

1165
01:11:14,200 --> 01:11:16,240
Speaker 1: heard a lot of people say they're they're aware of

1166
01:11:16,240 --> 01:11:19,360
Speaker 1: these statistics. They've they've heard a little bit that COVID

1167
01:11:19,439 --> 01:11:23,000
Speaker 1: nineteen is impacting people of color disproportionately, but they don't

1168
01:11:23,040 --> 01:11:26,720
Speaker 1: really understand why or how. And I'm wondering if you

1169
01:11:26,760 --> 01:11:29,280
Speaker 1: can offer some insight as to why this is happening

1170
01:11:29,960 --> 01:11:34,519
Speaker 1: and and any thoughts as a scientist, as a as

1171
01:11:34,520 --> 01:11:37,240
Speaker 1: a public health expert as to what we need to

1172
01:11:37,320 --> 01:11:43,160
Speaker 1: change in the future going forward, in addition obviously to

1173
01:11:43,240 --> 01:11:46,800
Speaker 1: healthcare not simply being based on employment, but health care

1174
01:11:46,840 --> 01:11:51,000
Speaker 1: being a human right. Yeah. Absolutely. You know, in public health,

1175
01:11:51,320 --> 01:11:54,280
Speaker 1: zip code is one of the biggest indicators for a

1176
01:11:54,320 --> 01:11:57,840
Speaker 1: community's health. You can search any zip code and you

1177
01:11:57,840 --> 01:12:03,679
Speaker 1: can see trends of disease, diseases having prevalence, of chronic

1178
01:12:03,720 --> 01:12:07,960
Speaker 1: illnesses having prevalence, and those things have so much to

1179
01:12:08,080 --> 01:12:10,840
Speaker 1: do with how we set up those communities to have

1180
01:12:10,920 --> 01:12:14,080
Speaker 1: access to health. There have been a number of reports

1181
01:12:14,080 --> 01:12:16,360
Speaker 1: that have come out during this pandemic that have showed

1182
01:12:16,600 --> 01:12:20,559
Speaker 1: that even access to testing is not equitable. That when

1183
01:12:20,560 --> 01:12:24,360
Speaker 1: there's an opportunity to put a testing facility in an area,

1184
01:12:24,800 --> 01:12:27,880
Speaker 1: it's most likely going to go to a white dominant

1185
01:12:27,960 --> 01:12:32,040
Speaker 1: neighborhood over a black dominant neighborhood. And you know, you

1186
01:12:32,080 --> 01:12:37,040
Speaker 1: can see that in how our health care systems are funded.

1187
01:12:37,760 --> 01:12:41,439
Speaker 1: You're seeing hospitals and clinics in more affluent areas have

1188
01:12:41,600 --> 01:12:45,360
Speaker 1: the resources that they need to manage an influx of cases,

1189
01:12:45,600 --> 01:12:49,960
Speaker 1: where you see hospitals in rural or more impoverished areas

1190
01:12:50,000 --> 01:12:55,680
Speaker 1: that have disproportionately high populations of people of color underfunded, overwhelmed,

1191
01:12:55,840 --> 01:12:58,640
Speaker 1: and not able to manage the burden of a pandemic.

1192
01:12:59,080 --> 01:13:03,200
Speaker 1: And so, you know, there are so many ways that

1193
01:13:03,320 --> 01:13:08,080
Speaker 1: we have disenfranchised people of color, not even set them

1194
01:13:08,160 --> 01:13:12,559
Speaker 1: up for the basic resources in health care so that

1195
01:13:12,600 --> 01:13:16,519
Speaker 1: they could even respond to this appropriately. And you know,

1196
01:13:16,760 --> 01:13:20,120
Speaker 1: I think a lot of people love to speculate about health,

1197
01:13:20,800 --> 01:13:24,479
Speaker 1: you know, as a India, as race as an indicator

1198
01:13:24,479 --> 01:13:26,639
Speaker 1: of health, and that's not what's happening here. We're talking

1199
01:13:26,680 --> 01:13:30,959
Speaker 1: about social systems. We're talking about structural and systemic racism

1200
01:13:31,240 --> 01:13:34,960
Speaker 1: that is perpetuating this and that existed pre COVID. You're

1201
01:13:35,000 --> 01:13:39,760
Speaker 1: dealing with the quality of air in certain neighborhoods that

1202
01:13:39,960 --> 01:13:46,000
Speaker 1: has resulted in chronic asthma, chronic respiratory illnesses in communities

1203
01:13:46,040 --> 01:13:49,240
Speaker 1: of color, and that has a direct impact on those

1204
01:13:49,280 --> 01:13:52,360
Speaker 1: co morbidities that we talked about in those populations and

1205
01:13:52,520 --> 01:13:56,000
Speaker 1: having worse outcomes with COVID nineteen infection there. I mean,

1206
01:13:56,040 --> 01:13:59,640
Speaker 1: it's all there, It's all there, and so um, you know,

1207
01:14:00,040 --> 01:14:02,280
Speaker 1: I think that it's part of the reason why I

1208
01:14:02,360 --> 01:14:04,840
Speaker 1: say the pre COVID normal was so broken and how

1209
01:14:04,880 --> 01:14:08,639
Speaker 1: we got here because we haven't taken care of our

1210
01:14:08,640 --> 01:14:12,679
Speaker 1: communities of color. We haven't invested in health care access

1211
01:14:13,160 --> 01:14:16,120
Speaker 1: to everybody in this country. We haven't invested in making

1212
01:14:16,120 --> 01:14:19,320
Speaker 1: sure that all health care systems have the right doctors

1213
01:14:19,400 --> 01:14:23,800
Speaker 1: and ventilators and ppe and all the things necessary to

1214
01:14:23,920 --> 01:14:26,639
Speaker 1: go to the hospital and actually survive in the hospital.

1215
01:14:27,360 --> 01:14:30,960
Speaker 1: Not to mention the fact that, you know, there's a

1216
01:14:30,960 --> 01:14:33,880
Speaker 1: lot of distrust in healthcare because of how people of

1217
01:14:33,880 --> 01:14:38,160
Speaker 1: color are treated. Women are treated. Black women, in particular,

1218
01:14:38,280 --> 01:14:42,760
Speaker 1: Latino women are treated terribly when it comes to prenatal care,

1219
01:14:43,120 --> 01:14:46,000
Speaker 1: when it comes to coming in with issues of pain management.

1220
01:14:46,360 --> 01:14:49,080
Speaker 1: And so if you have a system that already feels

1221
01:14:49,120 --> 01:14:51,920
Speaker 1: like it's against you, how on earth is that going

1222
01:14:51,960 --> 01:14:54,320
Speaker 1: to be like a safe place for those people to

1223
01:14:54,360 --> 01:14:58,080
Speaker 1: go in in such an emergency like this. Right, So,

1224
01:14:59,080 --> 01:15:02,720
Speaker 1: as we move for word, what do you think we

1225
01:15:02,880 --> 01:15:09,520
Speaker 1: can all do to demand change? Because obviously checking sources

1226
01:15:09,560 --> 01:15:13,240
Speaker 1: following the COVID tracking project, for example, knowing that you're

1227
01:15:13,280 --> 01:15:17,200
Speaker 1: sharing trusted science is a way to immediately affect your

1228
01:15:17,360 --> 01:15:21,280
Speaker 1: personal platform. Whether it's five people, fifty people, or five

1229
01:15:21,360 --> 01:15:27,000
Speaker 1: hundred people, you can become a trusted resource of real science.

1230
01:15:28,000 --> 01:15:31,760
Speaker 1: And that feels like an imperative as a citizen, re

1231
01:15:32,560 --> 01:15:35,400
Speaker 1: right out the gate. But what also feels imperative to

1232
01:15:35,439 --> 01:15:39,320
Speaker 1: me is talking about not going back to that version

1233
01:15:39,400 --> 01:15:43,000
Speaker 1: of normal post pandemic, if there is such a thing

1234
01:15:43,040 --> 01:15:47,040
Speaker 1: as a post pandemic. Ever, we need a new normal,

1235
01:15:47,320 --> 01:15:51,280
Speaker 1: We need a new system. And when I think about

1236
01:15:52,040 --> 01:15:55,640
Speaker 1: ways that I try to spend you know, my privilege

1237
01:15:56,439 --> 01:15:59,800
Speaker 1: as a white woman, for example, and make sure that

1238
01:16:00,000 --> 01:16:02,680
Speaker 1: all of the emotional labor of talking about issues of

1239
01:16:02,720 --> 01:16:05,360
Speaker 1: women of color doesn't fall solely on the shoulders of

1240
01:16:05,400 --> 01:16:09,280
Speaker 1: women of color. I think about how we as a

1241
01:16:09,320 --> 01:16:11,960
Speaker 1: society have a duty to our neighbors, how we as

1242
01:16:11,960 --> 01:16:15,000
Speaker 1: a citizen we need to say, if we're the richest

1243
01:16:15,040 --> 01:16:17,599
Speaker 1: country in the world, we need to be investing in

1244
01:16:17,680 --> 01:16:21,200
Speaker 1: public health more than we invest in war. What are

1245
01:16:21,240 --> 01:16:25,439
Speaker 1: we doing? What are our priorities? So I'm curious what

1246
01:16:25,560 --> 01:16:29,400
Speaker 1: you think the sort of average person who's sitting at home,

1247
01:16:29,560 --> 01:16:32,200
Speaker 1: myself included by the way, going how do I make

1248
01:16:32,240 --> 01:16:36,840
Speaker 1: a dent in this? Are are there actions that you,

1249
01:16:37,880 --> 01:16:41,240
Speaker 1: again as a public health expert, would tell us to take.

1250
01:16:41,560 --> 01:16:44,040
Speaker 1: Should we be calling our senators, should we be writing

1251
01:16:44,080 --> 01:16:46,960
Speaker 1: emails to the mayor? What what do we need to

1252
01:16:47,040 --> 01:16:50,760
Speaker 1: do to make some noise about access and equity in

1253
01:16:50,840 --> 01:16:55,960
Speaker 1: health care? So I will say to that, you know,

1254
01:16:56,600 --> 01:17:00,320
Speaker 1: nobody's sacrifice that they've made for COVID nineteen, but Asian

1255
01:17:00,400 --> 01:17:02,800
Speaker 1: is in vain. So I want to encourage people that

1256
01:17:02,880 --> 01:17:05,360
Speaker 1: the choices that they're making to not hang out with friends,

1257
01:17:05,560 --> 01:17:08,240
Speaker 1: to not go out, to wear a mask, to wash

1258
01:17:08,280 --> 01:17:12,200
Speaker 1: their hands, all these things are not for nothing, and

1259
01:17:12,240 --> 01:17:15,280
Speaker 1: to keep doing those things because those things have measurable

1260
01:17:15,360 --> 01:17:18,720
Speaker 1: impact on our public health, measurable and we should be

1261
01:17:18,760 --> 01:17:21,960
Speaker 1: thanking people for those things. And as depressing and as

1262
01:17:22,000 --> 01:17:26,560
Speaker 1: frustrating as the season is, like, truly truly commendable sacrifices

1263
01:17:26,600 --> 01:17:28,040
Speaker 1: and they're not in vain. So I just want to

1264
01:17:28,040 --> 01:17:30,920
Speaker 1: start with saying that the second thing would be, like

1265
01:17:30,960 --> 01:17:34,760
Speaker 1: I said earlier, to vote, to absolutely not sit on

1266
01:17:34,800 --> 01:17:38,040
Speaker 1: the opportunity to go out in November, and to vote

1267
01:17:38,080 --> 01:17:41,240
Speaker 1: for people who are going to promote science, to refund

1268
01:17:41,320 --> 01:17:44,720
Speaker 1: public health, to ensure that our science leader, that our

1269
01:17:44,840 --> 01:17:48,280
Speaker 1: leaders are trusting the science and speaking correctly about the science.

1270
01:17:49,600 --> 01:17:52,439
Speaker 1: And three to see if there are ways that you

1271
01:17:52,479 --> 01:17:56,760
Speaker 1: can participate in things like contact tracing or in volunteering

1272
01:17:56,840 --> 01:18:00,200
Speaker 1: even as a poll worker um or you know, doing

1273
01:18:00,240 --> 01:18:03,280
Speaker 1: all the things in our community that seem maybe high

1274
01:18:03,360 --> 01:18:07,080
Speaker 1: risk or part of COVID response. There are donate blood,

1275
01:18:07,200 --> 01:18:10,759
Speaker 1: donate plasma, like all those things have a tangible effect

1276
01:18:10,840 --> 01:18:13,400
Speaker 1: on how we can get our community back to normal.

1277
01:18:13,439 --> 01:18:15,320
Speaker 1: And I do want to say this pandemic is going

1278
01:18:15,360 --> 01:18:18,080
Speaker 1: to end. We will live in a post COVID world.

1279
01:18:18,400 --> 01:18:21,479
Speaker 1: We just don't know when it's gonna happen. It's probably

1280
01:18:21,520 --> 01:18:24,200
Speaker 1: gonna be a while. But you know, all of this

1281
01:18:24,400 --> 01:18:26,840
Speaker 1: is helping. And I think the more people that we

1282
01:18:27,160 --> 01:18:32,680
Speaker 1: can get on board with getting excited about supporting this

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01:18:32,960 --> 01:18:36,280
Speaker 1: as a team effort and not thinking about this individualistically,

1284
01:18:36,840 --> 01:18:38,720
Speaker 1: the faster we can get out of it. You know,

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01:18:38,920 --> 01:18:40,960
Speaker 1: we need to be thinking about this like a group

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01:18:41,120 --> 01:18:46,000
Speaker 1: and not as people. Thank you for that. That that

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01:18:46,120 --> 01:18:50,920
Speaker 1: reminds me to just not to not get tired, you know,

1288
01:18:51,080 --> 01:18:56,480
Speaker 1: to not get to not slip into less vigilance. It

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01:18:56,479 --> 01:18:59,320
Speaker 1: it's nice to be reminded that all of our actions

1290
01:18:59,360 --> 01:19:05,320
Speaker 1: really matter. I wonder we've we've run the gamut of

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01:19:06,840 --> 01:19:10,840
Speaker 1: you know, public health emergencies to hope for the future,

1292
01:19:10,880 --> 01:19:15,280
Speaker 1: and and in this moment, I'm curious, especially what your

1293
01:19:15,280 --> 01:19:17,240
Speaker 1: answer to this question will be. It's my favorite thing

1294
01:19:17,240 --> 01:19:21,800
Speaker 1: to ask everyone who comes on the show. This is

1295
01:19:21,800 --> 01:19:25,800
Speaker 1: a work in progress. And what feels like a work

1296
01:19:25,840 --> 01:19:29,439
Speaker 1: in progress in your life right now? Oh my gosh,

1297
01:19:29,479 --> 01:19:35,200
Speaker 1: this is the epitome of work in progress. Truly. Um,

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01:19:35,240 --> 01:19:40,040
Speaker 1: you know, in my life, I would say the work

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01:19:40,080 --> 01:19:42,360
Speaker 1: of science, and I am. I posted this on my

1300
01:19:42,400 --> 01:19:45,639
Speaker 1: Instagram the other day. The science is not finished until

1301
01:19:45,640 --> 01:19:49,600
Speaker 1: it's communicated, and so my job as a science communicator

1302
01:19:49,760 --> 01:19:53,880
Speaker 1: is not over and it's going to continue. It's going

1303
01:19:53,920 --> 01:19:56,360
Speaker 1: to get more complicated, it's going to get more nuanced,

1304
01:19:57,000 --> 01:20:00,920
Speaker 1: it's going to get more exhausting. And I'm excited about that.

1305
01:20:01,840 --> 01:20:06,080
Speaker 1: I'm excited because science is working. Science is a process.

1306
01:20:06,080 --> 01:20:09,920
Speaker 1: Science changes and evolves. It doesn't you know, does so

1307
01:20:10,120 --> 01:20:13,160
Speaker 1: it doesn't change so wildly that it causes you know, confusion.

1308
01:20:13,320 --> 01:20:17,720
Speaker 1: It's it's growing. It's like a living organism. And to

1309
01:20:17,840 --> 01:20:20,880
Speaker 1: watch that and to be able to invite people in

1310
01:20:20,920 --> 01:20:22,880
Speaker 1: that process so that it's not just sitting in a

1311
01:20:22,960 --> 01:20:27,240
Speaker 1: lab behind closed doors. UM is an honor, and I

1312
01:20:27,280 --> 01:20:30,360
Speaker 1: look forward to the future science that's coming out of

1313
01:20:30,479 --> 01:20:34,360
Speaker 1: vaccine research, of COVID nineteen research. I am thrilled to

1314
01:20:34,479 --> 01:20:36,800
Speaker 1: be a part of that translation and to be a

1315
01:20:36,840 --> 01:20:40,120
Speaker 1: part of inviting people into the process of science. M hmm.

1316
01:20:41,040 --> 01:20:43,479
Speaker 1: I love that. I like being invited to that table.

1317
01:20:44,439 --> 01:20:49,679
Speaker 1: Thank you for doing that with my joy. This show

1318
01:20:49,720 --> 01:20:53,200
Speaker 1: is executive produced by me Sophia Bush and sim Sarna.

1319
01:20:53,800 --> 01:20:58,080
Speaker 1: Our associate producer is Caitlyn Lee. Our editor is Josh Wendish,

1320
01:20:58,479 --> 01:21:00,920
Speaker 1: and our music was written by Jack Garrett and produced

1321
01:21:00,920 --> 01:21:03,559
Speaker 1: by Mark Foster. This show is brought to you by

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01:21:03,600 --> 01:21:09,280
Speaker 1: Grillian Anatomy m h