1 00:00:00,480 --> 00:00:00,800 Speaker 1: Hi. 2 00:00:01,160 --> 00:00:04,000 Speaker 2: Today we have a special bonus episode of Fast Politics 3 00:00:04,080 --> 00:00:08,920 Speaker 2: podcast with doctor Peter Hotez live from the Texas Tribune Festival. 4 00:00:09,160 --> 00:00:11,960 Speaker 2: What you don't hear on the podcast is we had 5 00:00:12,080 --> 00:00:16,200 Speaker 2: a quick and actually quite upsetting and terrifying visit from 6 00:00:16,400 --> 00:00:20,160 Speaker 2: RFK Junior, who stared weirdly at us and walked by 7 00:00:20,239 --> 00:00:23,800 Speaker 2: us several times. Anyway, I hope you enjoyed the interview. 8 00:00:24,160 --> 00:00:29,520 Speaker 2: I'm Mollie John Fast and I am oh, thank you, 9 00:00:30,200 --> 00:00:32,879 Speaker 2: thank you very much. I'm not you know, I have 10 00:00:32,960 --> 00:00:35,760 Speaker 2: three teenage children, so I'm not used to saying that 11 00:00:35,840 --> 00:00:36,800 Speaker 2: and have it clapping. 12 00:00:37,920 --> 00:00:39,559 Speaker 1: I've used to it. I'm used to them being like, 13 00:00:39,600 --> 00:00:40,600 Speaker 1: are you going to go out like that? 14 00:00:41,320 --> 00:00:44,760 Speaker 2: And it is my great honor and privilege to get 15 00:00:44,800 --> 00:00:46,639 Speaker 2: to interview doctor Peter Hotez. 16 00:00:48,600 --> 00:00:54,360 Speaker 3: Thank you, thank you. And I have four adult kids 17 00:00:54,400 --> 00:00:55,960 Speaker 3: and I'm not used to getting. 18 00:00:57,840 --> 00:01:00,680 Speaker 2: Yes, and our children are quite mean to us. 19 00:01:00,520 --> 00:01:02,720 Speaker 3: It's good experience right before. 20 00:01:02,480 --> 00:01:03,640 Speaker 1: We turn this into therapy. 21 00:01:04,160 --> 00:01:09,400 Speaker 2: So I'm very excited because I. 22 00:01:08,240 --> 00:01:09,360 Speaker 1: Love doctor Hotez. 23 00:01:09,520 --> 00:01:13,160 Speaker 2: He's my buddy, he's my go to, he's my man, 24 00:01:13,360 --> 00:01:15,800 Speaker 2: and I see him out there on the front lines 25 00:01:16,440 --> 00:01:18,720 Speaker 2: and I just respect the hell out of him. 26 00:01:18,880 --> 00:01:21,960 Speaker 3: The feelings mutual, Molly, because you know, so much, as 27 00:01:22,000 --> 00:01:23,679 Speaker 3: we'll talk about in the book, so much of the 28 00:01:24,040 --> 00:01:29,240 Speaker 3: targeting now against me around vaccines or COVID is politically motivated. 29 00:01:29,400 --> 00:01:31,440 Speaker 3: And who do you talk to about that? So you know, 30 00:01:31,480 --> 00:01:33,840 Speaker 3: you're been my go to person to help me understand 31 00:01:33,920 --> 00:01:36,679 Speaker 3: the underlying politics on so much of this. 32 00:01:37,040 --> 00:01:40,160 Speaker 2: We're like Captain and Taneil. We're going to break into song. 33 00:01:40,400 --> 00:01:44,240 Speaker 2: But so anyway, just dated right exactly. Oh yes, we 34 00:01:44,280 --> 00:01:45,160 Speaker 2: have a copy of the book. 35 00:01:45,200 --> 00:01:45,880 Speaker 1: Oh my god. 36 00:01:45,959 --> 00:01:48,680 Speaker 2: So I come from book World because I had all 37 00:01:48,720 --> 00:01:51,360 Speaker 2: these writers in my family, so I know we are 38 00:01:51,400 --> 00:01:55,400 Speaker 2: here to sell a product number one okay, and this 39 00:01:55,600 --> 00:01:56,120 Speaker 2: is the book. 40 00:01:56,160 --> 00:01:58,200 Speaker 1: So we're first, we're going to talk about this book. 41 00:01:58,280 --> 00:02:02,559 Speaker 2: Okay, the Deadly Rise of Anti Science a scientist warning. Okay, 42 00:02:02,640 --> 00:02:05,680 Speaker 2: I'm gonna Larry King this because I haven't read this book. Okay, 43 00:02:05,880 --> 00:02:08,320 Speaker 2: please forgive me. I didn't get it. Don't be mad. 44 00:02:08,520 --> 00:02:12,000 Speaker 2: So anyway, let's talk about this book first. Why did 45 00:02:12,040 --> 00:02:12,840 Speaker 2: you write this book? 46 00:02:13,040 --> 00:02:15,880 Speaker 3: Yeah? I mean I've been sort of in this dual life, 47 00:02:16,000 --> 00:02:19,320 Speaker 3: you know, for for decades. So I did my MD 48 00:02:19,440 --> 00:02:23,160 Speaker 3: and PhD in New York at Rockefeller University in Cornell 49 00:02:23,560 --> 00:02:26,120 Speaker 3: where the motto of the university is science for the 50 00:02:26,160 --> 00:02:29,000 Speaker 3: benefit of humanity, and I became a vaccine scientist with 51 00:02:29,040 --> 00:02:31,680 Speaker 3: that in mind. The hookworm vaccine I started as an 52 00:02:31,760 --> 00:02:34,320 Speaker 3: mdphd student in New York forty years later, is now 53 00:02:34,360 --> 00:02:37,399 Speaker 3: in phase two clinical trials, which is about the right 54 00:02:37,440 --> 00:02:39,919 Speaker 3: time frame for a lot of instances and so on. 55 00:02:40,240 --> 00:02:42,760 Speaker 3: What I set out to do forty years ago, I'm 56 00:02:42,760 --> 00:02:46,120 Speaker 3: actually doing. I'm actually living out my boyhood dream. I 57 00:02:46,360 --> 00:02:50,480 Speaker 3: believe so that that's wonderful. But twenty plus years ago 58 00:02:50,960 --> 00:02:53,120 Speaker 3: things kind of turned on its side where there were 59 00:02:53,160 --> 00:02:57,080 Speaker 3: false accusations at vaccines cause autism. That was the original 60 00:02:57,360 --> 00:03:00,240 Speaker 3: assertion from anti vaccine groups. That's when I benched mark 61 00:03:00,320 --> 00:03:02,440 Speaker 3: the start of the movement. And as I mentioned, I 62 00:03:02,480 --> 00:03:05,160 Speaker 3: have four adult kids, including Rachel, who has autism and 63 00:03:05,160 --> 00:03:08,520 Speaker 3: intellectual disabilities. And I wrote the book the book, the 64 00:03:08,560 --> 00:03:11,840 Speaker 3: original book called vaccines did not cause Rachel's autism. That 65 00:03:11,919 --> 00:03:14,919 Speaker 3: maybe public enemy number one or two with anti vaccine group. 66 00:03:15,000 --> 00:03:16,959 Speaker 3: So all of a sudden, I had a front row 67 00:03:17,040 --> 00:03:21,080 Speaker 3: seat to watching this anti vaccine movement grow and evolve. 68 00:03:21,120 --> 00:03:23,400 Speaker 3: And the reason I wrote the book this one because 69 00:03:23,440 --> 00:03:25,840 Speaker 3: I saw a pivot and a change, and it became 70 00:03:26,160 --> 00:03:29,720 Speaker 3: more politically motivated and become a political movement. But the 71 00:03:29,800 --> 00:03:32,760 Speaker 3: major reason was it was killing Americans. And in the 72 00:03:32,760 --> 00:03:36,040 Speaker 3: book I benchmarked that two hundred thousand Americans needless they 73 00:03:36,120 --> 00:03:38,880 Speaker 3: lost their lives because they refused the COVID vaccine. They 74 00:03:38,880 --> 00:03:44,720 Speaker 3: were victims from this very predatory, politically motivated enterprise. And 75 00:03:44,800 --> 00:03:48,400 Speaker 3: so for me, now, saving lives has a two pronged approach. 76 00:03:48,440 --> 00:03:51,160 Speaker 3: One making vaccines that no one else will make. And 77 00:03:51,200 --> 00:03:54,000 Speaker 3: we made two COVID vaccines, reached one hundred million people 78 00:03:54,000 --> 00:03:54,240 Speaker 3: in life. 79 00:03:54,280 --> 00:03:55,600 Speaker 1: Yeah, I want to tell you about it. 80 00:03:55,680 --> 00:04:00,040 Speaker 3: Thank you, thank you. But the other side of the 81 00:04:00,080 --> 00:04:04,400 Speaker 3: coin is also Saving lives is countering this anti vaccine 82 00:04:04,400 --> 00:04:07,680 Speaker 3: movement because so many Americans are dying because of including 83 00:04:07,720 --> 00:04:10,440 Speaker 3: forty thousand Texans who needlessly lost their lives in Texas. 84 00:04:10,840 --> 00:04:13,520 Speaker 2: That's so I want to talk about Rachel for a minute. 85 00:04:14,080 --> 00:04:17,960 Speaker 2: You have four kids, they're all adults. You have this 86 00:04:18,040 --> 00:04:22,919 Speaker 2: child who has autism, so you really have like the 87 00:04:23,120 --> 00:04:28,919 Speaker 2: story that these anti vaxxers, I mean, they're really using autism. 88 00:04:28,480 --> 00:04:31,000 Speaker 1: As a way to sell their. 89 00:04:30,960 --> 00:04:34,640 Speaker 3: Line, literally sell. That's how it started. They were monetizing 90 00:04:34,680 --> 00:04:41,000 Speaker 3: the internet, selling phony autism cures or nutritional supplements. I mean, 91 00:04:41,000 --> 00:04:42,760 Speaker 3: if you go to Amazon dot com and type in 92 00:04:42,800 --> 00:04:44,800 Speaker 3: the word vaccinations, you could do it right now on 93 00:04:44,800 --> 00:04:48,840 Speaker 3: your iPhone. It's almost all anti vaccine conspiracy books right there. 94 00:04:49,080 --> 00:04:52,479 Speaker 3: So there, it's an industry, and they're making money. And 95 00:04:52,560 --> 00:04:55,000 Speaker 3: so by my going up against it, I was hurting 96 00:04:55,040 --> 00:04:57,640 Speaker 3: their bottom line. And so what you saw was they 97 00:04:57,680 --> 00:05:02,159 Speaker 3: would continually change what their assertion was. The original assertion was, 98 00:05:02,200 --> 00:05:05,680 Speaker 3: I was sing tabella vaccine. That was the paper that 99 00:05:05,800 --> 00:05:09,040 Speaker 3: was fraudulent paper that was publishing the Lancet in nineteen 100 00:05:09,120 --> 00:05:10,600 Speaker 3: ninety eight. Wait so, and then. 101 00:05:10,520 --> 00:05:14,520 Speaker 1: Ellis the story. It started with a fraudulent paper when. 102 00:05:14,600 --> 00:05:17,040 Speaker 3: In nineteen ninety eight was when it was published, and 103 00:05:17,200 --> 00:05:21,719 Speaker 3: it claimed that the measles mumpsterbella vaccine, a live virus vaccine, 104 00:05:22,040 --> 00:05:24,760 Speaker 3: had the ability to replicate in the colon of kids, 105 00:05:24,760 --> 00:05:27,280 Speaker 3: and somehow that led to what they then called pervasive 106 00:05:27,279 --> 00:05:30,240 Speaker 3: developmental disorder. Now we call it autism. And it never 107 00:05:30,320 --> 00:05:34,080 Speaker 3: sounded right to me, how because clearly autism was something 108 00:05:34,120 --> 00:05:37,320 Speaker 3: that was beginning in pregnancy, right, I mean, that's why 109 00:05:37,360 --> 00:05:39,839 Speaker 3: the pervasive part comes in. And sure enough, you know 110 00:05:39,920 --> 00:05:42,520 Speaker 3: Rachel was diagnosed at the l Child Study Center when 111 00:05:42,520 --> 00:05:45,799 Speaker 3: I was on the faculty at Yale, and sure enough, 112 00:05:46,279 --> 00:05:50,640 Speaker 3: large cohort studies showed that kids who got the MMR 113 00:05:50,720 --> 00:05:52,720 Speaker 3: vaccine were no more likely to acquire auten than the 114 00:05:52,800 --> 00:05:55,359 Speaker 3: kids who didn't. The paper that was publishing the Lancet 115 00:05:55,400 --> 00:05:59,520 Speaker 3: was a twelve person study that had numerous errors and 116 00:05:59,600 --> 00:06:03,080 Speaker 3: there was conflicts of interest I weren't disclosed, and it 117 00:06:03,160 --> 00:06:06,200 Speaker 3: showed it to be not to be not true. And 118 00:06:05,520 --> 00:06:09,080 Speaker 3: and so the scientific community, though, responded in a very 119 00:06:09,160 --> 00:06:11,520 Speaker 3: robust way doing large coords. So he's sewing kids who 120 00:06:11,600 --> 00:06:14,040 Speaker 3: got the MMR no more likely to acquire autism than 121 00:06:14,200 --> 00:06:16,520 Speaker 3: kids who didn't get the MMR. And similarly, kids on 122 00:06:16,560 --> 00:06:18,760 Speaker 3: the autism spectrum were no more likely to have gotten 123 00:06:18,880 --> 00:06:21,440 Speaker 3: MMR the kids not on the autism spectrum. And that 124 00:06:21,480 --> 00:06:22,880 Speaker 3: should have been the end of it, But what you 125 00:06:23,000 --> 00:06:25,920 Speaker 3: started to see is they weren't taking no for an answer, 126 00:06:25,960 --> 00:06:29,640 Speaker 3: so they started all Propert F. Kennedy Junior got involved 127 00:06:29,640 --> 00:06:33,040 Speaker 3: in two thousand and five. He wrote an article claiming 128 00:06:33,120 --> 00:06:36,000 Speaker 3: it was the thimerosol preservative that it used to be, 129 00:06:36,200 --> 00:06:38,200 Speaker 3: and that got debunks some kind of thing, and then 130 00:06:38,240 --> 00:06:41,560 Speaker 3: it was spacing vaccines too close together an element. So 131 00:06:41,680 --> 00:06:43,320 Speaker 3: became this exhausting exercise. 132 00:06:43,360 --> 00:06:47,560 Speaker 1: So it started with the mercury. The mercury was MMR, 133 00:06:47,720 --> 00:06:48,480 Speaker 1: then MMR. 134 00:06:48,600 --> 00:06:51,960 Speaker 2: I then moved to mercury which wasn't even is now 135 00:06:52,240 --> 00:06:53,800 Speaker 2: not even used in vaccines. 136 00:06:54,279 --> 00:06:55,720 Speaker 1: And then it went to. 137 00:06:56,640 --> 00:06:59,640 Speaker 3: Spacing vaccines too close. Now that's when Jenny McCarthy got 138 00:07:00,320 --> 00:07:03,800 Speaker 3: so we had to talk about greening our vaccines. And 139 00:07:03,839 --> 00:07:06,479 Speaker 3: then it was ali in vaccine. Then they pivoted to 140 00:07:06,520 --> 00:07:10,080 Speaker 3: the HPV vaccine for cervical cancer and other cancers, has 141 00:07:10,120 --> 00:07:14,440 Speaker 3: said it was causing infertility or autoimmunity. How many people 142 00:07:14,520 --> 00:07:18,640 Speaker 3: here have heard that COVID vaccines caused infertility or autoimmunity. Yeah, 143 00:07:18,640 --> 00:07:20,360 Speaker 3: that's what they got it from. They copy pasted the 144 00:07:20,360 --> 00:07:24,040 Speaker 3: false assertion from FPV vaccine onto covid and then it 145 00:07:24,080 --> 00:07:27,280 Speaker 3: was then they pivoted something called chronic illness. You know, 146 00:07:27,320 --> 00:07:29,120 Speaker 3: how do you you know, how do you even start that? 147 00:07:29,280 --> 00:07:30,920 Speaker 3: And and I, you know, I think we did a 148 00:07:30,960 --> 00:07:33,040 Speaker 3: pretty good job taking the wind out of the sales. 149 00:07:33,040 --> 00:07:36,080 Speaker 3: I wrote the book about Rachel, which does it talks 150 00:07:36,120 --> 00:07:39,600 Speaker 3: about her life and also the evidence showing there's no 151 00:07:39,680 --> 00:07:43,520 Speaker 3: linking vaccines and autism but also providing an alternative narrative 152 00:07:43,720 --> 00:07:47,160 Speaker 3: that we did whole exum genomic sequencing on Rachel and 153 00:07:47,200 --> 00:07:50,080 Speaker 3: my wife Anne and I and found Rachel's autism gene 154 00:07:50,280 --> 00:07:53,040 Speaker 3: which was similar but different from the one hundred others 155 00:07:53,160 --> 00:07:55,600 Speaker 3: identified by the Broad Institute. 156 00:07:55,280 --> 00:07:57,920 Speaker 1: So it was it was genetic, right. 157 00:07:57,840 --> 00:08:01,040 Speaker 3: So it's genetics and epigenetic. So the point is there's 158 00:08:01,080 --> 00:08:04,560 Speaker 3: an alternative narrative right out, and that's important for parents 159 00:08:04,640 --> 00:08:08,600 Speaker 3: and I, and and that they loved me at that point. 160 00:08:08,680 --> 00:08:11,160 Speaker 3: So that's you know this, So I that's when I 161 00:08:11,200 --> 00:08:14,360 Speaker 3: started to get heavily targeted, but I get this front 162 00:08:14,440 --> 00:08:16,240 Speaker 3: row seat to the movement to see what it was 163 00:08:16,280 --> 00:08:16,800 Speaker 3: all about. 164 00:08:17,240 --> 00:08:20,280 Speaker 2: One of the things I think is so interesting about 165 00:08:20,280 --> 00:08:23,560 Speaker 2: the anti vax movement is it's not so different than 166 00:08:23,640 --> 00:08:27,400 Speaker 2: some of the other far right conservative movements, right that 167 00:08:27,520 --> 00:08:30,400 Speaker 2: they sort of come up with an idea, it gets debunked. 168 00:08:30,680 --> 00:08:33,560 Speaker 2: I mean, if you think of like the twenty twenty election, 169 00:08:33,960 --> 00:08:38,319 Speaker 2: right they were like Hugo Chavez voting machines, you know, 170 00:08:38,679 --> 00:08:41,240 Speaker 2: or you're like, no, Hugo, the ghost of Hugo Shaves. 171 00:08:41,280 --> 00:08:44,160 Speaker 2: I mean, right, Like we've debunked and debunked and debunked 172 00:08:44,200 --> 00:08:45,920 Speaker 2: and it just keeps what going. 173 00:08:46,040 --> 00:08:48,679 Speaker 3: But this is when it became so pernicious. I mean, 174 00:08:48,720 --> 00:08:51,360 Speaker 3: it was always problematic and kids weren't getting their full 175 00:08:51,360 --> 00:08:54,320 Speaker 3: complement of vaccines. But then it took this very dark 176 00:08:54,360 --> 00:08:57,360 Speaker 3: turn when it got adopted by the Republican d Party 177 00:08:57,400 --> 00:09:01,480 Speaker 3: in Texas and started getting PAC money, political action committee 178 00:09:01,480 --> 00:09:06,280 Speaker 3: money for anti vaccine activities. And that's what came off 179 00:09:06,320 --> 00:09:10,440 Speaker 3: the rails during COVID nineteen, because what you started to 180 00:09:10,520 --> 00:09:14,080 Speaker 3: see was Texas was one of the worst affected states 181 00:09:14,080 --> 00:09:18,880 Speaker 3: from COVID nineteen. One hundred thousand Texans perished in this pandemic, 182 00:09:19,679 --> 00:09:24,000 Speaker 3: but tragically, almost as many Texans died after vaccines became 183 00:09:24,040 --> 00:09:28,640 Speaker 3: widely available as before. Forty thousand Texans needlessly died because 184 00:09:28,640 --> 00:09:33,640 Speaker 3: they refused to COVID vaccine. And this was happening because 185 00:09:33,880 --> 00:09:37,440 Speaker 3: of a predatory movement coming from the far right. It 186 00:09:37,559 --> 00:09:41,640 Speaker 3: started at the SEAPAC conference in Dallas in twenty twenty one, 187 00:09:42,320 --> 00:09:44,760 Speaker 3: right as the Delta Wave was starting to take off 188 00:09:44,880 --> 00:09:47,120 Speaker 3: in the summer of twenty twenty one. You know, the 189 00:09:47,200 --> 00:09:49,280 Speaker 3: language was first they're going to vaccination, and then they're 190 00:09:49,280 --> 00:09:51,480 Speaker 3: going to take away your guns and your bibles, and 191 00:09:51,520 --> 00:09:55,000 Speaker 3: as ridiculous as that sounds to us people, especially in 192 00:09:55,040 --> 00:09:59,079 Speaker 3: the conservative parts of Texas, East Texas, Central Texas to Panhandle. 193 00:09:59,559 --> 00:10:03,760 Speaker 3: Except that, and then you saw the pilon. The pylon 194 00:10:03,880 --> 00:10:09,000 Speaker 3: came from members of Congress, Senator Groan Johnson, members of 195 00:10:09,040 --> 00:10:13,000 Speaker 3: the House Freedom Caucus, and then amplified on Fox News. 196 00:10:13,000 --> 00:10:16,959 Speaker 3: So it became this whole far right ecosystem right and 197 00:10:17,480 --> 00:10:21,240 Speaker 3: that was doing this, And it was very hard to 198 00:10:21,280 --> 00:10:24,360 Speaker 3: talk about, especially because you know, as a physician and scientist, 199 00:10:24,800 --> 00:10:27,080 Speaker 3: you're not supposed to talk about Republicans and Democrats. You're 200 00:10:27,120 --> 00:10:29,800 Speaker 3: not so conservative. But what do you do when this 201 00:10:29,880 --> 00:10:32,880 Speaker 3: aggressions coming from It's not that I care about someone's 202 00:10:33,000 --> 00:10:35,960 Speaker 3: extreme views, but somehow, I mean, that's your right as 203 00:10:35,960 --> 00:10:38,360 Speaker 3: an American. But my point was, somehow we have to 204 00:10:38,480 --> 00:10:41,600 Speaker 3: uncouple the anti science from it because it's killing too 205 00:10:41,640 --> 00:10:45,480 Speaker 3: many mess too many America. Two hundred thousand Americans needlessly 206 00:10:45,559 --> 00:10:48,280 Speaker 3: perished because they refused to COVID and there and they 207 00:10:48,280 --> 00:10:51,280 Speaker 3: were victims of this predatory movement. 208 00:10:51,480 --> 00:10:53,560 Speaker 2: So one of the things I wanted to ask you 209 00:10:53,640 --> 00:10:59,600 Speaker 2: about was doctor Fauci was kind of a villain for 210 00:10:59,640 --> 00:11:03,680 Speaker 2: the anti ivax crowd, and they were furious with him 211 00:11:03,880 --> 00:11:08,120 Speaker 2: for being a scientist and devoting his life to public service. 212 00:11:08,240 --> 00:11:11,960 Speaker 1: And then he resigned and they set their sights on you. 213 00:11:12,559 --> 00:11:15,360 Speaker 3: Yeah, it became sort of faucy light. They'd always had 214 00:11:15,360 --> 00:11:17,760 Speaker 3: their sights on me, but now with Tony out of 215 00:11:17,840 --> 00:11:21,679 Speaker 3: public office, he just wasn't as visible. So I became 216 00:11:21,720 --> 00:11:26,000 Speaker 3: the next rung down. And then the weirdness really unfolded. Well, 217 00:11:26,000 --> 00:11:28,400 Speaker 3: it was always weird, right I was, you know. I 218 00:11:28,480 --> 00:11:31,760 Speaker 3: this first happened on the far Rights on twenty twenty one, 219 00:11:32,360 --> 00:11:36,120 Speaker 3: Laura Ingram and Fox News and Governor desanct Us of 220 00:11:36,160 --> 00:11:39,800 Speaker 3: Florida started mocking me for predicting the Delta Way was 221 00:11:39,840 --> 00:11:42,640 Speaker 3: going to slam Florida in a couple of weeks, which 222 00:11:42,679 --> 00:11:45,600 Speaker 3: it absolutely did. It hammered Florida. But you know, that's 223 00:11:45,960 --> 00:11:48,360 Speaker 3: you know, when it first started, and then I became 224 00:11:48,400 --> 00:11:52,400 Speaker 3: a regular topic of targeting. Tucker Carlson went after me 225 00:11:52,880 --> 00:11:55,560 Speaker 3: actually on the on the same day I was co 226 00:11:55,720 --> 00:12:00,480 Speaker 3: nominated for the Nobel Peace Prize for our COVID vaccine. Tucker, 227 00:12:00,720 --> 00:12:03,760 Speaker 3: maybe because of it, he went on this horrible rant saying, 228 00:12:03,840 --> 00:12:06,880 Speaker 3: you know, I was totally unqualified. I'm with Charlotte and 229 00:12:06,960 --> 00:12:08,439 Speaker 3: I'd made a covid vaccine. What else? 230 00:12:09,200 --> 00:12:11,800 Speaker 2: And it's not just a will you talk about this 231 00:12:11,880 --> 00:12:15,040 Speaker 2: COVID vaccine, because it's not just a covid vaccine. It's 232 00:12:15,080 --> 00:12:18,800 Speaker 2: a covid vaccine that is for places where you can't 233 00:12:18,880 --> 00:12:22,959 Speaker 2: have the fancy Fizer vaccine, right talker. 234 00:12:23,280 --> 00:12:27,800 Speaker 3: So we, in addition to making vaccines for parasitic infections 235 00:12:27,800 --> 00:12:31,359 Speaker 3: for decades, about twelve years ago, we started making coronavirus 236 00:12:31,480 --> 00:12:35,640 Speaker 3: vaccines for SARS. Remember the original Severque respiratory syndrome came 237 00:12:35,679 --> 00:12:38,720 Speaker 3: out of southern China in two thousand and two, and mayors, 238 00:12:38,760 --> 00:12:42,120 Speaker 3: because they were orphaned as well, nobody cared about coronavirus vaccine. 239 00:12:42,120 --> 00:12:45,000 Speaker 3: So we became really good at making a low cost 240 00:12:45,160 --> 00:12:49,160 Speaker 3: COVID vaccine that could be done through microbial fermentation in yeast. 241 00:12:49,720 --> 00:12:53,400 Speaker 3: And so when the COVID nineteen sequence hit, we were 242 00:12:53,440 --> 00:12:56,400 Speaker 3: able to pivot and change our program to make the 243 00:12:56,440 --> 00:13:00,680 Speaker 3: COVID nineteen vaccine, which we then licensed no no patent, 244 00:13:00,720 --> 00:13:06,199 Speaker 3: no strings attached to India, Indonesia, Bangladesh, and they were 245 00:13:06,200 --> 00:13:08,000 Speaker 3: able to scale it up and make it so that 246 00:13:08,040 --> 00:13:10,880 Speaker 3: one hundred million people got either korby Vax, which was 247 00:13:10,920 --> 00:13:14,160 Speaker 3: the name of the vaccine in India or Indovac, which 248 00:13:14,200 --> 00:13:17,720 Speaker 3: is in Indonesia. Indovak became the first halal covid vaccine 249 00:13:18,040 --> 00:13:21,080 Speaker 3: because they came in and inspected our labs and realized 250 00:13:21,080 --> 00:13:24,040 Speaker 3: that all of our ingredients in the vaccine came from 251 00:13:24,040 --> 00:13:27,080 Speaker 3: a non animal, non human source. So in a sense, 252 00:13:27,080 --> 00:13:29,920 Speaker 3: it was a vegan vaccine. Although somebody pointed out, no, 253 00:13:30,040 --> 00:13:32,280 Speaker 3: it's not reductor, it's not a vegan vaccineuse you did 254 00:13:32,320 --> 00:13:35,400 Speaker 3: animal testing, so okay, so I don't know it's a 255 00:13:35,480 --> 00:13:36,560 Speaker 3: vegetarian vaccine se. 256 00:13:36,720 --> 00:13:40,160 Speaker 2: So one of the things about the original Fizer and 257 00:13:40,240 --> 00:13:44,000 Speaker 2: Maderna vaccines is that they need to be at least 258 00:13:44,080 --> 00:13:46,920 Speaker 2: in the earliest stages, they needed to be stored in 259 00:13:47,120 --> 00:13:50,319 Speaker 2: very cold storage. They were expensive to store, and there 260 00:13:50,320 --> 00:13:50,840 Speaker 2: were parts. 261 00:13:50,880 --> 00:13:52,719 Speaker 3: I mean, we told they have been that companies were 262 00:13:52,760 --> 00:13:55,600 Speaker 3: charging a lot and ours we were doing for two 263 00:13:55,600 --> 00:13:58,720 Speaker 3: to three dollars a dose. Yeah, was about the cheapest 264 00:13:58,760 --> 00:14:01,280 Speaker 3: you could get, and they were at the time charging 265 00:14:01,360 --> 00:14:04,280 Speaker 3: thirty dollars a dose. Of course, now after taking twenty 266 00:14:04,280 --> 00:14:07,520 Speaker 3: five billion dollars in taxpayer money for advanced purchase and 267 00:14:08,360 --> 00:14:10,120 Speaker 3: development costs, they jet what do they do for these 268 00:14:10,160 --> 00:14:12,199 Speaker 3: boot this new booster they jack up the price to 269 00:14:12,240 --> 00:14:14,400 Speaker 3: one hundred and thirty dollars a dose. Yeah, I say, 270 00:14:14,440 --> 00:14:16,760 Speaker 3: guys say that you're trying to get the American people 271 00:14:16,760 --> 00:14:18,480 Speaker 3: to hate you. I mean, you know, so they are. 272 00:14:18,760 --> 00:14:21,760 Speaker 3: Lack of situational awareness is something, but you know, we 273 00:14:21,840 --> 00:14:24,000 Speaker 3: continue to do this at very low But the other 274 00:14:24,040 --> 00:14:27,320 Speaker 3: interesting thing, of course is you know, when you know 275 00:14:27,360 --> 00:14:30,360 Speaker 3: people like RFK Junior, Joe Rogan go after me or 276 00:14:30,360 --> 00:14:33,600 Speaker 3: Elon Musk, you know, they they say the term the 277 00:14:33,680 --> 00:14:37,200 Speaker 3: uses a shill for the pharma companies, and I'm secretly 278 00:14:37,240 --> 00:14:39,760 Speaker 3: being paid by Fizer. Wait a minute, we made two 279 00:14:39,840 --> 00:14:44,680 Speaker 3: vaccines that kept Fighter indeed with five. Yeah, but you know, 280 00:14:44,760 --> 00:14:47,520 Speaker 3: the reality means nothing is fire. Pfizer is not. 281 00:14:47,600 --> 00:14:50,960 Speaker 2: Really they're not really fans, right, since your make a 282 00:14:51,040 --> 00:14:53,560 Speaker 2: low cost vaccine that's available for Fred. 283 00:14:53,600 --> 00:14:57,479 Speaker 3: Yeah, I'm in a funny position because we have our vaccine, 284 00:14:57,920 --> 00:15:00,680 Speaker 3: and yet the only vaccine really available in the US 285 00:15:00,720 --> 00:15:04,200 Speaker 3: pretty much is the mRNA vaccines from Pfizer Maderna, and 286 00:15:04,240 --> 00:15:07,040 Speaker 3: I want the American people to take that vaccine, so 287 00:15:07,080 --> 00:15:12,240 Speaker 3: I wind up having to also you know, promote those 288 00:15:12,320 --> 00:15:16,080 Speaker 3: as well, and so that's also problematic. So yeah, but 289 00:15:16,160 --> 00:15:19,480 Speaker 3: you know, our vaccine now has reached one hundred million people. 290 00:15:19,720 --> 00:15:23,040 Speaker 3: No patent. Those strings attached low cost to three dollars 291 00:15:23,080 --> 00:15:23,360 Speaker 3: a day. 292 00:15:23,440 --> 00:15:25,840 Speaker 1: So there are parts of India where you can't. 293 00:15:25,520 --> 00:15:30,440 Speaker 2: Refrigerate, parts of Africa where there is we're freeze or freeze. 294 00:15:30,520 --> 00:15:32,760 Speaker 2: What do you really need a vaccine that is more 295 00:15:32,880 --> 00:15:35,560 Speaker 2: durable And that was one of the things that your 296 00:15:35,640 --> 00:15:37,360 Speaker 2: vaccine was able to accomplish. 297 00:15:37,440 --> 00:15:40,000 Speaker 3: Yeah, it's low cost, no limited the amount you can 298 00:15:40,040 --> 00:15:44,040 Speaker 3: make super cheap, you know, and we did that technology deliberately. 299 00:15:44,080 --> 00:15:46,840 Speaker 3: So our whole approach is we make the prototype, then 300 00:15:46,880 --> 00:15:50,000 Speaker 3: we partner with vaccine producers in low and middle income 301 00:15:50,000 --> 00:15:53,400 Speaker 3: countries of scale up, kind of create this alternative model 302 00:15:53,520 --> 00:15:54,720 Speaker 3: to the big pharma company. 303 00:15:55,040 --> 00:15:57,400 Speaker 2: So I want to ask you another question about this 304 00:15:57,560 --> 00:16:01,640 Speaker 2: deadly rise of anti science. It's not just vaccines that 305 00:16:01,760 --> 00:16:04,240 Speaker 2: these people are against. 306 00:16:04,280 --> 00:16:05,280 Speaker 1: Will you talk about that? 307 00:16:05,600 --> 00:16:08,960 Speaker 3: Yeah, it's now spilling over to other Well, first of all, 308 00:16:09,040 --> 00:16:12,000 Speaker 3: it's I worry it's spilling over to vaccines for low 309 00:16:12,000 --> 00:16:16,440 Speaker 3: and middle income countries as well, and childhood immunizations. That's one. 310 00:16:16,560 --> 00:16:20,360 Speaker 3: And it's globalizing now, this US style anti vaccine movements, 311 00:16:20,400 --> 00:16:23,800 Speaker 3: you know, into Canada now with the Health Freedom convoys 312 00:16:23,840 --> 00:16:28,960 Speaker 3: and Central Europe, and in low and middle income countries 313 00:16:28,960 --> 00:16:32,560 Speaker 3: on the African continent, and now it's other aspects of 314 00:16:32,600 --> 00:16:38,360 Speaker 3: biomedical science. It's targeting the virologists falsely claiming that you 315 00:16:38,360 --> 00:16:41,720 Speaker 3: know that we made the COVID virus, which it's ridiculous. 316 00:16:41,760 --> 00:16:44,400 Speaker 3: And so what you're seeing is this kind of revisionist 317 00:16:44,520 --> 00:16:47,320 Speaker 3: history happened in the Congress. They you know, after I 318 00:16:47,360 --> 00:16:50,160 Speaker 3: pointed out that two hundred thousand Americans died because of 319 00:16:50,200 --> 00:16:54,680 Speaker 3: their predatory behavior. There's this revisionist history going on, and 320 00:16:54,800 --> 00:16:56,880 Speaker 3: no when they want to say, no, it was the 321 00:16:56,920 --> 00:17:00,080 Speaker 3: COVID vaccines that killed Americans, not COVID, total nonsense, and 322 00:17:00,680 --> 00:17:03,680 Speaker 3: the scientists made the virus. And you're seeing this play 323 00:17:03,680 --> 00:17:06,080 Speaker 3: out now in these ridiculous house hearings. 324 00:17:06,680 --> 00:17:07,760 Speaker 1: Yeah, so talk to me. 325 00:17:07,920 --> 00:17:11,120 Speaker 2: I want you to first talk about being targeted by 326 00:17:11,240 --> 00:17:15,600 Speaker 2: the dumbest Senator, Ron Johnson. I do not use the 327 00:17:15,640 --> 00:17:20,399 Speaker 2: phrase dumbest senator lightly because the competition is fierce. But 328 00:17:21,119 --> 00:17:25,080 Speaker 2: Ron Johnson I do think has brain worms, and he 329 00:17:25,200 --> 00:17:27,720 Speaker 2: has taken you as. 330 00:17:27,560 --> 00:17:30,520 Speaker 1: A real He is really into bully. 331 00:17:30,760 --> 00:17:33,760 Speaker 3: Well, they're they're all I mean, Marjorie Taylor Green. You 332 00:17:33,800 --> 00:17:36,680 Speaker 3: know when at Shawn the Senate going now in the House. 333 00:17:36,880 --> 00:17:39,040 Speaker 3: The House says the poenut power the Yeah, you know 334 00:17:39,040 --> 00:17:41,440 Speaker 3: he's going after me Steve Benn's war room a couple 335 00:17:41,480 --> 00:17:42,000 Speaker 3: of times. 336 00:17:42,600 --> 00:17:45,960 Speaker 2: But tell us about Ron Johnson because he has I mean, 337 00:17:46,440 --> 00:17:50,320 Speaker 2: I've seen, you know, Mandela Barnes ran against him and 338 00:17:50,400 --> 00:17:54,240 Speaker 2: really could have won had the polling not been so inaccurate. 339 00:17:54,400 --> 00:17:57,800 Speaker 2: And Mandela Barnes was selling a T shirt because Ron 340 00:17:57,880 --> 00:18:00,600 Speaker 2: Johnson had said on television that the f I was 341 00:18:00,640 --> 00:18:03,280 Speaker 2: after him. So Mandela Barnes has a tea shirt that 342 00:18:03,320 --> 00:18:04,880 Speaker 2: says the FBI is after. 343 00:18:04,720 --> 00:18:07,359 Speaker 1: Me Ron Johnson. So I have that T shirt still, 344 00:18:07,400 --> 00:18:08,359 Speaker 1: I mean, it's the beast. 345 00:18:08,800 --> 00:18:11,760 Speaker 2: But so I want to know how he targeted you, 346 00:18:11,840 --> 00:18:16,080 Speaker 2: and also what the repercussions of being targeted by elected 347 00:18:16,400 --> 00:18:19,760 Speaker 2: officials are, because if you think about that, it's so 348 00:18:20,080 --> 00:18:22,800 Speaker 2: scary to me to think of, like, these people have 349 00:18:22,920 --> 00:18:26,199 Speaker 2: been put in the United States Senate to represent the 350 00:18:26,200 --> 00:18:29,600 Speaker 2: people of Wisconsin and they're you know, I mean, going 351 00:18:29,640 --> 00:18:30,480 Speaker 2: after your doctor. 352 00:18:30,560 --> 00:18:32,040 Speaker 3: I mean I have Ram Paul. I mean, why is 353 00:18:32,119 --> 00:18:35,320 Speaker 3: Ram Paul, a center from Kentucky, you know, targeting me 354 00:18:35,400 --> 00:18:37,760 Speaker 3: on Twitter? Right? I mean, I mean, what is that 355 00:18:37,840 --> 00:18:41,240 Speaker 3: I'm a medical school professor in Texas, right, I mean, 356 00:18:41,880 --> 00:18:44,120 Speaker 3: or you know Marjorie Taylor Green, or you know, all 357 00:18:44,160 --> 00:18:46,879 Speaker 3: of these guys. And and that's the other reason I 358 00:18:46,880 --> 00:18:50,560 Speaker 3: wrote the book. That's scary that it's got this. It's 359 00:18:50,600 --> 00:18:55,000 Speaker 3: adopted by a major political party, It's adopted by a 360 00:18:55,320 --> 00:18:58,879 Speaker 3: section of the United States government targeting science and scientists. 361 00:18:58,920 --> 00:19:02,520 Speaker 3: And I said, look, this is a country that's built 362 00:19:02,920 --> 00:19:06,480 Speaker 3: on the greatness of our research universities and institutions. They 363 00:19:06,520 --> 00:19:10,520 Speaker 3: gave us the Manhattan Project in Silicon Valley and NASA, 364 00:19:10,760 --> 00:19:13,080 Speaker 3: and so you know, when you know, after one of 365 00:19:13,080 --> 00:19:15,320 Speaker 3: these guys goes after me, and or the war room 366 00:19:15,400 --> 00:19:18,160 Speaker 3: podcaster Steve Bannon calls me your criminal. Steve Banner called 367 00:19:18,160 --> 00:19:20,720 Speaker 3: me a criminal, right, And then you know the threats come, 368 00:19:20,800 --> 00:19:23,600 Speaker 3: the online threats, the physical stockings. What do they say? 369 00:19:23,600 --> 00:19:26,080 Speaker 3: They say, the army of patriots is coming to hunt 370 00:19:26,119 --> 00:19:28,399 Speaker 3: me down. And first I say, first of all, you 371 00:19:28,400 --> 00:19:30,360 Speaker 3: don't need an army of patriots, just me and Ann 372 00:19:30,440 --> 00:19:34,000 Speaker 3: and Rachel and Kat. Now, one patriot should do patriots 373 00:19:34,000 --> 00:19:35,800 Speaker 3: should do it. But I said, wait a minute, you 374 00:19:35,840 --> 00:19:38,639 Speaker 3: know who are the patriots here? The scientists are the patriots. 375 00:19:38,640 --> 00:19:41,760 Speaker 3: You know, we're not these chuckleheads. And and how we 376 00:19:41,840 --> 00:19:46,400 Speaker 3: bring that back, Yeah, I think is really critical because 377 00:19:47,080 --> 00:19:50,600 Speaker 3: once you start targeting science scientists, this, this will undermine 378 00:19:50,640 --> 00:19:54,000 Speaker 3: our democracy. It's a it's an attack on the intelligency. 379 00:19:54,040 --> 00:19:55,920 Speaker 3: And one of the things that you helped me to understand, Molly. 380 00:19:56,000 --> 00:19:58,800 Speaker 3: You and another person who's very helpful in this is 381 00:19:58,880 --> 00:19:59,600 Speaker 3: Ruth ben Gap. 382 00:20:00,040 --> 00:20:02,480 Speaker 1: Yeah, who studies extreme as well. 383 00:20:02,560 --> 00:20:05,919 Speaker 3: He studies extreme at NYU and New York University, and 384 00:20:06,440 --> 00:20:09,560 Speaker 3: you know, and she points out, this is what authoritarian 385 00:20:09,600 --> 00:20:13,399 Speaker 3: totalitarian states do. They target the intelligents. This is what 386 00:20:13,520 --> 00:20:16,280 Speaker 3: Stalin did. This is what Hannah Rent wrote about in 387 00:20:16,320 --> 00:20:19,720 Speaker 3: the Origins of Totalitarianism. So I had to take a 388 00:20:19,760 --> 00:20:23,240 Speaker 3: quick crash course in political science to write this book 389 00:20:23,320 --> 00:20:28,120 Speaker 3: because that's what it's about. This is about authoritarian political control. 390 00:20:28,480 --> 00:20:31,000 Speaker 2: One of the things I think is really interesting if 391 00:20:31,040 --> 00:20:33,960 Speaker 2: you look at someone like Elon Musk, my favorite, and 392 00:20:34,000 --> 00:20:39,160 Speaker 2: that's ironic. His business is electric vehicles. He does all 393 00:20:39,200 --> 00:20:42,600 Speaker 2: these rockets, right, these are all made possible by science. 394 00:20:43,000 --> 00:20:46,920 Speaker 2: He's on ozempic, right, or wavy or whatever it's called. 395 00:20:47,280 --> 00:20:49,200 Speaker 1: So he tweeted that he was on ozempic. 396 00:20:49,560 --> 00:20:52,359 Speaker 2: But he's critical of the vaccine, and a lot of 397 00:20:52,400 --> 00:20:56,560 Speaker 2: these people they're on some kind of steroid, they're on 398 00:20:56,600 --> 00:20:59,919 Speaker 2: a diet medicine, but they are worried about the vaccine, 399 00:21:00,080 --> 00:21:03,439 Speaker 2: which literally billions of people have taken. And so I 400 00:21:03,640 --> 00:21:08,840 Speaker 2: just wonder how you thread this anti science needle of 401 00:21:08,960 --> 00:21:13,520 Speaker 2: like I send rockets to Mars, I inject myself with 402 00:21:13,600 --> 00:21:17,600 Speaker 2: this drug that descends my bowels, but I don't trust 403 00:21:17,640 --> 00:21:20,760 Speaker 2: this vaccine that billions of people have taken. 404 00:21:21,440 --> 00:21:23,840 Speaker 3: Yeah, I don't think it's he could care. I don't 405 00:21:23,880 --> 00:21:25,760 Speaker 3: think he cares less one way or the other about 406 00:21:25,840 --> 00:21:29,359 Speaker 3: the vaccine. I think it's what the vaccine represents, that 407 00:21:29,440 --> 00:21:34,119 Speaker 3: it's targeted by the extremists, by the far right. Therefore 408 00:21:34,200 --> 00:21:37,639 Speaker 3: he's going to adopt that contrarian view to show that 409 00:21:37,680 --> 00:21:40,239 Speaker 3: he's part of the club, that he's part of it. 410 00:21:40,600 --> 00:21:43,040 Speaker 3: And that's what they did to so many Americans. I 411 00:21:43,040 --> 00:21:46,199 Speaker 3: mean I was. I gave medical grand rounds at University 412 00:21:46,240 --> 00:21:49,840 Speaker 3: of Texas Tyler, which is out in East Texas, very 413 00:21:49,880 --> 00:21:52,200 Speaker 3: conservative part of the state, and almost everyone you talk 414 00:21:52,320 --> 00:21:54,800 Speaker 3: to has lost a loved one because they refuse the 415 00:21:54,880 --> 00:21:57,320 Speaker 3: COVID vaccine. That's where you really start to see it. 416 00:21:57,800 --> 00:22:00,359 Speaker 3: And you know, these are the most extraordinary people. I 417 00:22:00,680 --> 00:22:04,160 Speaker 3: had given a medical grand routes to Stanford University and 418 00:22:04,880 --> 00:22:07,960 Speaker 3: Palo Alto, California, a very wealthy part of all of 419 00:22:08,000 --> 00:22:10,760 Speaker 3: California is expensive, but Palo Alto was uber expensive. And 420 00:22:10,760 --> 00:22:12,840 Speaker 3: I said, look, if you gave me the choice of 421 00:22:12,880 --> 00:22:17,000 Speaker 3: having my car broken down in Tyler, Texas or Palo Alto, California, 422 00:22:17,040 --> 00:22:20,159 Speaker 3: I'd take Tyler, Texas anytime, because they'd all be fighting 423 00:22:20,200 --> 00:22:23,360 Speaker 3: over who's going to help change your tire, right, I mean, 424 00:22:23,760 --> 00:22:25,800 Speaker 3: as you know, palow out the little drive right by. 425 00:22:25,920 --> 00:22:28,680 Speaker 3: But and the point is, these are the kindest, most 426 00:22:28,720 --> 00:22:31,159 Speaker 3: remarkable people you could ever hope to meet. And they 427 00:22:31,160 --> 00:22:36,040 Speaker 3: were victims. They were victimized by this Fox News jugger, 428 00:22:36,160 --> 00:22:39,959 Speaker 3: not together with the House Freedom Caucus, Media Matters and 429 00:22:40,800 --> 00:22:43,880 Speaker 3: Research group out of et Zurich, which is where Einstein studied, 430 00:22:43,920 --> 00:22:47,560 Speaker 3: you know, documented how Tucker Carlson, Laura and Graham Hannity 431 00:22:47,680 --> 00:22:51,800 Speaker 3: every night during that Delta Way filled their nighttime broadcast 432 00:22:51,920 --> 00:22:57,600 Speaker 3: three million viewers each with anti vaccine content, falsely discrediting 433 00:22:57,680 --> 00:23:00,879 Speaker 3: the effectiveness and safety of vaccines. People went down that 434 00:23:00,960 --> 00:23:03,320 Speaker 3: rabbit hole and paid for it with their lives. And 435 00:23:03,560 --> 00:23:05,879 Speaker 3: that's what we have to stop from ever happening again. 436 00:23:06,080 --> 00:23:08,879 Speaker 1: My last question for you, doctor Hotes is why do 437 00:23:08,920 --> 00:23:09,520 Speaker 1: you do it? 438 00:23:09,840 --> 00:23:12,160 Speaker 3: Yeah, I mean, I think part of it is there's 439 00:23:12,200 --> 00:23:14,919 Speaker 3: a vacuum you know, people, you know, because you're starting 440 00:23:14,960 --> 00:23:17,840 Speaker 3: to see the scientific societies and not really speaking out. 441 00:23:18,200 --> 00:23:21,680 Speaker 3: The college presidents aren't really speaking because nobody well when 442 00:23:21,720 --> 00:23:23,280 Speaker 3: they don't, they see how I get beat up and 443 00:23:23,280 --> 00:23:25,800 Speaker 3: they don't want that to happen. Right. But also I 444 00:23:25,840 --> 00:23:29,360 Speaker 3: think because of this commitment to political neutrality, they feel 445 00:23:29,400 --> 00:23:31,520 Speaker 3: they can't talk about it. And I said, look, I 446 00:23:31,520 --> 00:23:33,720 Speaker 3: don't know how to talk about it other than to 447 00:23:33,800 --> 00:23:35,760 Speaker 3: talk about it. So I talked about it, wrote about 448 00:23:35,760 --> 00:23:38,880 Speaker 3: it's again. It's this is what I feel is this 449 00:23:38,920 --> 00:23:41,520 Speaker 3: is part of the deal of being a vaccine scientist 450 00:23:41,560 --> 00:23:44,560 Speaker 3: and saving lives. And believe me, it's not fun, right, 451 00:23:44,600 --> 00:23:47,840 Speaker 3: I mean, it's not fun having the Houston Police Department 452 00:23:47,840 --> 00:23:49,800 Speaker 3: of Harris County Sheriff park down in front of your 453 00:23:49,840 --> 00:23:54,080 Speaker 3: house and dealing with the FBI because they're sending swastikas 454 00:23:54,119 --> 00:23:56,920 Speaker 3: to your home. And yeah, it's it's tough. It's tough, 455 00:23:57,040 --> 00:23:57,840 Speaker 3: it's tough sledding. 456 00:23:58,160 --> 00:24:00,960 Speaker 1: Yeah, doctor Hotes, thank you, thank you. 457 00:24:07,800 --> 00:24:12,320 Speaker 2: I'm very excited for your good saying not anti vax question. 458 00:24:12,720 --> 00:24:17,280 Speaker 1: So, and I'm very cranky and mean. So just remember I. 459 00:24:17,840 --> 00:24:19,680 Speaker 3: Got to bring you along with me everyone. No, no, 460 00:24:20,160 --> 00:24:21,280 Speaker 3: I'm your bodyguard. 461 00:24:21,320 --> 00:24:21,760 Speaker 1: Here we go. 462 00:24:23,160 --> 00:24:27,040 Speaker 4: Scientific research is getting really politicized, not only in terms 463 00:24:27,040 --> 00:24:31,920 Speaker 4: of vaccination, and our legislature here in Texas has been 464 00:24:32,200 --> 00:24:38,359 Speaker 4: very increasingly engaged in trying to regulate universities in different ways. 465 00:24:38,720 --> 00:24:43,880 Speaker 4: How concerned should we be about our research universities potentially 466 00:24:43,960 --> 00:24:46,679 Speaker 4: being subject to political interference in their mission? 467 00:24:46,920 --> 00:24:47,439 Speaker 3: Yeah, I know this. 468 00:24:47,640 --> 00:24:51,199 Speaker 1: That's a great question. More of those, very good, Thank you. 469 00:24:51,680 --> 00:24:54,080 Speaker 3: No, it's great, and it does worry me. And I 470 00:24:54,160 --> 00:24:56,360 Speaker 3: mean just and it's bizarre, right, I mean, you had 471 00:24:56,400 --> 00:24:59,360 Speaker 3: a professor at Texas A and I'm a pharmacy professor, 472 00:24:59,400 --> 00:25:01,879 Speaker 3: works on the fat mental crisis, gives a talk at 473 00:25:01,960 --> 00:25:06,040 Speaker 3: UTMB Galveston. I've been invited there many times, given talks 474 00:25:06,080 --> 00:25:09,200 Speaker 3: and apparently says something about the lieutenant governor that people 475 00:25:09,240 --> 00:25:12,080 Speaker 3: didn't like. Said they censure her for the place she's visiting, 476 00:25:12,119 --> 00:25:15,040 Speaker 3: which is and then she gets put on administrative leave 477 00:25:15,119 --> 00:25:17,919 Speaker 3: by her home institution. And it worked out okay at 478 00:25:17,920 --> 00:25:20,760 Speaker 3: the end. But that's very intimidating. I mean, I mean, 479 00:25:20,800 --> 00:25:22,919 Speaker 3: you have to draw a line in the sand. Otherwise, 480 00:25:23,520 --> 00:25:26,479 Speaker 3: you know, you don't have a university. And and you know, 481 00:25:26,760 --> 00:25:31,199 Speaker 3: I mean, this is part of the greatness of our state. Right, 482 00:25:31,240 --> 00:25:33,680 Speaker 3: I mean I didn't move to Texas because of the weather, right, 483 00:25:33,720 --> 00:25:36,640 Speaker 3: I mean I move I didn't move to Texas with this. Well, 484 00:25:36,680 --> 00:25:39,000 Speaker 3: I mean I moved to Texas because of the Texas 485 00:25:39,000 --> 00:25:42,800 Speaker 3: Medical Center, Baylor College of Medicine, and Texas Children's Hospital. 486 00:25:42,880 --> 00:25:44,600 Speaker 3: Then you know, University of Texas A and these are 487 00:25:44,640 --> 00:25:49,160 Speaker 3: amazing universities. Don't mess with them. They're also economic drivers 488 00:25:49,200 --> 00:25:52,280 Speaker 3: for our states. Yeah, so it's self so self defeating. 489 00:25:52,560 --> 00:25:54,800 Speaker 2: I wanted to just point out that you are in 490 00:25:54,840 --> 00:25:58,960 Speaker 2: a position where you're able to speak a lot of 491 00:25:59,119 --> 00:26:03,119 Speaker 2: younger profefe and people who are less established are. 492 00:26:03,000 --> 00:26:04,480 Speaker 1: Not able to write. 493 00:26:04,640 --> 00:26:07,840 Speaker 3: Yeah, I mean, you know, it's interesting the way offices 494 00:26:07,880 --> 00:26:11,639 Speaker 3: of communications work at universities. And they're pretty good at 495 00:26:11,640 --> 00:26:14,400 Speaker 3: Baylor and Texas Children's. But you know, usually the works 496 00:26:14,520 --> 00:26:17,120 Speaker 3: like this, They say, hey, you're an academic, you could 497 00:26:17,160 --> 00:26:20,600 Speaker 3: speak on whatever you want dot dot dot, but don't 498 00:26:20,600 --> 00:26:23,080 Speaker 3: screw this up and get the institution in trouble. So 499 00:26:23,160 --> 00:26:25,080 Speaker 3: you do this with kind of the sort of damocles 500 00:26:25,119 --> 00:26:26,840 Speaker 3: over your head. And of course if you out there 501 00:26:26,960 --> 00:26:30,320 Speaker 3: enough in the public public sphere, you will screw it up. 502 00:26:30,320 --> 00:26:33,120 Speaker 3: And it's only a matter of time. So we need 503 00:26:33,200 --> 00:26:38,440 Speaker 3: to make our faculty. And you're right, especially the junior people. 504 00:26:38,440 --> 00:26:41,199 Speaker 3: I mean, I have I have resources, right, I have 505 00:26:41,280 --> 00:26:43,760 Speaker 3: people I can go to. I'm established, I have you, 506 00:26:44,000 --> 00:26:46,840 Speaker 3: I have I do a weekly call with Mike Osterholme 507 00:26:46,920 --> 00:26:51,639 Speaker 3: and Peggy Hamburg and Bruce Gllen and Nark Topel that 508 00:26:51,720 --> 00:26:54,280 Speaker 3: can sort of guide me through this stuff. But young 509 00:26:54,320 --> 00:26:55,040 Speaker 3: people don't. 510 00:26:54,880 --> 00:26:59,119 Speaker 5: Have that right, and so it's you know, dogor again, 511 00:26:59,240 --> 00:27:02,879 Speaker 5: thank you for all your work. My question, it's a 512 00:27:02,920 --> 00:27:05,879 Speaker 5: sad question, is how many millions of kids are going 513 00:27:05,920 --> 00:27:09,040 Speaker 5: to have to die of measles, mumps, rebella or any 514 00:27:09,080 --> 00:27:13,280 Speaker 5: of the other diseases that were gone from this country. 515 00:27:13,359 --> 00:27:15,639 Speaker 3: Yeah, no, it's a century. It's a great question. You know, 516 00:27:15,680 --> 00:27:19,240 Speaker 3: in the past, before this new political phase, I would 517 00:27:19,240 --> 00:27:22,280 Speaker 3: have said, there's always this sort of inherent autocorrection mechanism. 518 00:27:22,400 --> 00:27:26,159 Speaker 3: Right if you stop vaccinating, the first breakthrough infection you 519 00:27:26,200 --> 00:27:29,600 Speaker 3: see is measles. Measles because it's so highly transmissible. Once 520 00:27:29,600 --> 00:27:32,399 Speaker 3: you go below in ninety percent. You see measles kids 521 00:27:32,440 --> 00:27:34,920 Speaker 3: go into the hospital, the intensive carrion. It word gets 522 00:27:34,960 --> 00:27:37,680 Speaker 3: around the community and the parents say, oh my god, 523 00:27:37,720 --> 00:27:40,200 Speaker 3: measles is really bad, and they start vaccinating agains. So 524 00:27:40,200 --> 00:27:43,400 Speaker 3: there was always this self correcting mechanism, but not two 525 00:27:43,480 --> 00:27:47,440 Speaker 3: hundred thousand Americans have just died for nothing right because 526 00:27:47,480 --> 00:27:50,560 Speaker 3: of this, And now you're just seeing this doubling down. 527 00:27:50,640 --> 00:27:54,280 Speaker 3: So I'm not seeing the self correcting mechanism, not seeing 528 00:27:54,280 --> 00:27:58,000 Speaker 3: the auto correction, and that worries me along because people's 529 00:27:58,040 --> 00:28:02,800 Speaker 3: political leanings now, people's political identity is time to this stuff, 530 00:28:02,800 --> 00:28:05,840 Speaker 3: and that that's new. Thank you, doctor Hotez. 531 00:28:05,880 --> 00:28:07,720 Speaker 6: You're are a hero to so many of us and 532 00:28:07,720 --> 00:28:08,480 Speaker 6: we appreciate it. 533 00:28:08,680 --> 00:28:14,320 Speaker 3: Thank you, Thank you well two thirds of the country. Anyway, 534 00:28:14,800 --> 00:28:17,159 Speaker 3: I'm hoping that's not watching fires. 535 00:28:17,840 --> 00:28:20,159 Speaker 7: I'm hoping you can help me understand the dynamic that 536 00:28:20,200 --> 00:28:23,320 Speaker 7: makes no sense to me, and that is why some 537 00:28:23,520 --> 00:28:29,080 Speaker 7: doctors are not necessarily anti vaxed, but anti precautionary measures. 538 00:28:29,400 --> 00:28:32,199 Speaker 7: The doctors that are saying no masks needed in the 539 00:28:33,320 --> 00:28:35,639 Speaker 7: in their offices, or the ones that don't wear it. 540 00:28:36,000 --> 00:28:38,440 Speaker 7: My own brother is a doctor who is on the 541 00:28:38,440 --> 00:28:41,960 Speaker 7: front lines of COVID in New York, and he mocks 542 00:28:42,000 --> 00:28:43,520 Speaker 7: me for wearing a mask in public. 543 00:28:44,200 --> 00:28:45,200 Speaker 3: I'm trying to understand that. 544 00:28:45,240 --> 00:28:47,000 Speaker 7: I don't think it's limited to a few people. I 545 00:28:47,000 --> 00:28:48,520 Speaker 7: think it's actually fairly widespread. 546 00:28:48,840 --> 00:28:54,200 Speaker 3: Yeah, I mean it runs the full spectrum. It's especially 547 00:28:54,240 --> 00:28:59,400 Speaker 3: demoralizing when you see physicians, even physicians scientists take these 548 00:28:59,480 --> 00:29:03,000 Speaker 3: contrarian in views and and and some of them are 549 00:29:03,320 --> 00:29:07,160 Speaker 3: medical school professors at places like Stanford and UCSF and 550 00:29:07,280 --> 00:29:11,360 Speaker 3: Johns Hopkins and and that, and not typically working on 551 00:29:11,440 --> 00:29:14,800 Speaker 3: vaccines or infectious disease. They're experts in other areas that 552 00:29:14,840 --> 00:29:17,440 Speaker 3: decide their way and what's their motivation and doing that. 553 00:29:17,800 --> 00:29:20,600 Speaker 3: And it causes so much damage because the average layperson 554 00:29:20,640 --> 00:29:23,440 Speaker 3: doesn't know the difference, you know, between someone like me 555 00:29:23,520 --> 00:29:26,200 Speaker 3: has worked on COVID vaccines for one hundred years and 556 00:29:26,200 --> 00:29:29,040 Speaker 3: and and and that and so this is a very 557 00:29:29,040 --> 00:29:32,600 Speaker 3: long I don't know the answer to your question, but 558 00:29:32,600 --> 00:29:35,560 Speaker 3: but I can tell you that it does and then 559 00:29:37,240 --> 00:29:40,959 Speaker 3: and then it and then everything from that to just 560 00:29:41,080 --> 00:29:44,720 Speaker 3: not you know, following the literature that closely and not understanding. 561 00:29:44,800 --> 00:29:50,640 Speaker 3: But it's it's that deliberate contrarian stuff from people who 562 00:29:50,640 --> 00:29:54,640 Speaker 3: should know better that I find it very alarming. Got 563 00:29:54,680 --> 00:29:56,920 Speaker 3: Gandhi towards the end of his life, did an interview 564 00:29:57,280 --> 00:30:00,800 Speaker 3: with an episco I think he's episcopalian. Minute disturb and 565 00:30:01,400 --> 00:30:04,720 Speaker 3: you know, is asked what disturbed him most during his 566 00:30:04,760 --> 00:30:07,360 Speaker 3: life and his answer was the hardness of heart of 567 00:30:07,400 --> 00:30:11,280 Speaker 3: the educated. And we've seen a lot of that in COVID. 568 00:30:11,640 --> 00:30:16,920 Speaker 8: So I'm an epidemiologist by training. I worked through COVID 569 00:30:17,280 --> 00:30:20,720 Speaker 8: doing social media campaigns and one of the things that 570 00:30:20,760 --> 00:30:23,920 Speaker 8: I noticed is not everyone who is refusing the COVID 571 00:30:24,000 --> 00:30:25,560 Speaker 8: vaccine is an anti vaxer. 572 00:30:25,680 --> 00:30:26,160 Speaker 3: A lot of the. 573 00:30:26,120 --> 00:30:29,960 Speaker 8: People who are literally just don't know better, Like they 574 00:30:30,040 --> 00:30:30,840 Speaker 8: want to know more. 575 00:30:30,880 --> 00:30:32,120 Speaker 1: There's an interest to know more. 576 00:30:32,320 --> 00:30:34,640 Speaker 8: And I have to admit, even as a scientist that 577 00:30:34,720 --> 00:30:37,920 Speaker 8: our communication throughout the pandemic was not the best. So 578 00:30:38,080 --> 00:30:41,160 Speaker 8: what can scientists and the science community do to one 579 00:30:41,360 --> 00:30:44,480 Speaker 8: rebuild that trust? Because it's a good question when when 580 00:30:44,640 --> 00:30:47,280 Speaker 8: like messaging is changing so much, I can understand that 581 00:30:47,320 --> 00:30:51,840 Speaker 8: fear and to how can we also improve like science 582 00:30:51,920 --> 00:30:54,520 Speaker 8: literacy or health literacy for the public so they can 583 00:30:54,520 --> 00:30:57,560 Speaker 8: make informed decisions because that seems to be a problem 584 00:30:57,640 --> 00:30:57,960 Speaker 8: as well. 585 00:30:58,040 --> 00:31:01,640 Speaker 2: Yeah, that's a really good question about ten different questions 586 00:31:01,680 --> 00:31:02,000 Speaker 2: and that. 587 00:31:03,200 --> 00:31:05,080 Speaker 1: What about rebuilding public trust? 588 00:31:05,920 --> 00:31:08,440 Speaker 3: Well, first of all, you know, I had a different 589 00:31:08,480 --> 00:31:10,880 Speaker 3: way of you know, when I was started in talking 590 00:31:10,920 --> 00:31:14,320 Speaker 3: to the public, some of the professional communications people said 591 00:31:14,320 --> 00:31:16,280 Speaker 3: to me, you've got to talk to the American people 592 00:31:16,320 --> 00:31:18,360 Speaker 3: like they're in the fourth grade or sixth grade. And 593 00:31:18,440 --> 00:31:20,880 Speaker 3: I found that what didn't work for my style? And 594 00:31:20,920 --> 00:31:23,720 Speaker 3: I found that wasn't the case. I mean, either you 595 00:31:23,760 --> 00:31:27,280 Speaker 3: were really worried about your family and wanted whatever it 596 00:31:27,320 --> 00:31:29,800 Speaker 3: took to protect your family or not and or why, 597 00:31:29,960 --> 00:31:32,800 Speaker 3: or you were watching Fox News and and if you 598 00:31:32,840 --> 00:31:35,760 Speaker 3: were in the former, I found that the American people 599 00:31:35,960 --> 00:31:39,560 Speaker 3: were willing to tolerate quite a level of complexity and 600 00:31:39,560 --> 00:31:41,680 Speaker 3: and went into that, and I think it was appreciated. 601 00:31:41,760 --> 00:31:44,520 Speaker 3: I think there was sort of this still old fashioned 602 00:31:44,520 --> 00:31:47,800 Speaker 3: way of communicating that often came out sounding like baby 603 00:31:47,840 --> 00:31:52,160 Speaker 3: talk and it didn't really ring true. So there was 604 00:31:52,440 --> 00:31:55,800 Speaker 3: those mistakes in communication. But of course you had the 605 00:31:56,000 --> 00:31:59,680 Speaker 3: anti vaccine and you know, anti science people weaponizing every 606 00:31:59,680 --> 00:32:03,080 Speaker 3: little slip up also, so that didn't help. So, yes, 607 00:32:03,480 --> 00:32:06,800 Speaker 3: there's a lot of room to improve communication, but it 608 00:32:06,920 --> 00:32:10,440 Speaker 3: was also every effort made to weaponize it as well. 609 00:32:10,720 --> 00:32:13,479 Speaker 2: One of the things I thought was really interesting about 610 00:32:13,520 --> 00:32:16,200 Speaker 2: you know, I wrote about all of this throughout, and 611 00:32:16,760 --> 00:32:20,840 Speaker 2: you know, they they were going with the information they had, right, 612 00:32:21,040 --> 00:32:24,520 Speaker 2: so they so when it started, the information they had 613 00:32:24,800 --> 00:32:28,640 Speaker 2: was they you know, remember the tape of Trump talking 614 00:32:28,960 --> 00:32:32,880 Speaker 2: to a journalist. I think it was the Watergate guy, 615 00:32:33,360 --> 00:32:36,600 Speaker 2: not Carl, but the other guy, Woodward, Right, the tapes 616 00:32:36,600 --> 00:32:38,560 Speaker 2: with Woodward were he's saying, it's airborne. 617 00:32:38,600 --> 00:32:41,000 Speaker 1: You know, we didn't know what it was. We didn't know. 618 00:32:41,040 --> 00:32:44,640 Speaker 2: I mean I remember, like we kept our Amazon boxes 619 00:32:44,720 --> 00:32:47,680 Speaker 2: outside because we were worried that we're going to get 620 00:32:47,720 --> 00:32:49,280 Speaker 2: something from it, you know, and like. 621 00:32:49,320 --> 00:32:51,360 Speaker 3: Remember those scrubbing down door dash. 622 00:32:51,520 --> 00:32:54,000 Speaker 2: Right and could you order food? I mean, we just were. 623 00:32:54,160 --> 00:32:57,440 Speaker 2: We just were sort of getting the information in real time, 624 00:32:57,920 --> 00:33:01,600 Speaker 2: which I think caused a lot of you know, just 625 00:33:02,040 --> 00:33:06,280 Speaker 2: information was changing because we were it was happening in 626 00:33:06,280 --> 00:33:09,480 Speaker 2: real time. And I think that the anti vax world 627 00:33:09,520 --> 00:33:10,680 Speaker 2: really weaponized that. 628 00:33:10,920 --> 00:33:14,120 Speaker 3: And for instance, when the when the two doses came 629 00:33:14,160 --> 00:33:16,560 Speaker 3: out of Pfizer, it was it was designed to prevent 630 00:33:16,600 --> 00:33:19,640 Speaker 3: symptomatic illness. And I was out there saying, well, you know, 631 00:33:19,680 --> 00:33:21,640 Speaker 3: it's two doses, but you know, if it's anything like 632 00:33:21,680 --> 00:33:24,280 Speaker 3: any pediatric vaccine. The way it works as you give 633 00:33:24,320 --> 00:33:27,640 Speaker 3: a series of immunizations, you wait six months to a year, 634 00:33:27,880 --> 00:33:31,200 Speaker 3: and then you boost. So it's don't be surprised if 635 00:33:31,200 --> 00:33:32,720 Speaker 3: it's not going to be one and done and two 636 00:33:32,720 --> 00:33:34,680 Speaker 3: and done, but it could be three and done. And 637 00:33:34,720 --> 00:33:36,480 Speaker 3: by the time the delta way fit, I was looking 638 00:33:36,520 --> 00:33:40,040 Speaker 3: really good. And then you know, the disappointment was the 639 00:33:40,200 --> 00:33:43,240 Speaker 3: mRNA just weren't holding up as boosters as well as 640 00:33:43,240 --> 00:33:46,760 Speaker 3: we thought, right, And that was a big disappointment for everyone. 641 00:33:46,880 --> 00:33:50,040 Speaker 3: So there was that need to, you know, say, there's news. 642 00:33:50,200 --> 00:33:52,120 Speaker 2: I want to ask you one more question about m 643 00:33:52,240 --> 00:33:56,760 Speaker 2: RNA because m RNA, with all of this anti vax nonsense, 644 00:33:57,120 --> 00:34:00,680 Speaker 2: we haven't talked about how mRNA vaccines could the future 645 00:34:00,720 --> 00:34:03,200 Speaker 2: of curing cancer and amazing stuff. 646 00:34:03,240 --> 00:34:04,440 Speaker 1: I mean, can you just. 647 00:34:04,480 --> 00:34:08,239 Speaker 2: Talk a little bit about what mRNA can do maybe 648 00:34:08,480 --> 00:34:09,200 Speaker 2: or eventually. 649 00:34:09,400 --> 00:34:12,200 Speaker 3: Well, most people know I've taken the mRNA vaccines. It 650 00:34:12,239 --> 00:34:18,399 Speaker 3: induces a pretty reactive inflammatory response, and the hope is 651 00:34:18,600 --> 00:34:21,600 Speaker 3: that this will work against not only infectious disease, but 652 00:34:21,680 --> 00:34:25,440 Speaker 3: noncommunicable diseases. I think I'm pretty sure that's why Pfizer 653 00:34:25,480 --> 00:34:28,360 Speaker 3: and Maderna and biotech are in this. They didn't because 654 00:34:28,400 --> 00:34:31,520 Speaker 3: the money to be made for infectious disease ordinarily is 655 00:34:31,560 --> 00:34:35,200 Speaker 3: not so high. The biggest, the big ticketized cancer autoimmune disease. 656 00:34:35,280 --> 00:34:37,760 Speaker 3: So the hope is that there will be some benefit 657 00:34:37,760 --> 00:34:37,960 Speaker 3: for that. 658 00:34:38,080 --> 00:34:41,200 Speaker 2: I mean, it'll be so ironic if that, if that 659 00:34:41,360 --> 00:34:44,800 Speaker 2: ushers in a whole new world of cancer treatment. 660 00:34:45,239 --> 00:34:46,920 Speaker 1: Oh sorry, Hi, let. 661 00:34:46,760 --> 00:34:49,040 Speaker 9: Me just say add my thank yous to everybody else's. 662 00:34:49,239 --> 00:34:51,840 Speaker 9: I really appreciate everything you've done. I love your books. 663 00:34:51,880 --> 00:34:55,240 Speaker 9: I'm learning a lot. So I'm a retired science teacher 664 00:34:55,560 --> 00:34:58,719 Speaker 9: and once a teacher, always a teacher. So I'm having 665 00:34:58,760 --> 00:35:03,080 Speaker 9: trouble educating people about vaccines and explaining what they are 666 00:35:03,719 --> 00:35:07,680 Speaker 9: and words like peer reviewed and all that. So how 667 00:35:07,680 --> 00:35:15,760 Speaker 9: do you suggest we educate others about common sense, factual science. 668 00:35:16,040 --> 00:35:18,080 Speaker 3: Well, the first thing is all K through twelve STEM 669 00:35:18,120 --> 00:35:20,120 Speaker 3: teachers need to be paid one hundred thousand dollars a 670 00:35:20,200 --> 00:35:25,040 Speaker 3: year started starting actually, and I shouldn't qualify the STEM 671 00:35:25,080 --> 00:35:26,799 Speaker 3: because we need to teach we a low to read, 672 00:35:26,920 --> 00:35:30,680 Speaker 3: write right and everything and write a sentence. So that's 673 00:35:30,719 --> 00:35:33,839 Speaker 3: the first thing we do. The second is we need 674 00:35:33,880 --> 00:35:38,200 Speaker 3: to get the American people to better understand what a 675 00:35:38,239 --> 00:35:42,319 Speaker 3: scientist actually does on a day basis, you know, and 676 00:35:42,360 --> 00:35:44,120 Speaker 3: what it means to go to a lab meeting or 677 00:35:44,239 --> 00:35:47,239 Speaker 3: do a major revision of a paper, or to get 678 00:35:47,280 --> 00:35:51,720 Speaker 3: a grant not discussed because it didn't make the first cut. 679 00:35:51,880 --> 00:35:55,280 Speaker 3: A great organization out in rest in Virginia or McLean 680 00:35:55,360 --> 00:35:58,920 Speaker 3: Virginia called Research America did a study. It said over 681 00:35:59,040 --> 00:36:02,960 Speaker 3: ninety percent of Americans cannot name a living scientist, and 682 00:36:03,040 --> 00:36:06,240 Speaker 3: of those who attempted to, they name people like Jonah 683 00:36:06,239 --> 00:36:10,640 Speaker 3: Salker Einstein, and then the others named you know, Fauci 684 00:36:10,719 --> 00:36:13,040 Speaker 3: or Bill Ny the science guy. So you know, the 685 00:36:13,080 --> 00:36:15,799 Speaker 3: point is the American people have no idea what we do. 686 00:36:16,040 --> 00:36:20,080 Speaker 3: And that's that's partly our fault as a scientific profession 687 00:36:20,080 --> 00:36:22,719 Speaker 3: because we're so inward looking and so focused on our 688 00:36:22,920 --> 00:36:25,399 Speaker 3: grants and our papers and writing and speaking for each 689 00:36:25,400 --> 00:36:28,160 Speaker 3: other that we've not done that public outreach. And so 690 00:36:28,239 --> 00:36:29,960 Speaker 3: there's got to be a sea change I think in 691 00:36:30,040 --> 00:36:32,400 Speaker 3: that as well, working hand in glove with the K 692 00:36:32,480 --> 00:36:35,480 Speaker 3: through twelve teachers to do so. The point is there's 693 00:36:35,480 --> 00:36:38,839 Speaker 3: a whole ecosystem or that has to be created that's 694 00:36:38,880 --> 00:36:40,879 Speaker 3: not there right now, Doctor Hotest minds, I see. 695 00:36:40,920 --> 00:36:43,640 Speaker 6: But the resident here in Austin speaking of trust, like 696 00:36:43,760 --> 00:36:46,840 Speaker 6: even the Biden administration regarding the actual origins of the 697 00:36:46,920 --> 00:36:50,640 Speaker 6: COVID has some of their intelligence has said with low 698 00:36:50,680 --> 00:36:53,040 Speaker 6: confidence it may have came from a lab in China. 699 00:36:53,160 --> 00:36:56,279 Speaker 6: As an evolutionary biologist like yourself, what evidence would you 700 00:36:56,360 --> 00:36:58,080 Speaker 6: have to have? I think you're still saying it's natural, 701 00:36:58,120 --> 00:37:00,680 Speaker 6: isn't it? And because this is a government that genocide 702 00:37:00,719 --> 00:37:02,439 Speaker 6: its own citizens, so if they have something to lose, 703 00:37:02,440 --> 00:37:04,360 Speaker 6: they're not going to you know, divulge it. So what 704 00:37:04,640 --> 00:37:06,759 Speaker 6: evidence would you need to say, Okay, yeah, this is 705 00:37:06,960 --> 00:37:08,320 Speaker 6: something that came from the last well. 706 00:37:08,320 --> 00:37:11,440 Speaker 1: So this question is what market versus. 707 00:37:11,120 --> 00:37:13,840 Speaker 3: Lab leak right or gain a function? I mean the 708 00:37:13,880 --> 00:37:16,360 Speaker 3: point is, you know, we've gotten now six or seven 709 00:37:17,120 --> 00:37:20,360 Speaker 3: published papers and some of the top journals like Science 710 00:37:20,440 --> 00:37:24,960 Speaker 3: magazine clearly showing the natural origins and zoanotic spillover of 711 00:37:25,040 --> 00:37:26,120 Speaker 3: the COVID nineteen virus. 712 00:37:26,160 --> 00:37:28,839 Speaker 1: So it's a crossover species like you. 713 00:37:28,760 --> 00:37:32,920 Speaker 3: Know, these viruses circulating bats, and then through a combination 714 00:37:33,040 --> 00:37:36,160 Speaker 3: of climate change and human migrations and deforestation, people are 715 00:37:36,160 --> 00:37:38,759 Speaker 3: coming into closer contact with bats. This is true not 716 00:37:38,800 --> 00:37:43,000 Speaker 3: only for the coronaviruses. It's true for NIPPA, which is 717 00:37:43,000 --> 00:37:45,759 Speaker 3: in India. It's true for Ebola. That's why we're seeing 718 00:37:45,760 --> 00:37:46,160 Speaker 3: the Ebola. 719 00:37:46,160 --> 00:37:46,440 Speaker 1: I'm right. 720 00:37:46,640 --> 00:37:49,040 Speaker 3: One of the most common questions I'm asked is, hey, Doc, 721 00:37:49,080 --> 00:37:51,239 Speaker 3: what the heck is going on? By that, they mean, 722 00:37:51,600 --> 00:37:54,480 Speaker 3: you know, Ebola, Zeka. This is all because of these 723 00:37:54,520 --> 00:37:58,760 Speaker 3: twenty first century forces. So the point is, COVID nineteen 724 00:37:58,840 --> 00:38:01,799 Speaker 3: is the third major corona pandemic of this century. We've 725 00:38:01,800 --> 00:38:04,800 Speaker 3: had SARS Mayor's now COVID nineteen pretty much all the 726 00:38:04,880 --> 00:38:08,680 Speaker 3: rising by the same set of circumstances. And that's why 727 00:38:08,760 --> 00:38:11,879 Speaker 3: we're going to have a fourth major coronavirus pandemic before 728 00:38:12,000 --> 00:38:15,799 Speaker 3: twenty thirty, I predict Wait what, yeah, that's right. Yeah, 729 00:38:15,800 --> 00:38:19,319 Speaker 3: I mean because we're not doing the outbreak investigation that 730 00:38:19,360 --> 00:38:22,440 Speaker 3: we need. On the other hand, there is not a 731 00:38:22,480 --> 00:38:25,480 Speaker 3: single published paper in a scientific journal on gain of 732 00:38:25,520 --> 00:38:29,640 Speaker 3: function or lab leak because nobody's been forthcoming and any 733 00:38:29,680 --> 00:38:33,959 Speaker 3: evidence for it. And I've said, look, if there's any 734 00:38:34,239 --> 00:38:36,480 Speaker 3: I said in the very beginning, if there's any evidence 735 00:38:36,640 --> 00:38:38,600 Speaker 3: for a lab leak, we could say that for any 736 00:38:38,680 --> 00:38:41,440 Speaker 3: epidemic or pandemic, you know, we'll look into it. And 737 00:38:42,000 --> 00:38:44,200 Speaker 3: I said that back in twenty twenty one. But since 738 00:38:44,239 --> 00:38:48,160 Speaker 3: then there's been no evidence forthcoming from lab leak or 739 00:38:48,280 --> 00:38:51,759 Speaker 3: gain of function, and a huge amount of evidence for 740 00:38:51,880 --> 00:38:54,640 Speaker 3: the natural origin, so much so that Michael Warabee looks 741 00:38:54,680 --> 00:38:58,880 Speaker 3: into this. I'm not an evolutionary biologist. I'm a vaccine scientist, 742 00:38:58,960 --> 00:39:02,319 Speaker 3: but Michael Warabe is evolutionary biologists. Has come out and 743 00:39:02,320 --> 00:39:06,080 Speaker 3: said there's probably more evidence for the origins of COVID 744 00:39:06,160 --> 00:39:09,520 Speaker 3: nineteen that we've had than compared to any other pandemic. 745 00:39:10,360 --> 00:39:14,160 Speaker 3: So it's pretty solid. And the evidence for that is 746 00:39:14,400 --> 00:39:17,000 Speaker 3: no one's written a paper on lab leak or gain 747 00:39:17,040 --> 00:39:21,000 Speaker 3: of function because there's no there there, And if there 748 00:39:21,040 --> 00:39:22,560 Speaker 3: is some there there, we'll look into it. 749 00:39:22,600 --> 00:39:25,000 Speaker 2: But right the right is obsessed with this idea that 750 00:39:25,040 --> 00:39:27,640 Speaker 2: it's a lab leak. But I don't think that you 751 00:39:27,680 --> 00:39:31,080 Speaker 2: guys can I mean, you just want to have vaccines 752 00:39:31,200 --> 00:39:32,760 Speaker 2: and solve the problem. 753 00:39:33,239 --> 00:39:36,600 Speaker 3: That's right. But the other reason it's important is because 754 00:39:36,640 --> 00:39:39,920 Speaker 3: we still don't understand at a very granular level how 755 00:39:40,000 --> 00:39:43,919 Speaker 3: these viruses are jumping from bats to people, maybe through 756 00:39:43,960 --> 00:39:48,360 Speaker 3: secondary intermediate animal hosts, maybe livestock. And that's a problem 757 00:39:48,480 --> 00:39:51,520 Speaker 3: because we're going to have the fourth one soon and 758 00:39:51,600 --> 00:39:54,680 Speaker 3: so we've never done that outbreak investigation that needs to 759 00:39:54,719 --> 00:39:59,360 Speaker 3: be done, which means going into Central China, collecting samples 760 00:39:59,360 --> 00:40:03,800 Speaker 3: from bats, some other animal hosts humans, and really detailing 761 00:40:03,800 --> 00:40:06,239 Speaker 3: that at a grandeur level. That's what needs to be done. 762 00:40:06,280 --> 00:40:08,600 Speaker 3: That's why I get so frustrated with the Chinese not 763 00:40:08,719 --> 00:40:11,840 Speaker 3: being transparent, because we're going to be set up to 764 00:40:11,920 --> 00:40:15,200 Speaker 3: have that same There's baths infective with coronavirus all over 765 00:40:15,239 --> 00:40:18,440 Speaker 3: the face to vasa Asia from Central China down the 766 00:40:18,560 --> 00:40:21,520 Speaker 3: unon on the Vietnam, going all the way up into 767 00:40:21,600 --> 00:40:25,000 Speaker 3: Japan and Korea, and we still don't really understand that 768 00:40:25,080 --> 00:40:27,799 Speaker 3: at a detailed level. And I worry that all of 769 00:40:27,840 --> 00:40:31,320 Speaker 3: this labily gainer function is a distraction because it's keeping 770 00:40:31,320 --> 00:40:33,279 Speaker 3: our eyes off the prize of what we really need 771 00:40:33,320 --> 00:40:36,680 Speaker 3: to do, which is to do that detailed outbreak investigation. 772 00:40:36,880 --> 00:40:38,280 Speaker 1: Okay, so one last question. 773 00:40:39,400 --> 00:40:42,240 Speaker 10: Hi, Thanks for your talk. I really appreciate everything you're doing. 774 00:40:42,320 --> 00:40:45,160 Speaker 10: I'm in medicine myself and I work in clinical trials, 775 00:40:45,280 --> 00:40:47,080 Speaker 10: but one of the things I think that go hand 776 00:40:47,080 --> 00:40:50,200 Speaker 10: in hand where you're talking about is medical misinformation basically, 777 00:40:50,600 --> 00:40:53,680 Speaker 10: and I heard that the NIH just solved that project. 778 00:40:53,719 --> 00:40:56,360 Speaker 10: They were going to do medical misinformation, and can you 779 00:40:56,400 --> 00:40:59,080 Speaker 10: tell us a little bit about what medical associations are 780 00:40:59,120 --> 00:41:02,319 Speaker 10: doing for people that are saying things that aren't untruthed 781 00:41:02,360 --> 00:41:05,480 Speaker 10: with like ivermectin or vaccines or any of those things. 782 00:41:05,520 --> 00:41:07,560 Speaker 3: Right, I think, you know, we don't know how to 783 00:41:07,640 --> 00:41:11,120 Speaker 3: manage this. And so the NIH, I think was going 784 00:41:11,200 --> 00:41:13,239 Speaker 3: to allocate funds, I forget what it was, maybe one 785 00:41:13,280 --> 00:41:16,520 Speaker 3: hundred and fifty million dollars to really support groups that 786 00:41:16,560 --> 00:41:19,040 Speaker 3: are looking at misinformation. But that's getting tired. Those groups 787 00:41:19,080 --> 00:41:22,040 Speaker 3: are getting targeted now by Jim Jordan and trying to 788 00:41:22,080 --> 00:41:24,839 Speaker 3: block it. And this is what's going to undermine our 789 00:41:24,920 --> 00:41:27,840 Speaker 3: country and it's a killer. And the evidence of that 790 00:41:27,960 --> 00:41:30,759 Speaker 3: of the two hundred thousand deaths, So we really need 791 00:41:30,760 --> 00:41:33,879 Speaker 3: to understand that. I just I'm reviewing a paper now 792 00:41:33,880 --> 00:41:39,200 Speaker 3: where now people can use AI artificial intelligence to generate blogs, 793 00:41:39,200 --> 00:41:43,880 Speaker 3: to generate all of this disinformation about health at a 794 00:41:44,000 --> 00:41:46,920 Speaker 3: very high level and very quickly. And so when people 795 00:41:46,960 --> 00:41:49,520 Speaker 3: start applying AI to this stuff, forget it. It's just 796 00:41:49,600 --> 00:41:52,480 Speaker 3: going to be this you know, over rush and and 797 00:41:52,520 --> 00:41:55,839 Speaker 3: make matters worse. You've got foreign actors, you know, using 798 00:41:55,840 --> 00:41:58,520 Speaker 3: this to destabilize the country. You have, you know, these 799 00:41:58,920 --> 00:42:02,040 Speaker 3: Russian bots and roles that what they're doing in Russia 800 00:42:02,080 --> 00:42:04,960 Speaker 3: is interesting. Is what Putin's doing is he's flooding our 801 00:42:05,000 --> 00:42:08,960 Speaker 3: internet with both anti vaccine and pro vaccine messages because 802 00:42:09,000 --> 00:42:11,600 Speaker 3: his deal is he looks at, you know, the big 803 00:42:11,640 --> 00:42:15,120 Speaker 3: issues that are destabilizing our countries and you know, the 804 00:42:15,160 --> 00:42:18,239 Speaker 3: elections one of them, but also vaccines, and so he 805 00:42:18,360 --> 00:42:22,439 Speaker 3: does this to so discontent and destabilized democracy. And guess 806 00:42:22,480 --> 00:42:24,880 Speaker 3: what it's working. And that's that's really worries. 807 00:42:25,239 --> 00:42:28,480 Speaker 2: Doctor Hotels is signing books at a different time. 808 00:42:28,680 --> 00:42:29,640 Speaker 1: I don't know anything. 809 00:42:29,760 --> 00:42:31,800 Speaker 3: It's the main place where you signed books, at. 810 00:42:31,640 --> 00:42:34,040 Speaker 1: The main place when you sign books, next. 811 00:42:33,840 --> 00:42:37,000 Speaker 3: To there and next to the Omni hotel in the Omni. 812 00:42:37,120 --> 00:42:39,399 Speaker 2: And he's the best. So thank you the best, thank you, 813 00:42:39,520 --> 00:42:44,520 Speaker 2: thank you very much. That's it for this episode of 814 00:42:44,520 --> 00:42:48,319 Speaker 2: Fast Politics. Tune in every Monday, Wednesday and Friday to 815 00:42:48,400 --> 00:42:51,239 Speaker 2: hear the best minds in politics makes sense of all 816 00:42:51,360 --> 00:42:54,839 Speaker 2: this chaos. If you enjoyed what you've heard, please send 817 00:42:54,880 --> 00:42:57,080 Speaker 2: it to a friend and keep the conversation going. 818 00:42:57,480 --> 00:42:59,160 Speaker 1: And again, thanks for listening. 819 00:43:00,840 --> 00:43:01,040 Speaker 5: Two