1 00:00:00,200 --> 00:00:02,800 Speaker 1: Up next, The Truth with Lisa Both part of the game. 2 00:00:04,760 --> 00:00:06,600 Speaker 1: Do you want to know what really happened behind the 3 00:00:06,600 --> 00:00:09,040 Speaker 1: scenes of the Trump White House during the COVID pandemic? 4 00:00:09,480 --> 00:00:11,879 Speaker 1: We're about to find out. This is The Truth with 5 00:00:11,960 --> 00:00:24,920 Speaker 1: Lisa Booth. Yeah. Dcor Scott Atlas is my guest this 6 00:00:24,960 --> 00:00:28,000 Speaker 1: week to talk about his new book, A Plague upon 7 00:00:28,320 --> 00:00:31,159 Speaker 1: Our House, My fight at the Trump White House to 8 00:00:31,160 --> 00:00:34,839 Speaker 1: stop COVID from destroying America. We get into all of it. 9 00:00:35,000 --> 00:00:38,200 Speaker 1: He shares his unfiltered insider account of serving on President 10 00:00:38,240 --> 00:00:41,880 Speaker 1: Trump's COVID task Force. He talks about the policy fights 11 00:00:41,880 --> 00:00:45,280 Speaker 1: and scientific disputes that he had with people like Dr Burke's, 12 00:00:45,720 --> 00:00:50,960 Speaker 1: Dr Anthony Fauci, raging egos, politically motivated lies, and the 13 00:00:51,000 --> 00:00:55,960 Speaker 1: worst cynical media willing to manipulate. He debunks myths about 14 00:00:55,960 --> 00:00:59,440 Speaker 1: the Trump administration's handling of the pandemic and explains who 15 00:00:59,480 --> 00:01:02,160 Speaker 1: on the task force was helpful and who caused the 16 00:01:02,200 --> 00:01:07,080 Speaker 1: American people unnecessary pain by dismissing scientific data. He calls 17 00:01:07,120 --> 00:01:11,240 Speaker 1: out the media, evaluates the lockdowns, and so much more. 18 00:01:11,560 --> 00:01:14,880 Speaker 1: We get into all of it on this show. Dr 19 00:01:14,920 --> 00:01:18,520 Speaker 1: Atlas is currently the Robert Wesson Senior Fellow and Healthcare 20 00:01:18,520 --> 00:01:22,240 Speaker 1: Policy at the Hoover Institute, and was previously a Professor 21 00:01:22,280 --> 00:01:26,319 Speaker 1: of Radiology and Chief nor Radiology at Stanford University Medical Center. 22 00:01:26,440 --> 00:01:30,000 Speaker 1: He has also advised several presidential candidates and leaders around 23 00:01:30,000 --> 00:01:34,440 Speaker 1: the world on healthcare and medical technology, including President Trump 24 00:01:34,480 --> 00:01:37,840 Speaker 1: on his White House Coronavirus Task Force. So I am 25 00:01:37,920 --> 00:01:41,440 Speaker 1: so so excited. I've been waiting to interview Dr Scott. 26 00:01:41,480 --> 00:01:43,960 Speaker 1: Atlas was a book coming out called A Plague Upon 27 00:01:44,000 --> 00:01:47,240 Speaker 1: Our House where he details his insider account about her 28 00:01:47,280 --> 00:01:50,360 Speaker 1: government's response to COVID and so many of the mistakes 29 00:01:50,880 --> 00:01:52,760 Speaker 1: that were made, particularly at the hands of people like 30 00:01:52,880 --> 00:01:56,840 Speaker 1: Dr Fauci and Dr Burke's. Uh. Dr Atlas, it's such 31 00:01:56,840 --> 00:01:58,760 Speaker 1: an honor free to be on the show. Well than 32 00:01:59,320 --> 00:02:01,520 Speaker 1: for me to do it, I appreciate it, and and 33 00:02:01,600 --> 00:02:04,120 Speaker 1: just for the folks at home to understand telling the 34 00:02:04,120 --> 00:02:06,600 Speaker 1: truth and being brave in today's society is not easy. 35 00:02:06,640 --> 00:02:08,959 Speaker 1: And you've done that, and you've been right all along. 36 00:02:09,080 --> 00:02:11,560 Speaker 1: So you're a warrior. So I am so thankful for 37 00:02:11,600 --> 00:02:14,080 Speaker 1: everything you've done, you know, sir. There there was this 38 00:02:14,200 --> 00:02:17,360 Speaker 1: article in the Brownstone Institute and one of the lines 39 00:02:17,360 --> 00:02:19,079 Speaker 1: read this. It said, not since the end of the 40 00:02:19,120 --> 00:02:23,639 Speaker 1: Second World War have so many renowned and acclaimed academics, scientists, 41 00:02:23,680 --> 00:02:27,400 Speaker 1: and medical doctors, including Nobel Prize recipients and nominees, have 42 00:02:27,520 --> 00:02:31,280 Speaker 1: been silenced, de platformed, and fired from the positions only 43 00:02:31,400 --> 00:02:34,120 Speaker 1: because they do not support the official or quote unquote 44 00:02:34,120 --> 00:02:37,720 Speaker 1: correct line. For those that aren't aware, how bad is it? Well, 45 00:02:37,760 --> 00:02:41,000 Speaker 1: I mean this is really the upshot of what what 46 00:02:41,240 --> 00:02:44,120 Speaker 1: is the big issues that I call them from this 47 00:02:44,200 --> 00:02:47,919 Speaker 1: pandemic because at some point it's not just about COVID 48 00:02:47,960 --> 00:02:52,360 Speaker 1: and the pandemic. We have been exposed as a society 49 00:02:53,040 --> 00:02:58,400 Speaker 1: of being unable to permit the free exchange of ideas. 50 00:02:58,480 --> 00:03:03,520 Speaker 1: We have politicization of science unprecedented in my view at 51 00:03:03,600 --> 00:03:08,280 Speaker 1: least in my lifetime um where we have academics and 52 00:03:08,480 --> 00:03:12,040 Speaker 1: scientists writing things like the letter to to the editor 53 00:03:12,360 --> 00:03:15,120 Speaker 1: on Lancet, one of the best journals in the world 54 00:03:15,160 --> 00:03:21,120 Speaker 1: from February claiming that anyone who said that the virus 55 00:03:21,200 --> 00:03:23,880 Speaker 1: could have originated in a lab as a conspiracy theorist. 56 00:03:23,919 --> 00:03:27,560 Speaker 1: That's not a scientific thing to say. Yet these people 57 00:03:27,600 --> 00:03:31,920 Speaker 1: felt compelled to say it, and that was intentionally to 58 00:03:32,040 --> 00:03:37,280 Speaker 1: intimidate or silence people. We see people at Stanford University 59 00:03:37,400 --> 00:03:42,480 Speaker 1: who have issued you know, uh rebukes that were really 60 00:03:42,680 --> 00:03:48,320 Speaker 1: irrational statements about things distorting my words. And you know, 61 00:03:48,400 --> 00:03:51,520 Speaker 1: this kind of stuff has been going on in a 62 00:03:51,520 --> 00:03:55,200 Speaker 1: lot of places, and it's it's political as well as 63 00:03:55,920 --> 00:03:59,480 Speaker 1: really harmful. This is the point. If you intimidate the 64 00:03:59,480 --> 00:04:03,080 Speaker 1: free exchange, your ideas will never arrive at the troops 65 00:04:03,120 --> 00:04:05,560 Speaker 1: that we need as in society. I mean, I got 66 00:04:05,800 --> 00:04:11,000 Speaker 1: over a hundred emails from scientists in this country during 67 00:04:11,040 --> 00:04:15,400 Speaker 1: this pandemic time saying to me, Scott keeps saying what 68 00:04:15,440 --> 00:04:19,000 Speaker 1: you're saying, You're exactly right, We're afraid to step forward. 69 00:04:19,440 --> 00:04:21,920 Speaker 1: And these scientists were from all over the country, some 70 00:04:22,040 --> 00:04:26,239 Speaker 1: outside the US, including inside the n I h Okay, 71 00:04:26,320 --> 00:04:30,000 Speaker 1: inside Stanford University. They were afraid to step forward. There 72 00:04:30,040 --> 00:04:33,120 Speaker 1: were other people that step forward. I'm not claiming otherwise, 73 00:04:33,200 --> 00:04:37,240 Speaker 1: but um, this sign kind of type of intimidation. It's 74 00:04:37,279 --> 00:04:41,960 Speaker 1: actually successful in stopping people, and that's that's very very 75 00:04:42,080 --> 00:04:44,320 Speaker 1: dangerous well. And there's also a chilling effect in the 76 00:04:44,360 --> 00:04:47,320 Speaker 1: media as well. I mean, even you look at the Facebook, Google, 77 00:04:47,320 --> 00:04:49,720 Speaker 1: and Twitter all really control the content that is out 78 00:04:49,760 --> 00:04:53,400 Speaker 1: there because they then censor networks. They then censor contact 79 00:04:53,560 --> 00:04:56,040 Speaker 1: or content that doesn't meet you know, the quote unquote 80 00:04:56,040 --> 00:04:59,080 Speaker 1: group think. You know, narrative as well, and in your acknowledgements, 81 00:04:59,160 --> 00:05:01,720 Speaker 1: you think of their journalist the most is honest network 82 00:05:01,720 --> 00:05:03,600 Speaker 1: for a supportive email they sent you. You You didn't want 83 00:05:03,640 --> 00:05:04,960 Speaker 1: to name their name, which is fair enough. I don't 84 00:05:05,000 --> 00:05:07,720 Speaker 1: care who it is, but that has a big role 85 00:05:07,720 --> 00:05:09,880 Speaker 1: in all this as well. So what's her assessment been, 86 00:05:10,080 --> 00:05:12,279 Speaker 1: you know, looking back from now, you know, from the 87 00:05:12,279 --> 00:05:16,520 Speaker 1: beginning of the media's coverage of COVID. Yeah, I mean 88 00:05:16,560 --> 00:05:22,000 Speaker 1: the media was the American media, specifically, the American media 89 00:05:22,320 --> 00:05:26,680 Speaker 1: was the outlier in its distortion of news and it's 90 00:05:26,800 --> 00:05:29,919 Speaker 1: very effective when they pile on you know, of course 91 00:05:29,960 --> 00:05:32,159 Speaker 1: we were, we were the country in the in the 92 00:05:32,200 --> 00:05:36,559 Speaker 1: election season. This all happened during a very volatile period 93 00:05:36,600 --> 00:05:39,400 Speaker 1: in the United States, as of course you and everyone knows. 94 00:05:40,160 --> 00:05:42,640 Speaker 1: But you know, this is not a political issue. I'll 95 00:05:42,640 --> 00:05:44,680 Speaker 1: give you an example of why I think the the 96 00:05:44,720 --> 00:05:49,159 Speaker 1: American media was unique. Uh, there's a study that people 97 00:05:49,160 --> 00:05:52,200 Speaker 1: should look up that was published I think November to 98 00:05:52,279 --> 00:05:55,120 Speaker 1: talked about what happened during the nine months of first 99 00:05:55,200 --> 00:05:58,000 Speaker 1: nine months of the year during the pandemic. The American 100 00:05:58,080 --> 00:06:03,520 Speaker 1: media stories about COVID were more than nine negative, but 101 00:06:03,720 --> 00:06:08,000 Speaker 1: international English speaking mainstream media was only half of those 102 00:06:08,000 --> 00:06:12,440 Speaker 1: stories were negative about schools opening. Half the stories were negative. 103 00:06:12,560 --> 00:06:16,320 Speaker 1: Roughly in the English speaking media outside the US, nine 104 00:06:16,640 --> 00:06:20,039 Speaker 1: plus percent were negative about schools opening, And the American 105 00:06:20,080 --> 00:06:24,520 Speaker 1: mainstream media when you had cases going up or down, 106 00:06:25,040 --> 00:06:29,279 Speaker 1: the number of the stories about cases going up out 107 00:06:29,360 --> 00:06:31,560 Speaker 1: numbered the number of stories about cases going down by 108 00:06:31,680 --> 00:06:35,240 Speaker 1: five to one in the American media even when cases 109 00:06:35,240 --> 00:06:39,320 Speaker 1: were going down. So, I mean, this was a gross 110 00:06:39,440 --> 00:06:43,840 Speaker 1: manipulation of the public. Whether it was political or not, 111 00:06:44,040 --> 00:06:48,320 Speaker 1: it was so harmful. It instilled a fear, a frenzy, 112 00:06:48,400 --> 00:06:52,840 Speaker 1: and it mainly impacted frankly young people in the United States. 113 00:06:52,839 --> 00:06:55,359 Speaker 1: And I can talk about how what the objective evidences 114 00:06:55,400 --> 00:06:59,240 Speaker 1: of that, but that there is a real harm that 115 00:06:59,360 --> 00:07:03,000 Speaker 1: the media did not just in censoring information, not just 116 00:07:03,120 --> 00:07:07,120 Speaker 1: in prohibiting, but in instilling fear and panic. Well, and 117 00:07:07,160 --> 00:07:10,480 Speaker 1: talk about that objective evidence. Sure, Well, I mean when 118 00:07:10,480 --> 00:07:14,000 Speaker 1: you look at young people who have you know, young 119 00:07:14,080 --> 00:07:18,400 Speaker 1: healthy people under twenty, under thirty even have an extremely 120 00:07:18,480 --> 00:07:22,280 Speaker 1: low risk from COVID and an extremely low risk of 121 00:07:22,320 --> 00:07:25,000 Speaker 1: death from COVID. This is factually proven. It's in all 122 00:07:25,080 --> 00:07:27,880 Speaker 1: the u S data. All the healthy people who are 123 00:07:27,920 --> 00:07:32,720 Speaker 1: young and simply are not at significant risk from this illness. 124 00:07:32,720 --> 00:07:35,160 Speaker 1: But when you look at the studies on what their 125 00:07:35,200 --> 00:07:40,400 Speaker 1: own assessment is of the risk, the young people, overwhelmingly 126 00:07:40,640 --> 00:07:43,960 Speaker 1: by the by the data, have like a fiftyfold increase 127 00:07:44,080 --> 00:07:46,720 Speaker 1: in their assessment of their own risk compared to people 128 00:07:46,760 --> 00:07:50,200 Speaker 1: who actually are old and at high risk. UH. And 129 00:07:50,240 --> 00:07:53,040 Speaker 1: what the evidence on how it's harm them are. Okay, 130 00:07:53,280 --> 00:07:56,160 Speaker 1: let's just look at the psychological data. The visits to 131 00:07:56,320 --> 00:07:59,960 Speaker 1: doctors for people who are teenagers and young adults German 132 00:08:00,000 --> 00:08:03,320 Speaker 1: actually decreased during the lockdowns because medical care was shut 133 00:08:03,360 --> 00:08:05,280 Speaker 1: off or people were afraid to go in. And we 134 00:08:05,360 --> 00:08:09,200 Speaker 1: know that. But the one disease that had marked spike 135 00:08:09,680 --> 00:08:14,120 Speaker 1: in doctor visits with psychiatric illness, cases of anxiety disorder 136 00:08:14,640 --> 00:08:19,240 Speaker 1: UH and other psychiatric illnesses exploded in teenagers and in 137 00:08:19,320 --> 00:08:23,120 Speaker 1: college age students in June, just from the spring lockdown 138 00:08:23,160 --> 00:08:26,920 Speaker 1: of schools. In the isolation in June of one of 139 00:08:27,040 --> 00:08:31,360 Speaker 1: four American college students start of killing himself. Okay, there 140 00:08:31,440 --> 00:08:36,400 Speaker 1: was a tripling of self harm visits by teenagers two 141 00:08:36,440 --> 00:08:41,560 Speaker 1: doctors during the lockdown. This means people are putting out 142 00:08:41,640 --> 00:08:45,840 Speaker 1: cigarettes on their skin or cutting their rists as a teenagers. 143 00:08:46,000 --> 00:08:49,600 Speaker 1: We had an explosion of opioid and drug abuse in 144 00:08:49,720 --> 00:08:54,320 Speaker 1: teenagers during the lockdown. We had an explosion of suicides 145 00:08:54,400 --> 00:08:57,760 Speaker 1: by teenage girls during the lockdown, all compared to one 146 00:08:57,840 --> 00:09:02,800 Speaker 1: year previous. So either way, more than half of people 147 00:09:02,920 --> 00:09:05,960 Speaker 1: in college age in the United States during the lockdown 148 00:09:07,440 --> 00:09:10,920 Speaker 1: had an unwanted weight gain, and that weight gain average 149 00:09:10,960 --> 00:09:14,200 Speaker 1: twenty eight pounds during the lockdown. These are all harms 150 00:09:14,200 --> 00:09:18,600 Speaker 1: from the isolation of the lockdown. This is not the virus. 151 00:09:18,600 --> 00:09:21,120 Speaker 1: The virus did not cause the lockdown. There was a 152 00:09:21,240 --> 00:09:26,240 Speaker 1: human decision. People in power implemented a lockdown, and that 153 00:09:26,400 --> 00:09:29,920 Speaker 1: lockdown didn't just fail to stop the sprout of the infection. 154 00:09:30,679 --> 00:09:35,520 Speaker 1: It killed people. It stopped it did not prevent people 155 00:09:35,600 --> 00:09:40,320 Speaker 1: from dying, and it destroyed families and particularly low income 156 00:09:40,400 --> 00:09:42,560 Speaker 1: families and children. Well, and that's such a good point 157 00:09:42,640 --> 00:09:44,320 Speaker 1: because you know, a lot of and a lot of 158 00:09:44,320 --> 00:09:47,400 Speaker 1: working people who weren't able to stay home they got COVID. 159 00:09:47,480 --> 00:09:49,679 Speaker 1: Yet now we're you know, once again, sort of subjecting 160 00:09:49,679 --> 00:09:51,480 Speaker 1: them to you gotta go out and get the vaccine 161 00:09:51,520 --> 00:09:53,439 Speaker 1: in order to get a job. Even though you already 162 00:09:53,440 --> 00:09:55,400 Speaker 1: put your life at risk if you're a nurse, you know, 163 00:09:55,400 --> 00:09:57,800 Speaker 1: on the front line. So we we just have sort 164 00:09:57,800 --> 00:10:00,280 Speaker 1: of had this ridiculous approach the entire time. It really 165 00:10:00,280 --> 00:10:01,840 Speaker 1: gets down to this one point you made in the 166 00:10:01,840 --> 00:10:04,200 Speaker 1: book which really just like stood out well you wrote 167 00:10:04,200 --> 00:10:06,400 Speaker 1: about it a couple of times, but about the lack 168 00:10:06,440 --> 00:10:09,439 Speaker 1: of critical thinking in the country. And right here you say, 169 00:10:09,600 --> 00:10:12,640 Speaker 1: is the herd mentality? So powerful? Is fear such a 170 00:10:12,720 --> 00:10:16,199 Speaker 1: dominant emotion that all critical thinking and values disappear? You 171 00:10:16,240 --> 00:10:19,440 Speaker 1: again write a basic question to yourself, where are the 172 00:10:19,440 --> 00:10:23,520 Speaker 1: critical thinkers? And I really believe we have this crisis 173 00:10:23,559 --> 00:10:26,080 Speaker 1: in this country of a lack of critical thinking from 174 00:10:26,600 --> 00:10:30,160 Speaker 1: you know, academia to public officials on down through the 175 00:10:30,200 --> 00:10:33,079 Speaker 1: media and just everyday Americans. Just a crisis of a 176 00:10:33,160 --> 00:10:36,400 Speaker 1: lack of critical thinking. That's right. And of course you 177 00:10:36,440 --> 00:10:40,040 Speaker 1: know fear, fear is important as a reason why people 178 00:10:40,720 --> 00:10:44,200 Speaker 1: have that, but also leadership, the leadership, and this is 179 00:10:44,240 --> 00:10:46,880 Speaker 1: what I saw on the task force. There's a big 180 00:10:46,880 --> 00:10:49,720 Speaker 1: difference between me and the other doctors on the task force, 181 00:10:49,840 --> 00:10:54,280 Speaker 1: the doctors Faucian Burkes, Uh. These people are bureaucrats and 182 00:10:54,280 --> 00:10:57,640 Speaker 1: they have been government jobs for forty years. Okay, I'm 183 00:10:57,640 --> 00:11:01,280 Speaker 1: a health I've been in healthcare policy for fifteen plus years, 184 00:11:01,280 --> 00:11:04,600 Speaker 1: and I have a year history of clinical medical practice 185 00:11:05,080 --> 00:11:09,000 Speaker 1: in a university setting with clinical research and reviewing papers 186 00:11:09,000 --> 00:11:12,920 Speaker 1: and grants. So where I am in my world, critical 187 00:11:13,000 --> 00:11:15,400 Speaker 1: thinking is the essence of it. You don't have to 188 00:11:15,400 --> 00:11:19,280 Speaker 1: be a scientist or a doctor to be a critical thinker, 189 00:11:19,400 --> 00:11:21,760 Speaker 1: but you must be a critical thinker if you're going 190 00:11:21,800 --> 00:11:23,600 Speaker 1: to be a scientist. And what I saw on the 191 00:11:23,600 --> 00:11:27,319 Speaker 1: task force where people when they made a point they 192 00:11:27,400 --> 00:11:31,840 Speaker 1: just said like a bottom line, mainstream narrative point. I 193 00:11:31,880 --> 00:11:36,360 Speaker 1: was bringing in dozens of research publications of the ongoing 194 00:11:36,360 --> 00:11:39,240 Speaker 1: scientific literature into the task force meetings. I never saw 195 00:11:39,440 --> 00:11:42,200 Speaker 1: in my any meeting I went to any of the 196 00:11:42,240 --> 00:11:48,240 Speaker 1: other doctors bring in scientific papers, critique a scientific study 197 00:11:48,280 --> 00:11:53,560 Speaker 1: that was talked about site scientific data. Except me, I 198 00:11:53,640 --> 00:11:56,640 Speaker 1: did all the time, and the answer was always you're 199 00:11:56,679 --> 00:11:59,600 Speaker 1: not mainstream, or the answer was they would run to 200 00:11:59,640 --> 00:12:03,360 Speaker 1: their friends in the media, uh and sort of you know, 201 00:12:03,520 --> 00:12:06,120 Speaker 1: use ad hominum attacks and talk about me being an outlier. 202 00:12:06,640 --> 00:12:08,880 Speaker 1: By the way I brought in it wasn't just me 203 00:12:09,240 --> 00:12:11,680 Speaker 1: part of this whole point of being an advisor in 204 00:12:11,720 --> 00:12:15,319 Speaker 1: a healthcare crisis, is you want as much information as 205 00:12:15,360 --> 00:12:18,840 Speaker 1: possible to be on the table. Here I was bringing in. 206 00:12:18,960 --> 00:12:21,320 Speaker 1: I brought in some of the best medical scientists in 207 00:12:21,360 --> 00:12:25,800 Speaker 1: the country from the from U c. L A. Toughs, Harvard, 208 00:12:25,920 --> 00:12:29,160 Speaker 1: Stanford to meet with the president. This is in the book. 209 00:12:29,760 --> 00:12:32,360 Speaker 1: No one even knows this, and the reason that they 210 00:12:32,360 --> 00:12:36,240 Speaker 1: don't know it is because it was viewed as politically 211 00:12:36,280 --> 00:12:39,720 Speaker 1: a hot potato because it would upset Dr Burkes. In fact, 212 00:12:39,800 --> 00:12:43,560 Speaker 1: Dr Burkes claims that she was excluded from meetings. She 213 00:12:43,760 --> 00:12:46,760 Speaker 1: we rescheduled that meeting so that she could attend, and 214 00:12:46,760 --> 00:12:49,360 Speaker 1: then the day before she sent an email saying she 215 00:12:49,440 --> 00:12:52,480 Speaker 1: can't go because not because she was out of time, 216 00:12:52,640 --> 00:12:54,520 Speaker 1: it wasn't good for her to go to that meeting. 217 00:12:55,000 --> 00:12:57,760 Speaker 1: We had. My view was the President of the United 218 00:12:57,800 --> 00:13:00,120 Speaker 1: States asked me to help in the healthcare crisis. The 219 00:13:00,120 --> 00:13:04,120 Speaker 1: answers yes, Number one, it's my country and the country's 220 00:13:04,160 --> 00:13:07,800 Speaker 1: off the rails. And secondly, I wanted to bring in 221 00:13:07,840 --> 00:13:11,120 Speaker 1: as much information as paul possible and the country should 222 00:13:11,120 --> 00:13:13,840 Speaker 1: be breathing a sigh of relief if they read my 223 00:13:13,960 --> 00:13:19,280 Speaker 1: book understanding that the President was actually listening at least 224 00:13:19,320 --> 00:13:22,160 Speaker 1: talking with people who are actually doing the research on 225 00:13:22,200 --> 00:13:25,599 Speaker 1: the pandemic, who are the top medical scientists in infectious 226 00:13:25,600 --> 00:13:30,880 Speaker 1: disease and epidemiology, in virology, talking to him as opposed 227 00:13:30,920 --> 00:13:33,920 Speaker 1: to what the task force doctors were worried about, which 228 00:13:34,000 --> 00:13:36,319 Speaker 1: was they didn't like it that there were quote, other 229 00:13:36,440 --> 00:13:39,559 Speaker 1: sources of information getting to the president, as if they 230 00:13:39,559 --> 00:13:42,559 Speaker 1: were supposed to be the filter for the information. That's 231 00:13:42,600 --> 00:13:46,280 Speaker 1: the exact opposite. Where I work in a university at 232 00:13:46,360 --> 00:13:49,440 Speaker 1: Hoover Institution, you walk into a room you're used to 233 00:13:49,480 --> 00:13:52,160 Speaker 1: being challenged. You win the argument on the basis being 234 00:13:52,160 --> 00:13:55,400 Speaker 1: prepared and knowing more, not on the basis of ad 235 00:13:55,440 --> 00:13:58,440 Speaker 1: hominem attacks of people who disagree with you. Well, and 236 00:13:58,440 --> 00:14:00,360 Speaker 1: actually that's why I was so glad that when you 237 00:14:00,400 --> 00:14:02,760 Speaker 1: were brought on, because my concern was that we were 238 00:14:02,760 --> 00:14:05,720 Speaker 1: all engaging in group think, and in that sort of environment, 239 00:14:05,760 --> 00:14:08,720 Speaker 1: it's important to have ideas challenged and have different ideas 240 00:14:09,120 --> 00:14:10,440 Speaker 1: uh come to. I mean we saw this in the 241 00:14:10,440 --> 00:14:12,880 Speaker 1: movie the media as well. I mean there's this sort 242 00:14:12,880 --> 00:14:16,080 Speaker 1: of like group think that overtook the nation, that actually 243 00:14:16,120 --> 00:14:19,040 Speaker 1: denied the ability for critical thinking and reasonable thoughts. So 244 00:14:19,080 --> 00:14:21,280 Speaker 1: I was so glad when you were brought on. You 245 00:14:21,320 --> 00:14:23,600 Speaker 1: talk about in the book how Dr Fauci wanted people 246 00:14:23,640 --> 00:14:26,640 Speaker 1: to live in fear, and after you challenged him, he 247 00:14:26,680 --> 00:14:29,360 Speaker 1: said that, yes, they need to be more afraid. What 248 00:14:29,480 --> 00:14:32,040 Speaker 1: has that impact of fear have on the country. You 249 00:14:32,080 --> 00:14:35,320 Speaker 1: look at sort of like the totality of the lockdowns 250 00:14:35,320 --> 00:14:37,400 Speaker 1: and our reaction to all this. What do you think 251 00:14:37,480 --> 00:14:41,720 Speaker 1: that the total damage is. I think there's a massive 252 00:14:41,760 --> 00:14:44,960 Speaker 1: amount of psychological damage. Now, not just from the statistics 253 00:14:45,000 --> 00:14:47,880 Speaker 1: that I cite on the basis of you know, young 254 00:14:47,920 --> 00:14:53,320 Speaker 1: people going to have uh, psychiatric illnesses and damage, but 255 00:14:53,440 --> 00:14:56,000 Speaker 1: this this is this is going co last. And you know, 256 00:14:56,080 --> 00:14:58,880 Speaker 1: certainly it's different in different parts of the country, but 257 00:14:59,000 --> 00:15:00,960 Speaker 1: I can tell you that where I am in California, 258 00:15:01,800 --> 00:15:05,160 Speaker 1: you see Stanford University students riding their bikes outside with 259 00:15:05,280 --> 00:15:10,920 Speaker 1: masks on, and riding skateboards with masks on. Or you 260 00:15:11,000 --> 00:15:15,720 Speaker 1: have people walking around, uh you know that they're vaccinated, 261 00:15:15,840 --> 00:15:17,960 Speaker 1: but they're they're still like afraid to go out. I 262 00:15:17,960 --> 00:15:21,160 Speaker 1: mean when you when you talk about what the fear 263 00:15:21,280 --> 00:15:23,160 Speaker 1: is there, there is a study I think it's a 264 00:15:23,200 --> 00:15:26,720 Speaker 1: Gallop poll that asked young people at people of all 265 00:15:26,760 --> 00:15:29,200 Speaker 1: ages and the and the most frightened were the young people, 266 00:15:29,320 --> 00:15:33,400 Speaker 1: eighteen twenty four year old people. Half of them said 267 00:15:33,400 --> 00:15:36,560 Speaker 1: they were quote afraid of all social nervous about all 268 00:15:36,640 --> 00:15:41,400 Speaker 1: social interactions, all social interactions. I mean, we are really 269 00:15:41,960 --> 00:15:44,880 Speaker 1: we we really have instilled a tremendous amount of fear. 270 00:15:45,400 --> 00:15:47,360 Speaker 1: By the way, there there's a study that one of 271 00:15:47,360 --> 00:15:50,640 Speaker 1: the student newspapers put out from Stanford that showed that 272 00:15:51,320 --> 00:15:55,920 Speaker 1: there are far more people wearing masks on bicycles Stanford 273 00:15:55,920 --> 00:15:59,480 Speaker 1: students than are wearing bike helmets. There's been a complete 274 00:15:59,600 --> 00:16:05,320 Speaker 1: perverse persian of risk and the assessment of risk, you know, 275 00:16:05,400 --> 00:16:08,680 Speaker 1: I mean the basic fundamental point about the viruses. It 276 00:16:08,840 --> 00:16:12,160 Speaker 1: is not true that everyone has a significant risk from COVID. 277 00:16:12,600 --> 00:16:15,480 Speaker 1: That's a simply false that's improven by the data, yet 278 00:16:15,480 --> 00:16:18,840 Speaker 1: it's still ignored almost two years into this where we 279 00:16:18,880 --> 00:16:21,640 Speaker 1: know that of deaths are in people who are over 280 00:16:21,720 --> 00:16:26,160 Speaker 1: sixty five. The average age of COVID death in many countries, 281 00:16:26,200 --> 00:16:31,000 Speaker 1: if not most, is older than the average age of 282 00:16:31,040 --> 00:16:36,040 Speaker 1: life expectancy. People that are under twenty have an extraordinarily 283 00:16:36,160 --> 00:16:39,720 Speaker 1: small risk of a serious illness or death from COVID 284 00:16:39,800 --> 00:16:42,840 Speaker 1: if they're healthy people. The people that are high risk 285 00:16:42,880 --> 00:16:45,480 Speaker 1: are high risk. Okay, if you have a child with leukemia, 286 00:16:45,840 --> 00:16:49,840 Speaker 1: I'd be worried. But you know, generally speaking, just as 287 00:16:49,880 --> 00:16:54,120 Speaker 1: another statistic to remember, two thirds of deaths in the 288 00:16:54,200 --> 00:16:57,640 Speaker 1: United States, according to the CDC study, two thirds of 289 00:16:57,720 --> 00:16:59,960 Speaker 1: deaths are in people who have greater than are equal 290 00:17:00,040 --> 00:17:05,639 Speaker 1: with six comorbidities, not just controlled hypertensions, which in and 291 00:17:05,680 --> 00:17:08,119 Speaker 1: of itself is not a significant risk factor according to 292 00:17:08,160 --> 00:17:13,080 Speaker 1: the CDC data and other data, but greater than six comorbidities, 293 00:17:13,160 --> 00:17:15,959 Speaker 1: greater than are equal to six co more abilities. So 294 00:17:16,080 --> 00:17:19,639 Speaker 1: you know, these are older people who are frail and 295 00:17:19,760 --> 00:17:23,919 Speaker 1: multiple underlying disorders who are at risk. Other people not 296 00:17:24,000 --> 00:17:27,280 Speaker 1: so much. Johnny and Needy statistic who's one of the 297 00:17:27,320 --> 00:17:31,639 Speaker 1: world renowned with epidemiologists and on this stuff. The point 298 00:17:31,640 --> 00:17:35,960 Speaker 1: the infection fatality rate for people under seventy is point 299 00:17:36,040 --> 00:17:40,920 Speaker 1: zero five. That means it's not very different from influenza. 300 00:17:41,160 --> 00:17:43,600 Speaker 1: But it's just crazy how all of this got distorted. 301 00:17:43,640 --> 00:17:46,119 Speaker 1: It really got distorted in the beginning as well. I mean, 302 00:17:46,200 --> 00:17:49,159 Speaker 1: you're right about the beginning of COVID. How you know 303 00:17:49,200 --> 00:17:52,400 Speaker 1: there's this knowledge or it wasn't even knowledge, but how 304 00:17:52,400 --> 00:17:55,040 Speaker 1: it was going around that you know, this exaggerated fatality 305 00:17:55,119 --> 00:17:58,040 Speaker 1: rate of three point four percent that was highlighted throughout 306 00:17:58,080 --> 00:18:00,880 Speaker 1: the media. But to your point, you were saying that, uh, 307 00:18:00,920 --> 00:18:03,800 Speaker 1: these fatality rates were based on people who were sick 308 00:18:03,840 --> 00:18:06,720 Speaker 1: enough to seek medical care rather than the much larger 309 00:18:06,800 --> 00:18:10,560 Speaker 1: population of infected individuals. So basically that number was entirely skewed. 310 00:18:11,000 --> 00:18:13,640 Speaker 1: And you noted that people like Dr Johnny and Needy 311 00:18:13,680 --> 00:18:16,720 Speaker 1: has noted that we have esteemed individuals who are noting this. 312 00:18:17,080 --> 00:18:19,560 Speaker 1: But then somehow that got lost with the White House 313 00:18:19,600 --> 00:18:22,760 Speaker 1: Task Force and the media. Why well, I mean, the 314 00:18:22,840 --> 00:18:25,480 Speaker 1: question the answer to why it is always very difficult. 315 00:18:25,600 --> 00:18:29,639 Speaker 1: As you know, Um, there's no question that, Okay, in 316 00:18:29,960 --> 00:18:33,040 Speaker 1: my opinion, in the beginning, I thought, okay, this is 317 00:18:33,080 --> 00:18:36,080 Speaker 1: an election year. There's a lot of political discussion in 318 00:18:36,119 --> 00:18:39,919 Speaker 1: the media. Uh, maybe it was political, but as it 319 00:18:40,000 --> 00:18:45,000 Speaker 1: went on that there was just an extraordinary lack of 320 00:18:45,160 --> 00:18:52,720 Speaker 1: acknowledgement of fundamental science, okay, fundamental obvious immunology. There were discussions. 321 00:18:52,760 --> 00:18:56,240 Speaker 1: I was pilloried for saying that there was immunity after 322 00:18:56,280 --> 00:19:00,159 Speaker 1: getting an infection. This was known, first of all, it 323 00:19:00,240 --> 00:19:04,600 Speaker 1: was known from previous stars and sections. The basic construct 324 00:19:04,640 --> 00:19:07,520 Speaker 1: of a three point four percent fatality rate. Johnny and Needs, 325 00:19:07,560 --> 00:19:10,600 Speaker 1: I think, was the first person to say this is absurd. 326 00:19:11,200 --> 00:19:14,800 Speaker 1: This number is meaningless unless we know how many people 327 00:19:14,800 --> 00:19:18,680 Speaker 1: were infected. Uh. And yet he was he was pilloried 328 00:19:18,920 --> 00:19:22,239 Speaker 1: by some of his colleagues for that, and so you know, 329 00:19:22,359 --> 00:19:24,960 Speaker 1: it's it's hard to say why. There was a tremendous 330 00:19:24,960 --> 00:19:28,639 Speaker 1: amount of fear. Everyone was afraid in the beginning, I think, 331 00:19:28,720 --> 00:19:30,720 Speaker 1: But then when you looked at the data with any 332 00:19:30,760 --> 00:19:33,240 Speaker 1: kind of critical thinking, you said, well, wait a second, 333 00:19:33,280 --> 00:19:36,159 Speaker 1: let's go through this. And so when people like me 334 00:19:36,280 --> 00:19:39,480 Speaker 1: started to go through it, it was already countered to 335 00:19:39,560 --> 00:19:43,439 Speaker 1: the conventional wisdom that was being perpetrated not just by 336 00:19:43,440 --> 00:19:46,280 Speaker 1: the World Health Organization and not just by the media, 337 00:19:46,359 --> 00:19:50,479 Speaker 1: but by these people who I will call modelers, okay, 338 00:19:50,600 --> 00:19:57,040 Speaker 1: computer science modelers, statistical modelers, who were taking grossly erroneous 339 00:19:57,119 --> 00:20:01,919 Speaker 1: assumptions and disregarding the natural history of viruses and putting 340 00:20:01,920 --> 00:20:05,680 Speaker 1: them into these models and and projecting these extraordinarily high 341 00:20:05,800 --> 00:20:09,240 Speaker 1: death rates which made the news until all of a sudden, 342 00:20:09,400 --> 00:20:11,919 Speaker 1: all over the place, the UK, but the United States, 343 00:20:12,400 --> 00:20:16,360 Speaker 1: there was all kinds of sort of exaggerated information being 344 00:20:16,359 --> 00:20:18,320 Speaker 1: put forward. Now we're we're not meant, I am not 345 00:20:18,520 --> 00:20:22,560 Speaker 1: minimizing that the seven hundred plus people thousand people in 346 00:20:22,600 --> 00:20:26,840 Speaker 1: the United States have died from COVID okay and millions 347 00:20:26,920 --> 00:20:30,280 Speaker 1: more have been destroyed by the lockdown policies. But to 348 00:20:30,400 --> 00:20:34,000 Speaker 1: say that the numbers uh that we're first put forward 349 00:20:34,000 --> 00:20:38,119 Speaker 1: were accurate is just grossly false. These people instilled the 350 00:20:38,160 --> 00:20:40,720 Speaker 1: tremendous amount of fear. Yet even to the end of 351 00:20:40,760 --> 00:20:43,680 Speaker 1: my three and a half months in Washington, I saw 352 00:20:43,800 --> 00:20:47,600 Speaker 1: people on the tasks were still relying on these totally 353 00:20:47,640 --> 00:20:50,639 Speaker 1: discredited models to make their statements. It was shocking. A 354 00:20:51,400 --> 00:20:55,080 Speaker 1: stunning lack of knowledge, a stunning lack of critical thinking, 355 00:20:55,680 --> 00:21:01,800 Speaker 1: and a complete lack of perspective or really diligence on 356 00:21:01,960 --> 00:21:04,639 Speaker 1: reading the scientific literature. Well, I actually got the White 357 00:21:04,640 --> 00:21:06,560 Speaker 1: House on record, I think it was back in April 358 00:21:06,560 --> 00:21:08,800 Speaker 1: about the need to do antibody testing too to get 359 00:21:08,800 --> 00:21:10,359 Speaker 1: to that very fact, to get to what the true 360 00:21:10,359 --> 00:21:12,680 Speaker 1: fatality rate was. Because I was reading people like Dr 361 00:21:12,760 --> 00:21:15,560 Speaker 1: e Needs and you know, people like you and these 362 00:21:15,600 --> 00:21:18,359 Speaker 1: individuals who are actually making sense, and it really resonated 363 00:21:18,400 --> 00:21:20,199 Speaker 1: with me, and so I just felt like this was 364 00:21:20,240 --> 00:21:23,359 Speaker 1: such a fight to get the truth out to the public. 365 00:21:23,400 --> 00:21:26,120 Speaker 1: But you talked about. I mean, I mean there are 366 00:21:26,160 --> 00:21:28,280 Speaker 1: some questions, like a lot of people have had questions 367 00:21:28,359 --> 00:21:31,520 Speaker 1: about even just you know, hospitalizations, or even the amount, 368 00:21:31,680 --> 00:21:33,320 Speaker 1: like the death toll that we have in the country, 369 00:21:33,400 --> 00:21:38,640 Speaker 1: like what percentage died from COVID versus what percentage died 370 00:21:38,720 --> 00:21:40,919 Speaker 1: with COVID. You know, for instance, I go into the hospital, 371 00:21:41,000 --> 00:21:43,720 Speaker 1: unfortunately I have cancer. That is what takes my life. 372 00:21:43,760 --> 00:21:45,960 Speaker 1: I happen to have COVID, but that's not what brought 373 00:21:46,000 --> 00:21:47,919 Speaker 1: me into the hospital, nor is it what killed me. 374 00:21:48,320 --> 00:21:51,800 Speaker 1: Are we classifying things correctly? Is the data wrong there 375 00:21:51,840 --> 00:21:53,960 Speaker 1: as well? Or what are your thoughts on that? But 376 00:21:54,200 --> 00:21:56,720 Speaker 1: I think that's a very good question. An important point 377 00:21:56,880 --> 00:22:01,400 Speaker 1: is that the statistics are are are seriously flawed. And 378 00:22:01,480 --> 00:22:04,960 Speaker 1: what do I mean by that? Well, number one hospitalizations 379 00:22:05,000 --> 00:22:10,280 Speaker 1: from COVID is a is incorrectly reported. That's been documented 380 00:22:10,359 --> 00:22:13,359 Speaker 1: in the literature already. There's been at least a few 381 00:22:13,400 --> 00:22:15,840 Speaker 1: studies that have shown this, one of which was from 382 00:22:15,840 --> 00:22:20,679 Speaker 1: Stanford University itself back in the spring, which reported that 383 00:22:20,880 --> 00:22:26,200 Speaker 1: almost half of kids that were called hospitalized with COVID 384 00:22:26,320 --> 00:22:31,080 Speaker 1: didn't have COVID COVID's illness. They had no symptoms. Literally asymptomatic, 385 00:22:31,359 --> 00:22:34,159 Speaker 1: they had a positive test for the virus. They were sick, 386 00:22:34,760 --> 00:22:37,280 Speaker 1: sick enough to be hospitalized, but with something else because 387 00:22:37,280 --> 00:22:40,280 Speaker 1: they had zero symptoms of COVID, yet they were called 388 00:22:40,760 --> 00:22:46,960 Speaker 1: COVID hospitalizations. So another study showed that of kids hospitalized 389 00:22:47,240 --> 00:22:51,399 Speaker 1: with COVID quote unquote, we're not COVID, they had no 390 00:22:51,520 --> 00:22:55,760 Speaker 1: symptoms of the illness we see on deaths from COVID. Okay, 391 00:22:55,760 --> 00:22:58,399 Speaker 1: if you're testing everybody who's entering a hospital and they 392 00:22:58,400 --> 00:23:01,760 Speaker 1: have a positive STARS to IRIS test and they died, 393 00:23:01,800 --> 00:23:04,040 Speaker 1: they were called COVID death even though they were in 394 00:23:04,160 --> 00:23:06,960 Speaker 1: COVID death. How do I know? Well, even the CDC 395 00:23:07,320 --> 00:23:14,720 Speaker 1: reported that one of deaths from COVID under eighteen in 396 00:23:14,840 --> 00:23:20,359 Speaker 1: hospital could not quote plausibly unquote the COVID because of 397 00:23:20,400 --> 00:23:23,080 Speaker 1: the chart review. They had no COVID symptoms, yet they 398 00:23:23,080 --> 00:23:26,359 Speaker 1: were called COVID death. So, yes, people died from COVID, 399 00:23:26,440 --> 00:23:29,800 Speaker 1: there's no doubt, But the statistics on hospitalizations and deaths 400 00:23:29,800 --> 00:23:32,320 Speaker 1: from COVID are flawed. And then there's another problem which 401 00:23:32,400 --> 00:23:35,639 Speaker 1: is maybe even bigger, and of course this is the 402 00:23:35,680 --> 00:23:40,359 Speaker 1: PCR testing. PCR testing is done as the defining feature 403 00:23:40,400 --> 00:23:44,080 Speaker 1: of do you have the infection or not yet. And 404 00:23:44,160 --> 00:23:46,760 Speaker 1: this was reported back in the summer before I even 405 00:23:47,080 --> 00:23:48,880 Speaker 1: brought it up. I brought this up at the task 406 00:23:48,920 --> 00:23:51,440 Speaker 1: Force and showed the data. It was never discussed or 407 00:23:51,480 --> 00:23:56,879 Speaker 1: even acknowledged on the task force that cent of positive 408 00:23:56,960 --> 00:24:03,439 Speaker 1: PCR tests diagnosing sar is infection the way it was 409 00:24:03,520 --> 00:24:08,360 Speaker 1: done are not live virus. That means they are and 410 00:24:08,960 --> 00:24:12,199 Speaker 1: they have just dead pieces of virus around and so 411 00:24:12,320 --> 00:24:16,760 Speaker 1: they're not contagious. So if you're diagnosis positive PCR testing 412 00:24:16,800 --> 00:24:20,280 Speaker 1: and you're quarantined but you're not contagious, or you're held 413 00:24:20,280 --> 00:24:22,200 Speaker 1: out of your job but you're not contagious, or you're 414 00:24:22,240 --> 00:24:26,120 Speaker 1: generating contact tracing studies on all your family, or you're 415 00:24:26,280 --> 00:24:29,960 Speaker 1: you're causing fear all these things, you don't even have 416 00:24:30,280 --> 00:24:33,760 Speaker 1: contagious virus. You have at such a tiny amount or 417 00:24:33,840 --> 00:24:37,560 Speaker 1: a dead piece of virus, you're not contagious. So the 418 00:24:37,640 --> 00:24:41,000 Speaker 1: way PCR testing has been done in the United States 419 00:24:41,000 --> 00:24:44,520 Speaker 1: and UK, in Israel and all over the world has 420 00:24:44,560 --> 00:24:49,040 Speaker 1: been really an over diagnosis of COVID. We and the 421 00:24:49,119 --> 00:24:51,159 Speaker 1: the reason I ask you this is obviously not to 422 00:24:51,440 --> 00:24:54,280 Speaker 1: you know, diminish any loss of life, but we're shaping 423 00:24:54,480 --> 00:24:58,560 Speaker 1: major policy decisions that profoundly impact both just the shape 424 00:24:58,560 --> 00:25:00,720 Speaker 1: and role of the federal government in our lives, the 425 00:25:00,760 --> 00:25:04,040 Speaker 1: loss of liberties, people being able to operate their business 426 00:25:04,200 --> 00:25:06,840 Speaker 1: or not, you know, kids going to school. Right, So 427 00:25:06,920 --> 00:25:10,960 Speaker 1: we're making these massive policy decisions. So the data is important. 428 00:25:11,000 --> 00:25:14,280 Speaker 1: And as you just pointed out that even the case 429 00:25:14,480 --> 00:25:16,479 Speaker 1: you know, the case loads and the talk about cases 430 00:25:16,560 --> 00:25:19,720 Speaker 1: is flawed, how hospitalizations are likely flawed, how or death 431 00:25:19,800 --> 00:25:22,240 Speaker 1: count could be flawed. So all of this is important 432 00:25:22,240 --> 00:25:25,280 Speaker 1: because it shapes public policy decisions, and it shapes perceptions 433 00:25:25,640 --> 00:25:28,119 Speaker 1: as well of how much should we fear coronavirus or not? 434 00:25:28,240 --> 00:25:30,520 Speaker 1: And so I mean, it just seems like throughout the 435 00:25:30,680 --> 00:25:33,679 Speaker 1: entirety of all of this, the data has always been flawed. 436 00:25:33,720 --> 00:25:36,720 Speaker 1: And then not only that, but just this complete misrepresentation 437 00:25:36,800 --> 00:25:39,000 Speaker 1: of the facts and the science that's right, And this 438 00:25:39,240 --> 00:25:41,680 Speaker 1: varied by the way from country to country, and how 439 00:25:41,720 --> 00:25:49,040 Speaker 1: they categorized COVID hobservations, COVID deaths and positive sours to 440 00:25:49,280 --> 00:25:52,800 Speaker 1: testing results, and this was all circulated. I even saw 441 00:25:52,840 --> 00:25:55,120 Speaker 1: it when I was in the White House, a studied 442 00:25:55,200 --> 00:25:58,359 Speaker 1: by the Rand Institute. Yet no one at the task 443 00:25:58,440 --> 00:26:01,439 Speaker 1: words about me was talking about the flags and PCR testing. 444 00:26:01,680 --> 00:26:05,720 Speaker 1: No one either they didn't know, or they weren't interested, 445 00:26:05,960 --> 00:26:09,760 Speaker 1: or they didn't understand how to analyze scientific uh fact. 446 00:26:09,840 --> 00:26:12,239 Speaker 1: That's just uh. I mean it kind of tells you 447 00:26:12,280 --> 00:26:14,879 Speaker 1: how we ended up where we ended up. Quick commercial break. 448 00:26:15,119 --> 00:26:18,760 Speaker 1: More on our government's response to COVID With Dr Scott Atlas, 449 00:26:25,960 --> 00:26:28,280 Speaker 1: you talk about how the human cost of lockdowns was 450 00:26:28,359 --> 00:26:31,040 Speaker 1: never discussed by the Task Task Force and ignored by 451 00:26:31,160 --> 00:26:34,840 Speaker 1: Dr five and Dr Burkes. Why was that ignored? I 452 00:26:34,840 --> 00:26:37,119 Speaker 1: mean you would think that their job would be to 453 00:26:37,200 --> 00:26:41,520 Speaker 1: take the totality of the situation into account. That's right. 454 00:26:41,560 --> 00:26:44,240 Speaker 1: I mean there was a gross what I call a 455 00:26:44,280 --> 00:26:48,880 Speaker 1: gross failure of morality in the public health leadership when 456 00:26:48,880 --> 00:26:54,199 Speaker 1: you're leading public health interventions. What they did was they 457 00:26:54,240 --> 00:26:58,040 Speaker 1: considered one and only one illness stopping COVID nineteen cases 458 00:26:58,080 --> 00:27:01,360 Speaker 1: at all costs. To me that it's a gross failure. 459 00:27:01,440 --> 00:27:05,040 Speaker 1: That's not what appropriate public health policy is. Public health 460 00:27:05,080 --> 00:27:10,119 Speaker 1: policy must take into account the totality of impact of 461 00:27:10,280 --> 00:27:15,320 Speaker 1: health from the policy itself. You could stop all cases 462 00:27:15,359 --> 00:27:19,679 Speaker 1: of COVID if you killed everybody for instance. Okay, of 463 00:27:19,720 --> 00:27:22,760 Speaker 1: course this is ludicrous. I'm just pointing out something sort 464 00:27:22,800 --> 00:27:25,879 Speaker 1: of to the absurd extreme. The point of a public 465 00:27:25,880 --> 00:27:29,160 Speaker 1: health policy is to take an account also what you're 466 00:27:29,200 --> 00:27:32,800 Speaker 1: doing to people, and we see from the data that 467 00:27:32,920 --> 00:27:36,639 Speaker 1: they You know, there's a study from UH in June, 468 00:27:36,760 --> 00:27:40,680 Speaker 1: from Rand Institute and USC that shows that the lockdowns 469 00:27:40,720 --> 00:27:44,680 Speaker 1: didn't just fail. The lockdowns increased the dusk. The earlier 470 00:27:44,720 --> 00:27:47,920 Speaker 1: the lockdowns were put on, the more deaths occurred. When 471 00:27:47,960 --> 00:27:51,960 Speaker 1: deaths we're falling and lockdowns were implemented, that's increased. And 472 00:27:52,000 --> 00:27:55,000 Speaker 1: this is analysis of forty three countries and in all 473 00:27:55,040 --> 00:27:57,640 Speaker 1: of the states in the United States. I think it's 474 00:27:57,680 --> 00:28:02,280 Speaker 1: a highly important paper to read from. And so yes, 475 00:28:02,359 --> 00:28:05,160 Speaker 1: it was a gross failure. In fact, this is why 476 00:28:05,200 --> 00:28:09,639 Speaker 1: you can't. We had the wrong people advising UH policy. 477 00:28:09,880 --> 00:28:13,359 Speaker 1: Don't forget. Dr Burke's was the task force coordinator, so 478 00:28:13,440 --> 00:28:16,560 Speaker 1: that means she was the official voice and head of 479 00:28:16,600 --> 00:28:20,760 Speaker 1: the medical policy. She wrote all of the policy advice 480 00:28:20,800 --> 00:28:23,520 Speaker 1: to all the governors that were the official policy of 481 00:28:23,600 --> 00:28:27,160 Speaker 1: the White House, the task force, and she visited dozens 482 00:28:27,200 --> 00:28:30,320 Speaker 1: of states, and you know with those visits all these 483 00:28:30,320 --> 00:28:35,000 Speaker 1: public health officials. I visited one state, Florida. Unfortunately, the 484 00:28:35,040 --> 00:28:39,000 Speaker 1: lockdowns were implemented. They succeeded. The Burke Spouci lockdowns were 485 00:28:39,040 --> 00:28:43,640 Speaker 1: successfully transmitted to the governors who implemented them throughout most 486 00:28:43,680 --> 00:28:48,400 Speaker 1: of the country throughout the entire year. And they failed. 487 00:28:48,440 --> 00:28:51,280 Speaker 1: They failed to stop the spread, they failed to protect 488 00:28:51,280 --> 00:28:54,040 Speaker 1: the elderly, and they destroyed families. And I've said this 489 00:28:54,120 --> 00:28:56,760 Speaker 1: many times, but it's so important because this is the 490 00:28:56,800 --> 00:29:00,520 Speaker 1: biggest failure of public health policy in modern history. Yeah, 491 00:29:00,600 --> 00:29:02,320 Speaker 1: I mean, I think it's the you know that the 492 00:29:02,320 --> 00:29:04,480 Speaker 1: biggest failure of the media. I mean, it's just I 493 00:29:04,600 --> 00:29:08,280 Speaker 1: don't think people really realized we've just had a complete 494 00:29:08,280 --> 00:29:12,600 Speaker 1: reshaping of American society throughout COVID, from the role of 495 00:29:12,600 --> 00:29:14,640 Speaker 1: the federal government and our lives to the role of 496 00:29:14,640 --> 00:29:17,560 Speaker 1: the media, place loss of loss and trust of institutions. 497 00:29:17,560 --> 00:29:21,200 Speaker 1: I mean, there's just been a complete and total societal breakdown, 498 00:29:21,600 --> 00:29:24,560 Speaker 1: and I don't think that's been fully you know, discovered yet. 499 00:29:24,600 --> 00:29:26,719 Speaker 1: You talk about too, like another big aspect of this 500 00:29:27,200 --> 00:29:30,240 Speaker 1: was just getting kids back to school, right, these these 501 00:29:30,280 --> 00:29:32,640 Speaker 1: low risk kids, of allowing them to live their lives. 502 00:29:33,080 --> 00:29:35,160 Speaker 1: And you talk about like, even in the presence of 503 00:29:35,240 --> 00:29:40,080 Speaker 1: actual data that people like Fauci, Berkes and Redfield were 504 00:29:40,120 --> 00:29:43,360 Speaker 1: still resistant to getting these kids back in school. Talk 505 00:29:43,400 --> 00:29:46,600 Speaker 1: about that, sure, well, I mean I distinctly remember, of course, 506 00:29:46,680 --> 00:29:50,320 Speaker 1: one of the UH, one of the earliest Task force 507 00:29:50,560 --> 00:29:52,880 Speaker 1: meetings that I went to, which was I think that 508 00:29:53,400 --> 00:29:58,240 Speaker 1: mid mid August, and the Vice President, who was administratively 509 00:29:58,360 --> 00:30:01,520 Speaker 1: running the Task Force UH, had wanted me to talk 510 00:30:01,560 --> 00:30:05,000 Speaker 1: about the risk to children these are these schools opening. 511 00:30:05,680 --> 00:30:07,640 Speaker 1: And I went through the data. In fact, I had 512 00:30:07,680 --> 00:30:11,080 Speaker 1: brought with me more than a dozen papers in my briefcase, 513 00:30:11,120 --> 00:30:12,640 Speaker 1: and I pulled them out and I went through. I 514 00:30:12,680 --> 00:30:15,320 Speaker 1: cited the data from all over the world on the 515 00:30:15,400 --> 00:30:18,920 Speaker 1: extremely low risk to healthy children, the extremely low risk 516 00:30:19,000 --> 00:30:24,400 Speaker 1: of transmission from children, and the documented lack of high 517 00:30:24,480 --> 00:30:27,680 Speaker 1: risk to teachers in schools from all over the world. 518 00:30:27,920 --> 00:30:29,600 Speaker 1: And I went through a little, you know, let's just 519 00:30:29,640 --> 00:30:32,600 Speaker 1: say five to ten minutes on this, and there was 520 00:30:32,720 --> 00:30:36,600 Speaker 1: silence from Dr Burke's, silence from Dr Fauci. There was 521 00:30:36,640 --> 00:30:39,000 Speaker 1: no refutation of what I said. They didn't cite a 522 00:30:39,080 --> 00:30:43,120 Speaker 1: single study. And the Vice President turned to Dr Redfield 523 00:30:43,120 --> 00:30:47,160 Speaker 1: and said, well, what do you think, Bob? And Redfield 524 00:30:47,600 --> 00:30:50,320 Speaker 1: leaned back, thought for a second. He said, let's just 525 00:30:50,360 --> 00:30:52,760 Speaker 1: say the jury is still out. That was the end 526 00:30:52,760 --> 00:30:55,920 Speaker 1: of the discussion. I mean, you know, this is no 527 00:30:56,160 --> 00:31:01,160 Speaker 1: scientific uh knowledge about the data, nothing to refute what 528 00:31:01,280 --> 00:31:05,160 Speaker 1: I said. And then uh, you know that this sort 529 00:31:05,160 --> 00:31:08,360 Speaker 1: of was very confusing to the public because and harmful 530 00:31:08,560 --> 00:31:11,680 Speaker 1: the advice. As we know, the schools in the United 531 00:31:11,680 --> 00:31:14,200 Speaker 1: States were closed in the fall term. All the pure 532 00:31:14,280 --> 00:31:19,280 Speaker 1: nations in Western Europe opened the schools. We were the outlier. 533 00:31:19,400 --> 00:31:26,480 Speaker 1: The American educational system was a complete really immoral abuse 534 00:31:26,560 --> 00:31:30,440 Speaker 1: of children by shutting down schools, and they had impact. 535 00:31:30,480 --> 00:31:34,360 Speaker 1: The isolation was extraordinarily harmful. I mean, the way I 536 00:31:34,400 --> 00:31:38,040 Speaker 1: think of it, there's nothing more important that a society 537 00:31:38,120 --> 00:31:41,880 Speaker 1: does and educate its children. Yet we chose out of 538 00:31:41,920 --> 00:31:45,200 Speaker 1: fear and ignorance, and in my view it was too 539 00:31:45,440 --> 00:31:50,760 Speaker 1: with this uh miss misinappropriate idea of protecting adults by 540 00:31:50,840 --> 00:31:56,520 Speaker 1: using children, sacrificing children even though the children, healthy children, 541 00:31:56,520 --> 00:31:59,640 Speaker 1: did not have a significant riskrom COVID and that's been proven, 542 00:31:59,680 --> 00:32:03,400 Speaker 1: that was known then it's not new information, by the way, 543 00:32:03,800 --> 00:32:07,440 Speaker 1: So you know, this is really it's almost unspeakable what 544 00:32:07,600 --> 00:32:09,480 Speaker 1: was done. Well, you know they're doing that now with 545 00:32:09,640 --> 00:32:12,320 Speaker 1: vaccines as well. You know, even children who aren't at risk, 546 00:32:12,360 --> 00:32:15,680 Speaker 1: they're essentially you know, trying to to push kids to 547 00:32:15,680 --> 00:32:19,440 Speaker 1: to get them, which sort of defies logic. In my book, um, 548 00:32:19,480 --> 00:32:21,160 Speaker 1: so I thought you'd get a kick out of this. 549 00:32:21,280 --> 00:32:25,680 Speaker 1: So Political Political Playbook wrote about how Fauci was at 550 00:32:25,720 --> 00:32:29,920 Speaker 1: a Jonathan Carl book party, and it notes that he 551 00:32:30,000 --> 00:32:32,120 Speaker 1: kept taking the mask on and off for cockers who 552 00:32:32,120 --> 00:32:33,720 Speaker 1: are trying to take pictures of them with it out 553 00:32:33,720 --> 00:32:36,600 Speaker 1: his mask on, and then when asked about it, he said, 554 00:32:36,640 --> 00:32:38,880 Speaker 1: I just decided that if anyone came up to me 555 00:32:38,920 --> 00:32:40,600 Speaker 1: that I didn't know, I would put my mask on. 556 00:32:41,360 --> 00:32:45,160 Speaker 1: Doesn't that summarize the theater surrounding masks and also explain 557 00:32:45,200 --> 00:32:48,239 Speaker 1: Fauci a lot. Well, let's hits with this. Anybody who 558 00:32:48,400 --> 00:32:52,480 Speaker 1: thinks that that makes sense is I mean, you know, 559 00:32:53,120 --> 00:32:54,480 Speaker 1: I don't want to be rude, but I mean that 560 00:32:54,640 --> 00:32:57,080 Speaker 1: is just ludicrous. I mean, what what is the what 561 00:32:57,240 --> 00:33:00,640 Speaker 1: is the basis of putting on a map because you 562 00:33:00,720 --> 00:33:04,560 Speaker 1: don't know people? If it's just to uh have an image, 563 00:33:04,600 --> 00:33:07,320 Speaker 1: I think that it's very revealing. I mean, let's put 564 00:33:07,360 --> 00:33:11,280 Speaker 1: it this way. It was not uncommon. I heard the 565 00:33:11,320 --> 00:33:13,480 Speaker 1: people on the task force, the doctors on the task 566 00:33:13,560 --> 00:33:16,360 Speaker 1: Force refer to people in the media, their media friends 567 00:33:16,360 --> 00:33:19,960 Speaker 1: by the first name. Okay, this is you know, when 568 00:33:20,000 --> 00:33:23,080 Speaker 1: I was speaking about the data, the result of that 569 00:33:23,200 --> 00:33:27,000 Speaker 1: was not talking back to me. The result of that 570 00:33:27,560 --> 00:33:30,000 Speaker 1: was going outside to their friends and the media and 571 00:33:30,120 --> 00:33:33,720 Speaker 1: using ad hominum attack and distorting my words. So this 572 00:33:33,840 --> 00:33:37,600 Speaker 1: was sort of all about the image the power. I 573 00:33:37,600 --> 00:33:40,320 Speaker 1: don't know what was going on. I hate to ascribe motive, 574 00:33:41,320 --> 00:33:44,000 Speaker 1: but there was a gross failure there, and I think 575 00:33:44,080 --> 00:33:47,400 Speaker 1: this is really uh, we can never let this happen again. 576 00:33:47,480 --> 00:33:52,560 Speaker 1: We cannot have people who aren't critical thinkers, uh, you know, 577 00:33:52,680 --> 00:33:55,120 Speaker 1: in charge of of a crisis. I mean that this 578 00:33:55,240 --> 00:33:58,800 Speaker 1: should goes without saying, but I mean, this was just 579 00:33:59,000 --> 00:34:03,040 Speaker 1: a shocking failure. And and you know, they got what 580 00:34:03,080 --> 00:34:05,760 Speaker 1: they wanted, which were the lockdowns, and the lockdowns failed. 581 00:34:05,800 --> 00:34:08,279 Speaker 1: So now we're seeing this or welly in attempt to 582 00:34:08,320 --> 00:34:12,319 Speaker 1: rewrite history, blaming people like me who are opposed to 583 00:34:12,400 --> 00:34:15,719 Speaker 1: what was implemented for the failure of what was implemented. 584 00:34:16,000 --> 00:34:19,440 Speaker 1: You know, this is just another kind of thing that 585 00:34:19,480 --> 00:34:22,279 Speaker 1: shows I mean, it's important for people to understand who 586 00:34:22,320 --> 00:34:24,359 Speaker 1: are listening. I'm sorry I'm going on, but when you 587 00:34:24,400 --> 00:34:29,319 Speaker 1: see people who are issuing erratic statements, who are obviously 588 00:34:29,480 --> 00:34:31,960 Speaker 1: just saying, oh, it's the science, but they don't know 589 00:34:32,040 --> 00:34:34,720 Speaker 1: the science, or they're doing things that are contrary to science. 590 00:34:35,160 --> 00:34:37,959 Speaker 1: This is how you decide who to trust. You must 591 00:34:38,000 --> 00:34:41,720 Speaker 1: be a critical thinker. Now, the burden is on individuals, okay, 592 00:34:41,800 --> 00:34:45,000 Speaker 1: all of us to become more involved in our own 593 00:34:45,440 --> 00:34:49,680 Speaker 1: critical thinking, in our own beliefs, in short of our 594 00:34:49,719 --> 00:34:53,840 Speaker 1: own filter here to decide what is credible what isn't. 595 00:34:54,200 --> 00:34:57,120 Speaker 1: It's more of an effort but to trust. The blind 596 00:34:57,200 --> 00:35:01,080 Speaker 1: trust in people who are deemed experts should be gone. 597 00:35:01,480 --> 00:35:04,959 Speaker 1: That should be gone. We've learned that now. Uh, it's 598 00:35:04,960 --> 00:35:06,719 Speaker 1: not you know, I don't know if I said this, 599 00:35:06,800 --> 00:35:10,720 Speaker 1: but you know, we all need to be critical thinkers 600 00:35:10,760 --> 00:35:13,960 Speaker 1: here to make the best decisions for ourselves in our family. Well, 601 00:35:14,000 --> 00:35:16,799 Speaker 1: and our mask policy has always been done because I mean, 602 00:35:16,840 --> 00:35:19,279 Speaker 1: who thinks it's a good idea to wear one when 603 00:35:19,320 --> 00:35:21,040 Speaker 1: you enter a restaurant and then you take it off 604 00:35:21,040 --> 00:35:23,000 Speaker 1: when you're seated next to people, or to wear it 605 00:35:23,040 --> 00:35:25,399 Speaker 1: on a flight as we currently do, and then take 606 00:35:25,400 --> 00:35:27,000 Speaker 1: it off when you're next to people. I mean, none 607 00:35:27,000 --> 00:35:29,920 Speaker 1: of it has ever made sense, yet they continues to 608 00:35:29,960 --> 00:35:32,200 Speaker 1: be pushed. I mean, what is the data and the 609 00:35:32,200 --> 00:35:35,719 Speaker 1: science say about masks, do they work? Okay? So you 610 00:35:35,719 --> 00:35:40,319 Speaker 1: know in terms of widespread population masks, uh, there there 611 00:35:40,360 --> 00:35:42,600 Speaker 1: are several good studies out there, and people should look 612 00:35:42,600 --> 00:35:46,200 Speaker 1: at the good studies. The best study, the randomized controlled 613 00:35:46,239 --> 00:35:50,560 Speaker 1: trial of Denmark shows uh. They took more than six 614 00:35:50,600 --> 00:35:54,240 Speaker 1: thousand people. It took people with masks and people without masks. 615 00:35:54,239 --> 00:35:59,040 Speaker 1: They tested them by blood test for not just sours too, 616 00:35:59,120 --> 00:36:03,120 Speaker 1: but other leven other viruses, and they found that people 617 00:36:03,160 --> 00:36:08,960 Speaker 1: that wear masks do not have a significantly less incidents 618 00:36:09,000 --> 00:36:12,560 Speaker 1: of stars to infection. Neither do they have a less 619 00:36:12,760 --> 00:36:15,719 Speaker 1: lesser incidence of any of the other eleven viruses. By 620 00:36:15,760 --> 00:36:18,200 Speaker 1: the way, and this corroborates what was known in the 621 00:36:18,320 --> 00:36:21,840 Speaker 1: randomized trials about influenza that was published by the CDC 622 00:36:22,000 --> 00:36:27,760 Speaker 1: in May that mass general population masks do not prevent 623 00:36:28,040 --> 00:36:32,600 Speaker 1: the spread of infection or the becoming infect that the 624 00:36:32,640 --> 00:36:36,200 Speaker 1: recipient side of it in influenza. Why is that relevant 625 00:36:36,239 --> 00:36:40,400 Speaker 1: because influenza is the same size as roughly as the 626 00:36:40,400 --> 00:36:43,280 Speaker 1: stars To virus, which is smaller than the poor size 627 00:36:44,040 --> 00:36:46,640 Speaker 1: in the mask. Smaller the virus is smaller in the 628 00:36:46,680 --> 00:36:49,399 Speaker 1: whole in the mask. Secondly, we look at the other 629 00:36:49,480 --> 00:36:52,759 Speaker 1: studies look at that Denmark. The next study to look 630 00:36:52,800 --> 00:36:56,040 Speaker 1: at is the University of Louisville did a very detailed 631 00:36:56,040 --> 00:37:01,520 Speaker 1: analysis of all the states and their conclusions that mask 632 00:37:01,640 --> 00:37:06,279 Speaker 1: wearing mask mandates did not stop the spread of infection 633 00:37:07,120 --> 00:37:10,759 Speaker 1: and mask wearing did not stop the spread of infection 634 00:37:11,040 --> 00:37:14,360 Speaker 1: in states. And then you look at the next UH 635 00:37:14,800 --> 00:37:17,280 Speaker 1: paper that came out, which is the study that received 636 00:37:17,280 --> 00:37:20,960 Speaker 1: a lot of attention on Bangladesh villages. And then you know, 637 00:37:21,000 --> 00:37:22,719 Speaker 1: people were, you know, this is after a year and 638 00:37:22,760 --> 00:37:26,480 Speaker 1: a half, they're still desperately trying to find something that 639 00:37:26,600 --> 00:37:30,239 Speaker 1: validates masked widespread mask using. And they look at this 640 00:37:30,320 --> 00:37:34,520 Speaker 1: Bangladesh study, which was a study where villagers were instruct 641 00:37:34,600 --> 00:37:38,279 Speaker 1: some villages, some villages were instructed to wear masks, other 642 00:37:38,360 --> 00:37:41,360 Speaker 1: villages were not instructed to wear masks. And what the 643 00:37:41,400 --> 00:37:46,040 Speaker 1: authors reported was an eleven percent decrease in symptomatic COVID 644 00:37:47,200 --> 00:37:50,080 Speaker 1: in villages that were instructed to wear masks, not in 645 00:37:50,120 --> 00:37:53,080 Speaker 1: the mass wearers necessarily. Just so they said there was 646 00:37:53,120 --> 00:37:56,680 Speaker 1: a small decrease in symptomatic COVID. And when you look 647 00:37:56,719 --> 00:38:00,760 Speaker 1: at the data and the studies, not only was that small, 648 00:38:01,560 --> 00:38:04,200 Speaker 1: but the only people they had to reduce incidence of 649 00:38:04,239 --> 00:38:07,840 Speaker 1: symptomatic COVID were people in a single age bracket fifty 650 00:38:07,960 --> 00:38:11,640 Speaker 1: and above. People forty to fifty didn't have a decrease, 651 00:38:11,719 --> 00:38:14,840 Speaker 1: People thirty to forty didn't have a decrease in symptomatic COVID. 652 00:38:15,040 --> 00:38:16,920 Speaker 1: So you have to wonder did the people who were older, 653 00:38:16,920 --> 00:38:19,239 Speaker 1: were they more cautious, did they avoid groups that they 654 00:38:19,239 --> 00:38:21,800 Speaker 1: do other things that might have reduced it. The second 655 00:38:21,840 --> 00:38:24,120 Speaker 1: part of this that was the surgical mass site. The 656 00:38:24,160 --> 00:38:27,440 Speaker 1: regular mass that people were by the way, had zero 657 00:38:27,520 --> 00:38:30,960 Speaker 1: significance in terms of its reduction. So the study was 658 00:38:31,040 --> 00:38:35,160 Speaker 1: lauded in the lay media by these mask uh mask 659 00:38:35,480 --> 00:38:38,880 Speaker 1: advocates as proving mass work, but the in fact it 660 00:38:38,920 --> 00:38:41,920 Speaker 1: was supportive that mass do not have a significant impact 661 00:38:42,400 --> 00:38:45,560 Speaker 1: on limiting the spread of COVID and so enough in 662 00:38:45,560 --> 00:38:48,439 Speaker 1: the infection. So I mean, you know, you know, there's 663 00:38:48,480 --> 00:38:51,479 Speaker 1: this new study that came out recently and basically that's 664 00:38:51,480 --> 00:38:53,920 Speaker 1: just a meta analysis of a bunch of studies in 665 00:38:54,000 --> 00:38:57,600 Speaker 1: which they in this article admitted that the studies themselves 666 00:38:57,880 --> 00:39:01,280 Speaker 1: were poor research. Yet some how this is now the title. 667 00:39:01,480 --> 00:39:06,360 Speaker 1: So people are desperate, Okay, masks, I'm not for forbidding, masks, 668 00:39:06,360 --> 00:39:09,680 Speaker 1: just like I'm not for forbidding wearing a copper bracelet 669 00:39:09,719 --> 00:39:11,560 Speaker 1: for arthritis. If you want to wear a mask, if 670 00:39:11,560 --> 00:39:14,279 Speaker 1: you want to wear a six masks, that's fine, but 671 00:39:14,520 --> 00:39:18,799 Speaker 1: it's very dangerous to say what Dr Redfield said under Oath. 672 00:39:19,440 --> 00:39:21,520 Speaker 1: He said two things, and this is in my book 673 00:39:22,120 --> 00:39:26,319 Speaker 1: Under Oath he said, where if everybody wore a mask, 674 00:39:26,400 --> 00:39:30,080 Speaker 1: we'd get rid of the pandemic in six to eight weeks. Okay, 675 00:39:30,120 --> 00:39:34,080 Speaker 1: that's just completely nonsense, and it's been proven all over 676 00:39:34,120 --> 00:39:41,200 Speaker 1: the world, including societies where people are wearing masks. Secondly, 677 00:39:41,680 --> 00:39:44,680 Speaker 1: he said he held up a mask and infamously said 678 00:39:45,360 --> 00:39:48,560 Speaker 1: this mask is more protective than a vaccine. Now, why 679 00:39:48,640 --> 00:39:51,160 Speaker 1: is that a problem? First of all, it's it's completely 680 00:39:51,239 --> 00:39:56,560 Speaker 1: irrational and and bizarre. Second of all, it's dangerous. If 681 00:39:56,640 --> 00:40:00,440 Speaker 1: you think then an old person who's high risk is 682 00:40:00,480 --> 00:40:05,120 Speaker 1: more protected by wearing a mask, You're endangering them if 683 00:40:05,160 --> 00:40:08,080 Speaker 1: they think that, and so, uh, you know, this is 684 00:40:08,200 --> 00:40:11,399 Speaker 1: very dangerous. The misstatements coming out of some of our 685 00:40:11,480 --> 00:40:17,120 Speaker 1: most trusted public health leaders are shocking. We saw it publicly, 686 00:40:17,520 --> 00:40:20,080 Speaker 1: I saw it inside the task Force, as I note 687 00:40:20,480 --> 00:40:24,880 Speaker 1: multiple times multiple conversations in the book. And I think again, 688 00:40:24,920 --> 00:40:29,279 Speaker 1: we've destroyed the trust that is necessary for people when 689 00:40:29,280 --> 00:40:31,640 Speaker 1: a crisis arises, and so we have a long way 690 00:40:31,719 --> 00:40:34,040 Speaker 1: to dig out from that hole that was created by 691 00:40:34,160 --> 00:40:38,359 Speaker 1: these people in public health leadership. Quick commercial break more 692 00:40:38,400 --> 00:40:50,920 Speaker 1: with dr Atlas. Are we even getting the truth about 693 00:40:50,920 --> 00:40:53,719 Speaker 1: things like even like vaccines for instance, like the FDA's 694 00:40:53,760 --> 00:40:57,000 Speaker 1: requested the courts give them until two thousand seventy six 695 00:40:57,120 --> 00:40:59,920 Speaker 1: to review and fully released documents pertaining to the approval 696 00:41:00,040 --> 00:41:04,080 Speaker 1: the Fiser vaccine. The FDA recently released information about the 697 00:41:04,080 --> 00:41:07,840 Speaker 1: Fiser vaccine trials and we saw that all cause mortality 698 00:41:07,960 --> 00:41:10,759 Speaker 1: was actually higher and the people who are vaccinated than 699 00:41:10,840 --> 00:41:13,680 Speaker 1: received the police ebo. So, I mean, how do we 700 00:41:13,800 --> 00:41:17,719 Speaker 1: really know how effective these vaccines truly are? Yeah, well 701 00:41:17,760 --> 00:41:20,960 Speaker 1: that's a good question. So I would answer it, uh 702 00:41:21,160 --> 00:41:24,959 Speaker 1: a couple of ways. Uh. First, I I never thought 703 00:41:25,000 --> 00:41:27,000 Speaker 1: I would say this, but I actually look at the 704 00:41:27,040 --> 00:41:31,400 Speaker 1: other countries data because I actually, uh, you know, I 705 00:41:31,480 --> 00:41:35,280 Speaker 1: feel like I'm getting more I feel more for trust 706 00:41:35,360 --> 00:41:37,000 Speaker 1: in some of these other countries. Dare So I look 707 00:41:37,040 --> 00:41:39,920 Speaker 1: at the data from a country Sweden just published some 708 00:41:40,040 --> 00:41:44,040 Speaker 1: data just this month, within the past two weeks, and uh, 709 00:41:44,160 --> 00:41:48,480 Speaker 1: you know, Qatar of the population I think is vaccinated 710 00:41:48,719 --> 00:41:51,680 Speaker 1: has data. The UK has data. And when we look 711 00:41:51,719 --> 00:41:54,320 Speaker 1: at why do I trust the data more wealth? Some 712 00:41:54,520 --> 00:41:57,759 Speaker 1: of it I trust because they're rational and what they say. 713 00:41:57,800 --> 00:42:01,520 Speaker 1: I'll give you an example. The official organization, the Consulting 714 00:42:01,560 --> 00:42:06,840 Speaker 1: Group to the UK Government on Vaccinating people, issued a 715 00:42:06,880 --> 00:42:11,160 Speaker 1: statement in September saying because of the risk benefit calculation 716 00:42:11,200 --> 00:42:15,120 Speaker 1: of the disease versus the vaccine, they do not recommend 717 00:42:15,160 --> 00:42:20,120 Speaker 1: at this time vaccinating people under sixteen. Okay, we we're 718 00:42:20,160 --> 00:42:23,520 Speaker 1: busy in a country mandating vaccines for children. We're gonna, 719 00:42:23,560 --> 00:42:26,160 Speaker 1: We're we're about to be. We're mandating them all over 720 00:42:26,200 --> 00:42:28,640 Speaker 1: the university campasis for people who are eighteen years old, 721 00:42:29,200 --> 00:42:32,000 Speaker 1: and we're gonna. I wouldn't be shocked if children in 722 00:42:32,520 --> 00:42:35,200 Speaker 1: most of the United States will be will be mandatory 723 00:42:35,239 --> 00:42:38,560 Speaker 1: to get this vaccine, even though other countries are saying, 724 00:42:38,920 --> 00:42:40,839 Speaker 1: you know, it looks like it doesn't really add up 725 00:42:40,880 --> 00:42:43,480 Speaker 1: and there's some danger to the vaccine. So I mean 726 00:42:43,680 --> 00:42:46,040 Speaker 1: I start to look at other countries. I think people 727 00:42:46,080 --> 00:42:49,279 Speaker 1: need to be familiarizing them with that, with themselves with 728 00:42:49,360 --> 00:42:53,360 Speaker 1: that data. And you know, secondly, I think we're Another 729 00:42:53,400 --> 00:42:56,440 Speaker 1: point to make is that the people in our government, 730 00:42:57,320 --> 00:43:01,919 Speaker 1: including our CDC, which is a government agency, have done 731 00:43:01,960 --> 00:43:05,160 Speaker 1: more harm to the confidence in the vaccine than anyone 732 00:43:05,280 --> 00:43:07,920 Speaker 1: could have done who was against the vaccine. They have 733 00:43:07,960 --> 00:43:12,680 Speaker 1: been still doubt in people by their irrational mandates. They're 734 00:43:12,719 --> 00:43:16,759 Speaker 1: mandates that are contrary to science, and they're overt ignoring 735 00:43:17,840 --> 00:43:21,360 Speaker 1: of natural immunity. We are the I was, I was 736 00:43:21,400 --> 00:43:24,200 Speaker 1: in Europe recently, and you know these other countries they 737 00:43:24,280 --> 00:43:28,680 Speaker 1: count if you've had the UH COVID infection COVID and 738 00:43:28,719 --> 00:43:33,359 Speaker 1: you've recovered, that counts our country. We don't even mention it. 739 00:43:33,640 --> 00:43:36,279 Speaker 1: You realize the c d C even says that more 740 00:43:36,320 --> 00:43:41,919 Speaker 1: than for people who are young have had the infection. Okay, 741 00:43:41,960 --> 00:43:44,719 Speaker 1: they have immunity, but we're busy trying to get them 742 00:43:44,719 --> 00:43:49,040 Speaker 1: to take vaccines and boosters without even caring about these 743 00:43:49,280 --> 00:43:53,879 Speaker 1: the natural immunity. Similarly, for people in hospitals, I mean, 744 00:43:53,880 --> 00:43:58,000 Speaker 1: I don't understand why people would be fired UH from 745 00:43:58,480 --> 00:44:02,600 Speaker 1: any job if they you've had documented recovery from COVID, 746 00:44:02,719 --> 00:44:06,560 Speaker 1: they have protection. And the protection according to the data 747 00:44:07,440 --> 00:44:10,680 Speaker 1: in Israel, which is the best study of this, the 748 00:44:10,760 --> 00:44:16,040 Speaker 1: protection from infection is better than the protection from non 749 00:44:16,200 --> 00:44:20,719 Speaker 1: infected vaccinated people. But see, I just feel like, you know, 750 00:44:21,040 --> 00:44:24,279 Speaker 1: even beyond like teenagers, like someone like myself thirty six, 751 00:44:24,320 --> 00:44:27,120 Speaker 1: still underlying conditions. It doesn't make sense, my opinion, for 752 00:44:27,160 --> 00:44:29,240 Speaker 1: someone like me to get it, particularly when there hasn't 753 00:44:29,280 --> 00:44:33,279 Speaker 1: been transparency surrounding the effectiveness of vaccines. There seems to 754 00:44:33,280 --> 00:44:36,320 Speaker 1: be an expiration date in terms of loss of effectiveness 755 00:44:36,360 --> 00:44:38,600 Speaker 1: after a short period of time. And personally, I don't 756 00:44:38,600 --> 00:44:41,839 Speaker 1: think vaccine injury or risk has been fully explored. I mean, 757 00:44:41,840 --> 00:44:43,640 Speaker 1: how can you do that in less than a year. 758 00:44:44,040 --> 00:44:46,760 Speaker 1: So I mean then you're pointing out, Yeah, you're pointing 759 00:44:46,760 --> 00:44:49,319 Speaker 1: out something very important. If I can interrupt and say 760 00:44:49,400 --> 00:44:54,080 Speaker 1: that the lack of transparency on the on the vaccine 761 00:44:54,080 --> 00:44:56,239 Speaker 1: and the in the side effects or the potential side 762 00:44:56,239 --> 00:45:00,960 Speaker 1: effects has been really a huge prob and a cause 763 00:45:01,760 --> 00:45:05,279 Speaker 1: of the vaccine tsidency, and rightfully so, you know, when 764 00:45:05,280 --> 00:45:08,120 Speaker 1: you look at the safety data, First of all, this 765 00:45:08,239 --> 00:45:10,520 Speaker 1: is a vaccine that's experimental. We've never had an m 766 00:45:10,640 --> 00:45:14,560 Speaker 1: RNA vaccine before. Second of all, we don't have long 767 00:45:14,680 --> 00:45:18,239 Speaker 1: term safety data, and generally vaccines have five to ten 768 00:45:18,320 --> 00:45:21,360 Speaker 1: years of safety data before they're fully approved five to 769 00:45:21,400 --> 00:45:25,040 Speaker 1: ten years. This vaccine was approved on an emergency use 770 00:45:25,080 --> 00:45:29,120 Speaker 1: of authorization on you know, let's just say forty thou subjects. 771 00:45:29,719 --> 00:45:34,239 Speaker 1: And once that approval came, they stopped with the placebo. 772 00:45:34,320 --> 00:45:36,879 Speaker 1: They offered the vaccine to everybody in the placebo arm 773 00:45:37,320 --> 00:45:41,840 Speaker 1: and something like ninety there's no significantly sized placebo group. 774 00:45:41,960 --> 00:45:45,520 Speaker 1: We don't have even long term affricacy, a real clinical 775 00:45:45,560 --> 00:45:48,200 Speaker 1: trial on the vaccine. And then you look at the 776 00:45:48,560 --> 00:45:53,040 Speaker 1: safety data on something like boosters. There's no safety data 777 00:45:53,080 --> 00:45:56,120 Speaker 1: on the booster to speak of the booster safety data 778 00:45:56,160 --> 00:45:59,280 Speaker 1: from the from Israel because they were early in giving 779 00:45:59,280 --> 00:46:03,760 Speaker 1: boosters is on like you know, several single digit thousand 780 00:46:03,920 --> 00:46:07,080 Speaker 1: or several thousand people, which is very small, and it's 781 00:46:07,120 --> 00:46:10,799 Speaker 1: for thirty days. I mean, that's not safety data. So 782 00:46:10,960 --> 00:46:14,680 Speaker 1: we're busy here making these decisions and mandates on drugs 783 00:46:14,680 --> 00:46:18,600 Speaker 1: and they're they're simply not flushed out in terms of safety. 784 00:46:18,600 --> 00:46:23,759 Speaker 1: There's not been transparency and a good study of the complications. 785 00:46:24,200 --> 00:46:27,560 Speaker 1: And so this is really what I say is, you know, 786 00:46:27,800 --> 00:46:31,839 Speaker 1: the vaccines work to protect death. That's what the data 787 00:46:31,920 --> 00:46:35,200 Speaker 1: shows so far. Uh. And the Sweden data is the 788 00:46:35,239 --> 00:46:39,000 Speaker 1: longest assessment, which is something like nine plus months. It 789 00:46:39,040 --> 00:46:42,400 Speaker 1: shows that Okay, there's some laming of protection for people 790 00:46:43,040 --> 00:46:46,279 Speaker 1: even against death who are over eighty, but for the 791 00:46:46,800 --> 00:46:49,799 Speaker 1: vast majority of people, the protection against death is very high. 792 00:46:49,800 --> 00:46:51,560 Speaker 1: So if your high risk, meaning you have a chance 793 00:46:51,600 --> 00:46:54,000 Speaker 1: of significant chance of dying, it makes sense to take 794 00:46:54,040 --> 00:46:57,680 Speaker 1: the vaccine. But the vaccine does not last in its 795 00:46:57,680 --> 00:47:01,480 Speaker 1: protection about infection in their first out of infection more 796 00:47:01,560 --> 00:47:04,879 Speaker 1: than three to six months. And this is validated all 797 00:47:04,920 --> 00:47:08,200 Speaker 1: over the world. And so the vaccine, the way I 798 00:47:08,280 --> 00:47:13,120 Speaker 1: think of it, I think it's reasonable have significant protection 799 00:47:13,239 --> 00:47:16,840 Speaker 1: for people are high risk, and it's a personal protection. 800 00:47:17,000 --> 00:47:22,720 Speaker 1: But after several months, the vaccine does not significantly protect others, okay, 801 00:47:22,760 --> 00:47:26,360 Speaker 1: and so it's a personal health benefit and not a 802 00:47:26,440 --> 00:47:29,280 Speaker 1: public health benefit to speak of after a few months. 803 00:47:29,760 --> 00:47:32,239 Speaker 1: And therefore it makes sense that you should make the 804 00:47:32,360 --> 00:47:37,040 Speaker 1: decision personally based upon your own risk assessment, in conjunction 805 00:47:37,640 --> 00:47:40,640 Speaker 1: with your your medical your health care provider, your doctor. 806 00:47:40,719 --> 00:47:43,280 Speaker 1: But that's making an assumption that your doctor is rational 807 00:47:43,320 --> 00:47:45,600 Speaker 1: and a critical think o thinker, which I don't think 808 00:47:45,600 --> 00:47:48,280 Speaker 1: it's true for and for for a lot of people. 809 00:47:48,400 --> 00:47:52,040 Speaker 1: But uh, suffice it to say that it's an individual decision. 810 00:47:52,040 --> 00:47:56,080 Speaker 1: In my book about the risks and the benefits for you. 811 00:47:56,239 --> 00:47:59,160 Speaker 1: Not for people who are young and healthy, there's an 812 00:47:59,160 --> 00:48:03,200 Speaker 1: extremely low risk from COVID and it's in a near 813 00:48:03,280 --> 00:48:07,040 Speaker 1: zero risk from death for healthy young people. For people 814 00:48:07,040 --> 00:48:11,400 Speaker 1: with comor abiliefs that are young, there that are high risk. Okay, 815 00:48:11,440 --> 00:48:15,040 Speaker 1: that's a different equation. They might want to opt for 816 00:48:15,160 --> 00:48:18,520 Speaker 1: the vaccine or even for the booster, as are older 817 00:48:18,560 --> 00:48:21,640 Speaker 1: people with high risk. But for everybody else, you know, 818 00:48:22,040 --> 00:48:23,360 Speaker 1: you know, you have to you have to make the 819 00:48:23,400 --> 00:48:26,880 Speaker 1: calculation on your own. I think that's rational. It's supposed 820 00:48:26,920 --> 00:48:29,799 Speaker 1: to be a free society. Yet what we're saying is 821 00:48:29,960 --> 00:48:33,600 Speaker 1: countries like Austria are putting unvaccinated people under house arrest. 822 00:48:33,680 --> 00:48:36,279 Speaker 1: You can't dine in New York City if you're not vaccinated. 823 00:48:36,320 --> 00:48:39,280 Speaker 1: So we're saying, quite literally, human rights abuses being carried 824 00:48:39,320 --> 00:48:41,680 Speaker 1: out against people who have chosen not to get something. 825 00:48:41,719 --> 00:48:43,680 Speaker 1: As you mentioned that we don't have long term data 826 00:48:43,719 --> 00:48:45,880 Speaker 1: on that they might not need if they're young and healthy, 827 00:48:45,960 --> 00:48:49,720 Speaker 1: or potentially they have prior immunity. So again with you know, basically, 828 00:48:49,760 --> 00:48:52,200 Speaker 1: the theme of all this is nothing makes sense in 829 00:48:52,280 --> 00:48:54,520 Speaker 1: our decisions, and there's no rationale, and a lot of 830 00:48:54,560 --> 00:48:57,640 Speaker 1: the policy decisions that are being implemented. You know, one 831 00:48:57,640 --> 00:48:59,120 Speaker 1: thing that has been on my mind too that a 832 00:48:59,120 --> 00:49:01,200 Speaker 1: lot of people don't talk about as you look at 833 00:49:01,239 --> 00:49:04,920 Speaker 1: something like monoclonal antibodies, which is pretty much universally accepted 834 00:49:05,040 --> 00:49:10,520 Speaker 1: as a high a very good therapeutic that can cut hospitation, hospitalization, 835 00:49:10,520 --> 00:49:14,520 Speaker 1: and death by you know upwards to However, the vaccine 836 00:49:15,080 --> 00:49:17,840 Speaker 1: is what most gets discussed. We don't really talk about 837 00:49:17,880 --> 00:49:19,959 Speaker 1: these other therapeutics. So my question to you is, have 838 00:49:20,040 --> 00:49:23,560 Speaker 1: we have people died with our vaccine only approached by 839 00:49:23,640 --> 00:49:27,120 Speaker 1: not prioritizing therapeutics or are not really having this all 840 00:49:27,200 --> 00:49:29,480 Speaker 1: of the above approach where where we just seek to 841 00:49:30,080 --> 00:49:32,200 Speaker 1: you know, to provide you know, a bunch of things 842 00:49:32,280 --> 00:49:34,160 Speaker 1: to the public to try to save lives, as opposed 843 00:49:34,200 --> 00:49:38,920 Speaker 1: to this vaccine only approach. I'm sure that it's that 844 00:49:39,040 --> 00:49:41,200 Speaker 1: it's true. I don't have the data on that, but 845 00:49:41,320 --> 00:49:45,200 Speaker 1: we know that the monocole antibodies that were developed under 846 00:49:45,239 --> 00:49:49,400 Speaker 1: Explanitay fashion this is part of Operation Warp speed Um, 847 00:49:49,680 --> 00:49:53,200 Speaker 1: they were not uh, they were available, but they were 848 00:49:53,239 --> 00:49:57,040 Speaker 1: not being prescribed. You know, there's a lot of a 849 00:49:57,160 --> 00:49:59,399 Speaker 1: lot of the early data that I saw that there 850 00:49:59,480 --> 00:50:02,520 Speaker 1: was different guilty and getting them. Doctors weren't prescribing at 851 00:50:02,600 --> 00:50:06,640 Speaker 1: hospitals weren't administering it, and and that's very sad. I mean, 852 00:50:06,680 --> 00:50:10,480 Speaker 1: there's no question that the monoclon antibodies were significant breakthrough 853 00:50:10,520 --> 00:50:14,880 Speaker 1: in treatment, Like you said, seventy plus percent reduction and 854 00:50:15,000 --> 00:50:18,440 Speaker 1: hospitalizations for the highest risk people. By the way, the 855 00:50:18,560 --> 00:50:21,440 Speaker 1: data on the monocla anybodys was in people who have 856 00:50:21,760 --> 00:50:27,239 Speaker 1: the highest risk categorization older with obesity, seventy plus reduction 857 00:50:27,239 --> 00:50:31,239 Speaker 1: and hospitalization. So yes, this has been a problem. And 858 00:50:31,280 --> 00:50:32,960 Speaker 1: you know, we can look back in history. As I 859 00:50:33,040 --> 00:50:35,680 Speaker 1: mentioned in my book, it's it's fascinating to read the 860 00:50:35,760 --> 00:50:39,040 Speaker 1: history of HIV and AIDS where the focus of many 861 00:50:39,040 --> 00:50:41,560 Speaker 1: of these same people that are on the task force, 862 00:50:41,640 --> 00:50:46,680 Speaker 1: the medical people, they were also in the AIDS era 863 00:50:47,200 --> 00:50:52,080 Speaker 1: pushing almost single mindedly, almost single mindedly for a vaccine 864 00:50:52,280 --> 00:50:54,480 Speaker 1: and we still don't have a vaccine for for HIV 865 00:50:54,640 --> 00:50:57,880 Speaker 1: eight and so, uh, you have to wonder what happened. 866 00:50:57,880 --> 00:51:01,359 Speaker 1: I don't know why, but there's a lot of sort 867 00:51:01,400 --> 00:51:05,400 Speaker 1: of compromise of people. There are there are conflicts of 868 00:51:05,440 --> 00:51:10,200 Speaker 1: interest galore that we know about both in the private 869 00:51:10,239 --> 00:51:13,840 Speaker 1: sector ties to the people that are giving out public 870 00:51:13,880 --> 00:51:18,239 Speaker 1: advice as well as other things and organizations. I think 871 00:51:18,280 --> 00:51:21,319 Speaker 1: it's it's it's very dangerous, uh you know. I mean 872 00:51:21,600 --> 00:51:24,040 Speaker 1: there's a there's something else that we didn't touch on, 873 00:51:24,120 --> 00:51:27,960 Speaker 1: which is seeming conflict of interest. They might ask, why 874 00:51:28,040 --> 00:51:32,239 Speaker 1: would science become politicized? Okay, because this is a very 875 00:51:32,280 --> 00:51:35,640 Speaker 1: important problem right now. It may may be the most 876 00:51:35,680 --> 00:51:39,560 Speaker 1: problematic of all because without science, I don't know what 877 00:51:39,640 --> 00:51:42,360 Speaker 1: kind of society we have, as science isn't seeking truth. 878 00:51:42,800 --> 00:51:45,200 Speaker 1: And we we can explain some of this by looking 879 00:51:45,239 --> 00:51:50,440 Speaker 1: at the way science and academics fund their research. Science 880 00:51:50,520 --> 00:51:53,680 Speaker 1: is funded by NIH in the United States as well 881 00:51:53,680 --> 00:51:56,319 Speaker 1: as other agencies, but the most important funding is from 882 00:51:56,440 --> 00:52:00,320 Speaker 1: ni H, from people like Dr Fauci and the ni AGE. 883 00:52:00,600 --> 00:52:02,520 Speaker 1: And what do I mean by it's funded. I mean 884 00:52:02,600 --> 00:52:04,839 Speaker 1: that's the only way you get your research. It's also 885 00:52:04,920 --> 00:52:08,920 Speaker 1: the way that academic scientists get their promotions. It's also 886 00:52:09,000 --> 00:52:11,680 Speaker 1: the way that they get their papers published, because the 887 00:52:11,680 --> 00:52:14,480 Speaker 1: people that are reviewing papers and the journal editors are 888 00:52:14,520 --> 00:52:19,040 Speaker 1: the same people reviewing the applications for science research grants 889 00:52:19,040 --> 00:52:23,799 Speaker 1: from the ni H. So there's this that controls everyone's 890 00:52:23,920 --> 00:52:28,680 Speaker 1: career in science and the funding stream of science of 891 00:52:28,800 --> 00:52:33,080 Speaker 1: scientific research. This is very dangerous. This is this is 892 00:52:33,120 --> 00:52:36,239 Speaker 1: a sort of an explanation of why their people are 893 00:52:36,280 --> 00:52:42,160 Speaker 1: reluctant to come forward and criticize something about say people 894 00:52:42,200 --> 00:52:45,200 Speaker 1: in the NIH for instance, their whole careers depend on 895 00:52:45,280 --> 00:52:48,200 Speaker 1: not making waves or their their funding stream could be interrupted. 896 00:52:48,600 --> 00:52:50,600 Speaker 1: This is a hidden secret of what's going on in 897 00:52:50,680 --> 00:52:54,560 Speaker 1: the United States. There is a massive problem with seeking 898 00:52:54,560 --> 00:52:58,840 Speaker 1: out and being able to say, uh, the science, the 899 00:52:59,239 --> 00:53:02,439 Speaker 1: truth about the data, or even raise debate about what's 900 00:53:02,480 --> 00:53:05,680 Speaker 1: being said to very fundamental problem in our society. Well, 901 00:53:05,719 --> 00:53:07,600 Speaker 1: also might be why they don't want to talk about 902 00:53:07,640 --> 00:53:10,480 Speaker 1: the origins of the coronavirus as well. I think that's 903 00:53:10,520 --> 00:53:12,920 Speaker 1: exactly that's that's sort of one of the things I'm 904 00:53:12,920 --> 00:53:15,879 Speaker 1: alluding to it as I may have mentioned that letter 905 00:53:15,960 --> 00:53:20,920 Speaker 1: to Lancet in February from virologists who said that anyone 906 00:53:20,960 --> 00:53:24,720 Speaker 1: who says it's not of natural origin is a conspiracy theorist. 907 00:53:25,200 --> 00:53:28,360 Speaker 1: That artist that letter was completely false. That was a 908 00:53:28,400 --> 00:53:31,160 Speaker 1: lie that they said. Because what was a lie was 909 00:53:31,239 --> 00:53:35,600 Speaker 1: they said it was known in February without a doubt, 910 00:53:35,640 --> 00:53:39,799 Speaker 1: with certainty, that the virus was of natural origin. They 911 00:53:39,920 --> 00:53:41,879 Speaker 1: could not have been known because it's still not even 912 00:53:41,960 --> 00:53:45,160 Speaker 1: known today. And so they were lying. And the question 913 00:53:45,160 --> 00:53:47,560 Speaker 1: is why would they be prompted to even write a 914 00:53:47,640 --> 00:53:52,200 Speaker 1: letter like that. The letter was written to stop people 915 00:53:52,320 --> 00:53:58,440 Speaker 1: from claiming anything else to intimidate, to silence scientific debate. 916 00:53:58,800 --> 00:54:03,800 Speaker 1: There is no such is science without scientific debate. None. 917 00:54:04,520 --> 00:54:07,879 Speaker 1: And so that's a fundamental problem. And you know, one 918 00:54:07,920 --> 00:54:12,319 Speaker 1: possible explanation is their fundings in their own careers, their 919 00:54:12,320 --> 00:54:16,760 Speaker 1: own promotions in their universities were dependent on the people 920 00:54:16,920 --> 00:54:19,160 Speaker 1: in the n I H. At the highest level. What 921 00:54:19,360 --> 00:54:21,400 Speaker 1: was working with President Trump? Like, you know, working with 922 00:54:21,440 --> 00:54:25,000 Speaker 1: President President was always very gracious to me. He thanked 923 00:54:25,000 --> 00:54:30,480 Speaker 1: me always for you know, giving up my anonymity and 924 00:54:30,560 --> 00:54:34,600 Speaker 1: my job to help the country at at you know, 925 00:54:34,680 --> 00:54:37,360 Speaker 1: at personal risk. I mean, you know, I think everyone's 926 00:54:37,400 --> 00:54:42,800 Speaker 1: aware that half the country despises the president. Uh and 927 00:54:43,120 --> 00:54:45,680 Speaker 1: um that that had nothing to do with anything for me. 928 00:54:46,160 --> 00:54:49,120 Speaker 1: This is too important. He was also as I mentioned 929 00:54:49,120 --> 00:54:52,200 Speaker 1: the book, uh, you know, if he had he asked questions, 930 00:54:52,400 --> 00:54:55,840 Speaker 1: and he asked the right questions about the infection, about immunity, 931 00:54:55,880 --> 00:55:00,359 Speaker 1: about Sweden, about drug treatments, about uh, you know, why 932 00:55:00,440 --> 00:55:04,239 Speaker 1: uh certain people might be resistant to the infection and 933 00:55:04,239 --> 00:55:06,680 Speaker 1: and you know, he he wanted to know the information, 934 00:55:06,719 --> 00:55:10,040 Speaker 1: and I think that's critical. He was also receptive to 935 00:55:10,239 --> 00:55:15,760 Speaker 1: hearing experts tell him information. As I mentioned may have mentioned, 936 00:55:15,760 --> 00:55:18,040 Speaker 1: we had doctors from all over the country come in 937 00:55:18,400 --> 00:55:20,680 Speaker 1: and speak to the President and speak to the Vice president. 938 00:55:21,040 --> 00:55:24,160 Speaker 1: He wanted to hear. What started off as being a 939 00:55:24,200 --> 00:55:27,000 Speaker 1: five minute session ended up going for forty five minutes 940 00:55:27,040 --> 00:55:30,359 Speaker 1: because he kept asking questions after questions. The American people 941 00:55:30,360 --> 00:55:33,640 Speaker 1: should be relieved to know that the President wanted to 942 00:55:33,719 --> 00:55:37,320 Speaker 1: know information. I mean, you know, this is not something 943 00:55:37,360 --> 00:55:42,000 Speaker 1: that uh that should be making people anxious. Uh you know, yes, 944 00:55:42,160 --> 00:55:45,279 Speaker 1: people that didn't want him to know information inside the 945 00:55:45,280 --> 00:55:48,240 Speaker 1: task force that they were personally threatened because maybe they 946 00:55:48,280 --> 00:55:51,600 Speaker 1: maybe they felt they couldn't stand up to the academic 947 00:55:51,640 --> 00:55:55,239 Speaker 1: scholarship of the people that were brought in. But in 948 00:55:55,360 --> 00:55:59,600 Speaker 1: any event, you know, this was very important and I think, uh, 949 00:55:59,719 --> 00:56:01,600 Speaker 1: you know that that was very good. I also think 950 00:56:01,640 --> 00:56:06,360 Speaker 1: the President was as as everyone knows, using common sense 951 00:56:06,360 --> 00:56:09,279 Speaker 1: when he thought about policies. He he wanted schools to open. 952 00:56:09,320 --> 00:56:12,160 Speaker 1: He knew the lockdowns were a disaster. The problem was 953 00:56:12,160 --> 00:56:17,720 Speaker 1: the official voice of recommendation ended up being Dr Burke's 954 00:56:18,160 --> 00:56:21,719 Speaker 1: and Dr Fauci from the Task Force that was the 955 00:56:21,760 --> 00:56:25,880 Speaker 1: policy arm of the White House, and their recommendation was 956 00:56:25,960 --> 00:56:30,800 Speaker 1: contrary to the President. Their recommendation was business restrictions, curfews, 957 00:56:30,960 --> 00:56:35,640 Speaker 1: school closures, lockdowns, and that was heard by the governors 958 00:56:35,640 --> 00:56:38,839 Speaker 1: and implemented almost the entire country. When I always get 959 00:56:38,880 --> 00:56:41,880 Speaker 1: to how can you be against getting this vaccine but 960 00:56:42,040 --> 00:56:44,760 Speaker 1: for Operation Warp Speed, I always say, it's an entirely 961 00:56:44,800 --> 00:56:47,759 Speaker 1: different thing to get something to the market under emergency 962 00:56:47,760 --> 00:56:50,840 Speaker 1: Youth authorization for extremely high risk people under you know, 963 00:56:50,920 --> 00:56:52,759 Speaker 1: essentially right to try, right, like, if they're really high 964 00:56:52,840 --> 00:56:54,399 Speaker 1: risk they want to try this to try to save 965 00:56:54,400 --> 00:56:57,080 Speaker 1: their lives. It's an entirely different thing to coerce people 966 00:56:57,120 --> 00:56:59,320 Speaker 1: to get something that isn't really thoroughly studied and we 967 00:56:59,360 --> 00:57:01,880 Speaker 1: don't have full answers on But is there is there 968 00:57:01,920 --> 00:57:04,960 Speaker 1: anything I've missed in this conversation, anything in the book 969 00:57:04,960 --> 00:57:07,920 Speaker 1: that you really want to highlight, uh, in this conversation 970 00:57:08,000 --> 00:57:10,480 Speaker 1: that I haven't well, I think, you know, I I 971 00:57:11,320 --> 00:57:13,160 Speaker 1: might want to sum up by saying, you know that 972 00:57:13,239 --> 00:57:14,799 Speaker 1: why did I write the book? I mean, you know, 973 00:57:15,080 --> 00:57:18,400 Speaker 1: one people need to know who was in charge of 974 00:57:18,400 --> 00:57:20,360 Speaker 1: the task for us what they said and what the 975 00:57:20,400 --> 00:57:23,000 Speaker 1: results of that. What they said was, this is very 976 00:57:23,080 --> 00:57:26,160 Speaker 1: important because we can never left this kind of people 977 00:57:26,880 --> 00:57:31,440 Speaker 1: be in charge again. Secondly, people need to know the 978 00:57:31,520 --> 00:57:35,440 Speaker 1: unfiltered data on COVID, on on everything that we experienced, 979 00:57:35,680 --> 00:57:40,200 Speaker 1: without the filtering by the media, without the distortion, without 980 00:57:40,200 --> 00:57:42,440 Speaker 1: the political lens put on it. And I try to 981 00:57:42,440 --> 00:57:44,880 Speaker 1: do that in the book. And third I try to 982 00:57:44,920 --> 00:57:49,080 Speaker 1: make people aware of what has been exposed the media, 983 00:57:49,280 --> 00:57:53,720 Speaker 1: what we now know about the politicization of science, about unit, 984 00:57:53,920 --> 00:57:59,240 Speaker 1: the lack of honesty, and the intimidation to stop the 985 00:57:59,280 --> 00:58:02,920 Speaker 1: free exchange of ideas on university campuses. This stuff the 986 00:58:02,960 --> 00:58:07,400 Speaker 1: big issues here I need to be solved, or really 987 00:58:07,840 --> 00:58:11,520 Speaker 1: we're in danger of of not being able to solve 988 00:58:11,600 --> 00:58:14,720 Speaker 1: any future crisis. So I encourage people they want to 989 00:58:14,720 --> 00:58:18,000 Speaker 1: hear the unburned truth. And I think everybody knows, uh 990 00:58:18,120 --> 00:58:20,040 Speaker 1: what I'm saying is true. I mean, you know that 991 00:58:20,160 --> 00:58:22,800 Speaker 1: this is Anybody who knows me knows this. But I 992 00:58:22,800 --> 00:58:25,800 Speaker 1: can guarantee not only is why everything in this book true, 993 00:58:25,880 --> 00:58:27,840 Speaker 1: but I can guarantee you that the people who are 994 00:58:27,840 --> 00:58:31,280 Speaker 1: spoken of in this book will deny it's true, and 995 00:58:31,320 --> 00:58:33,240 Speaker 1: that will not come and should not come as a 996 00:58:33,320 --> 00:58:37,120 Speaker 1: surprise to people, because this book exposes people for what 997 00:58:37,200 --> 00:58:40,520 Speaker 1: they said and what they what they did without the 998 00:58:40,600 --> 00:58:43,680 Speaker 1: protection of their friends in the media. Well, in my book, 999 00:58:44,080 --> 00:58:46,439 Speaker 1: you are a hero. So is Dr John eating Ned 1000 00:58:46,600 --> 00:58:50,200 Speaker 1: s J. Battacharya and Martin Colder, who have all been 1001 00:58:50,240 --> 00:58:53,480 Speaker 1: extremely brave in the face of the Bob to tell 1002 00:58:53,520 --> 00:58:55,360 Speaker 1: the truth. And I really think that if we didn't 1003 00:58:55,360 --> 00:58:58,320 Speaker 1: have you guys out there as truth tellers, who knows 1004 00:58:58,400 --> 00:59:01,880 Speaker 1: where we would be as a country. So I I 1005 00:59:01,920 --> 00:59:05,600 Speaker 1: am so appreciative of you and respect you immensely. And 1006 00:59:05,600 --> 00:59:08,160 Speaker 1: I've already purchased the book. I said, just everyone does purchase. 1007 00:59:08,320 --> 00:59:11,320 Speaker 1: It's called A Plague upon Our House. It's out December seven, 1008 00:59:11,400 --> 00:59:14,400 Speaker 1: but you can pre order it. It's it's remarkable. Everyone 1009 00:59:14,440 --> 00:59:17,640 Speaker 1: should go and get it. Dr Atlas. I thank you 1010 00:59:17,720 --> 00:59:21,560 Speaker 1: so much. I truly appreciate it. It's an honor. Thank you, 1011 00:59:21,760 --> 00:59:24,000 Speaker 1: Thank you, Lisa, thank you for the kind words, and 1012 00:59:24,280 --> 00:59:36,920 Speaker 1: thank you for any opportunity. I love this interview. I 1013 00:59:36,960 --> 00:59:39,600 Speaker 1: hope you did too, And thank you so much to 1014 00:59:39,720 --> 00:59:42,520 Speaker 1: Dr Scott Atlas again for such an incredible interview and 1015 00:59:42,560 --> 00:59:44,640 Speaker 1: taking so much time with us. I want to thank 1016 00:59:44,640 --> 00:59:47,400 Speaker 1: you at home for listening. If you enjoy today's show, 1017 00:59:47,480 --> 00:59:49,760 Speaker 1: please leave us a review and rate us five stars 1018 00:59:49,760 --> 00:59:52,760 Speaker 1: and Apple Podcast. You can find me on Twitter, Facebook 1019 00:59:52,760 --> 00:59:56,040 Speaker 1: and Instagram and at least some rebooth special thanks to 1020 00:59:56,120 --> 01:00:00,320 Speaker 1: our producer John Cassio, researcher Aaron Kleigman, and ex Ecketive 1021 01:00:00,320 --> 01:00:03,280 Speaker 1: producers Jebbie Myers and speak renew Gangridge, all part of 1022 01:00:03,320 --> 01:00:07,280 Speaker 1: the Gingridge three sixty network and team