1 00:00:00,240 --> 00:00:03,760 Speaker 1: It has been one hundred seventy days since fifteen days 2 00:00:03,800 --> 00:00:05,920 Speaker 1: to slow the spread, So it seems like good at 3 00:00:05,960 --> 00:00:08,520 Speaker 1: time as any to take stock where we are now, 4 00:00:08,760 --> 00:00:13,200 Speaker 1: how the coronavirus pandemic stands, and most importantly, when we 5 00:00:13,280 --> 00:00:16,400 Speaker 1: will be able to reopen our country. This is Verdict 6 00:00:16,440 --> 00:00:25,320 Speaker 1: with Ted Cruz. Welcome back to Verdict with Ted Cruz. 7 00:00:25,400 --> 00:00:28,479 Speaker 1: I'm Michael Knowles, joined as ever by the Senator and 8 00:00:28,880 --> 00:00:33,040 Speaker 1: a very special guest, Steve Dace of the Steve Dace 9 00:00:33,280 --> 00:00:36,360 Speaker 1: Show over at the Blaze. I'm sure you've seen him 10 00:00:36,400 --> 00:00:39,680 Speaker 1: everywhere and he's a longtime friend, not just of the show, 11 00:00:39,920 --> 00:00:43,120 Speaker 1: but of Senator Cruz as well. Steve, Welcome, It's good 12 00:00:43,120 --> 00:00:45,159 Speaker 1: to have you. You know, Steve and I have spent 13 00:00:45,840 --> 00:00:50,040 Speaker 1: thousands of hours together on the road traveling. I gotta 14 00:00:50,080 --> 00:00:55,720 Speaker 1: tell you if Steve is brilliant, he is a passionate conservative. 15 00:00:56,280 --> 00:00:58,400 Speaker 1: But I gotta tell y'all so there may be no 16 00:00:58,440 --> 00:01:01,080 Speaker 1: one in the country who is my phone up with 17 00:01:01,160 --> 00:01:06,680 Speaker 1: more texts during this whole pandemic than Steve at every stage, 18 00:01:06,760 --> 00:01:11,319 Speaker 1: because because he has been diving in from the beginning 19 00:01:11,319 --> 00:01:14,400 Speaker 1: of this pandemic to the numbers to what the numbers 20 00:01:14,480 --> 00:01:18,319 Speaker 1: mean to what the testing tells us, to what the 21 00:01:18,360 --> 00:01:21,080 Speaker 1: antibody numbers tell us, to what the impacts of the 22 00:01:21,120 --> 00:01:24,400 Speaker 1: shutdown tell us. And so Steve and I have talked 23 00:01:24,400 --> 00:01:28,000 Speaker 1: about many, many issues at great length, but but I 24 00:01:28,040 --> 00:01:31,960 Speaker 1: think this pod in particular, it's it's it's valuable to 25 00:01:32,319 --> 00:01:35,760 Speaker 1: get in to what's going on with the pandemic and 26 00:01:36,120 --> 00:01:39,319 Speaker 1: the country right now. So I know that there are lies, 27 00:01:39,840 --> 00:01:43,320 Speaker 1: damned lies and statistics, and everybody seems to have their 28 00:01:43,360 --> 00:01:46,480 Speaker 1: own statistics on this pandemic. And even I try to 29 00:01:46,560 --> 00:01:48,880 Speaker 1: keep my head into it, I can't really make heads 30 00:01:48,920 --> 00:01:51,080 Speaker 1: or tails of it. I don't know what to believe. So, Steve, 31 00:01:51,560 --> 00:01:54,560 Speaker 1: where do we stand on the coronavirus? I think if 32 00:01:54,640 --> 00:01:57,400 Speaker 1: I could choose one point for us to center the 33 00:01:57,440 --> 00:02:01,160 Speaker 1: conversation on, it would really come down to what we've 34 00:02:01,240 --> 00:02:04,680 Speaker 1: learned about our testing metric guys, because it goes to 35 00:02:04,760 --> 00:02:06,520 Speaker 1: the heart of why we You know, none of us 36 00:02:06,560 --> 00:02:09,800 Speaker 1: are epidemiologists. We're all fairly intelligent guys, but it's not 37 00:02:09,880 --> 00:02:12,840 Speaker 1: our field of expertise. And so I like to keep 38 00:02:12,880 --> 00:02:17,280 Speaker 1: the conversation where it impacts public policy as much as possible, 39 00:02:17,320 --> 00:02:20,040 Speaker 1: because that is each of our areas of expertise. And 40 00:02:20,120 --> 00:02:22,239 Speaker 1: if you look at the number one concern for why 41 00:02:22,240 --> 00:02:25,400 Speaker 1: we did these shutdowns across the country, it's because we 42 00:02:25,400 --> 00:02:28,640 Speaker 1: were concerned about masses of asymptomatic spread that all kinds 43 00:02:28,639 --> 00:02:32,280 Speaker 1: of people who were otherwise healthy would get the virus, 44 00:02:32,320 --> 00:02:36,080 Speaker 1: go home, infect grandma, grandpa, and or have these mass 45 00:02:36,280 --> 00:02:38,600 Speaker 1: spread or events and then go home. And then we 46 00:02:38,639 --> 00:02:41,200 Speaker 1: get to an R two R three situation. Right now, 47 00:02:41,280 --> 00:02:44,239 Speaker 1: let me stop, stop you right there and just ask 48 00:02:44,320 --> 00:02:47,040 Speaker 1: for folks listening, what is an R two R three 49 00:02:47,120 --> 00:02:49,840 Speaker 1: What does what does that mean? It means the rate 50 00:02:49,919 --> 00:02:53,000 Speaker 1: of who's infected or how many people you infect based 51 00:02:53,040 --> 00:02:56,440 Speaker 1: on who's infected. Right, So does two people get infected 52 00:02:56,480 --> 00:02:59,480 Speaker 1: for every person that's infected, three people, etc. The goal 53 00:02:59,480 --> 00:03:01,040 Speaker 1: in a paid MC is to get to R one 54 00:03:01,080 --> 00:03:04,080 Speaker 1: and then hopefully to R zero. Okay. And so if 55 00:03:04,120 --> 00:03:07,080 Speaker 1: you go way back to March twenty sixth, there's a 56 00:03:07,080 --> 00:03:09,639 Speaker 1: guy that we used to think was brilliant named Didier 57 00:03:09,880 --> 00:03:13,960 Speaker 1: Rayalt was considered the leading infectious disease expert in the 58 00:03:14,000 --> 00:03:17,000 Speaker 1: world until March twelfth, and that's when he had the 59 00:03:17,080 --> 00:03:22,360 Speaker 1: unfortunate circumstance of having President Trump site positively and affirmatively 60 00:03:22,720 --> 00:03:26,520 Speaker 1: his research on hydroxy chloroquin as a treatment for COVID nineteen. 61 00:03:26,560 --> 00:03:29,360 Speaker 1: So all over the world, yes, well it was for 62 00:03:29,480 --> 00:03:31,600 Speaker 1: him at the time. All over the world. We were 63 00:03:31,639 --> 00:03:34,880 Speaker 1: beginning to use hydroxy chloroquin until it was orange man 64 00:03:34,920 --> 00:03:37,440 Speaker 1: bad and now suddenly we could not write well. On 65 00:03:37,520 --> 00:03:41,840 Speaker 1: March twenty sixth, he issued a piece on PCR testing 66 00:03:42,240 --> 00:03:44,400 Speaker 1: for COVID nineteen and if you go back to the 67 00:03:44,440 --> 00:03:47,920 Speaker 1: first stars, the World Health Organization was very concerned about 68 00:03:47,920 --> 00:03:51,320 Speaker 1: the amount of false positives with PCR testing because of 69 00:03:51,320 --> 00:03:54,560 Speaker 1: how sensitive they were, and they wanted two positives before 70 00:03:54,600 --> 00:03:59,120 Speaker 1: they would report. And so a PCR test is the 71 00:03:59,160 --> 00:04:02,960 Speaker 1: test that's the it's used most frequently. It's the one 72 00:04:03,000 --> 00:04:05,000 Speaker 1: where they stick the thing way up your nose and 73 00:04:05,040 --> 00:04:06,720 Speaker 1: it feels like it's in the back of your brain, 74 00:04:06,840 --> 00:04:11,280 Speaker 1: and it takes often a couple of days or even 75 00:04:11,440 --> 00:04:14,000 Speaker 1: a week or two in some circumstances to get the 76 00:04:14,040 --> 00:04:18,640 Speaker 1: result back correct. It's a great testing module. They're very sensitive, 77 00:04:18,880 --> 00:04:21,920 Speaker 1: they're very accurate, but like any other algorithm, it comes 78 00:04:21,960 --> 00:04:24,040 Speaker 1: down to what do you program it for the setting 79 00:04:24,080 --> 00:04:26,719 Speaker 1: that you want. And so back on March twenty sixth, 80 00:04:27,080 --> 00:04:30,680 Speaker 1: Rail put out a paper in France saying, hey, what 81 00:04:30,880 --> 00:04:35,640 Speaker 1: we're finding is when we get beyond thirty cts, all right, 82 00:04:35,720 --> 00:04:39,000 Speaker 1: which is cycle thresholds, all right, meaning how many times 83 00:04:39,000 --> 00:04:41,760 Speaker 1: they have to zero in on a sample before they 84 00:04:41,760 --> 00:04:43,680 Speaker 1: detect a virus, like when you're zooming in on something 85 00:04:43,760 --> 00:04:47,039 Speaker 1: on your phone or your computer. Okay, when we have 86 00:04:47,120 --> 00:04:50,400 Speaker 1: to zoom zoom beyond thirty times, these people are not 87 00:04:51,200 --> 00:04:54,320 Speaker 1: they're not contagious, they're probably not infected. He even in 88 00:04:54,360 --> 00:04:57,120 Speaker 1: his papery refers to them as quote viral artifacts. And 89 00:04:57,120 --> 00:04:59,520 Speaker 1: we all know what an artifact is. It's something that's 90 00:04:59,640 --> 00:05:02,200 Speaker 1: long since gone. It's a remnant of something that's long 91 00:05:02,240 --> 00:05:05,840 Speaker 1: since dead. Right, And so he recommended that no one 92 00:05:06,000 --> 00:05:08,680 Speaker 1: set their PCR tests above a cycle threshold or a 93 00:05:08,720 --> 00:05:12,440 Speaker 1: CT of thirty three, and recommended thirty for whatever reasons. 94 00:05:12,480 --> 00:05:14,120 Speaker 1: And we don't know the answer to this, and this 95 00:05:14,200 --> 00:05:16,120 Speaker 1: is probably where it becomes your job as a senator 96 00:05:16,160 --> 00:05:19,839 Speaker 1: to help us find out our CDC and CDC like 97 00:05:20,200 --> 00:05:24,840 Speaker 1: institutions across the world decided to set their sensitivity levels 98 00:05:25,000 --> 00:05:28,599 Speaker 1: anywhere from thirty five to forty. In our country, it's 99 00:05:28,680 --> 00:05:31,120 Speaker 1: thirty seven to forty. And so what the New York 100 00:05:31,160 --> 00:05:33,760 Speaker 1: Times found when they did this survey across the country 101 00:05:34,000 --> 00:05:36,400 Speaker 1: is that if your state is at a thirty seven 102 00:05:36,520 --> 00:05:40,000 Speaker 1: or at a forty, anywhere from forty to ninety percent 103 00:05:40,520 --> 00:05:44,239 Speaker 1: of our positive test are false positives because these people 104 00:05:44,240 --> 00:05:47,440 Speaker 1: are either asymptomatic to the point they're not contagious, they're 105 00:05:47,440 --> 00:05:50,599 Speaker 1: not contagious at all, or it's a viral artifact. We're 106 00:05:50,600 --> 00:05:52,960 Speaker 1: picking up a remnant of an exposure that just is 107 00:05:53,000 --> 00:05:56,120 Speaker 1: no longer any kind of a live culture. Well, I 108 00:05:56,160 --> 00:05:58,599 Speaker 1: can't begin to express what that means from a public 109 00:05:58,960 --> 00:06:01,880 Speaker 1: is Steve Steven Let me let me stop you for 110 00:06:01,880 --> 00:06:04,320 Speaker 1: a second, because I want to underscore something that you 111 00:06:04,400 --> 00:06:06,640 Speaker 1: mentioned there, but that a lot of folks listening and 112 00:06:06,680 --> 00:06:11,400 Speaker 1: watching may not know. It would be easy for some 113 00:06:11,520 --> 00:06:15,520 Speaker 1: skeptics perhaps to dismiss the three of us as crazy 114 00:06:15,600 --> 00:06:19,560 Speaker 1: right wingers. But but but you mentioned The New York Times, 115 00:06:19,600 --> 00:06:22,280 Speaker 1: which I think it's fair to say, whether or not 116 00:06:22,360 --> 00:06:25,200 Speaker 1: we're crazy right wingers, the New York Times is not 117 00:06:25,320 --> 00:06:27,800 Speaker 1: a crazy right wing institution. I don't think that's going 118 00:06:27,839 --> 00:06:31,080 Speaker 1: too far out on a limb to say that. And 119 00:06:31,160 --> 00:06:35,039 Speaker 1: The New York Times wrote a stunning article just a 120 00:06:35,080 --> 00:06:39,360 Speaker 1: few days ago that that lays out exactly what you're saying. So, 121 00:06:39,360 --> 00:06:43,120 Speaker 1: so if you're skeptical of what you're hearing right now, 122 00:06:43,160 --> 00:06:45,400 Speaker 1: I'm going to say something I have never said before 123 00:06:45,440 --> 00:06:47,760 Speaker 1: and probably will never say again. Go look up the 124 00:06:47,760 --> 00:06:50,600 Speaker 1: New York Times. Go read the article from the New 125 00:06:50,680 --> 00:06:55,520 Speaker 1: York Times. And by the way, if the New York 126 00:06:55,520 --> 00:06:59,120 Speaker 1: Times and Steve Dace and Cruise and Knolls are all agreeing, 127 00:06:59,800 --> 00:07:02,800 Speaker 1: that may actually be in the Book of Revelations a 128 00:07:02,880 --> 00:07:07,720 Speaker 1: sign of the end times, I think so. And by 129 00:07:07,760 --> 00:07:09,840 Speaker 1: the way, the way it was reported in the New 130 00:07:09,920 --> 00:07:12,760 Speaker 1: York Times seemed to be this kind of stunning revelation 131 00:07:12,960 --> 00:07:14,760 Speaker 1: that you could have up to ninety percent of people 132 00:07:14,880 --> 00:07:16,560 Speaker 1: who are not contagious. And I think that's how a 133 00:07:16,600 --> 00:07:18,000 Speaker 1: lot of people took it. It's how I took it. 134 00:07:18,160 --> 00:07:19,960 Speaker 1: But Steve, it seems to me what you're saying is 135 00:07:20,280 --> 00:07:22,960 Speaker 1: this was built into the testing from the beginning, that 136 00:07:23,080 --> 00:07:27,120 Speaker 1: by making the tests so hyper sensitive beyond what would 137 00:07:27,120 --> 00:07:30,320 Speaker 1: be the usual convention, that you were setting yourself up 138 00:07:30,320 --> 00:07:33,120 Speaker 1: for this kind of scenario. Now, this is where from 139 00:07:33,160 --> 00:07:35,480 Speaker 1: a public policy standpoint, we have to get into what 140 00:07:35,560 --> 00:07:37,640 Speaker 1: was the motivation for this And if you want to 141 00:07:37,680 --> 00:07:40,119 Speaker 1: give everyone the maximum benefited it out back in March. 142 00:07:40,160 --> 00:07:43,720 Speaker 1: It is a novel coronavirus. Now, it's not a novel virus. 143 00:07:43,760 --> 00:07:47,280 Speaker 1: We have been studying coronaviruses for seventy years. The common 144 00:07:47,320 --> 00:07:50,120 Speaker 1: cold is one of the coronaviruses, for example. But it 145 00:07:50,200 --> 00:07:51,680 Speaker 1: was the first time we had seen one of these 146 00:07:51,760 --> 00:07:54,480 Speaker 1: mutate from animal to animal to animal to human and 147 00:07:54,600 --> 00:07:57,280 Speaker 1: behave like this. And we also understood that we couldn't 148 00:07:57,320 --> 00:07:59,880 Speaker 1: trust China's data. So if we all went into this saying, 149 00:08:00,240 --> 00:08:03,040 Speaker 1: let's be hyper cautious, We're still in the cold flu 150 00:08:03,120 --> 00:08:05,360 Speaker 1: season anyway. There's not a lot going on in this 151 00:08:05,400 --> 00:08:09,680 Speaker 1: country in March anyway, except for spring breakers, so let's 152 00:08:09,720 --> 00:08:13,000 Speaker 1: be hyper sensitive about this. Fine, But why we have 153 00:08:13,080 --> 00:08:16,160 Speaker 1: continued to do this now for five for six months? 154 00:08:16,520 --> 00:08:18,680 Speaker 1: You know, there was an interesting there's an interesting situation 155 00:08:18,720 --> 00:08:21,240 Speaker 1: happening at the University of Alabama as we speak. Last 156 00:08:21,280 --> 00:08:25,119 Speaker 1: I heard they have reported twelve hundred positive cases since 157 00:08:25,120 --> 00:08:27,640 Speaker 1: the students returned. But news we went and did a 158 00:08:27,720 --> 00:08:30,360 Speaker 1: survey of these cases and found almost all of them 159 00:08:30,360 --> 00:08:34,640 Speaker 1: were asymptomatic and there were zero hospitalizations. LSU and Clemson, 160 00:08:34,679 --> 00:08:37,160 Speaker 1: the top two teams in college football last year, when 161 00:08:37,160 --> 00:08:39,680 Speaker 1: they brought the student athletes back to campus in June 162 00:08:39,679 --> 00:08:44,080 Speaker 1: and started testing, they had fifty four combined positives, all 163 00:08:44,120 --> 00:08:49,440 Speaker 1: almost all asymptomatic, zero hospitalizations. So how so that actually 164 00:08:49,480 --> 00:08:52,920 Speaker 1: dovetails with the New York Times report, meaning that because 165 00:08:52,960 --> 00:08:55,439 Speaker 1: we have this case demit going on right now and 166 00:08:55,760 --> 00:08:58,840 Speaker 1: which we're creating so many cases, that's not we're doing 167 00:08:58,840 --> 00:09:01,520 Speaker 1: too much testing. I love the fact we're doing too 168 00:09:01,600 --> 00:09:04,240 Speaker 1: much testing because it shows that the virus is actually 169 00:09:04,280 --> 00:09:07,040 Speaker 1: not as strong or as lethal as we originally feared 170 00:09:07,040 --> 00:09:09,720 Speaker 1: back in March. But there's a difference between too much 171 00:09:09,840 --> 00:09:13,959 Speaker 1: testing and too many cases. We had sixty million cases 172 00:09:14,000 --> 00:09:16,839 Speaker 1: of H one N one guys when the Obama administration 173 00:09:16,920 --> 00:09:20,040 Speaker 1: finally decided to cut off the testing because they it 174 00:09:20,160 --> 00:09:22,920 Speaker 1: wasn't going anywhere. This is what we're doing now, and 175 00:09:23,520 --> 00:09:26,240 Speaker 1: we've got to realize what is our ultimate metric to 176 00:09:26,280 --> 00:09:29,840 Speaker 1: reopen the country. When deaths plummeted around Memorial Day weekend, 177 00:09:29,880 --> 00:09:31,840 Speaker 1: we were told, yeah, but then the cases we're too high. 178 00:09:32,120 --> 00:09:34,840 Speaker 1: Well now we've had six straight weeks of cases going down, 179 00:09:35,040 --> 00:09:37,400 Speaker 1: and we're being told, well, now now it's about deaths. 180 00:09:37,480 --> 00:09:40,560 Speaker 1: We need a defined metric of what it is that 181 00:09:40,640 --> 00:09:43,920 Speaker 1: actually says we're beating this thing, and I'll leap more. 182 00:09:44,160 --> 00:09:46,960 Speaker 1: Go ahead, Well, Steve, you know I'm here in Los 183 00:09:46,960 --> 00:09:50,880 Speaker 1: Angeles and in California. The new metric for reopening, to 184 00:09:50,960 --> 00:09:53,680 Speaker 1: be almost fully reopened, is that you've got to get 185 00:09:53,679 --> 00:09:57,440 Speaker 1: down to a two percent rate of positive tests. So 186 00:09:57,559 --> 00:09:59,920 Speaker 1: if we have this issue of the tests that you're 187 00:10:00,080 --> 00:10:02,480 Speaker 1: describing and that the New York Times is describing, then 188 00:10:02,640 --> 00:10:04,520 Speaker 1: then you're in a situation where it looks it looks 189 00:10:04,520 --> 00:10:07,440 Speaker 1: like we're never gonna right. Last week, Los Angeles County 190 00:10:07,480 --> 00:10:11,160 Speaker 1: was at its lowest rate for hospitalizations since April second. 191 00:10:11,840 --> 00:10:15,199 Speaker 1: Nationwide for COVID symptoms, we are at the lowest rate 192 00:10:15,200 --> 00:10:19,319 Speaker 1: of hospitalization since March twenty first. Nationwide, we are below 193 00:10:19,360 --> 00:10:23,199 Speaker 1: two percent of er visits are for COVID like symptoms. Now, guys, 194 00:10:23,200 --> 00:10:27,160 Speaker 1: I ask you without not a therapeutic, a meaningful vaccine, 195 00:10:27,600 --> 00:10:30,880 Speaker 1: Without a meaningful vaccine, and since we all since now 196 00:10:30,920 --> 00:10:34,200 Speaker 1: apparently the natural herd immunity that saved human civilization from 197 00:10:34,240 --> 00:10:37,319 Speaker 1: plagues for six thousand years is now suddenly voodoo. So 198 00:10:37,400 --> 00:10:40,560 Speaker 1: without without herd immunity and without a meaningful vaccine, in 199 00:10:40,640 --> 00:10:43,160 Speaker 1: a nation of three hundred and thirty one million. How 200 00:10:43,160 --> 00:10:45,360 Speaker 1: do we do better than less than two percent of 201 00:10:45,640 --> 00:10:48,480 Speaker 1: er visits for COVID? Wh When are the numbers low enough? 202 00:10:48,520 --> 00:10:51,080 Speaker 1: I think that's the question instant. And let me drill 203 00:10:51,160 --> 00:10:54,760 Speaker 1: down a little bit in the testing information you're talking about, 204 00:10:54,760 --> 00:10:57,720 Speaker 1: and what's in this New York Times article, which is 205 00:10:58,400 --> 00:11:04,000 Speaker 1: we're not saying that COVID isn't real, that it isn't serious, 206 00:11:04,120 --> 00:11:07,679 Speaker 1: and and if you're very elderly, if you've got serious 207 00:11:08,400 --> 00:11:15,160 Speaker 1: other health conditions, COVID can be lethal. But for a 208 00:11:15,160 --> 00:11:18,400 Speaker 1: great many people who are not elderly, a great many 209 00:11:18,440 --> 00:11:23,640 Speaker 1: people who don't have other serious health ailments, the fatality 210 00:11:23,760 --> 00:11:27,520 Speaker 1: rate for COVID is much much much lower. And the 211 00:11:27,640 --> 00:11:30,840 Speaker 1: point you're emphasizing here, and it's actually something as you 212 00:11:30,880 --> 00:11:32,920 Speaker 1: read the New York Times article that was really stunning, 213 00:11:33,760 --> 00:11:38,480 Speaker 1: is the testing is producing a massive number of false positives, 214 00:11:38,480 --> 00:11:44,480 Speaker 1: over ninety percent. And these false positives are people. You know, 215 00:11:44,520 --> 00:11:46,520 Speaker 1: it's worth drilling down a little bit at what it 216 00:11:46,559 --> 00:11:49,320 Speaker 1: means if the test is said at thirty seven or 217 00:11:49,360 --> 00:11:52,520 Speaker 1: at forty, that's that. I like the analogy of sort 218 00:11:52,520 --> 00:11:55,400 Speaker 1: of zooming in, zooming in zooming in So that's super 219 00:11:55,520 --> 00:11:59,560 Speaker 1: zoomed in, so it detects a little bit of virus 220 00:11:59,559 --> 00:12:03,120 Speaker 1: in you, but not much, not enough virus typically to 221 00:12:03,160 --> 00:12:09,840 Speaker 1: make you sick. And interestingly, and really importantly, not enough 222 00:12:09,920 --> 00:12:13,480 Speaker 1: virus probably, although we're still learning how this operates, but 223 00:12:13,600 --> 00:12:16,440 Speaker 1: not enough virus very possibly, let me put it that way, 224 00:12:16,960 --> 00:12:21,600 Speaker 1: not enough virus very possibly to be contagious. And this 225 00:12:21,800 --> 00:12:24,880 Speaker 1: insight is important because if you want to stop a pandemic, 226 00:12:24,920 --> 00:12:28,160 Speaker 1: what you want to focus on is people who are contagious. 227 00:12:28,200 --> 00:12:30,760 Speaker 1: You want to stop someone, even if they're healthy, from 228 00:12:30,840 --> 00:12:34,400 Speaker 1: giving it to someone else who's very vulnerable. And if 229 00:12:34,400 --> 00:12:38,240 Speaker 1: the vast majority of these false positives are not having 230 00:12:38,240 --> 00:12:41,520 Speaker 1: symptoms and not contagious, it means we're focusing our energy 231 00:12:41,559 --> 00:12:45,840 Speaker 1: the wrong place rather than directly on the people that 232 00:12:45,920 --> 00:12:51,240 Speaker 1: actually have a significant amount of virus, a significant viral 233 00:12:51,280 --> 00:12:56,440 Speaker 1: loading their body where they could well be symptomatic and 234 00:12:56,520 --> 00:12:58,760 Speaker 1: getting sick and they could well be contagious. Is that 235 00:12:58,800 --> 00:13:04,240 Speaker 1: am I characterizing that fairly? Steve, you nailed it. You 236 00:13:04,320 --> 00:13:05,959 Speaker 1: nailed its, Senator. And then this goes back to the 237 00:13:06,040 --> 00:13:10,280 Speaker 1: beginning of the lockdowns, where we didn't secure America's nursing 238 00:13:10,280 --> 00:13:12,880 Speaker 1: homes up until about the end of July, something like 239 00:13:12,920 --> 00:13:16,280 Speaker 1: forty five percent of all COVID deaths in America had 240 00:13:16,320 --> 00:13:19,640 Speaker 1: taken place in a long term care facility. Gentlemen, only 241 00:13:19,720 --> 00:13:22,439 Speaker 1: zero point six percent of Americans live in a long 242 00:13:22,559 --> 00:13:26,520 Speaker 1: term healthcare facility. So we didn't lock down the vulnerable 243 00:13:27,000 --> 00:13:30,319 Speaker 1: because we put in this incredible effort to lock everybody 244 00:13:30,360 --> 00:13:33,800 Speaker 1: else down, and it was over the sphere of asymptomatic spread. 245 00:13:34,000 --> 00:13:36,520 Speaker 1: The largest contact tracing study that was done in this 246 00:13:36,559 --> 00:13:40,720 Speaker 1: world so far was about two weeks ago, over five cases. 247 00:13:40,880 --> 00:13:43,880 Speaker 1: Eight percent of them they could trace back to some 248 00:13:44,000 --> 00:13:47,640 Speaker 1: form of asymptomatic spread, eight percent out of over three 249 00:13:47,720 --> 00:13:52,680 Speaker 1: thousand cases. So we made this huge investment. We went essentially, 250 00:13:52,679 --> 00:13:54,719 Speaker 1: we went out were like it, We went honey with 251 00:13:54,760 --> 00:13:57,960 Speaker 1: mice with an elephant gun. We made this huge investment 252 00:13:58,000 --> 00:14:02,120 Speaker 1: in locking everyone down over the canarative asymptomatic spread and 253 00:14:02,160 --> 00:14:04,720 Speaker 1: didn't protect the most of vulnerable among us. If I'm 254 00:14:04,800 --> 00:14:07,600 Speaker 1: elderly in Alabama, why are we testing all these students 255 00:14:07,600 --> 00:14:10,240 Speaker 1: at Alabama? What are we why are we protecting well 256 00:14:10,280 --> 00:14:14,320 Speaker 1: and Steve? You know, you know it's interesting that I 257 00:14:14,360 --> 00:14:18,120 Speaker 1: can tell you firsthand. I've seen how the understanding of 258 00:14:18,280 --> 00:14:23,640 Speaker 1: doctors and scientists and epidimimologists about this disease has changed 259 00:14:23,720 --> 00:14:26,480 Speaker 1: and been uncertain, which is Michael and I were observing 260 00:14:26,520 --> 00:14:29,880 Speaker 1: earlier today that it was back in March, actually on 261 00:14:29,920 --> 00:14:34,000 Speaker 1: the Verdict podcast where we did a podcast from the 262 00:14:34,040 --> 00:14:37,440 Speaker 1: stage at Sepack with Ronald McDaniel, the head of the RNC, 263 00:14:38,280 --> 00:14:40,640 Speaker 1: and we did it live. It was a fun episode 264 00:14:40,720 --> 00:14:45,280 Speaker 1: at Spack, and you'll recall at Spack Michael and I 265 00:14:45,360 --> 00:14:50,400 Speaker 1: both encountered an individual who subsequently tested positive and was symptomatic. 266 00:14:50,400 --> 00:14:55,000 Speaker 1: He got he got ill. And in the wake of 267 00:14:55,040 --> 00:14:58,680 Speaker 1: that that that's when I decided initially to self quarantine. 268 00:14:59,080 --> 00:15:02,760 Speaker 1: And this is right the beginning of when COVID was 269 00:15:02,840 --> 00:15:07,280 Speaker 1: starting to become a meaningful issue in the US, and 270 00:15:07,400 --> 00:15:12,720 Speaker 1: the physicians all told me, if you're asymptomatic, and if 271 00:15:12,760 --> 00:15:15,360 Speaker 1: the person was not actively sick when you encountered him, 272 00:15:15,960 --> 00:15:18,640 Speaker 1: you don't have a concern, you don't need to quarantine, 273 00:15:18,880 --> 00:15:22,080 Speaker 1: You're fine. And I ended up decide I'm going to 274 00:15:22,160 --> 00:15:27,320 Speaker 1: stay home to protect everyone else around. But what's interesting is, 275 00:15:27,360 --> 00:15:29,600 Speaker 1: having seen the months that have gone on, I have 276 00:15:29,720 --> 00:15:35,400 Speaker 1: seen the experts at CDC say categorically, asymptomatic people cannot 277 00:15:35,440 --> 00:15:38,000 Speaker 1: transmit it, which is what they told me in March. 278 00:15:38,120 --> 00:15:41,200 Speaker 1: Categorically too, there was a period of time where they 279 00:15:41,200 --> 00:15:44,440 Speaker 1: were focused on, Okay, the whole worry is asymptomatic, and 280 00:15:44,480 --> 00:15:47,160 Speaker 1: I have to admit that felt a little weird, a 281 00:15:47,240 --> 00:15:51,600 Speaker 1: weird focus. And then we seem to be moving back 282 00:15:51,640 --> 00:15:55,480 Speaker 1: into an area of a greater common sense that we 283 00:15:55,560 --> 00:16:00,760 Speaker 1: need to focus on who's actually seriously contagious. And you know, 284 00:16:00,840 --> 00:16:04,840 Speaker 1: as you were talking about nursing homes, here's a question 285 00:16:04,880 --> 00:16:09,480 Speaker 1: for you, Steve. Can you think of a more catastrophically 286 00:16:09,640 --> 00:16:14,320 Speaker 1: damaging public health decision than the public policy of the 287 00:16:14,320 --> 00:16:17,680 Speaker 1: New York State government and Governor Cuomo, who was just 288 00:16:17,880 --> 00:16:22,880 Speaker 1: lionized at the DNC then his policies of sending people 289 00:16:22,880 --> 00:16:28,040 Speaker 1: into nursing homes who were who were sick with COVID 290 00:16:28,120 --> 00:16:31,120 Speaker 1: and were contagious, and the incredible death told that that 291 00:16:31,440 --> 00:16:35,080 Speaker 1: resulted from that. I cannot, And Ted, I gotta tell you, 292 00:16:35,440 --> 00:16:38,080 Speaker 1: I'm pretty cynical, as you well know, this is the 293 00:16:38,120 --> 00:16:41,200 Speaker 1: worst gas lighting I've ever seen. I mean, this is 294 00:16:41,600 --> 00:16:45,640 Speaker 1: what the retconning of Cuomo's record where this is concerned. 295 00:16:45,800 --> 00:16:49,320 Speaker 1: I mean, we're sitting here the early September and right 296 00:16:49,400 --> 00:16:51,520 Speaker 1: now if New York was its own country, it would 297 00:16:51,520 --> 00:16:54,400 Speaker 1: still be a sixth worst country in the world for 298 00:16:54,520 --> 00:16:57,840 Speaker 1: COVID nineteen death like the seventh worst country in the 299 00:16:57,880 --> 00:17:01,080 Speaker 1: world for COVID cases per one million. Still about one 300 00:17:01,120 --> 00:17:04,159 Speaker 1: out of every five deaths in America from COVID occurred 301 00:17:04,560 --> 00:17:08,240 Speaker 1: in New York or New Jersey. And and so the 302 00:17:08,600 --> 00:17:11,000 Speaker 1: way that this has been retcon and we've been gas 303 00:17:11,119 --> 00:17:13,119 Speaker 1: lighted that he's some kind of hero. And you look 304 00:17:13,160 --> 00:17:15,440 Speaker 1: at a guy like Ron DeSantis in Florida, for example, 305 00:17:15,680 --> 00:17:18,359 Speaker 1: where he's got a larger population, he's got a larger 306 00:17:18,440 --> 00:17:23,520 Speaker 1: elderly population, and his CFR is lower than the countries 307 00:17:23,600 --> 00:17:26,560 Speaker 1: a case fatality rate which is easy to divide, which 308 00:17:26,680 --> 00:17:28,800 Speaker 1: is just simply the amount of cases divided by the 309 00:17:28,840 --> 00:17:32,159 Speaker 1: amount of people who who sadly died, and it's one 310 00:17:32,200 --> 00:17:35,639 Speaker 1: point nine percent in Florida, below the national average, and 311 00:17:36,080 --> 00:17:38,680 Speaker 1: the one in New York is seven point one percent. 312 00:17:38,760 --> 00:17:41,320 Speaker 1: So he's almost seven times lower than the one in 313 00:17:41,359 --> 00:17:44,720 Speaker 1: New York, with the second largest elderly population per capita 314 00:17:45,080 --> 00:17:47,359 Speaker 1: in the country. And he gets ripped as some kind 315 00:17:47,400 --> 00:17:50,520 Speaker 1: of a grim raper and Cuomo gets elevated. So what 316 00:17:50,680 --> 00:17:53,399 Speaker 1: did New York do wrong, and what did New Jersey 317 00:17:53,480 --> 00:17:55,560 Speaker 1: do wrong? What New York did wrong? He is there, 318 00:17:55,680 --> 00:17:58,080 Speaker 1: and there is a debate about whether this came from 319 00:17:58,080 --> 00:18:02,159 Speaker 1: the fence. There is a bureau that did recommend that 320 00:18:02,440 --> 00:18:05,359 Speaker 1: nursing homes because they were concerned coming off the Imperial 321 00:18:05,480 --> 00:18:08,520 Speaker 1: College and especially the IHMME surveys, that we were going 322 00:18:08,560 --> 00:18:12,320 Speaker 1: to overload the hospitals. There was a memo that suggested 323 00:18:12,359 --> 00:18:15,960 Speaker 1: that states could take a look. Some minor bureaucracy you've 324 00:18:15,960 --> 00:18:18,639 Speaker 1: probably never heard of, did put out a memo suggesting 325 00:18:18,680 --> 00:18:21,880 Speaker 1: that states take a look at the possibility of reinserting 326 00:18:22,000 --> 00:18:25,600 Speaker 1: COVID infected patients back into nursing homes if they weren't 327 00:18:25,640 --> 00:18:30,280 Speaker 1: immediately in danger of perishing, because they were concerned about 328 00:18:30,520 --> 00:18:33,520 Speaker 1: ICU overload, all right. And so six states took the 329 00:18:33,600 --> 00:18:36,119 Speaker 1: lead on this. Five of them were governed by Democrats, 330 00:18:36,359 --> 00:18:39,200 Speaker 1: and then there was Massachusetts, which as a Republican governor 331 00:18:39,280 --> 00:18:42,440 Speaker 1: who's basically a Democrat. All right. New York was the 332 00:18:42,520 --> 00:18:44,200 Speaker 1: one that took the lead out of these six states. 333 00:18:44,560 --> 00:18:46,200 Speaker 1: And if you look at the death rate in these 334 00:18:46,280 --> 00:18:49,480 Speaker 1: six states that made made this decision compared to the 335 00:18:49,560 --> 00:18:52,960 Speaker 1: rest of the country, it's really just not even close. 336 00:18:53,040 --> 00:18:55,880 Speaker 1: And what they did is they brought a bomb into 337 00:18:55,920 --> 00:18:59,000 Speaker 1: their nursing homes. And if nursing homes or anything they are, 338 00:18:59,400 --> 00:19:03,320 Speaker 1: they are gets of autoimmune deficiencies. Yeah, you're talking about 339 00:19:03,320 --> 00:19:05,720 Speaker 1: the elderly obviously, And so they brought them in and 340 00:19:05,840 --> 00:19:09,080 Speaker 1: re exposed them to COVID with these reinsertions of these 341 00:19:09,160 --> 00:19:12,240 Speaker 1: COVID patients. And there are some estimate. Phil Kirpin's a 342 00:19:12,280 --> 00:19:15,159 Speaker 1: phenomenal researcher out there. He estimates that it could be 343 00:19:15,280 --> 00:19:18,239 Speaker 1: twenty thousand people in New York died in New York 344 00:19:18,359 --> 00:19:20,840 Speaker 1: nursing homes. The AP has been passed pointed out on 345 00:19:20,960 --> 00:19:23,880 Speaker 1: numerous occasions that the numbers that Cuomo and his stand 346 00:19:23,920 --> 00:19:26,480 Speaker 1: are putting out are false and inaccurate. And the other 347 00:19:26,600 --> 00:19:28,919 Speaker 1: day Cuomo said, well, you know, it's probably gonna take 348 00:19:28,920 --> 00:19:31,359 Speaker 1: tim arow November fifth or so thrust to get an 349 00:19:31,359 --> 00:19:34,440 Speaker 1: accurate account. Gee, I wonder why we might take until 350 00:19:34,480 --> 00:19:37,040 Speaker 1: November fifth. Anybody know why that's a magic date. What's 351 00:19:37,040 --> 00:19:40,679 Speaker 1: going on in thirds? Just coincidence? I would say, Steve, Well, 352 00:19:40,840 --> 00:19:43,320 Speaker 1: I think this is the point. You put it so well, 353 00:19:43,760 --> 00:19:46,280 Speaker 1: it's this gas lighting, it's it's some of the greatest 354 00:19:46,320 --> 00:19:50,240 Speaker 1: gaslighting we've ever seen, and that isn't even coming from 355 00:19:50,320 --> 00:19:52,200 Speaker 1: you know, the scientists or people looking at the data. 356 00:19:52,440 --> 00:19:55,119 Speaker 1: That is coming from the politicians and you, miy I. 357 00:19:55,200 --> 00:19:58,000 Speaker 1: I want to ask him two questions. Number one, for 358 00:19:58,119 --> 00:20:01,920 Speaker 1: people listening, if you want to understand more about the numbers, 359 00:20:01,960 --> 00:20:05,240 Speaker 1: if you want to dig down more deeply, are their names? 360 00:20:05,359 --> 00:20:09,159 Speaker 1: Are their people? Are their scientists? Are their researchers that 361 00:20:09,720 --> 00:20:13,080 Speaker 1: folks to look for and read what they're saying. I 362 00:20:13,119 --> 00:20:15,560 Speaker 1: would urge people to go back to Johnny and Eades 363 00:20:15,600 --> 00:20:19,919 Speaker 1: at Stanford University his very first white paper on March seventeenth, 364 00:20:20,280 --> 00:20:22,399 Speaker 1: which all he did, He's the head of their public 365 00:20:22,480 --> 00:20:25,119 Speaker 1: health department at Stanford, which is a top five medical 366 00:20:25,440 --> 00:20:27,840 Speaker 1: school in the country. All he did was break down 367 00:20:27,920 --> 00:20:30,560 Speaker 1: the i FR and the CFAR from our original guinea pig, 368 00:20:30,640 --> 00:20:33,440 Speaker 1: the Diamond Princess cruise ship, and project out what that 369 00:20:33,520 --> 00:20:36,720 Speaker 1: would be for our American population. And he nailed those 370 00:20:36,840 --> 00:20:41,000 Speaker 1: numbers back on March seventeenth exactly. He was considered a quack, 371 00:20:41,400 --> 00:20:44,520 Speaker 1: but he's turned out to be exactly right. Oxford University, 372 00:20:44,600 --> 00:20:47,840 Speaker 1: the number one university in the world. Numerous epidemiologist at 373 00:20:47,840 --> 00:20:51,280 Speaker 1: Oxford had been calling bs on this all along. So 374 00:20:51,600 --> 00:20:53,439 Speaker 1: I mean I would look at a doctor. Tony Katz 375 00:20:53,560 --> 00:20:56,480 Speaker 1: at Yale University is another one. I mean, there's a 376 00:20:56,600 --> 00:20:59,239 Speaker 1: long list. That's what's been fascinating about this guy's from 377 00:20:59,280 --> 00:21:02,400 Speaker 1: the very beginning. When I started poking at the Imperial 378 00:21:02,480 --> 00:21:04,960 Speaker 1: College model and realized that their math did not add up, 379 00:21:05,240 --> 00:21:06,480 Speaker 1: I was like, you know, this is going to be 380 00:21:06,680 --> 00:21:10,080 Speaker 1: like a climate change debate. It's gonna be Steve days Breitbart, 381 00:21:10,240 --> 00:21:14,159 Speaker 1: Michael Knowles against academia. Right. What blew me away is 382 00:21:14,240 --> 00:21:17,719 Speaker 1: how much of academia all over the world has been 383 00:21:17,800 --> 00:21:20,879 Speaker 1: pushing back on the It's Steve, let me let me 384 00:21:20,920 --> 00:21:26,520 Speaker 1: ask you. I mean, look the institutions you mentioned, Stanford, Oxford, Yale. 385 00:21:26,680 --> 00:21:29,480 Speaker 1: I mean those are not fly by night institutions. Okay, 386 00:21:29,560 --> 00:21:32,440 Speaker 1: Yale is, but but the other two are not. I 387 00:21:32,560 --> 00:21:35,320 Speaker 1: knew that was coming. You can't give me a hagging 388 00:21:35,400 --> 00:21:38,680 Speaker 1: curveball like that and not expect me to swing. But like, like, 389 00:21:38,920 --> 00:21:44,320 Speaker 1: how do you get researchers and physicians and doctors at 390 00:21:45,280 --> 00:21:48,399 Speaker 1: the most esteemed academic institutions on the face of the planet. 391 00:21:48,800 --> 00:21:51,640 Speaker 1: How do you get them dismissed over and over again 392 00:21:51,680 --> 00:21:55,400 Speaker 1: as quacks that seems an odd, an odd dynamic. What's 393 00:21:55,400 --> 00:21:58,240 Speaker 1: going on? I wish I knew? Now I will tell 394 00:21:58,240 --> 00:22:00,239 Speaker 1: you this. You mentioned the whole thing that you were 395 00:22:00,280 --> 00:22:03,399 Speaker 1: told at Sepack about asymptomatic spread. Guys, when somebody in 396 00:22:03,480 --> 00:22:05,800 Speaker 1: your office is come ins and says, you know, my 397 00:22:05,920 --> 00:22:08,199 Speaker 1: kid at home, I think has the flu. If they 398 00:22:08,320 --> 00:22:10,720 Speaker 1: have no fever, no cough, no symptoms, do you make 399 00:22:10,760 --> 00:22:13,720 Speaker 1: them go home? No? Nobody does that, right, Okay, So 400 00:22:13,920 --> 00:22:16,240 Speaker 1: why did we do that with this? You know? Doctor 401 00:22:16,280 --> 00:22:18,919 Speaker 1: Scott Atlas was on my show on April twenty seventh 402 00:22:19,280 --> 00:22:22,280 Speaker 1: and he said something very interesting, which is, we have 403 00:22:22,480 --> 00:22:26,600 Speaker 1: suspended the natural laws of biology, immunology and virology. We've 404 00:22:26,640 --> 00:22:28,720 Speaker 1: acted like we have We don't have hundreds of years 405 00:22:29,000 --> 00:22:31,720 Speaker 1: and decades of established science on this, and I can't 406 00:22:31,800 --> 00:22:34,640 Speaker 1: figure out why. Thankfully, he was brought into the White 407 00:22:34,640 --> 00:22:37,320 Speaker 1: House Coronavirus Task Force about a month ago, and I 408 00:22:37,440 --> 00:22:40,480 Speaker 1: think you'll notice the difference and messaging from the White 409 00:22:40,520 --> 00:22:43,800 Speaker 1: House since he was brought in. He gave a fantastic 410 00:22:43,880 --> 00:22:46,119 Speaker 1: press conference a couple of days ago with Governor De 411 00:22:46,200 --> 00:22:49,440 Speaker 1: Santis down in Florida. Because I don't think this is 412 00:22:49,480 --> 00:22:52,000 Speaker 1: about science. The question that you asked, had I think 413 00:22:52,040 --> 00:22:55,200 Speaker 1: that we've gotten into the politics of this, and I 414 00:22:55,359 --> 00:22:57,960 Speaker 1: think that's what's really sad is it's made it so 415 00:22:58,119 --> 00:23:01,199 Speaker 1: that suddenly a drug that's been ft approved for sixty 416 00:23:01,280 --> 00:23:04,399 Speaker 1: years is not healthy, despite all the studies around the 417 00:23:04,440 --> 00:23:07,240 Speaker 1: world that showed that it has at least some marked 418 00:23:07,359 --> 00:23:10,920 Speaker 1: success early on as a treatment. The level of politicization 419 00:23:11,040 --> 00:23:15,200 Speaker 1: of this is just frankly despicable given to human lives 420 00:23:15,240 --> 00:23:19,480 Speaker 1: that are at stake. College football, you have strong thoughts 421 00:23:19,560 --> 00:23:24,640 Speaker 1: on this, Share your thoughts on college football. Well. According 422 00:23:24,680 --> 00:23:27,040 Speaker 1: to CDC, those fifteen to twenty four right in the 423 00:23:27,080 --> 00:23:29,639 Speaker 1: age you're playing high school and college football are twelve 424 00:23:29,680 --> 00:23:32,440 Speaker 1: point nine percent of the population, and yet there's zero 425 00:23:32,520 --> 00:23:35,600 Speaker 1: point two percent of people who have died with COVID. 426 00:23:36,000 --> 00:23:39,320 Speaker 1: Only one point five percent of deaths since March of 427 00:23:39,680 --> 00:23:43,200 Speaker 1: those in that age group have been with COVID. There's 428 00:23:43,240 --> 00:23:45,600 Speaker 1: not a single recorded case that we can point to 429 00:23:45,760 --> 00:23:48,959 Speaker 1: around the world of a student giving a teacher COVID 430 00:23:49,400 --> 00:23:53,080 Speaker 1: why we're not playing football when co morbidities are the 431 00:23:53,240 --> 00:23:55,720 Speaker 1: number one cause of death with COVID, ninety four percent 432 00:23:55,760 --> 00:23:57,960 Speaker 1: of the deaths have been with co morbidities. Because the 433 00:23:58,040 --> 00:24:00,679 Speaker 1: number one thing this virus does is weaken your immune system. 434 00:24:00,920 --> 00:24:03,000 Speaker 1: So if you're well, Steve, I have to ask, is 435 00:24:03,119 --> 00:24:05,720 Speaker 1: I notice you're using very specific language. You're saying dying 436 00:24:05,920 --> 00:24:08,640 Speaker 1: with COVID, which I think ties into this ninety four 437 00:24:08,680 --> 00:24:11,440 Speaker 1: percent six percent number that has been going around. What 438 00:24:11,560 --> 00:24:13,240 Speaker 1: does that mean? I mean, what's the distinction here and 439 00:24:13,320 --> 00:24:15,879 Speaker 1: what are we talking about with the consent? Through August fifteen, 440 00:24:15,960 --> 00:24:19,080 Speaker 1: CD says six percent of deaths were people who walked 441 00:24:19,119 --> 00:24:22,359 Speaker 1: in who are otherwise healthy, got COVID nineteen and died. 442 00:24:22,760 --> 00:24:25,000 Speaker 1: The other ninety four percent were people who had an 443 00:24:25,000 --> 00:24:30,040 Speaker 1: average of two point one comorbidities, meaning that this virus 444 00:24:30,240 --> 00:24:33,400 Speaker 1: weakened they're already weakened immune systems. It does not mean 445 00:24:33,760 --> 00:24:36,520 Speaker 1: that ninety four percent of deaths are fake new That's 446 00:24:36,560 --> 00:24:38,600 Speaker 1: not what it means. What it means, though, is the 447 00:24:38,680 --> 00:24:41,359 Speaker 1: way that this virus attacks the human body. Is it 448 00:24:41,480 --> 00:24:46,080 Speaker 1: specifically targets those who already have an immune deficiency. So somebody, 449 00:24:46,119 --> 00:24:48,000 Speaker 1: you guys well know that I work with Glenn Beck 450 00:24:48,040 --> 00:24:51,320 Speaker 1: at the Blaze. He has autoimmune disease. He would not 451 00:24:51,560 --> 00:24:54,959 Speaker 1: normally have to self quarantine during a typical flu season, right, 452 00:24:55,320 --> 00:24:59,480 Speaker 1: but because this virus specifically goes after weakened immune systems 453 00:25:00,000 --> 00:25:02,800 Speaker 1: and did self quarantine from our studios for about two 454 00:25:02,840 --> 00:25:06,159 Speaker 1: to three months. And so it is a very vicious virus. 455 00:25:06,240 --> 00:25:08,920 Speaker 1: I don't want to understate that whatsoever, but there's a 456 00:25:09,119 --> 00:25:12,360 Speaker 1: very targeted demo that it goes after. And that's why 457 00:25:12,640 --> 00:25:15,520 Speaker 1: this policy that we have done of attacking mice with 458 00:25:15,680 --> 00:25:18,320 Speaker 1: elephant guns. Guys, I'll leave you with this. I mean, 459 00:25:18,400 --> 00:25:21,040 Speaker 1: look at Hawaii. Hawaii has had some form of a 460 00:25:21,119 --> 00:25:24,840 Speaker 1: mask mandate since April twenty fourth. They're two thousand miles 461 00:25:24,880 --> 00:25:28,280 Speaker 1: away from the next closest civilization. They've seen as seven 462 00:25:28,440 --> 00:25:32,320 Speaker 1: hundred percent increase in cases there Hong Kong, where they've 463 00:25:32,359 --> 00:25:34,680 Speaker 1: been masking up since they're on their third wave of 464 00:25:34,760 --> 00:25:37,840 Speaker 1: lockdowns in Hong Kong. Now the Philippines on their second 465 00:25:37,880 --> 00:25:41,320 Speaker 1: wave of lockdowns, where again these are isolated places. The 466 00:25:41,400 --> 00:25:45,280 Speaker 1: Philippines Hawaii high mask use. And yet in the end 467 00:25:45,440 --> 00:25:48,439 Speaker 1: the virus makes its way through. So we're not going 468 00:25:48,520 --> 00:25:51,320 Speaker 1: to stop it from getting through. The question is can 469 00:25:51,400 --> 00:25:53,680 Speaker 1: we stop it from getting to the people that it's 470 00:25:53,760 --> 00:25:57,160 Speaker 1: most going to hurt? That is the question. And stay 471 00:25:57,240 --> 00:25:58,960 Speaker 1: to be clear, I'm not sure you can refer to 472 00:25:59,040 --> 00:26:05,760 Speaker 1: California as civilization also a very true point. You might 473 00:26:05,840 --> 00:26:08,320 Speaker 1: even say that's a scientific point, gentleman. That's all the 474 00:26:08,359 --> 00:26:10,320 Speaker 1: time we have. Steve, thank you so much for being here. 475 00:26:10,359 --> 00:26:12,840 Speaker 1: You can always go, and I would highly recommend you 476 00:26:12,920 --> 00:26:15,879 Speaker 1: go check out the Steve Days show over at the 477 00:26:15,960 --> 00:26:19,440 Speaker 1: Blaze and the Senator Cruz. I will see you in 478 00:26:19,640 --> 00:26:21,840 Speaker 1: just a little while for our next episode. In the meantime, 479 00:26:22,200 --> 00:26:33,879 Speaker 1: I'm Michael Knowles. This is Verdict with Ted Cruz. This 480 00:26:34,119 --> 00:26:36,920 Speaker 1: episode of Verdict with Ted Cruz is being brought to 481 00:26:37,000 --> 00:26:40,399 Speaker 1: you by Jobs, Freedom and Security Pack, a political action 482 00:26:40,480 --> 00:26:45,480 Speaker 1: committee dedicated to supporting conservative causes, organizations, and candidates across 483 00:26:45,560 --> 00:26:48,920 Speaker 1: the country. In twenty twenty two, Jobs Freedom and Security 484 00:26:48,960 --> 00:26:52,440 Speaker 1: Pack plans to donate to conservative candidates running for Congress 485 00:26:52,600 --> 00:26:55,280 Speaker 1: and help the Republican Party across the nation.