VAERS show HOT LOTS Within Lots!

Video Transcript:

Welcome to EGLE Tuesday, December 7th, Silicon Valley. And this is my weekly audit and housekeeping. I feel good, making progress. So let's just jump in. So currently we're at a 927,000 adverse events with 19,532 total deaths. Now let's jump into my dashboard. And here's that 19,532 total deaths. This is my death only dashboard. Okay, so I'm just going to walk by and in steps here. So now for just domestic. I'll give that a second is saying 8986, 8986 deaths. So let's just jump over. So walk through here. Whoops, that's the foreign. The 8986 deaths. Okay, and foreign says that it's 10,546 deaths. So let's just bet that real quick. And come over here to just click on just forans. Give that a second 10,546. Okay, so now this tab. Just this, this dashboard here is organized by a lot numbers. And you could see that the lot numbers that didn't have anything populated is way up at the top and skewing all of the numbers. Look at that. That's 3,800 deaths that don't have any lot number. And then an additional 888 that are unknown. It just had question marks or some strange characters or it says they don't have it or requested. I just lumped them. I lumped them all in there. And stuck them under that. So there is my vetting process so that you know my stuff is true and blue. I mean, truth. Okay, so now let's we're looking at just domestic. So now I want to eliminate, I want to get rid of the blank right here blank in the unknown unknown is in here somewhere. Oh. Okay, so. And let's take a look at it too. Let's see blank. It's all foreign. So this popping up in here that happens to all before and wow. Is that true? Oh, because I'm looking at foreign. I'm sorry. Let me jump in and look at them just domestic only. And now we're looking at domestic. And let's see for blank. It'll show me real quick. Oh, okay. So the blue states have a thousand. The red states have 800 unknown where they didn't populate, but it is domestic has 641 and the territories. Here's my color scheme right here. So this is the green. Now this is the overall. This little graph here is the overall. How many there are total of this number up here of the 8900. Okay, so that's how that works. So you get the color scheme going. I got a little color scheme going. But I want to eliminate the first two because it's throwing off. And it's making all these little. Histogram little bars real tiny. So what I'm going to do here is I'm going to come down here. Do this little maneuver and say just filter only the selected. So I just selected everything but. But those two. So now it it changes right. And now let me do something else here. I'm going to come over here to. Give it table analysis and I'm going to expand everything. So grouped all together there and think. And then. Expanded. I just expanded everything. Now it's this. So you see the gray, the un. See the unknown really the unknown states domestic states really screws stuff up. So. You know, this is this is basically kind of how I found how I found the Puerto Rico. The Puerto Rico one. But anyways, this is this is how it goes. And here's the other thing. I could look at something and go let me find something that looks out of the ordinary. Now what I'm looking for. Is the red and blue close together, but something like. Like something like this. This blue one is so much bigger than this red one. And make sure that they're the same. See, these are the same. Batch number. E and 62 O2. And there's so many more blue ones. So 62 O2. Now I could either scroll down here. It is right here. It's actually this one right here. And at first glance, yeah, at first glance, you could say, well, yeah, there's there's more. There's a lot more. Blue states than red states. It drilled down a little further and see that that. You know, is it one state in particular? Is it that much in comparison? So. I'm doing it old school. I. And this impart is for Mr. Craig and the Craig, the Craig's and the Jessica's of the world. And by that, I mean this guy Craig. Part of Cooper. Who. I. I made contact with. So he's actually, if you guys have followed me, you know what's going on. Jessica did an analysis on his stuff. But he's he's carrying out. Death only as the first. The first stuff, you know, his initial offerings, he's calling it toxicity. And. But, you know, there's, there's a lot, there's a lot of. There's a lot of reports that are not serious. None of the above, they're not, they don't have office visit or emergency or hospital or not. And the box is checked off. Therefore, it goes into none of the above and has to do with a lot of administration. Heirs. Ghosts and chairs, temperature, heirs, excursion, air, they're calling them excursion. All kinds of airs. There's a ton of airs. There's over 40,000. There's. But that really spew the numbers when you're doing this kind of analysis. So I had said. I had a lot of, you know, I, you know, I, in my analysis of, uh, desifices stuff and, and crag stuff to say. You know, it'd be ideal to. You know, do this analysis. Yes, but also do an analysis where we basically exclude. The low level stuff and just leave the high level stuff. And that's what Jessica. I mentioned as much to when she called it, you know, explaining the definition of toxic lots and. So be your adverse effects and all of that stuff. So. Here's I'll leave a link to her, to her article as well. It's, it's amazing. So is crag's work. And I think my work's pretty cool too. So. So jumping back over here. And now this is death only, right? And. And so something like this. This is, this is what I'm running into is that there's so much stuff under the unknown state. That excuse the numbers so much like right here, I'd like this one right here. And so, you know, I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. 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I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I think that's what I'm going to do. I'm planning things for this. So, let's look at what that's being done. Okay, I think that's going to be nicer. I think that's going to be better. That's going to be better. Alright, let's see how we're going to do this, I think I'm going to do this, okay, so let's try next? Mixed. Let's see how we're going to do it. Let's see how we're going to do this. Okay, let's try next. Oops. Okay. Hold on. Find All. And it's quality 3220 mh. Let me see here. Let me see here. One second. Okay, here, okay. Here it is. Here it is on this list, right here where There's 25. Right there off the bat, that looks crazy. How could that be? And what territories are there? Is it just Puerto Rico? Is it other ones? Let's, we want to know all that stuff. But here's how it looks on the graph here, on the right here. It looks like, let me just keep scrolling up. I'm looking for a green that sticks out like a sore thumb. Right there. That's what it looks like right here. I mean, wouldn't anybody go, what's this? All right. So that was, that was that batch numbers 032H28. So let's take a look at it. What do we know about that? What do we know about this? Okay, so first of all, there's only 206 total cases for this lot. So it's not, you know, from a quick glance, from a bird's eye view, from an eagle's eye view. This ain't going to pop up on anybody's hot lot list, on anybody's scatter plot 200. When you're looking at 5,000, okay? Maybe when you're talking about that toxicity and exclude, you know, the low level stuff, it may show up, but still it's hard to see. Okay, so, you know, this is everything. So I organized it by the location and then organized it by all the different event levels. So there's nothing that jumps out at you. Looking at just wheeling down the wheeling down the list until you look at Puerto Rico, this right here. That number pops out at you. And they didn't have too much of every of all the other stuff. Emergency rooms, you know, life threatening. None of that stuff. Just bam, just not serious, couple not serious. And bam, 25 deaths. Okay. All right. These are all of the deaths. So it's, you know, I did a kind of a micro view of just, just show me the deaths just for this. So again, here it's starting to, it's starting to rise and starting to say, hey, this looks curious. 80% of all of the deaths are issued to Puerto Rico. Out of the 31 total deaths, something, something doesn't smell right. All right. What else can we. So, what else can we do? What else can we do? Surmise or deduce from Puerto Rico? Well, Puerto Rico. Just, just from the. Birds, I view they only have 100 while they have 115 deaths total in all the back to everything. I'm attributed to Puerto Rico. And by back. I think they're going to be able to say their deaths. And what's what's interesting is that. You know, ones these two Z's, three Z's, four Z's, five Z's, and six Z's, but they haven't had a, they haven't recorded a death by a back date since July 7th. Since July 7th. Wow. Wow. And so that just accentuates all this. Holy Toledo. What's really going on? Okay. All right. So back. Puerto Rico. Let's, let's learn everything about Puerto Rico. Puerto Rico has a toe. This is their total. Their total. There's 115 deaths. 2700 total cases. You know, half of it's half of it's not serious. You know, under 100 hospitalizations. 125 permanent disabilities and 115 deaths. Okay. Puerto Rico. It's a whole country, little country, little island. But okay. The next thing is just a look on the CDC, which I already did. Look at my past videos on the Puerto Rico analysis that I already did. But. Against their uptake. Well, how many, how many shots did they, how many shots in the arms? I'm not, you know, how many people got at least one dose in Puerto Rico? That's apples to apples. I don't care about two doses or three doses and boosters and all of that. Just watch. How many people got at least one dose? And that was what my previous video was all about. But anyways. This. Is a great example. Okay. So. Okay. So now come over here. Let me show you now. I'm going to kind of poke a hole in my own in my own. Audit here. So this is, this is the source code where all my, the pivot tables and my graphs are pulling from all this data. And right here, I can see. I'm looking at just this, just this lot. And this is deaths only. And I'm getting a. You know, the, the 30. What was it? The 31, the 31 deaths. And 31 deaths. Okay. So then I can see, well, here, here's my biggest problem. Here's one that came. That was received by received by bears on 1115 on November 15th. We're at December 4th. Okay. Let's take a look at it. Okay. So here it is. There is the lot in question. From Michigan. He was vaxed on January the first, but his onset was 1031. And he died November the 13th. When did it first appear? I mean, does it first appear November 19th of that's day to date. It actually dropped to the public. So we're able to see it very, for the very first time on November 26th. Okay. So first appeared is the week before it's by data. Data up to November 19th. First appeared to us. To the public November 26th. So not this last drop on this pass Friday, but the week before that. So here is the quandary that everybody has. And I figured out even the expose. And so it's the same thing. And the reason that it's. There's a set up. Janky. Um. It's the one. Everyone. Um. See it's a while. It's still coming in. It's still coming in. And. You know, I mean, it's, it's even it. Accentuates it. A little, a little tiny little bit less. Like take. Add one more death and apply it to the domestic. So that the 25 don't look so bad. Um, but you know, no big deal. I just wanted to point that out. I actually was trying to point out something. A little bit here. Is this guy? Oh, that is weird. 207. Oh, it first appeared here. I'm sorry. Look at that. Look at that. It first appeared here. And that's 207. Man, this is eagle eye. That's where it first appeared. But. Look at this one. It's got both of them. It's. It's both of them. So they added. So they added this one. This, this one. Is this. Now it has two. See. Boy, these guys are shady. I just realized this. Making my video right here. This is what it looks like. So then that means. 86. 69207. Hold on a second. 86. 92. 07. 186. 186. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. 192. I guess I did, but it's the first and second. This believes that there's only a couple left reasons. when I was doing my prep work here trying to say how janky this is, when did this, did this guy, how do you attribute this death? Do you attribute it to this slot or this slot? Or when you're doing your scatter plots and your SQL or your coding in R and you say apply it to, is it popping up twice for you guys? Once in this one and once in that one, the on my spreadsheet, I apply one, the highest level event is what you get and you don't get the extra stuff. There's the event. So I'm not, so I eliminate this whole disclaimer bullshit right over here about, where is it? About, oh shit, about this right here. This right here, 170%. See, I asked, I asked, while I asked med alerts, show me all the people that died. These are all the people that died. That's 100%. That's perfect. That matches this. But then those, out of those, 8,900 records, there's some records that also say permanent disability and death. All 698 also say office visit and death. See like that. So that's how we arrive at a number like this saying, oh, there's 15,000 total event outcomes. So it's semantics. It's the minutiae of the terminology. What are we talking about? Event outcomes or cases? So that's why I, you know, I had to say there's a priority level here. And even the way they organize this is not the priority level. Death is priority one. That's the that and then permanent disability. And then I put birth defect. That's the only way I get it to work out. That's priority number three. And then life threatening. That's four. And then hospitalizations, both of them lump them and then emergency and then office visit. And they throw in this recover. Doesn't mean shit. But when it's not, when it's when none of these are checked off, it comes out as none of the above. If you look at the wonder, they categorize it as none of the above. So anyways, that's that's that's what I'm saying is that's what I realized when expose was doing their doing their article and I was crunching that. And I'm like, how did they get to 5,000 5,000 events or cases or that I realize, oh, shoot. No, they're doing it from like SQL. They're not, you know, so it's stuff is being counted, is doubly counted, triply counted in some cases. And so I'm wondering, I'm wondering, and this now this question is specific to Craig and to just I'm wondering, how do you, how do you come back against that in your in your queries? You know, I asked my buddy Wayne, the Mr. SQL guy at Veris analysis dot info and pose that question to him and talk turkey with them. I said, you know, I know in SQL, you can do it. And embed that logic to say, hey, you know, only one ID gets one event level. And then, but this, this, this is a little harder to, you know, and which one do you, so where do you apply this death? I mean, this guy died a positive for SARS on Halloween. I mean, and then in the fine print, it says that he was vaccinated January and February. Wow, I mean, you can apply it to either, but you should only apply it to one, not to both. Where does that come out at your scatter plot? That's a good question. I don't know. And for me, I do, doing it in my little simple Aztec methodology, you know, I don't get all high tech, you know, but, you know, the sun goes up and the sun go down. That's one, sun go up, sun go down. That's two and start from there. Takes a little longer, but, you know, you know, it's just as valid. So anyways, so I apply it, you know, by the way, I copy and paste and do all my pivot tables and all of that stuff. I basically apply it to the first one. You know, that had I, you know, that's my simple method. I apply it to the first one for no for no good reason. Maybe I should apply it to the second one, actually. But, you know, maybe, maybe there's maybe there's some elixir in the first one. It really is like a bio weapon. So basically what I'm basically saying is that is that I want to make a finer point and not just say, not just be just kind of general and broad about saying hot lots. I'm starting to realize when I'm doing my, my analysis, I'm starting to realize, wait a minute, it's like, it's like, there's a hot lot within a lot. Could that be possible? A hot lot within a lot? Yeah, yeah, I think that I think something like that's going on. I need to do more research. And, you know, I am, but I just wanted to take a break and pull over and make a video and show you guys, you know, the general public and the Jessica's and the craigs of the world. You know, God bless you guys. Collaborate craig. Thank you for reaching out. Jessica, I know you're busy. I, you know, I try to sharpen iron with you, but, you know, you're the doctor. You know, you know what you're doing. So I just point this out then. So carry on with your bad self. You know, I love, I love your, I love everybody's work. I love anybody who attempts to use their skills to show the masses because the masses don't get to see this information. They're depending on people like us to show them in some digestible manner. Um, you know, what, what this complicated data means. You know, so here it is. This is my offering. So you guys got bless you. I love you. Everybody, even the Jews, even China, even, you know, all those people that continue to bash and say, you know, it's just a Trump vaccine or the China did it or it's Jews, you know, bullshit. Take your, you know, take your show someplace else. Get off my channel. I don't like that stuff. We got to take off our uniforms and the vaccine is the litmus test as far as I'm concerned to tell me what side you're on. You're either being paid or you're being played, which is it? This vaccine needed to stop. Uh, it should never have come out. I knew that from the beginning. And I can say that because as you can go back to all my videos, this was bullshit from the very beginning. I'm just waiting for everybody else to catch up. And that's the truth, Ruth. Everybody, everybody knows that. Um, you know, just look at all my oldies videos. And even before I was on this shoot, I was already, I was already saying something is not according to oil. This don't, you know, this the censorship alone was enough to say, nope, I ain't even going to get a Vax because Facebook and Google and YouTube are acting all goofy about this stuff. Nope. I don't even want to take the Vax because something's not right. And now low and behold, now we have data. So, hey, you know, I don't need a PhD to tell me that this is garbage. I just felt it in my bones. Now I'm showing you guys is garbage is bullshit. So, you know, that's the only credit I could take is just I felt it in my bones and I had to be able to prove it to myself. I don't trust anybody. I ain't going to believe anybody. I got to see it for myself. I got to bet it for myself. And I did. And here it is. It's bullshit. It's garbage. It's eugenics. It's a deep population. It's it's it's a binary bio weapon. It's a try, a trinary. What's a three way? Maybe, maybe your flu Vax in 1988 and your shingles Vax in 2002 or 2018 coupled with this new elixir is this is this gives you the Willy Wonka death ticket. You know, these these these this elite cabal is smart. They're not stupid. Maybe it's some kind of some kind of crazy thing like that. Let's look at everybody's Vax history and sink it up if we could do it right. We'll never know. But you know, I wouldn't put it past them some some some multi level thing here that a big ingredients that's going to you know, going to cause you death. All right, you guys. I'm gibber jabbering. You guys got blessed Eagle out.