UVC: Dr. Jessica Rose: VAERS: Key Ways to Prove C19 Jab Harm Causation
The inaugural Understanding Vaccine Causation Conference, convened by World Council for Health Steering Committee Member, Shabnam Palesa Mohamed, took place on Feb. 5, 2022. The WCH Law and Activism Committee brought together legal practitioners, doctors, scientists, and jab victim data and advocacy groups to explore a key question: How are jab adverse events proved?
Dr. Jessica Rose joined the Science in Action panel for her presentation, VAERS: Key Ways to Prove C19 Jab Harm Causation.
[00:00:05] Shabnam Palesa Mohamed: We’re now onto the very last session “Science in Action” and it is my great pleasure to welcome both an expert and my friend, Dr. Jessica Rose, who will be talking to us, I believe, about Bradford Hill as well as VAERS, key ways to prove C 19 jab harm causation. Jessica, you are warmly welcomed. Tell us a little about yourself and why this conference is important and then launch into your presentation.
[00:00:34] Dr. Jessica Rose: First of all, wow, you guys are troopers for putting this together. It’s so important. Thanks for everyone for staying around too, it’s five hours now, guys, we’re heroes already. I am a Canadian researcher. This is my cat checkpoint. He’s always with me in these debates and conversations.
[00:00:55] I currently reside in Israel. I came here to complete a PhD. I stayed to do two post-docs and now I’m kind of here whether I like it or not, and I’ve become a data analyst, I suppose. That’s what I do with most of my time. I became very interested in what’s going on in the vaccine adverse event reporting system of the states a little over a year ago.
[00:01:17] So yes, without further ado, by the way that the talks today have been kick ass. Ted, your talk was amazing, I just have to tell you. And Ryan as well, like really, really good. Everyone’s were. But, um, so I’m going to provide some evidence to support the Bradford Hill criteria, which pretty much is the way that you can provide evidence of causation from biological or epidemiological data.
[00:01:47] All right. So the title of the talk is Assessing Causality from Adverse Event Data. You’ll never hear me say that they’re proved because it’s almost impossible. So I’m going to provide some background for those of you who aren’t aware. I’m sure everybody is. The vaccine adverse event reporting system, or VAERS, is a collection of data reservoir, which was created by the CDC and the FDA in 1990, in order to monitor side effects or adverse event reports that weren’t detected in pre-market tests or clinical trials.
[00:02:22] Basically it came about as the product of vaccine companies having blanket immunity from liability. This was the, kind of ‘solution’, quote on quote, to that problem. But of course we all know that’s not a solution at all. It’s important, but we need to make sure that these companies are liable at some point in human history.
[00:02:45] An adverse event is just a side effect that’s reported in temporal proximity to an injection. Severe adverse event is a death or disability, hospitalization, emergency room visit, a life-threatening illness or a birth defect, by definition. Pharmacovigilance, VAERS is a pharmacovigilance tool. All of these adverse event data collection systems are pharmacovigilance tools, which means that they’re designed to detect safety signals. Causal inference is the process by which we can use data to make claims about causal relationships.
[00:03:24] And how do we provide evidence of causality? So like I said, there’s this thing called the Bradford hill criteria, which I’ll get into which we can use to provide very strong evidence of causal effects.
[00:03:36] So pharmacovigilance, it’s a very important concept. So the science and activities relating to the detection, assessment, understanding and prevention of adverse events, most importantly. And this applies equally before and after approval, which these products still haven’t been, throughout the life cycle.
[00:03:56] So it’s not just that, ‘Okay. We did a clinical trial and everything looks fine,’ then you just start ignoring everything, like the adverse event data that’s coming into VAERS. No, no, no. You have to maintain your diligence and your pharmacovigilance throughout the life of the product.
[00:04:14] And if causation is suspected, which in this case, I think it’s more than suspected. I think it’s very clear. Then it’s of utmost relevance and importance to inform the public. And if you willingly withhold safety data from the public and know that there’s a problem with the safety profile and you continue to administer those products, you’re guilty of malfeasance. I think it’s pretty clear.
[00:04:41] So the talking points today will be on the WHO criteria, that’s the World Health Organization criteria of causality, applying the Bradford Hill criteria to, say, VAERS data. And to ask the question, why is there such a strong denial of causation when it comes to vaccines? It’s not even scientific to do that.
[00:05:04] So, I didn’t know this before I started working on this presentation, you guys probably do, that the WHO actually have a criterion list to disprove or prove causation. They use five criteria, which actually are five of the Bradford Hill criteria that are listed here on the left. And so they’re using the Bradford Hill criteria and they only need five to tick off in the boxes in order to make a good case for causation. They even have a form, which I didn’t know about. Now, I don’t know if they use them or not, but I did find this online and for which, once you fill out this form and you go through their methodology or assessment program, they have six possible outcomes. And one of them actually is that the vaccine caused the injury. So, they do have this process. That’s good, I think that’s good. You’ll find out in the end why. So that’s the WHO definition of causality.
[00:06:04] This presentation is going to be my attempt to go through every single one of the 10 criteria in which nobody does. You don’t have to prove 10, as I just said, you can prove five and then provide good evidence, but I’m going through all 10. And I’m going to try my best using VAERS data and papers and all sorts of reports that I’ve collated to provide evidence for each point.
[00:06:28] So these are the 10 Bradford Hill criteria. There are strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, analogy, and reversibility. A lot of these have overlap and you’ll notice that as I’m presenting data, but they each kind of have their own special quality.
[00:06:52] So let’s get into this. Strength of association can be measured using Chi-square test, which is a categorical test. Recently, I wrote a Substack article on a set of data that came out, which is basically the efficacy data from the Moderna products that came out November 4th. What’s interesting in this document, or the parts of the document that are hidden in the appendix, are not the efficacy data, it’s the safety data that come out. It’s crazy what’s in this document. I think everyone should run after this presentation and go read it.
[00:07:26] This is one of the things that they report in their appendix. When you compare the, SAE here stands for severe adverse event, when you compare the number of severe adverse events in the drug arm, the mRNA 1, 2 7, 3 arm versus placebo, you find a statistically significant difference between the two groups. And you can see that the number of severe adverse event reports was actually higher in the drug arm.
[00:07:54] The P-value was very, very low, which means that this indicates a very strong association. I also looked at the correlation. So if you take any standalone adverse event from VAERS, these are the disability reports from VAERS plotted against the doses administered for Pfizer, and each dot represents the number of each per week. So it’s the intersection point. And then you do a linear regression. You see that the R-value, which is basically, this is the correlation coefficient, and the closer to one that it is, the stronger the correlation. So this is almost one. This is R equals 0.99, which is a really high number and the P values indicate significance. So there’s strong correlation here as well. And like I said, you can pick just about any adverse event from VAERS and see this trend. And I tried out a negative control. I took smallpox data from 2018 against the Covid- 19 adverse events, and I lost the significance. So it actually worked out to be a good negative control.
[00:09:04] This is across the board. The lowest R-value that I found in the standalone adverse events that I looked at was death. And it’s still a 0.94 for the R-value. So it’s reproducible and consistent. I also plotted the data. The number of people fully injected from Our World in Data as a data source against all the adverse events reported in VAERS against the dates. And I did a significance test and, or a correlation linear regression, sorry. And I still found a high R-value.
[00:09:32] You’ll also notice in this plot that the, well, this significance is one in the logistic phase of the curve, which are, sorry, the exponential phase of these curves. And just as a point of interest, eventually I hope the injection rollout is going to slow down. It already is. People are starting to say, no, I don’t want any more of this crap. So we would expect the rate of injections to start to go down soon. But my suspicion is that the rate of adverse events is not going to slow down and you’ll see why as I go through here.
[00:10:02] So the trend toward the continuing rise in the adverse events, I think is going to continue. So to answer our question, I want you guys to answer this question in your own minds. There are going to be 10 yes or no questions. Just keep note of what you think based on what I showed you. The next Bradford Hill criteria is consistency. And the question you can ask pertaining to this point is do all the existing data indicate that A causes B, A being the drugs and B being the adverse events.
[00:10:31] And so these are three of the big, sorry, I didn’t throw SAVAERS in here, but these are three of the big adverse event data collection systems across the world. There’s VAERS on top, the Yellow Card in the UK, and the EudraVigilance System for the EU. And each of these systems has over a million reports in them, which is consistent for all three. And also it’s consistent based on the fact that this has never happened before in any of these systems. Never have we seen, within a year, a million reports for a single product. It’s crazy.
[00:11:05] So consistency, do all the data indicate that A causes B? Bradford Hill criteria is specificity. So is A causing B in specific populations? Now, what you have to do here is look at subpopulations of people who are having something happen to them. So let’s, let’s look at healthy people, healthy young people. I chose two groups here. I chose the athletes. And everyone’s heard the stories of perfectly healthy fit, young athletes just dropping dead on the field. And this is a phenomenon and you see the number here. It’s 108. And the background rate, according to Josh [inaudible], who’s done research on this is about five per year. So we’re way above background for this. This is an NFL player here and he died at 37, shortly after his, I think his second dose. Don’t quote me on that. Pfizer.
[00:11:59] And another example of this, it can be found in our kids. Everyone has heard, of course here that myocarditis is becoming a thing in children, which is bizarro world. And they’re calling it rare and mild, and it’s neither of those things. And this is the CDC’s own bloody data. And you can see that the above background reported a number of cases that they observed – it’s off the charts. So the specificity for the subgroup of athletes, healthy, dropping dead from heart attacks and the young people who are young and healthy and fit, you know, no senescent cells. Succumbing to myocarditis and heart problems. I mean, it’s pretty clear to me. You can answer the question is A causing B specific populations?
[00:12:45] Temporality. This one is easy. Does A come before B? That’s really the only question you have to answer, but I want to push it further and say that the shorter the timeframe between those two points, makes an even stronger case for this point and causality itself.
[00:13:01] So I published a paper back in May, which provided supportive evidence of causation by generating plots where the X axis was the difference between the injection date and the onset date, against the percentage of the adverse events, according to standalone adverse event or groups. And what I found was that there was a significant clustering around zero and one, which meant that in most of them, it was about 50% of the reports were being made within 48 hours.
[00:13:31] A lot of people said, well, that’s just because of the psychologic, you know, nobody’s going to report after a certain amount of time, that’s just that phenomenon. And I’m not denying that that’s a factor, but it doesn’t explain everything. And it definitely doesn’t rule out causation.
[00:13:46] So this is an example. Using anaphylaxis as an internal positive control. Everybody here knows that anaphylactic shock is an acute reaction to a trigger. So 92% of the reports in the anaphylaxis reports were made within 48 hours. That’s great. You know, 87 were made within 24. And by the way, I want to remind everyone, this is done by day. So within 24 hours could literally mean 10 minutes after the shot. And within 48 hours could be, you know, 25 hours. So just bear that in mind, these are loose. And it takes time to file a VAERS report and get it into the front end system.
[00:14:24] So if we’re seeing something within 24 hours, that’s fast. I also show an example of the cases of reports of children aged five through 11. There are millions of injected kids, five to 11 in the states. Now within one week of them giving the go ahead on November 4th, there were over a million children injected. And the percentage of the reports that were made immediately, I mean like really immediately, as I explained, within 24 hours was very high. Now, as of last Friday, yesterday, it’s 84%. So this is pretty strong evidence of temporality.
[00:15:00] So does A come before B? Dose response is next. Everybody’s heard of myocarditis, I’ve already mentioned it in this presentation. But this is the heart, the human heart. So just a schematic, but it does the job. The myocardium is the muscly layer in the middle of the heart that allows the heart to do this gorgeous beating thing via the blood. So these are the inflammation of this inner lining.
[00:15:26] I’ll also point out that there is the phenomenon of pericarditis and endocarditis, which is inflammation of the outer layer of the heart and the inner layer. So if you look at all the myocarditis reports, only in the domestic dataset, and you plot the data by age group, and according to dose, you’ll see that in children, especially 15 year old boys, you can’t see gender data here, but take my word for it. 15 year old boys, there’s a four times higher reporting rate of myocarditis. So something happens. A lot of people are claiming, and I also agree with this, that there’s a cumulative effect of these shots. The more you give them and the shorter duration between the two is giving like a double punch to whatever organ system or whatever, you’re, you’re having the injury in. So this is indicative of a dose response whereby the cumulative effect is showing upon the second dose. So does, does more of A result in B?
[00:16:31] Plausibility. I’m going into this from two points of view, first one is biological plausibility. And there are two mechanisms of action that I can think of that would answer this question pretty easily. First of all, the spike proteins have been pronounced to be cytotoxic.
[00:16:48] Dr. Jessica Rose: I think that’s pretty clear by now and something that’s not being talked about as much, which I’ve taken a keen interest in lately are the toxicity of the lipid nano particles themselves. Maybe toxicity isn’t the right word, but maybe it is. So in order to go into that, I want to just give everyone a good background on both of these components. The lipid nanoparticles, the fat bubble, that encases the mRNA as a protective thing, which is used to administer the payload of the mRNA into the body. These are manufactured out of four different fats in the Pfizer products. Some of these fats are for evasion of immune components, macrophages, for example, but all of them come in a certain concentration and they’re mixed together in a certain way to enhance the deliverability of the the payload, which is the mRNA.
[00:17:43] One of these fats is called a Cationic lipid and I’ve been talking about this for like two years. These are highly, highly toxic to cells. What happens is the mixture of these, these fats, and when they gain entry into cells, they actually damage the membrane upon entry. So they’re harmful to cells as they enter and once they’re inside us. The cationic lipids are the ones that kind of surround and protect the mRNA within the fat bubble. And just a side interest here because I love scale diagrams. This is the SARS virus on the right, uh, the top, right, compared to the size of the LNPs. And they’re both about a hundred nanometers, which is really cool. I’m just a nerd. So I think that’s really cool. for those of you who don’t know what the spike protein is and the difference between the spike protein and the SARS virus and the alleged spike protein that’s being manufactured according to the mRNA template, in these fat bubbles, this is a spike protein on the coronavirus. Corona actually means crown in Latin. So the reason why we call these Coronaviruses is because they’re crowned with all these spike jewels on their surfaces, which are actually embedded in the membrane. These undergo confirmational changes as they meet their cognate receptor, which is the ACE-2 receptor in just about every human cell you can find. And so they undergo this confirmational change to allow entry into cells via fusion of membranes, eventually. And these spike proteins are different in two really big ways that I can think of off the top of my head, than the spike proteins that are encoded by the Pfizer and the Moderna products.
[00:19:31] These are different by the way, these are modified spike proteins. There are two pro liens substituted for two other residues in the formulation. I think of the Moderna product and, and the Pfizer don’t quote me, which result in the confirmation of the spike protein staying in the locked, like the closed locked phase, locks it in the closed phase. So the hypothesis was that this would prevent binding and downstream signaling of ACE-2 because once ACE-2 in the, in the human cell, uh, the receptor’s found then this whole series of downstream signaling events, ensues, and dah, dah, dah, we won’t get into that.
[00:20:11] Another huge modification that they made was that they replaced the uridines with pseudo uridines. And the reason they did this was definitely on purpose because it makes them less immunogenic. The toll-like receptors are, it masks them from toll-like receptors and the innate immune response is general. So it makes it more polarizable by the human body and the immune system. And I stole this little far right bottom picture from Robert Malone’s latest presentation, which was really good. This is a scale, uh, schematic of a spike protein on the SARS virus and an antibody. Cause I think a lot of people think that antibodies are like way smaller than the spike protein, but they’re not. So you basically get one or two that can find the spike protein to neutralize a a virus. I just thought that was cool too.
[00:21:08] So spike proteins, uh, mRNA, which encodes these spike proteins is processed. And as part of the processing, and as part of our gorgeous immune response inside the cell, these little bits of the proteins are processed and mounted on these special receptors called MHC class one and two molecules, which basically tells the T-cells and the CD four positive and the CD eight positive. And also the B cells that, hey, we have these proteins that are not supposed to be here, which basically makes them targets for extermination by CD eight positive T-cells.
[00:21:51] These spike proteins can also embed in the, impregnate themselves, I love that terminology, into cells and also make some targets for neutralization. Which is problematic for our cells, right? And here’s just a sample of some of the papers that do address the problem of toxicity of Cationic lipids. It’s a very, it’s a well-known thing. They claim that, you know, well the concentration’s not high enough to cause problems…agh, I’d rather not have it injected into me, personally. And also some problems with the spike and, and I’m missing a whole bunch here. So yeah, I, I know that there are a lot more papers.
[00:22:30] But I will point out here that there was recently a paper that came out that provided evidence that the spike protein sticks around for a long time. So even if you stopped getting injected with this stuff and you, you stopped the cumulative effect, it’s still going to take your body a little while to clear these things.
[00:22:49] And this is the second point of plausibility. These two companies that are producing the mRNA based gene therapies, they don’t have a good track record. Ted knows this better than anyone. Pfizer has had to pay out the most money in, in, in harm, you know, anyway, not, not a good track record. Moderna, if you go to the Wikipedia site, not that I’m promoting going to Wikipedia or anything, but they actually say in this bottom little blurb, you probably can’t read it, but I’ll give you my slides, that these treatments would never be safe for humans. They Actually say that. Anyway, you can read about that.
[00:23:28] And so the question, coherence, does it make sense that A can cause B? Is this in line with with what we’re seeing. So, what I’m going to do is provide another example of the Moderna clinical trial lab data, it’s their own data, versus what I’m calling epidemiological data, which is the adverse event data in the VAERS system.
[00:23:49] So what you can see here is another screenshot of one of the tables in the appendix of this efficacy data thing that they sent out. And you can see here, that this is the data for Bell’s Palsy. There are 10,910 different metric codes in, in VAERS right now in the context of Covid products. So this isn’t just about Bell’s Palsy or myocarditis or Guillain Barre, there’s a huge list here. So I picked Bell’s Palsy because I have data in three different ways. I can see there’s a 2.7 times higher rate of Bell’s Palsy in the drug arm. Think about that.
[00:24:29] Like, if you think about that and you say, well, okay, what’s going to happen if we inject a billion people with this and we collect adverse event data, what will we see all? Well, I think we’d probably see something like we see them on the bottom figures here. This is the data from VAERS only from the domestic data set associated with the Covid products, only. Compared to the Bell’s Palsy report for Bell’s Palsy compared to the Bell’s Palsy reports for every single year, going back to 2016. So we have like nothing, nothing, nothing, nothing kind of something, which I dare say is because of the tail end of 2020. And we have a whole lot. So, this is, it’s pretty clear cut.
[00:25:08] And this is also backed up by papers that are coming out in case reports that show that there is something going on here in the context of the injections within temporal proximity with Bell’s Palsy. And specifically, like I said, there’s a ton of other adverse events that have the same pattern, but I just picked Bell’s Palsy as an example experiment.
[00:25:32] Now, most people would say, well, the only real way to determine causation is to do a double blinded randomized control trial. And yeah, okay, that’s, that’s a good way, and we’re not really in a randomized controlled trial now, but we’re definitely in an experiment.
[00:25:47] So I’m going to go out on a limb here and I’m going to say, well, if this is an experiment, if every single person who has gotten an injection is a volunteer member of this experiment and all the data that’s collected thereafter is good to go for providing evidence of causation. So all of the adverse event data that’s been collected in my opinion is good to go, for providing evidence.
[00:26:10] This is very general here. The bar plot on the left represents all the adverse event data for 2021 only against all the adverse event data collected for all the non-Covid vaccines. And there were millions of vaccines administered in the U.S in 2021. Millions. Probably hundreds of millions. So here you see a stark contrast between the number of reports. And on the right is the same concept but for death. So there’s no comparison here. There’s definitely something, there’s something going on here in the data, which is the by-product, the result of this experiment that we’re in.
[00:26:51] And just to back this up, because I really like papers, the deployment is definitely seemingly followed by many studies that are signaling danger. I’m really glad that Ryan pointed out this paper, which has been my favorite paper since it came out. This comprehensive investigations revealed consistent pathophysiological alterations, because I’m all about CD eight T-cells and NDK cells. I love these guys. They’re the killers.
[00:27:19] So this is showing weird gene profiles in the context of TNF alpha, interferons, and CD eight positive T cells and all of this. And the other papers that are coming out are giving strong, they’re strong indicators, I’m not saying they’re proof, but they’re indicators that these things are causing immune deficiencies, not just hyper inflammation, immune deficiencies in at least subpopulations of the people. And we need to figure out which people those are.
[00:27:50] Are they the people who had autoimmune conditions? Are they only the ones that had a cancer that was in remission? Are they the people who didn’t get a placebo? We don’t know the answers to very basic questions here. So there’s a lot of papers, including my own very famous paper and it’s still withdrawn and missing in action.
[00:28:13] And the last one, I’m sorry, the second, last one. I’m almost done. Analogy. Has this happened before in history? Ted knows very well, the answer to this question. I’m going to give you two specific examples. This very famous one of intussusception that was actually, it was VAERS that they used to detect the safety signal in children. And thanks to VAERS the rotavirus vaccine was pulled. Intussusception is not a happy thing. Definitely not if you’re a child. And acute encephalopathy, which is also not a happy thing, associated with permanent brain injury, in the context of MMR vaccines. And we all know, well whoever knows about the whistleblower story in Vaxxed, we know that they hid the data that showed that autism was much more prevalent in young boys, if they got the the MMR shot before 18 months, et cetera. So there are, there are many analogies, not just the ones that I’ve listed here.
[00:29:14] And this is the final .Point reversibility. And everybody who talks about Bradford Hill says, no, you don’t have to show this one, and I think I understand why, in this context.
[00:29:24] If we stop the shots, will the adverse events stop too? And my answer to this question is no, because of what I just said about the dysregulation of the immune system in, in either, I don’t know why, nobody knows why. We have to figure out who this is affecting that way, very quickly actually. And I would also like to quote Ruben, except I want to turn his little sentence around. And I’d say, we won’t know if these products are unsafe until we stopped giving them to people. That’s just the way it goes.
[00:30:02] So I don’t know what the answer to this question is. And I fear that we, we just won’t be able to find out because, like it’s actually, it might be a point that they can use against us. They’re going to be like, well, we stopped the shots, but people are still having these adverse events, so it couldn’t be that they were caused by the shots. But anybody who’s been looking at this will say, well, actually, yeah, it really, really could be, because of the claim of dysfunction of the immune system.
[00:30:30] So, why the causation denial? It’s not just, I really loved that people talked about this like it’s a religion because it’s. This isn’t science that’s like backing up these very ferocious defenders of the narrative. It’s really weird. And it seems to be the same group of people who absolutely deny even the possibility that there could be a causal effect in the context of any vaccine, which, like I said before, it’s not even scientific to say that. You can’t say that none of them will ever cause anything. That’s just stupid. Of course, sometimes it’s going to happen. And the responsibility’s on the manufacturers and the regulators to find out when it’s happening and if it’s happening – we need to stop the things that is happening in.
[00:31:20] So my question is, if the WHO used these same criteria, I don’t know how many yeses you guys have, I have 10. But if the WHO uses the same criteria – they only used five. Why were they not admitting the likelihood or the possibility, even on the cause effect relationship? I wonder.
[00:31:41] There is something going on in VAERS. It’s not deniable. Everybody’s probably seen these plots before. These are up to date. This is only the domestic data. If you combine this with the foreign data set, it’s much worse. This is going back 10 years. VAERS data, all vaccines combined. There’s no comparison between what happened historically and what’s happening now. The left is total reports. The right is deaths. And these are, this is from my new website.
[00:32:15] You could see the grouped adverse events, the immunological, pedological, neurological, cardiovascular. It’s insane, how many reports there are. And here, I want to reiterate what Shabnam said about the under-reporting factor. None of these numbers I’ve reported to you today from VAERS have considered the under-reporting factor. So like she said, the most conservative estimate that’s been calculated is mine, which is 31. So you can multiply all of these numbers by 31, which is insane.
[00:32:48] I also want to go down to the bottom because somebody mentioned prion diseases, which I’m really happy about and unhappy about at the same time, because there is nothing that I am more concerned about them this. There are 24 reports that, sorry, the bracket number is the total number from the foreign and domestic data and the one on the left without the brackets is the domestic data alone. So there are 24 reports of prion diseases in VAERS right now. And it’s possible that those represent the background reports that would always be there. Not saying that it’s because of the products. I’m also not saying anything else because prion diseases don’t get reported in VAERS before 2021. So this is a point of real concern.
[00:33:38] And the final message, it’s easier to disprove causality than to prove it. All you have to do is take one of these points and p. Anybody who thinks there’s a problem with any one of these points, I welcomed the opportunity to discuss it with you. But the onus is on the manufacturers to prove that there’s no causal effect here.
[00:34:11] And this is where you could find me. And that’s the end of my presentation. I have no idea what time it is. So I hope I’m on time,
[00:34:20] Shabnam Palesa Mohamed: Slightly over Jessica Rose, but outstanding presentation and talking to people criteria. That’s exactly what we needed to see. And I know that, I think it was Ryan that mentioned the Bradford [Hill] as well, and then you just brought it in. It was great.
[00:34:33] Megha Velma will be moderating the science in action panel. Mega, you can ask Jessica two questions before we hand over to our next speaker, who is the very patient Dr. Rob Verkerk. Megha, questions for Jessica.
[00:34:49] Megha Verma: I’m just checking the chat for the Q and A. I thought it was a fantastic presentation. I was just taking so many notes.
[00:34:57] Dr. Jessica Rose: I can send you, I want everyone to have these slides. It’s been too long that I didn’t do this. I’m really genuinely grateful to every single person who made this happen. Shabnam, you’re, you’re a heroine. I mean, it needed to happen. And every single person in the world, I think, needs to see what everybody, or hear, what everybody here today has said, because everyone contributed something and everything dovetailed really beautifully together. I think it’s been a really extraordinary conference, I really do.
[00:35:31] Megha Verma: Yeah, you’re totally right. This was a long time coming, I think ever since the eighties, when they remove liability from the pharma companies. But there are a lot of great questions in the chat. One of them is someone from named Tango, he said, playing the devil’s advocate, what do you say when someone argues that all of these adverse events, including , could be caused by the Covid infections themselves as well?
[00:35:55] Dr. Jessica Rose: So you’re saying that everybody who got the shot got Covid and the reason they had the symptoms would be because of the Covid and not the shots? I would say that’s highly unlikely. But it’s worth investigating man. Take a crack at that. If there’s a way, I mean, in VAERS, they actually do report whether or not somebody has Covid in the context of say myocarditis sometimes. Um, yeah, you can investigate that.
[00:36:25] Megha Verma: This is a great question because this comes up often when we say that there are adverse events, they say that, well, we have to balance them with the adverse events of getting Covid as well. And all of the sequella of Covid.
[00:36:39] Another thing that I wanted to point out that hasn’t been pointed out yet, is that when patients are reported as unvaccinated in the data, they can still be vaccinated, but it hasn’t crossed the two week mark, which makes absolutely no sense to include in the vaccinated group because the two week mark is just for one day claim that the vaccines become “effective”. Not that their adverse events may not be visible.
[00:37:05] Dr. Jessica Rose: And you know what, that, that just confounds all the data sets and me and a group in the UK are attacking this in the UK data and Pierre Kory revealed from his personal experience that there’s a form. When someone comes into the hospital in the, into the emergency room with whatever, you know, they, they just end up in the ER for a broken arm or something. On the form there’s a new section on the top, right that says that you have to click off if you’re vaccinated or what do you think the other one is? On?
[00:37:36] Megha Verma: Has it been two weeks?
[00:37:38] Dr. Jessica Rose: It’s unknown. So it’s really weird, what’s going on in terms of collecting data in the categorizations of – and nobody knows what it means to be fully vaccinated anymore because there’s no definition. It’s like, is it two, is it three? Is it four? Is it for your green pass? Like what, what is it? So, yeah, I’m getting a bit riled up because it’s really a non…
[00:38:00] Megha Verma: I understand this is a very emotional topic and for good reason.
[00:38:04] Dr. Jessica Rose: ….from a data point of view though, because you can’t really make an assessment, you know what I mean?
[00:38:08] Megha Verma: I think it’s a feature, not a bug that they’re collecting the data like this.
[00:38:12] Dr. Jessica Rose: Nicely put.
[00:38:15] Megha Verma: More confusion helps to support their point of view because if you confuse the people who are trying to logically dismantle what you’re saying. then, you win.
[00:38:25] Dr. Jessica Rose: Yeah, but they don’t know how type A we, data scientists are. I mean, my, my friend Joel, is like, wow, it’s like dangling like a carrot in front of someone who loves carrots. It’s like, I’m going to get that carrot. You can make it go farther and farther away, but I’m going to go after it and I’m going to get it. So, uh, yeah. Good luck guys.
[00:38:46] Megha Verma: I understand. I used to do data science and I just remember making enormous scripts on Python and like, it’s just so sad.
[00:38:52] So another question they asked was from Feisal Mansoor. My question is how do we respond to the CDC claim that unvaccinated people are several times more likely to die from the disease? This is, I guess interesting because now the narrative has shifted that even if you get Covid following vaccination, you’re still less likely to die. Do you know of any data that contradicts that?
[00:39:18] Dr. Jessica Rose: Well, all the data that’s in indicates that the chance of dying from Covid for most people is zero. If you’re under like 55, it’s like zero. Kids, kids do very well with Covid. The balance is, is completely tipped now, if you ask me. if you actually look at the data, not just listen to legacy media, but look at the data, the Covid data, the SARS related Covid data and the adverse event data, it just seems very apparent to me, the risk. It’s not just present and clear, but it seems like it’s much bigger in the, in terms of the injections, especially if you’re healthy and you don’t need it anyway. It doesn’t prevent transmission. It doesn’t provide protective immunity. So why the hell would you take it? Just get Omicron, get over it. Like that’s, that’s what I would say.
[00:40:09] And on the subject of Omicron, what a gift that is, right. It’s basically a live attenuated vaccine. It’s a gift instead of getting a true variant of concern, which is a variant that’s more transmissible and more virulent, what we get is this beautiful version that gives people the sniffles, in most cases. Some people not, but here we go with the risk bennefit. Isolate the vulnerable or take care of the vulnerable, treat the vulnerable with known off-label drugs, for example. We have many doctors who’ve spent two years now and treated thousands of patients with centuries of experience between them who develop
[00:40:54] Megha Verma: Little risk. Nope, no less.
[00:40:56] Dr. Jessica Rose: Right. They’ve established these gorgeous treatment protocols. So if, if you do get Covid and you do get to symptomatic phase where it’s looking bad for you, we can treat it. So the, these injections are totally pointless. I mean, you can look at it from any way you want to. You have to back that question up and ask, do I need this? And most people can say no.
[00:41:22] Megha Verma: Thank you for answering all of our questions and for your fantastic presentation.
[00:41:26] Dr. Jessica Rose: You’re welcome. I’m really happy to have participated, genuinely.
[00:41:31] Shabnam Palesa Mohamed: Thank you. Once more, Jessica Rose, Dr. Jessica Rose, just such a stalwart in this movement for freedom that we’re in, but through the lens of data and actual evidence. So Jessica, they are some comments and questions, if you can address them in the chat in Q&A, we would absolutely appreciate.
[00:41:47] Thank you. Once again, we look forward to seeing you at the next UVC.