Presentation for PANDA Open Science, 29 November 2023
This is a previously-unpublished video of a presentation that Dr. John Khademi (@professorakston on X) and I gave to a small group in PANDA in November 2023, a few weeks after PANDA published “Does New York City Spring 2020 Make Any Sense?”
Dr. Khademi and I had “e-known” each other on “COVID Twitter” for several years before connecting more specifically around “the New York problem”.
A complete transcript with our slides embedded is below, followed by a PDF of the same.
This post will eventually be filed under the actual presentation date.
John Khademi (self-introduction): I was going to go to graduate school in physical chemistry and I—I took advanced quantum mechanics and advanced laser spectroscopy. As an undergrad. I took some graduate classes. Uh, two years of math, linear algebra, differential equations, all that kind of stuff. But as a younger person my objective was actually to go to become an orthodontist. Braces. And I took a look at things and I really felt like acute care was a better fit for me. I was the only one in my class to do an externship, a one-month externship in oral surgery in Chicago. Then my girlfriend at the time who was in school with me, she got into a specialty school in Baylor and I went with her and I’m like, this is—this looks like the specialty for me. Then I got into [University of ] Iowa and I specialized in endodontics, which is root canals.
The first couple of years of professional school, it’s the same whether you’re a DO, an MD, a DDS, a DMD: anatomy, physiology, biochemistry, all that stuff’s all pharmacology, that stuff’s all the same. We have the same lower division professional school classes and then the upper division, they learn more general body. And just as—as me. That’s like 12 years up there. So I actually have more education and more years in school than a family physician. One of those is a master’s in digital imaging. I just have this very odd math and computer science background for a clinician, and it’s given me some pretty nice insights into outcomes, doing analysis, probability, statistics, all that kind of stuff. I’m kind of known as ‘that guy’ in my specialty.
The Official Narrative
Jessica can help me here because we worked on this stuff together and you’ll see in a couple of slides that I, we actually predicted what was going to happen in New York.
You send people home. We knew at the end of March. This clip isn’t in there, but I posted online several times: you get COVID inside. You get the flu inside. That’s why—that’s one of the many reasons I think why these things were such a disaster, the policies.
The WHO—E.D. Ryan, the executive director—I have him on video in March saying people aren’t getting it outside, they’re getting it at homes. And I know early on that’s exactly what happened is moms and dads had to work and those snot-nosed little kids stayed home with grandma and grandpa. Duh. So that’s certainly what happened in the United States and happened around here. They thought that this was some sort of spread of some novel deadly virus.
New York City got hit harder and New York City was overwhelmed or going to be overwhelmed by COVID. This is what we were all told. The official story. The official narrative. New York City killed a lot with the nursing home policy, and I bought that at first. I did. Because we didn’t have the [finalized] CDC WONDER data on place of death [for 2020] until the end of 2021.
Ventilators killed a lot. And if you understand the pharmacology, the biology, the physiology, that actually makes perfect sense. We have many interventions in Western medicine that when subject to a randomized trial later on, we found that they were bad.
I lecture on overdiagnosis and overtreatment. And they do it a little bit in dentistry but mostly in medicine because telling people in my audience that they’re hurting people doesn’t land well. But if I tell them physicians do, they buy that, and then the smarter ones figure out we’re doing that too.
From a physiological standpoint, the diaphragm is a skeletal muscle. It’s like any other skeletal-type muscle. If you—these are people that are struggling already with inflation and perfusion. They’re struggling breathing already, and then you breathe for them, and what little function they had atrophies, and you can’t get them off the vents. And of course that’s exactly what happened. And I think that’s been going on for decades.
People were afraid of the hospital and were afraid of going to the hospital. I think there’s some truth to that. But also—and we predicted pattern of mortality after that. Also, Jessica has some data that contradicts that.
Some people are going to be afraid, more afraid of getting COVID and dying of COVID, and other people are going to be afraid, and that’s going to manifest in different ways, I think.
So, we’ve got a few talking points and I’ve kind of hinted at what those are going to be already.
A Test Deployment Curve, NOT a Disease-Spread Curve
Very early on we had a lot of people trying to model this. I made some initial predictions on mortality, and I treated this as a probabilistic event because flu season is really variable.
With a factor of five, maybe even a factor of 10 if you look at a long enough timeline in mortality. And a lot of very smart, very sophisticated people—smarter and more sophisticated than me—were trying to model this using a gamma distribution in terms of the arrival and then the tail-off of the disease.
For me, this was not a natural event. The gamma would be useful to model a natural event. Initially, early on, everybody was trying to model what was really a test deployment curve as a spread curve. This was a classic mistake.
And I’ll tell you guys, because it’s a small audience: if you want to measure the rate of something, it has to meet what’s called Nyquist criteria. And Nyquist criteria is whatever you want to measure the rate of, you have to sample that at twice the speed that you’re sampling it.
Of course, we were bandwidth-limited with our testing rate. And the only way you could measure the rate of spread was if it was spreading—just making the numbers up—if it was spreading 100,000 people a day. You’d have to be testing everybody and you’d have to be testing at the rate of 200,000 people a day to measure the spread.
To tie this back to something we all are old enough to probably remember: compact discs, CDs. To capture the sounds on a compact disc, you have to sample the sound at at least twice the rate of what we’re able to hear, which is about 20,000 hertz, and CDs are sampled at about 40,000 hertz.
So this is a critical error that was made across the board by people with enough mathematical sophistication like [Michael] Levitt [of Stanford University] but did not have the right medical background to understand how tests and testing worked. So, there was a lot of that going on. This was really clear to me from day zero.
And I’m not sure how accurate my labels are on this anymore, but it doesn’t matter. When you see something like this, this is absolutely not a disease spread. This is a test deployment curve.
And then up here, initially, we see this variability. Looking at it at a longer time now, we actually see this not as just random variation on a maxed-out testing rate but with longer timeline you can go, Oh that’s actually a weekly pattern: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday…kind of thing.
So right now with this just very short timeline it just looks like random variation on a maxed out testing rate. And so we estimated that there was about 150,000 CDC tests and this is all open right now. So this was information that I had when I did this analysis and this is March/April of 2020. I back-calculated about how many positive this number would be right here, this window.
If we said there’s 150,000 CDC tests that got deployed around the world, there were this many positives from those tests, and that number translates out from — just giving myself incredibly wide error bars — that number translates out to 15 million to 1.5 billion positives at that time. So there’s no way that this was somehow “novel” and “hit” the United States as some sort of novel virus. This was a 100% test deployment because we didn’t have tests back then. And of course, you don’t have tests, you’re capped out, maxed by the Nyquist thing.
I made a video on this in April of 2020 as well. (SARS, H1N1, and now COVID-19: Why we keep getting this wrong….)
Only a handful of people were actually doing what needed to be done, which was looking at the mortality data and then back projecting how many cases would have been required to generate that mortality at an arbitrary case fatality fatal fatality rate. Ethical Skeptic was one of those.
Early on he did not realize that this was not a novel virus and, like many people at that time,
Emia, [Michael] Levitt, he was fitting a Gompertz curve and I stopped — once the initial mortality wave came by I stopped doing any of that kind of projections about what future mortality was because it was clearly not a natural phenomenon.
Repeat Performance
It’s not like we haven’t seen this before either. I did my residency in San Francisco and I have a long history with Fauci, and I’ve seen the kind of person that he is, and there’s some people that just want to watch the world burn and he’s one of them. I mean, he was he was on TV, you can go back and find the clips, in the 1980s.
I’m rotating through San Francisco General Hospital and working I’m seeing HIV positive and AIDS patients, and we’re making those primary diagnosis on the clinic floor. Like you’ve got you’ve got Kaposi sarcoma, you might want to go get yourself, you know. A lot of these people are like, I don’t want to hear it. It was really wild.
So, he did that with SARS. Then again, in my mind with H1N1, as well and that’s all that is that’s a testing spike right there. This is UK, so this happened everywhere.
The whole world freaked out — that out-of-bound testing spike — and then in orange is the mortality and there just wasn’t any mortality because it was basically summertime. It was like May and the mortality went away just from a seasonal standpoint and people didn’t really seek care from that.
This is May of 2020, and it’s like the stay-at-home/shelter in place, this did not do anything.
Not a Natural Event
If anything, if there was a novel circulating pathogen, it just delayed a traditional seasonal spike. And sure enough, this is earlier in May, and this is later in May. And sure enough, we see that actually happened.
And the timing of that spike is the exact latency between when you get the flu and die of the flu. Ethical Skeptic and I have both done that latency analysis and it’s 18, 20, 21, 23 days. Both of us have around the same number.
So you see us tell people to stay at home, they get sick, they go to the hospital or they don’t go to the hospital or whatever else. and then we see this completely non-natural mortality spike afterwards. All of the stuff that I’m going to show you is based on this particular kind of modeling that we did.
I generated what looks like basically a sine wave to fit the data, and we actually got a very, very good fit the data over about a dozen years from 2009 to 2020. This is all cause data.
And then to do z-scores, we computed a seasonal standard deviation on that, because we see in the winter months the standard deviation, the variability is much higher than in the summer months. And so people that were doing Gompertz’s model and Imia and all of those people that were trying to fit this and model this, this is what happened afterwards. This is absolutely not the spread of some sort of novel pathogen. This is there’s just no way that something like that happens. And when you see the z-scores, the probability of that having anything to do with natural event is vanishingly small.
Questions so far? Good. Okay.
Re-Examining NYC/Tri-State
New York: Where the Money Is
I would never have done this re-analysis had it not been for Jessica.
I looked at New York. It was very, very, very, very clear early on that New York and New Jersey were gaming the system. Their mortality was off the charts compared to everybody else. Nobody was even close.
Why New York City? Why tri-state? Because they actually ended up behaving together. There were some issues with Connecticut that kept what was going on there hidden from view for a while. But why New York City? Why tri-state?
Well, “I rob banks because that’s where the money is,” Willie Sutton said.
And you know, why New York? Why New York City?
Well, that’s where the criminals are. I mean, it’s not like they’ve done this before. Anytime four New Yorkers get into a cab together without arguing, a bank robbery has just taken place. So, nobody’s talking. And Jessica, is anybody talking to you from New York?
Jessica: Uh, no. No, nobody’s talking. Doing everything to avoid any discussion of this. Head down. Yeah.
John: Yeah. Nobody’s talking. And that’s just like—in my mind—that’s just—this is Johnny Carson, of course, who’s a New Yorker. Just smoking gun evidence that a robbery’s just been done.
So I’m—this is tin—little tinfoil hat theory—but it’s not like they haven’t done this before.
What bank are they going to rob? Well, they’re going to rob the Federal Reserve. And it’s—it’s again not like they’ve not done that before.
I mean, look at—look at that. This is M2. That’s a couple trillion dollars there. Bang. All at once.
There’s our national debt. It went up like 10—it’s gone up 10 trillion. We were at around 20 trillion when this started. We’re over 30 trillion now. It’s insane. The amount of money is absolutely mind-boggling. This is our public debt.
Like I said—it’s not like they’ve done this before. This is the great financial crisis, and of course the Federal Reserve stepped in. You can look at that on this timeline going back 70 years.
This was a massive infusion at that time, but it absolutely pales compared to what was done in recent times. This is actually the 2019 liquidity crisis right here. Again, it’s just absolutely dwarfed when the Federal Reserve stepped in and had to fix the reverse repo facility.
So it’s not like New York hasn’t done this before. Whatever it is, they go—New York’s still the center of the financial world. And whenever they have a problem, they go rob the Federal Reserve.
“Spread” of a novel deadly virus
I actually got this idea from an economist. He was the first one that I saw do this. And this is—the Ethical Skeptic, I think, was doing this as well. Past testing needs to be interpreted through current testing rates. Here we back-project to positives.
If we’re going to have a mortality peak right here in the middle of February, we need to have a case peak about three weeks earlier. This is national data right here. This is from whenever I did it. So summer of 2020, it looks like. Here’s our mortality data. Our COVID deaths, whatever.
We can, at an arbitrary case fatality rate, backtrack what the number of positives or cases need to be. And that was an area that was confused initially too, and in my mind still is. People confused a case for a positive. A positive is not necessarily a case because there are false positives.
I told Eric Svenson, “You’re confusing tests and positives, of course, with cases.”
This is a central error that sent us down this road of destruction. Zero tests doesn’t make zero cases. And, of course, Trump was kind of crack-up on that. He said, “Well, let’s stop testing,” which actually, it could have happened.
So it’s summer 2020. In spring of 2020, I’m thinking, We’re going to snap out of this. People are going to realize this is bullshit and we’re going to snap out of it. But by summer of 2020, it was clear that that was not going to happen.
And again, at Jessica’s prompting, we actually, I actually did the back-projection on what New York would have had to look like. And I told [Alex] Berenson, I said, “You know, New York City probably had a million COVID positive cases in March.” You know, a million of them.
I wrote this in 2020 before we had the data to say anything. And sure enough, you do the back-projection at some sort of reasonable case fatality rate. March 7th, half a million cases in New York.
And anybody that understands anything about any kind of spread can look at this and go, this is absolutely not a natural process. Because this is the case, the number of sick people that would have been needed to create that number of deaths, with a latency. You can put any numbers you want in there in terms of case fatality rate or latency. But they’re all going to look like that. They’re going to—it’s just going to completely contradict the idea of natural spread.
To generate the reported number of COVID related deaths with a case fatality rate of 0.2 percent, which is about double flu, this would have required a spread from essentially zero in late February to 2.5 million in the middle of March in a matter of three weeks. This kind of stuff just doesn’t happen. So this was not some sort of spread of some novel deadly virus. That was obvious to me right away.
I think people that are more sophisticated genetically and analytically than me have completely falsified that idea. There’s every reason to believe that “coronaviruses” and what we’re calling COVID specifically is probably COVID-17 and maybe as old as COVID-12, 13, or 14 or something like that. In other words, a decade old by the time we, shall we say, tested it. Because we started getting positives—unspecified coronavirus mortality—in 2016 and 2017.
New York tri-state mortality vs “Fat States”
So again, this is New York City.
We didn’t have this data until a little bit later on in this format, especially where people died. This is, same kind of model down here, mean/SD kind of model. Again we’re going from, you know, 1,500–2,000 deaths at peak maybe, to 8,000.
Not a chance. This is not a natural phenomenon. It’s open, in my mind, what caused this. And I think it’s a combination of things. But this is man-made. One hundred percent.
And, as it turns out, my analysis along these lines—before Jessica prompted me to look at New York State specifically, or New York City specifically, and the places of death—inpatient, nursing homes, hospital—before I looked at that, I was looking at the tri-state area because they all moved together differently than the other states.
In the US, we had about 96,000, 98,000 excess all-cause mortality in this spring mortality event window. And about half of that mortality came from the tri-state area. It was insane. So it followed geographic lines.
Here’s all of our states: New York, New Jersey, Connecticut—basically all timed together. [JH Note, 17 Feb 20206: More accurately, it’s really tri-state corridor counties, not the entire states] West Virginia is elevated over the others and it’s noisier as well. But West Virginia is number one in the US in smoking and number one in obesity, which is a fairly deadly combination.
For the people outside the United States, this is kind of what it looks like. New York, Connecticut, and New Jersey. This is what we call the tri-state area. It’s known that way in the US.
And I’ve pulled out all of the states and I’ve left what we would call interesting states—or at least interesting to us in the United States. South Dakota has a huge motorcycle rally that happens. A big biker rally that happens in the summer. They didn’t have any ensuing—what they call a mass spread event. And bikers tend to not be the healthiest people. Jessica can chime in on that if she has anybody in her family. They tend to smoke, tend to drink, tend to be overweight.
I was actually almost expecting South Dakota to have some kind of bump after that, but they didn’t. But they’re pretty far up north, so they get winter earlier than everybody else.
And then HoldLLC [on Twitter]. What’s his name, Jessica? Remind me again of his name.
Jessica: Clayton Cobb.
John: Clayton did some analysis that showed what we called the FAT states: Florida, Arizona, California, Texas. And I would call those, kind of the air conditioning states. They actually had some excess mortality kind of a little bit more in the summer, which was a little bit unsurprising, because if you’re going to tell people to stay at home, well, those people are going to stay at home when it’s 100 billion degrees outside, not in the winter. So we might have seen some mortality there.
Jessica: Those are also states that get more people from the southern border.
John: All of the above. I don’t think that’s a cause. But again—New York, New Jersey, and Connecticut all moved together. They moved in what we call out-of-band way. They moved in a way that this kind of mortality just doesn’t happen. And I have a hundred years of data: There’s no flu mortality events in the middle of April in a hundred years. They all moved together.
New York: Gaming the System?
John: So, something fishy was going on there with New York—and New York City definitely at the leaderboard. That’s why a lot of my analysis looked at tri-state. And then, with Jessica’s prompting, New York City too. This was—again, this was absolutely clear in summer of 2020.
New York and New York City are at the leaderboard. It looks like gaming the system at twice the national average.
And so nationally we were about 16 percent—this later became PIC mortality – pneumonia, influenza, and COVID mortality—but early on it was still P&I mortality at 16 percent nationally. And of course they’re gaming the system, so they’re at 27 percent. Because we had kickers for labeling things as COVID mortality. And that screenshot is in July of 2020.
We’re going to see that same point—week 15, week 16 of 2020. Keep an eye on what happens over time.
So, US week 15, week 16. As we get more data, we see that actually wasn’t the peak week. It was one week later. But again, you could see where we are in time. These are screenshots that I was doing at that time because I knew they were going to be changing numbers. And sure enough, they did. Week 15–16, 15.7 versus 26.8.
If you look at the massive excess – what became PIC mortality – when you had all that extra capacity and put all those people on those hands with ventilators, with over double the national average died in your hands. This is January 4th of 2022, that mortality peak that was 15–16 percent in the US was now 20.78 percent. So the percentage of COVID deaths went up over time—in the past. How does that happen?
And then New York again, not to be outdone, they’re at about 70 percent. New York City. This was—in my mind—I was screenshotting these because I expected this to happen. And sure enough, it’s happening.
These percentage numbers are going up in time when the percentage of deaths from a particular cause should not be going up over time – unless someone is gaming the system to rob the bank and not talking about it.
Where They Died: The Nursing Home Myth
John: Jessica, thanks for giving me a little kick here to take a look at where they died, because early on, I was like yeah, you send a bunch of sick people back to nursing homes and they’re going to die there. My mom had MS. She was bedridden in a nursing home for many, many, many years. Flu goes around. These are people that don’t have good respiratory and cardiovascular function because they’re sedentary. Some of them are sedentary because they’re disabled.
It was very reasonable to think that’s where people would die. Especially if Cuomo sends known positive patients back there. No surprise. So I never looked at that again.
My mom had MS and far as I know she died of COVID 1994. For all we knew she died of a respiratory problem. Sure, those are going to be places where people are going to die. I’d expect that, especially if Cuomo sends known positive patients back there. No surprise. So, I never looked at that again.
And Jessica’s like, “They didn’t die there. That’s not where they died.”
And we didn’t have that data to make that statement until the end of 2021. She said, “You need to go look at that.” And I was like, “Oh my God, you’ve got to be kidding me. They died inpatient.”
If you look at this, this is combined hospice and long-term care. Combined hospice and long-term care with what you would think of are the most vulnerable people combined was about the same as the number that died of COVID [self-correction] or died at home died at home.
This is all cause mortality. The bulk of these excess deaths were actually inpatient which was, oh my God, there were definitely a spike in outpatient and ED deaths too.
So this whole idea that that New York had all these excess deaths because of nursing homes that is not just not supported by the data; it’s actually contradicted by it.
And we can look at place of death in terms of the percentages and the timings of this. And this is where I wish I had daily data for that. Jessica’s convinced me in the value of daily data, too. I like longer data sets because they’re just less noisy and they’re more amenable to quieter analysis.
If you look, here’s our inpatient death peak right here, and we see these clear shifts in where people died. And, in fact, Decedent’s home — I’ve got data going farther back, but that she likes to focus on this short-term event, and I agree with that – but you can see that, it looks like elevated died at home over baseline in terms of the percentage of deaths. And if anything, if anything, hospice and nursing home, if anything, that dropped down and stayed depressed for quite some time. And so it’s one hypothesis is maybe that they transferred some of these people to the hospital and they died or got killed there as well.
There’s a there’s a whole — I don’t think this lends itself to the explanation. I think there are many explanations in combination of events that led to this unholy mess. This is Jessica’s slide. So this was like oh my god because she had this. I’ll let her chime in.
Hospital Intake versus Hospital Deaths: Counter-Expectation
Jessica: Sure. When you look at place of death, which I think I had started doing in early 2021 just for the US as a whole, you can really you can really start to say, “Okay if this many people died in this kind of setting, and this many people died in this kind of setting, we can start to ask questions about what contributed to the deaths in in those settings and start to look at the mechanics.”
John is right, and I think all of you heard have heard me say this many, many times is that the most death was in hospital inpatient, by a long shot.
John: Way long shot.
Jessica: And so I had started looking at the mechanics and saying, “Okay who was coming into the hospital? Who were these people that died as inpatients? When did they come into the hospital? Why did they come into the hospital?”
And this graph is from the article that Panda published, the “Eight Reasons” [article] co-authored with Thomas [Verduyn] and Jonathan [Engler] and others.
I like this one because you if you really think about it, it’s super confusing alongside the government’s claim that we had the “sudden spread of a novel deadly pathogen”.
What would you expect to see? Well, something I would expect to see is just more people coming to the emergency room. If it’s an emergency, you should see a rise, not a 60 to 70% decline in emergency room visits. We can see in New York City that ED visits— this is ED visits just going back to early 2019 — but they peaked in January and then they began to decline and then completely plummeted simultaneous to a massive 600-700% rise in hospital inpatient [deaths].
At the same time, ambulance transports, meaning people calling 911, an ambulance coming, and the ambulance taking the patient from the point of pickup to somewhere else. That’s what that data represents. Could be the hospital, it could be somewhere else, but that declined as well. We did not see an increase in ambulances picking up patients and bringing them somewhere else. So, you know, who all is coming the hospital?
One of my hypotheses is that New York City must have seen, or is claiming to have seen, a massive death event involving people who were already in the hospital. I call it sinking the ships. Sinking the damaged ships. [Metaphor later turned into an article: https://woodhouse76.com/2024/03/30/the-allegory-of-the-damaged-ship/] Even then I have questions about the body management of that and what would have had to have occurred at that time.
So, as a lot of you know, I have said I think that some of this data is just plain fraudulent. They may have pulled forward, literally, or reclassified deaths that had already occurred in prior weeks or months. Somebody may have hit an extra digit, an extra zero.
There may be some double counting. There’s a lot of possible explanations. [Later proffered in “The F Word” https://woodhouse76.com/2024/04/15/the-f-word/]
I’ve had a very difficult time getting the city or the state to cooperate regarding the number of hospital inpatient admissions. I need the daily data, but I have been able to cobble together through Internet Archive, from past reports, I’ve been able to cobble together some monthly data for 55 of the 60 or so hospitals in New York City.
And we can see that even though we would expect — or if you just ask somebody what they would expect — you’d expect admissions to go up, especially if, you know, 20,000 or so inpatients died in that period. You would expect to see higher admissions, and that’s just plain not what we see. We see the opposite during the biggest mortality event their hospital systems claim to have ever seen.
Also, as a lot of you know, I’ve focused a lot on Elmhurst Hospital because early on the media pushed Elmhurst Hospital as the so-called “epicenter of the epicenter.”
Even today, people will say to me, well, some hospitals were overwhelmed in New York or overrun with patients. I’ll shoot back, “Okay, which ones?” And they’ll say, “Well, Elmhurst.” I’m like, “Nope, not Elmhurst either. I’m really sorry.” [Complaints related to Elmhurst and other New York public hospital data later filed: https://woodhouse76.com/2026/02/03/complaint-against-new-york-city-health-hospitals-filed-with-prac-and-hhs-oig-submission-date-3-february-2026/]
In fact, people were coming to the emergency room at a level comparable to the 2017-2018 flu season, but that was back in in January [2020]. And then it plummeted in — well certainly in March and April, but even in February it went down, and we saw a little bit, they saw a little bit of peak back up. A lot of that was people just coming to be to be tested and in “the testing tent,” as I call it, outside Elmhurst that a lot of hospitals set up at that time. But we just don’t see the data that you would expect to see.
Lack of substantiation & accountability for hospital death event
John: And I would say – and I want to put a level of indirection in there. The reported data contradicts the narrative. It doesn’t just not support it. It contradicts it. The reported data. So, it’s like, are they are they lying? It’s New York.
Jessica: Yeah. Exactly. One way — I’ve tried to come at this data from a lot of different ways, but one way I put it on Substack recently is just to try to give people a visual. https://woodhouse76.com/2023/10/06/wait-did-this-really-happen-in-new-york-city-hospitals/
I’ve never worked in a hospital. I’ve barely been in a hospital as a patient. I mean, not even a handful of times in my life, twice to have babies. But I was in the restaurant industry. My family was in the restaurant industry. I think about tables turning in a restaurant and what has to happen.
In that 11-week spring period. According to state data, the peak patient occupancy in those 11 weeks in New York City hospitals was around 20,000 patients. And that’s also the normal number of beds as well.
Well, when we look at how many inpatients died in that period, it’s almost 20,000. So from a practical standpoint, I’m trying to match that idea of hospitals, between them — and I understand that some hospitals saw more death than others, or report that they saw more deaths than others — but I don’t understand the body management of that time and why people aren’t talking about it. Where is the three-year documentary about how New York City hospitals handled the biggest mass casualty event that any hospital system in the world, I would say, has ever seen?
Like John said earlier, it’s what people aren’t saying. It’s people not talking that, at some point you’re like, “Did this happen? Did this happen in the manner that it’s presented in real time?”
So there’s real value in looking at this setting of death data no matter what country you’re in. And I’ve encouraged people in the UK and elsewhere to break the data down by place of death — daily if you can get it — so that you can place some parameters around what may or may not have occurred. And, if you can, compare it to other kinds of what I would call mechanistic data that suggest, or try to suggest, what actually happened on the ground in real time and whether that actually makes sense, because with New York it does not. It does not.
John: This was, again, I snagged that screenshot very early on because I actually sent an email to [Howard] Zucker, and I know he had eyes on it, telling him, “Dude, no. This is this is going to be a disaster. And they went ahead with it anyway. And then of course they issued this nursing home order that they later took down. But it’s like this was one of those where screenshot everything.
And so when you see that circling around, that’s where that screenshot came from. That was, this was used against Cuomo later on. Of course, Zucker, I think, had resigned fairly shortly after all of this stuff went sideways, directing nursing homes to take back patients. No resident shall be denied readmission or hospital solely on the basis of confirmed or suspected COVID. So it was very reasonable at that time, and for quite some time afterwards, to believe that that there was this huge nursing home debacle because of that. The other states around there did the same thing. Many states had similar kinds of directives following New York’s lead that directed them to take back these sick patients. But that’s just not where the deaths occurred. It didn’t occur in New York, didn’t occur in tri-state, and didn’t occur in the US. The deaths were hospital inpatient.
Jessica: This is something that when I was removed from Twitter in July 2022, I began looking at the whole nursing home policy thing more in depth and trying to get actual data to support that idea about you know all these nursing home patients dying. And people will say —people still say, Naomi Wolf said this to Jonathan Engler and I in an interview a couple weeks ago — but people will still say, “Well, you know, Cuomo was hiding where people died.”
It’s funny that people say that because, in a way, he could not have done that because CDC WONDER at the same time was reporting exactly where people were dying.
What was unusual in the US at the time is that governors like mine, Governor Pritzker [of Illinois] – actually, I would say most other states when they were when they were announcing the number of nursing home resident COVID deaths — they were not saying how many were in the hospital.
So, it’s really funny that people think, “Well Cuomo was trying to hide it.” It’s like, “Actually, everybody else is still hiding how many nursing home residents died in the hospital, including New York.”
The place of death data is literally where people died. It’s not, we don’t know from this data, we still don’t know how many New York City nursing home residents were sent into hospitals but never left. [Related: https://woodhouse76.com/2026/01/30/the-new-york-nursing-home-scapegoat-and-hospital-black-box/]
I’ve shown people this view many times.
If you just look at, for that 11-week period, what the straight-up all-cause increase is, you can see hospital inpatient is the most. Yes, there was nursing home increase. Yes, deaths at home. But we don’t know from that inpatient and/or outpatient/emergency department. We don’t know how many of those people were nursing home residents.
If we just look at the data for deaths that attributed COVID-19 as underlying cause, we don’t have the most of those deaths in nursing homes, which sort of makes sense because nursing homes were not getting reimbursed by the federal government per COVID patient, right? Where was that happening? Going back to the theft and stealing from the feds, so to speak, that was happening at the hospital. So, you know, you get more of what you pay for, as Jonathan Engler has said. And so, it’s no surprise that if you died of COVID in New York City, it was very likely that you died in the hospital. Seventy-six percent of all of those deaths were in hospital inpatient. A relatively small number, or smaller number, was in nursing homes.
So this idea that people were sent into nursing homes where they “spread” COVID is simply not substantiated. It’s part of the spread narrative, right?
Go ahead, John. Chime in and then I’ll show the next one.
John: Yeah. And I’d say as you said, contradicts the data, contradicts that story. It’s more powerful than that. It doesn’t just not support it or leave you maybe, maybe not. It’s contradicted by it.
Jessica: Well, and not only that: I’ve tried to tell people like Janice Dean, the Fox News lady that’s gone after Cuomo who wants to see him in jail. And I get it. I’d like to see him in jail as well. But I think when we focus on the nursing home policy, we actually let these people get away with everything else, right? And it’s deflecting attention away from hospitals.
I would even say that when we have people on “our side” focusing on so-called lockdown harms, it’s funny, lockdown harms never seem to include what happened in hospitals. I don’t hear a lot of those people calling for an investigation of what actually occurred.
I’ve tried to get this data daily, but they’re not giving it to me. I’d have to fill out a full research request and probably be affiliated currently with a university, which I’m not. But this is discharge data from the New York Statewide Planning and Research Cooperative System for the year. This also contradicts this narrative because we see a 15,000 plus decline for the year in the number of patients discharged to skilled nursing home facilities [of 15,000].
15,000 is roughly the increase in hospital in impatient deaths in that period. Right? So we simply don’t have the data to make some of the claims that the New York City narrative continues to make.
John: The data contradicts that, by and large. They need to, they’re getting paid for this and they’re calling it this. They need a supporting data for it.
Jessica: Duncan said in the chat that people who are close to death in a nursing home do usually tend to be picked up by an ambulance and sent to the hospital. That’s actually not what I’ve seen in data and studies prior to 2020 in the US. Most people who are in a nursing home die in the nursing home. I think the ratios — I even looked at it at one point globally to see where I could get other studies that would report that — and the ratio was about 70 to 80% of nursing home residents dying in the nursing home. [Related: https://woodhouse76.com/2026/01/04/how-many-residents-of-u-s-nursing-homes-died-in-2020-we-still-dont-know-but-hhs-needs-to-find-out/]
So we see – and I can speak to this with Illinois because I do have the full data for that – we see the reverse in 2020. It’s like they were being sent in and then the hospitals were being paid for those deaths. And I can tell you at the time I did not assume that the numbers I was hearing on the news were nursing home residents that were sent into the hospital to die there. I just did not realize that.
I’m still waiting on — actually it was actually due two days ago — I’m still waiting on data from one of the New York City agencies on how many ambulance transports were patients from nursing homes to hospitals. I requested daily data. I also requested it for from jails or prisons to hospitals [and] homeless shelters to hospitals because, again, I’m trying to see who was being sent into the hospital here. So that’ll be that data will be interesting, assuming that they give it to me. [JH, 26 Feb 2026: They didn’t.] That’s all I have to say about that right now.
What they died of
John: Okay, what they died of. Interestingly, in the tri-state area more people died with COVID than the cumulative excess cumulative all-cause mortality excess. So this is some evidence and we see this with other analyses that I’ve done as well that they were dipping into the pool of would have died of something else and then got labeled as died of died of COVID as well.
For instance, this is tri-state circulatory deaths, which includes heart attacks and things like that, and this is weekly data showing died at home of those things. And you see this is the percentage that’s underlying causes of death and multiple cause of death died of and died with and that actually remains fairly stable.
But in medical facilities we see those dips in the percentage relationships of those happen and they’re timed exactly with COVID mortality peak. So people are coming in that normally historically would have died of a circulatory problem and instead they died of COVID and died with the circulatory problem. And at home, it’s the other way around. We see a lot of died of circulatory at home and the relationship between those two things stayed the same.
So these and Jessica can —- Jessica may have a little divergence of opinion here, although I don’t know, we’ll find out in a second. I think these were patients at least partially that were so afraid of going to the hospital and getting COVID that they stayed of stayed at home and died of a heart attack instead. And we predicted this particular pattern of mortality would happen with many diseases. With circulatory, we see this exact sharp spike of these people stayed at home and very loosely circulatory is the category but stayed at home and died of a heart attack with no change in the relationship between died of a heart attack and died with a heart attack.
We see a massive change in that relationship in the medical facilities where they died of and died with. We see that percentage stable here. And in medical facilities, fewer people died of a heart attack. That actually went down. Fewer people died of a heart attack in medical facilities, of course, because they died of COVID.
Jessica has, I think, ambulance data. Chime in, Jessica, please.
Jessica: I didn’t put it in this presentation because we’re still — another colleague and I are still going back and forth with one of the city agencies about anomalies that we see in in the data. But I would put it like this: I used to think that as well – what John is saying: Okay, they told people to stay home, save lives. They did. New York City is just a very tense place in in general. You have people who live in very small spaces even the wealthy people live in smaller spaces and people were afraid and they stay home they stayed home and died.
I think that there is reason to believe that that occurred. But the speed of the rise in the deaths at home is so great and so high, and if you look at it alongside 911 call data — I can’t remember what people in other countries call that — but you’re picking up the phone and there’s an emergency. The 911 call data spikes up right with 15 days to slow the spread on March 16th, 2020. It’s like an automatic. It goes straight up. Massive increase in cardiac arrest calls.
But you also see that, or I’ve also figured out that, there were a series of orders given to ambulance/emergency medical services and some people have told me that this was true in the UK too. I haven’t looked at it, but there was a series of orders that basically amounted to telling ambulance dispatch EMTs to stand down from administering full life-saving measures to cardiac arrest patients in in particular and to avoid bringing people to the hospitals because the hospitals were overwhelmed, which was untrue. Untrue. [Discussed further a few months later in this interview: https://rumble.com/v4ifmhr-jessica-hockett-woodhouse76-deaths-at-home-new-york-city-april-2020-covid-e.html]
John: Right.
Jessica: So, it was basically sort of a DNR order, if you will, to the EMTs that was in effect for about a month.
And you can see – I have a draft timeline of it, you can see that’s exactly when ambulance dispatches that pronounced people dead spike up. And deaths at home spike up. The idea that people were afraid of the hospital again is true, but it’s also contradicted by the 911 data.
So people were calling, dispatches were going, but people were apparently not being helped. One thing that I have not figured out is why the sudden rise at that level of heart attacks or cardiac events. Is that consistent with the number of events that the New York City fire department and EMTs handle on a regular basis and so that’s enough – “standing down” would just be enough [to create a cardiac arrest death event], or was there something else that happened, especially in select neighborhoods?
People have floated the idea of a water poisoning event or some other kind of release. I don’t think that that can be excluded because the data are so strange.
I also haven’t, honestly, excluded the possibility that a simulation was being run and some of the 911 call data is simulated by the military. I know that sounds crazy, but when you start to piece together different things and different news reports that were happening, I can’t exclude that possibility at this point. [See also this later article: https://woodhouse76.com/2026/02/02/the-sound-of-sirens-new-york-city-and-london-spring-2020/]
John: Yeah. So, I think in this spike, there’s what Jessica said about the ambulance data, but we see died at home of circulatory remains elevated over baseline. And so this could be the people that call, that got called, just we don’t — they won’t – really, nobody’s talking. Nobody’s talking.
So there a chunk of that’s probably, they called 911 and they did not get resuscitated because if you get to a heart attack in time it’s very survivable. But we also see after that it remains elevated. And we see that with other natural causes of death too.
Okay. Ventilators, what did they die of?
Jessica: Insofar as, you know, what caused the mortality? I still hear all the time, well, it was ventilators and remdesivir. I’m not opposed to those explanations, but we don’t have the data to blame the massive hospital impatient spike on those factors. You know, with ventilators, first of all, we have incomplete data. Okay? Yeah, we have a data set that starts on March 26th. I think I have a I there’s a there’s a date error at the top. That actually should say 3/26/2020. My apologies for that. [Dates at the bottom are 2/29/20 onward] This data doesn’t even say ventilated It just says ICU intubated COVID patients. New York does not have all patients intubated. So that’s being uh hidden, kept from us. [Correction: See graph under “Ventilator data” in this earlier presentation: https://woodhouse76.com/2026/01/10/toward-a-new-york-city-hypothesis-may-july-2023-presentation-for-panda-open-science-transcript/]
But we see this census.
John: I’d like to add an interesting point in there that I picked up from you is when was the –EPIC is this hospital EMR [electronic medical records system] that is fairly widely used and everybody hates it. When was EPIC actually fully implemented in New York?
Jessica: On that day, in the middle of their cataclysmic disease spread event, they released a press release. New York Health and Hospitals did anyway, on the transition to EPIC that had begun in 2018 had been completed. [https://www.nychealthandhospitals.org/pressrelease/nyc-health-hospitals-completes-transition-to-electronic-medical-records-system/] For me, that’s a plausible deniability thing.
John: Right. Absolutely.
Jessica: “We’re gonna switch systems”— it’s so New York — “we’re going to switch systems right in the middle of this event so that later on we can blame X Y and Z on switching systems.” That’s basically what that is.
But it’s also the same day that, or the day after the CARES Act was passed. So the timing there. I have opined that the data are pushed from the left, without having a baseline. Like a lot of people look at this data and they’re like, “Oh, look, you know, it went up so fast.” I don’t know. Did it? For all I know, there’s data from the left that’s dumped in there to try to substantiate the event that was allegedly occurring.
When I think about the speed of the intubation that had to have occurred for that to peak rise in the time — and I know we’re talking about individual hospitals — this is all the hospitals, but it’s suspiciously fast. We just can’t determine the rate. And when people try to cite that Northwell study that was released in April 2020, they didn’t have the full set of data reported, right? They cut off the date. They didn’t have data for all the patients. That’s what I’m trying to say. But, you know, oh, 80% of, you know, people in New York City who were on ventilators were dying. Okay. Yeah. At that end point in that study.
But we don’t know how many total patients were intubated in this period. There’s no data. So, I just don’t have a way of figuring out or blaming the ventilators entirely. Or even substantially. I wish I could, but it’s not there. [See also October 2022 article https://brownstone.org/articles/april-was-the-cruelest-month/] And I’m not sure that they necessarily stopped doing anything. They really fast, according to this data, and then we’ve got a long slow decline. So I don’t know about Oh they figured out that they were messing up with ventilators. No, I don’t think so. Nobody’s talking.
U.S. ACM with and without NYC/Tri-State
John: Nobody’s talking. Nobody’s talking. What US all-cause mortality looks like in the context of New York and Tristate. So early on New York and New Jersey were really the only places that had some excess mortality. They were our version of Bergamo. Pretty much everywhere else was within random variation. This is May of 2020 and in the final analysis, kind of, nobody died anywhere else. Half the time we’re going to go be above half the time we’re going to be below.
I didn’t have the data to do this analysis until later on, and Jessica prompted me to do that. The top blue line here is US all-cause mortality.
And here’s the “bad flu season” of 2017-2018. And here it is right here with New York, New Jersey, and Connecticut included. But if you impute an older season, you pull that out. Here’s what the US all-cause mortality looks like with a normal New York, New Jersey, Connecticut season. We can actually see that we’re below the historical peak that just went unscathed. And when you look at the z-scores on this — and this is without this Tri-State massive mortality event. If we use a normal, say a 2018-2019, if I use this season which is a fairly normal season, impute that data to 2019-2020 flu season, we see that the all-cause mortality peak is actually less than the 2017-2018 flu season.
Now the z-score is higher because these deaths occur out of band where the standard deviation during the winter is quite large, which is going to lower the z-score and the standard deviations at this time of year are smaller, which are going to raise the z-score.
But I actually did some stuff where I slid that back. I phase-shifted the mortality and the z-score ends up being right around two, which is a once every 20 years kind of event, well within random variation. So the key thing here, the take-home here, is the massive excess mortality that we saw in the United States came from New York, New Jersey and Connecticut. It was the tri-state area. Connecticut didn’t contribute as much in terms of raw bodies, but we saw they had the same timing in the mortality peak, and their z-score was very high as well.
And when you plot the z-scores to give some context, the tri-state z-score 260. These are just simple z-cores. New York is 204. And here’s the reported US, with the actual reported New York. It’s a z-core of about seven. And here’s the z-score out of band with this imputed New York. So it gives a sense of how different were things in the tri-state area than the rest of the United States. [Raw numbers version here: https://woodhouse76.com/2024/06/03/weekly-deaths-all-causes-january-2018-december-2023-united-states-with-and-without-nyc-metro/]
And the answer is oh my God this was, and again from the raw-numbers standpoint, half of that excess mortality came from the tri-state area. So I think we at least have, we don’t have any supporting data for any of these points.
Could the all-cause curve and/or total be manipulated?
I think we largely have falsifying data for all of the talking points that the standard the standard story, the standard narrative says. If they actually died, that’s an open item. I’m with Jessica’s husband: I think if they didn’t actually die, they just made it up. And I’ll let her elaborate on that – if these reported deaths actually happened.
Jessica: I mean, that’s what a lot of people — and people said it to me in the [PANDA] science coordinators meeting. A lot of my friends say it: I think they just made stuff up – and I think that’s possible. I don’t want to lead with that, because if that’s true, that doesn’t make me look bad. I’m trying to give them as much of the benefit of the doubt as I can. In truth, I think it’s a whole bunch of things happened, including maybe some fraudulent death certificates.
John, did you want to mention real quick? One thing we talked about last night that you didn’t bring up is the “COVID only” death certificates.
John: It was late, and I quickly sniffed for those numbers, but PRider180 on Twitter, he’s kind of my go-to guy for that kind of stuff and he’s really taught me a lot. And one of the things he taught me that’s clear from the CDC’s guidance on how to fill out death certificates is a death certificate that lists COVID only is an incomplete death certificate. And Jessica got some FOIA data where New York, New York City – right, Jessica? – processed 11,000 deaths in three days, which is like…
Jessica: The medical examiner, who doesn’t process all deaths anyway, I asked for the number of deaths processed each day and was shocked. It’s just out of control. The medical examiner in New York City normally processes…maybe 50 deaths a day. (Again, not all deaths go through the ME.) Then you see those start to rise with “15 days to slow the spread,” into the hundreds, and then all of a sudden there’s a massive dump of 11,000 deaths at the end of April, in three days.
John: Not possible.
Jessica: An additional FOIA that I submitted to the ME showed that almost all of those are deaths that occurred in hospitals, which makes it event more like, “Why is the ME processing hospital deaths? Is that just a computer dump?” Even tech people have told me, “I have questions about that kind of processing, even from a computer systems standpoint.” The military was in New York at that time. The military could assist with that kind of processing. So, we’re wondering about that.
But John also applied some of his – we each tried a different method – the insider said your method was correct. But we applied some ninja skills to CDC WONDER and we think there’s about 4,000 or so death certificates in WONDER, from New York City, that have COVID only [i.e., only COVID] on the death certificate. That is a signal, I would call that a fraud signal – with that number of death certificates that are incomplete death certificates. [Note: The 4,000 figure was U.S. Figures later cited in article using method confirmed with CDC are correct. NYC’s ratio is disproportionate: https://woodhouse76.com/2024/09/01/nycs-covid-only-deaths-in-spring-2020-revised/]
Unfortunately, right-wing media here especially, in the U.S., has taken the statistics on death certificates that only have COVID on them — the CDC reports that in aggregate and they say it’s 5% or 3% or something like that – and say, “Those are the only people who’ve died of COVID.” That’s not what that means. It’s actually sort of a fraud signal is what it is [i.e., when there is a very high proportion of such certificates].
John: Yeah it is.
Jessica: I have put in a FOIL to the New York City Department of Health. I have all these workarounds with them since they don’t, you can’t FOIL actual death certificates. So, I’ve said, how many death certificates were processed each week that list only COVID as underlying cause of death? I want to see what they say. That’s going to be pretty interesting, but that’s something we’re looking into.
John: Yeah, that this has not been addressed and nobody’s talking about it? There’s no way that people that process 50 or 100 a day can process 11,000 in 3 days, or 2,000 in one day. You know, maybe they worked, you know, around the clock and some weekends, and they pulled some extra people in and they maybe labeled some hasty things to to cover this bump. Could they double their workload for a short period? Yeah, maybe. Can they 10x it? Absolutely not.
There’s just no way. And if this was a one-day event, I’d be like, “Yeah, maybe someone typed the wrong number in.” But actually it’s spread over three days.” So, yeah. Could that, is it on the table? Yeah. Something this needs an explanation.
Jessica: Right, and I wrote about it on Substack. [https://woodhouse76.com/2023/11/15/why-and-how-did-the-new-york-city-medical-examiner-process-11000-deaths-in-three-days/] I just put the link to the article in the chat. And thank you to Thomas Verduyn for emphasizing that point with me when we were talking about this. The office of the medical examiner, this is a taxpayer-funded entity. They’ve basically got one job, right? Determining cause of death under certain situations. And they haven’t even issued, at least as far as I’ve been able to tell, they haven’t even issued a report on how they handled the biggest mass casualty event the city has ever seen. Right. So, goes back to the nobody’s talking.
And at some point, for me, the only thing worse than this event – I don’t know that John would put it this way, but the only thing worse than this event happening is that it didn’t. Right? Is that some percentage, let’s call, I’ll lowball it –let’s call it 20%—is fraudulent in some way, shape, or form. Deaths pulled from another time, death pulled from other jurisdictions, something like that. For me, if we’re only a little right about that, it changes a lot.
John: Yeah.
Jessica: And that’s why I think [it’s] a reason that nobody is talking.
No New York/Tri-State? No Pandemic
John: Yeah. This doesn’t get resolved until there’s litigation and a congressional inquiry. So, if anybody has those kinds of contacts, we need people to be subpoenaed on this and we need to, you know, Jim Jordan or Schmidt or some of those people that are suing the government.
Jessica: Well, and it’s interesting because not even Rand Paul, I mean, we think about these elected officials who ostensibly “on our side,” and they’re all talking about lab leak and all manner of other things, and vaccine deaths. And that’s fine, I’m saying we don’t need to focus on that. But why does nobody want to go back and talk about what really happened on the ground in these first four or five months, with New York, with this outlier? And it’s like nobody’s saying anything. That’s mafia to me.
John: Yeah. This, in my mind, this paints the picture of what was reported in the United States, what happened without New York, and what happened in New York in a tri-state. It’s just insane.
Jessica: Without New — there’s no pandemic. I think people forget, I can’t speak for people in the UK or South Africa or wherever, but New York was used as, “This is a serious virus. Look what’s happening in New York, Chicago! We don’t want to be New York! So, your kids have to be locked out of school!” Right? So that’s always been my interest in this. There wouldn’t have been anything without New York.
John: There was nothing. Look at all the other states. There was nothing. There was nothing.
Jessica: Or, John, like I’ve said to you and others: The other uncomfortable possibility is that with, Oh, there was no excess til lockdowns. Maybe there was. And that’s the thing nobody wants to say, which is why lately I’ve said, “Let us not assume that all-cause mortality is —
John: —has not been doctored
Jessica: —the cornerstone. Exactly. Because these people have lied about everything.
John: Everything.
Jessica: Why are we giving them the benefit of the doubt about all-cause mortality? It’d be so easy. It’s so easy, especially with everything digital, to move stuff around, play a little shell game, right? So I would just — I’m not convinced. I’m skeptical.
John: Skeptical.
Jessica: Cautious, but…
John: End of planned presentation. Yes. That’s what we got.
Nick Hudson: I’ve got eight minutes here before I go on to an interview. I’ve been listening to most of it. It’s been fascinating. Thank you.
John: Thanks, Nick. Thanks for what you’re doing.
Nick: Thank you, too. We’re all in it together, unlike certain others.
John: I don’t know if you got there and saw this slide early on, but it’s, you know, I rob banks because that’s where the money is. You know, nobody’s talking. Nobody’s talking.
Nick. Yep. That’s a great line. I’m going to use that possibly in a few minutes. I’m an unrepentant robber myself, but just of sound bites.
John: By all means. Yeah. All right.
Jessica Anything else? Any other questions? I mean, I know we’re sort of preaching to a choir here.
Italy, China, and other Early Episodes
Nick: I have a quick question if I can. What, do you have a theory on Italy? You know, because that was the other event that we kind of took aim at and that looked a little bit staged. Why would they select that place?
Jessica: I mean I — John, do you have one? I do.
John: So, I had eyes on the ground in Italy and I’m emailing him. This is February, March maybe. I’m like, “Pasquale, what’s going on?” He says, “John, it’s the fear virus that’s circling.” He said where he is, there’s a couple of million people and there’s 50 deaths. And in Rome, there’s a couple of million people and 50 deaths. He says, “Nothing’s happening anywhere else.” But he says, “If you go outside your house and one of your neighbors sees you outside the house, they’ll call the police.” He says, “It’s insane.” He says in an area of where, he was in, there’s two million people and there was there were 50 deaths. And in the meantime, of course, what we’re seeing is Bergamo is like burning to the ground. And so I don’t know. I haven’t looked at that data but I know, by and large Italy, at least at that time, Italy at that time Bergamo was their New York. Could it have been staged? Yeah.
Nick: And what about the lockdown in China? How do we — do you have any sense of how much that was staged?
John: I have no idea. It’s the Chinese. I mean those people like dropping dead in the street of COVID?
Nick: No, that was staged.
Jessica: Well, but here’s my question. Here’s my question. What, like with that example or with the China propaganda early on: Why weren’t US officials speaking, saying anything about it? I mean, Things That People Would Do if They were Involved in the Staging, right?
I think in northern Italy there was a mass, like an intentional euthanasia event. I actually think that happened here too, but I’m on a little firmer ground, maybe, with Italy. Bergamo Province daily death data shows that they didn’t have any excess after those six weeks. None. [Later article: https://woodhouse76.com/2024/11/24/yes-we-believe-the-bergamo-italy-all-cause-death-curve-is-fraudulent/]
John: None.
Jessica: No excess. So, we’re talking about vaccine deaths? They’ve got nothing in their data. With all those little provinces and the daily data, it would be very easy to move some things around.
But I think from a propaganda standpoint, if you look at the timing of the events — and I’ve tried to chart these locations on different, or on the same timeline. So, Italy was before New York City. And I don’t think you convince New York City health care workers that something is going on just with China. I mean, weird stuff goes on in China all the time, you know, from people’s perspective. But if something’s going on in Italy? Okay, now you have an Italian-heritage mayor, you have an Italian-heritage governor, you have a tons of Italians in New York City. In America, we just have a general love of all things Italian, period. Right? So, if something’s happening in Italy, something is really happening.
John: It could happen here.
Jessica: Right? It could happen here. Nick, I think I may have shown it to you at one point. I know I showed it to Todd and Jonathan, but JAMA [Journal of the American Medical Association] did like this – what’s his name? Howard Bauchner? Is that his name? The editor of JAMA, or the previous editor of JAMA. But they did an interview with this Italian intensivist about what was going on there and what he was seeing. [https://edhub.ama-assn.org/jn-learning/audio-player/18317079] I think the interview was recorded on March 13, 2020. [Note: Jessica had confirmed the recording date via email with the doctor that was interviewed.] So there was, I call it “The Italian Connection”. There was this connection where – I believe that was intentional. I don’t think US officials were “duped” by China and Italy. I just don’t.
John: I don’t either.
Jessica: It makes no sense to me. No sense to me. But Italy was key and New York was key.
And then Iran. Remember all their officials, there was like a political hit job involving COVID. Remember, they had like 10 leaders who died of COVID, allegedly? Do you remember that? [Later article: https://woodhouse76.com/2024/07/16/the-time-a-coronavirus-hit-iran/] Like in February. I mean, come on people. Madrid, remember that? So all of these — Diamond Princess — these sequences of events. These aren’t accidental. I used to think that they were, and I just can’t see it as any other thing except as this very strategic set of things that happened. Stinks. But that’s where I’m at.
Harms of Western Medicine & “Pandemic Planning”
John: Yeah. The role of panic is not to be underestimated. And in medicine, especially in New York, when I lecture, a lot of the questions about treatment planning and treatment decisions and treatment from East Coast people, Washington DC, New York, a lot of those questions are along the lines of, “Well, what if you get sued?”
And so, from the standpoint of intervening, you’re far more likely to get litigated for failing to do something than doing something. It’s the sins of omission that are, tend to get as opposed to the sins of commission. And Western medicine is all about you gotta do something, you gotta do something, you gotta do something. And we have ample evidence that many of our interventions when actually subjected to a randomized trial make things worse.
And so I think Bergamo, the message from Bergamo is ventilate hard, ventilate early. And I think New York did that, and that’s why they called for all those ventilators because they thought that was going to save lives and they, very that’s certainly a piece of the puzzle. They very easily could have killed all of those people. And then rather than owning it, it spun out of control. And we saw we saw the later waves happen in the other cities, other states here. And I think as those other states adopted those policies. So I think these are, these are at least partially intervention related waves of mortality here, but they’re nothing like New York or New Jersey or Connecticut.
Jessica: I do want to say with ventilators, Martin Neil and Jonathan Engler and I did a shorter article on this. [https://wherearethenumbers.substack.com/p/us-covid-19-ventilation-policy-made] Ventilator use had long been part of pandemic bioterrorism and disaster medicine planning in US documents going back to the early 2000s. New York State had a ventilator allocation guide. They still do. Guidelines. Huge, thick document that was first drafted, I think, in 2007 and then redone in 2015. So I don’t fully understand why ventilators are associated with a disaster event, or with a pandemic flu event. I don’t get where that comes from, but that was entrenched in our preparedness here. And then I think, John, you’re right: What Italy did is it gave the green light like, Oh, it’s time to use that. Right? It’s time to do what we’ve all — this is what we’ve been planning for! [Also mentioned in previous PANDA presentation. See heading “Ventilator Guidance: Who’s Responsible?”: https://woodhouse76.com/2026/01/10/toward-a-new-york-city-hypothesis-may-july-2023-presentation-for-panda-open-science-transcript/ ]
John: Yep. Yep.
END TRANSCRIPT FOR POSTED VIDEO
There was additional discussion about antibody testing with a participant who elected to not appear in the published video.
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