Commenting on this: "It is, therefore quite possible that nothing unusual happened in NYC in the spring of 2020 (even as nothing unusual happened in China [45]), and that the 24% average rise in 911 calls [46] during this period of time, along with the inexplicable and disturbing increase in cardiac arrests [47] were not caused by Covid but by media induced fear [48]."
The cardiac arrest event cannot - in my opinion - be reasonably attributed to media-induced fear. At minimum, the redirection in call types and directives to FDNY/EMS must be acknowledged as deadly -- and the RMA increase as deeply disturbing. See under 9) and 10) here: https://www.woodhouse76.com/p/eleven-serious-problems-with-the
The "official" government-anointed explanations for the OHCA event are "COVID-19" and "people staying away from the hospital." The latter would be related to the media-induced fear idea, but as I note in the article above, there is no great explanation for why media would induce fear to the point of cardiac arrest (including of the nature/characteristics described in the OHCA study) in NYC but not in Chicago. Detroit, which is not far from Chicago, also had a significant OHCA event, as did London and Paris. Lombardy's OHCA came close to NYC's, but not quite.
For all cities that saw significant OHCA events, I am hard-pressed to explain why the events "are" a curve, irrespective of cause. Let's imagine it was pure panic (whatever we want to say that means): Why would we see a curve, versus a more random distribution?
Finally, even if the cardiac event were fear induced (fear on the part of EMS and patients), it's not reasonable to characterize that as "nothing unusual" happening. It's incredibly unusual - as is that fact it officials haven't been compelled to explain it.
Respectfully, the questions I have posed and points I have made in comments have not yet been sufficiently addressed by the author, as far as I can tell.
More dodgy data being used to support 'the vaccine'...
QUOTE:
In the absence of a vaccination program, there would have been approximately 1.1 million additional COVID-19 deaths and more than 10.3 million additional COVID-19 hospitalizations in the U.S. by November 2021.
Without the U.S. vaccination program, COVID-19 deaths would have been approximately 3.2 times higher and COVID-19 hospitalizations approximately 4.9 times higher than the actual toll during 2021.
If no one had been vaccinated, daily deaths from COVID-19 could have jumped to as high as 21,000 per day — nearly 5.2 times the level of the record peak of more than 4,000 deaths per day recorded in January 2021.
Even though I receive emails notifying me of new articles, I receive so many Sub stack notifications that I generally do not look at them when I receive them but go through them when I have some spare time then I do some 'binge reading'. I had such a binge reading session on 15 April, prior to this article being published. I posted a comment on the 'Where Aare the Numbers' article about MHRA still not releasing data regarding pregnancies, etc. https://wherearethenumbers.substack.com/p/mhra-still-wont-release-critical
Today I am doing a similar binge read and have just read this article about data on the JHU dashboard. Whilst reading what it said, it resonated so much with what I had said in my post on 15 April, namely:
"Bill Malcolm
Apr 15
I'm lost for words...
I have 2 points I wish to make.....
My second point is that what can be so difficult about summarising and publishing data that is contained within the systems that they administer/ monitor? It is not rocket science!
From early 2020, every country in the world (allegedly) reported, ON A DAILY BASIS, the new number of cases and deaths linked to CV19. The logistics of doing such (worldwide) reporting always puzzled me.
When did the reporting system get put in place and how was it administered? Were additional staff employed just to do this daily reporting? The logistics of this are quite mind boggling. Such data was being fielded from every health centre/ doctors practice/ hospital within each NHS region and reported centrally every day!! Even if such daily reporting was done, which I very much doubt, who (if anyone) checked/ verified the figures reported?..."
My suspicion has always been that much of the data reported in real time (news media, Covid dashboards like JHU) must have been educated estimates (guesses) or, worse still, completely made up!! This article confirms my suspicions and, if anything, it seems more likely that it was the latter!
My go-to source for CV19 data during the 'Restrictions' or 'Period of Madness' (much more apt descriptions than 'pandemic') was Worldometer, which gets prominently & extensively mentioned in your article.
As you correctly point out this website provides information (on a world wide basis) in respect of populations. What is remarkable is that this website has just 2 main topics, Population & Coronavirus
As an aside, given the date of your article, there is a note that now appears in a banner displayed (from 14 April) at the top of of the Worldometer Covid page, which says:
"NOTE: As of April 13, 2024, the Coronavirus Tracker is no longer being updated..."
What is peculiar (gleaned from using the Wayback Machine) is that prior to 2019, the site only had one tab on its home page (Population). In May 2019, it added a "GDP" tab and in September it added a "CO2" tab. The peculiar thing about the CO2 tab is that it only shows data from 1971 up to 2016. The tables & graphs in the tab look like a test out of the tables & graphs that would used in the (still in the future) Coronavirus tab.
On 29 January 2020, the Coronavirus tab was added.
Whoever did this must have had amazing forethought (like 'Mystic Meg'!!) to decide to set up a completely new section on the website, which according to the figures it reported that day, only had deaths (1,133) in one single country (China). The table this day has 20 countries listed. The other 19 countries, in aggregate, have zero deaths and only 101 cases (range 1 to 14), including USA with just 5 cases.
On this first day of Coronavirus reporting, Worldometer ditch their GDP tab. And the next day (30 Jan), Worldometer ditches its CO2 tab!!
Another remarkable thing about 30 January is that 963 previously dead people in China came back to life because the total deaths for China dropped from 1,133 to only 170!!!
Prior to 29 January 2020, The Worldometer website would not have required much administration because it was not providing real word up to date data. On the "About" page, the following is stated (December 2019) regarding how the website works:
"The live counters show the real-time estimate as computed by our proprietary algorithm, which processes the latest data and projections provided by the most reputable organizations and statistical offices in the world."
This begs the question, how did Worldometer magically have the resources to not only obtain real world live data (if it existed, which I very much doubt) from all countries around the world but also verify it on an ongoing 24 hour basis?
Below is what was stated on the About page of their website (again from December 2019):
"Trusted Authority
Worldometers was voted as one of the best free reference websites by the American Library Association (ALA), the oldest and largest library association in the world.
We have licensed our counters at the United Nations Conference on Sustainable Development (Rio+20), to BBC News, and to the U2 concert, among others.
Worldometers is cited as a source in over 3500 published books, in more than 2000 professional journal articles, and in over 1000 Wikipedia pages."
I am just throwing out a hypothesis. If, prior to 2020, Worldometer was widely perceived to be such a trusted source (similar probably to how JHU would have been perceived) then, if somebody, or a group or groups, wished to spread false information/ propaganda (and wanted it to be trusted as being true) spreading such information through a trusted source (like Worldometer) would be a good way of doing it.
Particularly so because journalists and politicians are widely regarded as not trustworthy (journalists, politicians, Govt ministers are all near the bottom, around 20% trusted, in the annual Ipsos Veracity Index). Therefore a long established, very well trusted and apparently "independent" website would be excellent for providing support for what the media/ governments report, should any non-trusting citizens want to verify/ try to fact-check what they are being told.
The trustworthiness of this website seems to have even improved since December 2019 as the About page now says the following. Note the 10+fold increase in citations in journal articles (2,000 to 25,000).
"Trusted Authority
Worldometer was voted as one of the best free reference websites by the American Library Association (ALA), the oldest and largest library association in the world.
Worldometer is a provider of global COVID-19 statistics for many caring people around the world. Our data is also trusted and used by the UK Government, Johns Hopkins CSSE, the Government of Thailand, the Government of Pakistan, the Government of Sri Lanka, Government of Vietnam, Financial Times, The New York Times, Business Insider, BBC, and many others.
Over the past 15 years, our statistics have been requested by, and provided to: Oxford University Press, Wiley, Pearson, CERN, World Wide Web Consortium (W3C), The Atlantic, BBC, Milton J. Rubenstein Museum of Science & Technology, Science Museum of Virginia, Morgan Stanley, IBM, Hewlett Packard, Dell, Kaspersky, PricewaterhouseCoopers, Amazon Alexa, Google Translate, the United Nations Conference on Sustainable Development (Rio+20), the U2 concert, and many others.
Worldometer is cited as a source in over 10,000 published books and in more than 25,000 professional journal articles."
Worldometer uses computer simulations in general. That's a given. They say: "For the live counters on the home page, we elaborate instead a real-time estimate through our proprietary algorithm which processes the latest data and projections provided by the most reputable organizations and statistical offices in the world."
For Covid numbers, they claim otherwise: "Our sources include Official Websites of Ministries of Health or other Government Institutions and Government authorities' social media accounts. Because national aggregates often lag behind the regional and local health departments' data, part of our work consists in monitoring thousands of daily reports released by local authorities. Our multilingual team also monitors press briefings' live streams throughout the day. Occasionally, we can use a selection of leading and trusted news wires with a proven history of accuracy in communicating the data reported by Governments in live press conferences before it is published on the Official Websites."
Thank you Thomas V. It makes much but not all of our work on estimating infectious disease outbreaks and COVID-19 dependent on questionable data. We did use UK Office for National Statistics for the UK model not Worldometer. Published in the International Journal of General Medicine after near 3 years of rejections.
Cook M, Puri B, A Novel and Accurate Method for Estimating Deaths and Cases During Outbreaks of Infectious Diseases Including COVID-19.
Yup all fake modelling as reliable as Dr Pants Down Fergusons! All on purpose, to create the fear needed to roll out an injection based toxic synthetic weapon. Nothing biological about so called covid. Its not even made in a lab from bats etc. Its just a weaponised synthetic protein, or other chemical poison, crafted after years of prior also fake vaccines, for fake diseases, which are all just cover stories for industrial toxins, or sanitation/malnutrition issues.
> Every State Health Authority reported their “covid” numbers daily. This data was then collated by various websites and dashboards like JHU.
To clarify what you are saying: are you asserting that you have personal, hands on technical(!) knowledge (as opposed to belief) of all(!) of the various data pipelines involved in this project, across all(!) states?
> It wasn’t that the data was false, it was just misinterpreted.
Please explain what you mean by this.
> The data actually proved everything they said and did was wrong.
How could you possibly know:
a) Everything that they said and did?
b) Whether that was proven out with perfection by the data? Can you even name the numerous extremely complicated disciplines involved in pulling something like that off?
Before you reply: I am quite familiar with your kind, a typical response will avoid addressing the questions, but rather engage in various forms of rhetoric, insults, evasion, etc. Please try to resist natural Allistic urges to engage in that behavior, this is a serious topic.
Reflecting on my early 2020 self/impressions, I did check the JHU dashboard but I did not think it was “ahead” of my state. The impression I and people I knew had re: JHU dashboard is that it was an aggregator.
The state health department was reporting daily “COVID” case, hosp., and death numbers in press releases and daily press conferences. In addition, my county medical examiner was publishing every COVID-attributed death in the county it’s public register. I could see each death (line by line w/age, causes, city of death, etc).
I initially looked at JHU to see what it ranked my county vs other counties and to see what it was saying about other countries. (Whole thing felt like something out of the 1980s movie “War Games” 🙄) But the novelty quickly wore off when I saw the local info wasn’t different from what the county/state was reporting.
The CDC provisional data pages had more info for my purposes. I don’t recall ever downloading data from JHU for analysis.
If the JHU dashboard was “leading” jurisdictions with modeled data, that would be a different impression than I had at the time re: what the dashboard was doing.
I understand your point: even taking covid numbers at face value, covid was not the big scary virus worthy of global lockdowns, like so many claimed.
The article is clear: covid case and death numbers as reported by JHU were meaningless for 3 reasons:
1. very few health authorities in the world are capable of producing daily data,
2. so JHU filled in the gaps with computer models.
3. JHU models were corrected with data from health authorities, but this ended up confounding the data.
So, it's not that the data is "false" like a PCR test might be. Rather, the data is meaningless in the same way faulty models are meaningless.
Case in point, only 9% of countries in the world could provide the WHO with weekly ICU numbers for covid, let alone daily numbers, let alone daily numbers of cases and deaths. See: https://t.co/Rs6RObeeL6
But the United States is one of the countries that theoretically could.
In addition to the questions I've already posted, I ask:
1. What are you saying/not saying about the JHU dashboard showed/represented when it comes to what it showed for the U.S./U.S. states and other countries which did have the ability to provide such data?
2. What do you mean by the models being "corrected" and how did it confound the data?
3. What did/do the "corrected" data reflect, and how do we know?
Your 2nd and 3rd questions are answered in the article.
As for your 1st question, if it took from 7 months to 2 years for the CDC to tabulate influenza cases and deaths, why do you think the US has the ability to track covid cases and deaths in real time?
I do not see the answers to my questions in the article. To be clear, I am referring to the U.S. and U.S. states, where state health department data was the source for the JHU dashboard. Are you saying that the JHU dashboard was consistently "ahead" of states?
I did not say that the U.S. has this ability. They do not and I said so earlier this week when discussing fraud in NYC. https://www.woodhouse76.com/p/the-f-word
But that's why I'm trying to gain clarity on what you think the JHU case and death numbers represented and their overall relationship to all-cause deaths.
Regarding flu, the time taken to "estimate" flu cases and deaths is part of the CDC's/WHO's longtime effort to pretend flu is deadlier than it is. IIRC, the page you cited was being updated in 2020 and 2021, as people kept comparing COVID deaths and flu deaths. But the purpose of flu surveillance is to give estimates of what is occurring in a flu season. Arguably, that's what governments were trying to "sell" the population on with the JHU dashboard.
The number of deaths with influenza on the death certificate is reported in the CDC WONDER Provisional database and finalized for a particular year ~12 months later.
I think we would agree an overall purpose & effect of the JHU dashboard early on was to show “global spread” and feed the “virus coming from afar to your hometown soon” story.
As far as I remember – like the data on the CDC’s COVID-19 pages and the respective states health department numbers – everything was presented as provisional, subject to change, etc. Federal, state, and local officials in the U.S., at least, were constantly saying that numbers reported (regardless of source) were an undercount due to delays, not enough testing, and so on. The claim that a disease was being or could be “tracked” in near real-time was absurd, but officials also weren’t saying all cases etc were being captured by health departments or dashboards. They were saying the opposite.
Thank you for deep and clear analysis of details underlying JHU dashboard. Indeed, at the beginning of 2020 I thought (as millions of people around the world) that it presented number of real cases (simply speaking- people suffering from severe pneumonia caused by novel virus).
To supplement above analysis- all C19 statistics are based on massive testing of people with highly unreliable tests. As a result- official C19 data are rubbish.
It is indeed, all persuasive writing is technically propaganda, but not all propaganda is bad. Figuring out which kind you're dealing with can be very difficult, but luckily the vast majority of Humans on either side are not able to care what is trueso don't get into such matters.
I'm not following what is being shown regarding the probables, the difference, and what it necessarily shows or doesn't show. It may be because, for me, "Whence the probables?" has always been a critical question of the NYC mass casualty event.
I therefore offer the following context/comments, with questions:
1) You say, "In general, both NYCH and JHU included what they called 'probable deaths' in their daily numbers."
I think it’s important to note that inclusion of the probables began at a certain point in time.
I have not investigated the role of the timing/magnitude of “probables” in all states or in other countries.
Do you have a sense of how the JHU dashboard integrated the probables across the board?
2) The addition of the NYC probables was preceded by officials and media reports/experts quoted by media saying that "coronavirus"/COVID deaths in NYC were being undercounted because totals didn't include all potential coronavirus deaths at home, there wasn't enough testing, etc. Their claims were hogwash, in my opinion, but as relevant insofar as the contemporaneous government narrative is concerned.
Is there evidence that JHU’s dashboard was (at that point) stating a higher number than NYC was stating – or stating a very high case number and using a model that pushed the forecasting for deaths higher? (What I’m really asking is if we have reason to believe that federal or local officials were shown or saw something behind the scenes and had an “uh oh” moment in need of course-correction.)
3) Andrew Cuomo fueled the "Undercount!!" fire [intentionally IMO] when he said on April 8, 2020 that the state coronavirus death numbers were coming directly from hospitals. Because New York City, at least, launched mass testing in hospitals - and tested many existing hospital patients - I'm sure that was largely true, even if numbers were inflated. Of course, it raises questions about how numbers were getting from hospitals to the state/city health departments, whether those were model based, etc., but it may explain what both JHU and other dashboards were showing, i.e., positive tests and positive-test deaths in hospitals.
I'm not suggesting JHU was accurate, mind you - only suggesting that what appears to have been happening via hospital data in NYC could've been happening elsewhere.
Related excerpt from NYT article:
Mr. Cuomo said on Wednesday that the official death count numbers presented each day by the state are based on hospital data. Our most conservative understanding right now is that patients who have tested positive for the virus and die in hospitals are reflected in the state’s official death count.
The city has a different measure: Any patient who has had a positive coronavirus test and then later dies — whether at home or in a hospital — is being counted as a coronavirus death, said Dr. Oxiris Barbot, the commissioner of the city’s Department of Health.
“To date, we have only been recording on people who have had the test,' she said on Thursday morning.”
Do you think it's possible that hospital systems in some cities in the U.S. and elsewhere had any new or upgraded abilities were feeding data to health departments very quickly?
4) I don't see in the article where you defined or cited a reference for "probable death" or made/observed any distinctions between NYCDOHMH's definition and JHU's.
The city's vital statistics report for 2020 (published in April 2023) gives a definition and suggests that a good portion of the probable COVID deaths are NOT included in the COVID-19 mortality section. https://www.nyc.gov/assets/doh/downloads/pdf/vs/2020sum.pdf
I note that JHU page you linked doesn't say "probable COVID death" but rather "probable deaths."
What is your interpretation of the JHU “probable deaths”? Are those people who probably died, but may not have died at all?
5) Given the above background, I'm not seeing how "The similarities between the blue and green lines provide almost irrefutable proof that the 'probable death' numbers were artificially generated on a computer." Can you explain?
6) Finally, you said, "All of this suggests, and that rather strongly, that Covid death numbers for NYC were invented on a calculator rather than counted in a morgue."
As you may know, I believe and have written about how the New York City all-cause daily curve very likely represents a fantasy.
In your statement above, it's not clear whether you're implying a) that the deaths DID occur in real-time, as presented by official data, and were merely attributed to COVID-19 based on a model, b) some portion of the deaths did not occur in real-time, were fabricated, etc., or c) something else entirely.
Thanks for the comments. To answer your questions:
1) no.
2) yes and no. Yes because by their own testimony the dashboard was almost always higher than most health authorities numbers (they claimed this was because JHU was more accurate). No, because the repositories I used changed in time as per what is said in the article.
3) That info is unavailable, but it is quite unlikely they could do it in real time. They didn't have this ability in 2019, to be sure.
4) JHU defines probable deaths expressly in their Aug 31,2020 announcement about NYC by borough (link in the previous thread). I had no reason to invent a new definition.
5) I suggest you reread that section. I wouldn't know how to make it more clear.
6) my statement was solely about covid death numbers.
1) Okay, but I didn't get the impression from the article that you were aware of the overall probable/confirmed history.
2) and 5) I am not seeing how NYC example works for what you're trying to show.
I would like it to, because I believe the NYC death curve is fraudulent and was driven by a model - specifically, by a lower-bound estimate from a 2008 Pandemic Influenza Surge Plan published by the OCME of ~27,000 deaths occurring in 8 weeks.
It doesn't matter if the dashboard was usually higher in most places; I'm asking about New York City in particular.
"Methods differ even within states. New York state only reports test-confirmed Covid-19 deaths in its official online tally. New York City, on the other hand, started adding probable cases to city-specific data in mid-April, conforming with CDC guidelines. Dr. Gardner’s team manually added thousands of probable deaths to the dashboard as a result. Dr. Gardner said it isn’t her team’s role to adjudicate these differences. Where authorities report probable cases, she’ll include it. 'How am I supposed to figure out how many probable deaths there are in all 4,000 dots on my dashboard?' Dr. Gardner says. 'It is an impossible thing for me to do. It is hard enough to just to pull in the data that’s been reported.'"
The JHU GitHub notes say "April 24 2020: New York City, NY | Back distribution of probable deaths, removal of probable deaths as probable cases | time_series_covid19_confirmed_us.csv, time_series_covid19_deaths_us.csv | This change is in line with CDC reporting guidelines.
The way I interpret all of this is that (at least in public), JHU was adjusting in response to NYC DOHMH.
You wrote, "In general, both NYCH and JHU included what they called “probable deaths” in their daily numbers. Since they kept track of these amounts separately and on a daily basis, we opted to include them in the NYCH daily values (blue line) but not for JHU (red line). In this way, the difference between the blue and red lines in the above graph is on account of “probable deaths.” The similarities between the blue and green lines provide almost irrefutable proof that the “probable death” numbers were artificially generated on a computer."
I'm not seeing why JHU making adjustments to what NYCDOHMH was reporting is "almost irrefutable proof that the probable death numbers were artificially generated on a computer." I would like to understand, because I have an interest in amassing as much evidence as possible about the NYC death event.
4) I did not say you needed to invent a new definition. I see JHU links to NYC pages but not an explicit definition on their page. After poling around, I do now see JHU's probable/confirmed definitions on a different page (which are the CSTE/CDC definitions).
5) [addressed above]
6) What about the COVID death numbers? If I'm not mistaken, like me, you don't think there was a sudden-spreading coronavirus or a new cause of death - only a new cause of death attribution/code. What is the underlying contention regarding Figure 2? We agree (and have previously discussed elsewhere) that the curve is too smooth and is a modeled event, not a real-time/disease spread event. What are the implications of the saying the COVID death numbers are invented on a calculator? How did that direct what is on death certificates? How does it relate to all deaths? Did those deaths occur on the days claimed, irrespective of cause?
Chapter, verse and receipts on the Dempanic that somehow magically came and went with the 2020 election season.
The aggregate costs of the various models, “remediation” policies, economies collapsed, jobs lost, families split, government trust cratered, and children not educated is incalculable but certainly in the billions of dollars.
That is how much the globalists are willing - and can / have access to - to spend to crush the middle class and self-government.
Anyone thinking they won’t remove Trump from the board is not thinking.
The probable deaths came from the JHU link, which was listed as "unassigned new York" (state) until May 17, which is why it was decided to include probable deaths in the NYCH blue line, but not the JHU red line of figure 1. It took the better part of 2 weeks to sort that out, but it was cleared up with this post on the JHU github: https://github.com/CSSEGISandData/COVID-19/issues/3084
In other words, the source for NYCH had the probable deaths included in the total deaths, but the JHU source had the probable deaths in a separate category.
No, no effort was made to find a different NYCH source, nor to find their breakdown of confirmed and probable deaths. It wasn't felt to be necessary for this article, especially given that the difference between the NYCH numbers and those of JHU were explained by the probable death classification.
Here’s a recent paper claiming “The Australian vaccination campaign was successful in reducing mortality over 2022, relative to alternative hypothetical vaccination scenarios.”
Commenting on this: "It is, therefore quite possible that nothing unusual happened in NYC in the spring of 2020 (even as nothing unusual happened in China [45]), and that the 24% average rise in 911 calls [46] during this period of time, along with the inexplicable and disturbing increase in cardiac arrests [47] were not caused by Covid but by media induced fear [48]."
The cardiac arrest event cannot - in my opinion - be reasonably attributed to media-induced fear. At minimum, the redirection in call types and directives to FDNY/EMS must be acknowledged as deadly -- and the RMA increase as deeply disturbing. See under 9) and 10) here: https://www.woodhouse76.com/p/eleven-serious-problems-with-the
The "official" government-anointed explanations for the OHCA event are "COVID-19" and "people staying away from the hospital." The latter would be related to the media-induced fear idea, but as I note in the article above, there is no great explanation for why media would induce fear to the point of cardiac arrest (including of the nature/characteristics described in the OHCA study) in NYC but not in Chicago. Detroit, which is not far from Chicago, also had a significant OHCA event, as did London and Paris. Lombardy's OHCA came close to NYC's, but not quite.
For all cities that saw significant OHCA events, I am hard-pressed to explain why the events "are" a curve, irrespective of cause. Let's imagine it was pure panic (whatever we want to say that means): Why would we see a curve, versus a more random distribution?
Finally, even if the cardiac event were fear induced (fear on the part of EMS and patients), it's not reasonable to characterize that as "nothing unusual" happening. It's incredibly unusual - as is that fact it officials haven't been compelled to explain it.
This explains why the entire continent of Africa had fewer deaths than the United States.
Respectfully, the questions I have posed and points I have made in comments have not yet been sufficiently addressed by the author, as far as I can tell.
I've made reference to my comments in responding to a related article by Martin Neill and colleagues here: https://open.substack.com/pub/wherearethenumbers/p/exaggerated-estimates-from-epidemiological?r=jjay2&utm_campaign=comment-list-share-cta&utm_medium=web&comments=true&commentId=55919095
I would appreciate if Thomas would help me understand his conclusions in view of what I've pointed out and am continuing to ask.
Thank you.
More dodgy data being used to support 'the vaccine'...
QUOTE:
In the absence of a vaccination program, there would have been approximately 1.1 million additional COVID-19 deaths and more than 10.3 million additional COVID-19 hospitalizations in the U.S. by November 2021.
Without the U.S. vaccination program, COVID-19 deaths would have been approximately 3.2 times higher and COVID-19 hospitalizations approximately 4.9 times higher than the actual toll during 2021.
If no one had been vaccinated, daily deaths from COVID-19 could have jumped to as high as 21,000 per day — nearly 5.2 times the level of the record peak of more than 4,000 deaths per day recorded in January 2021.
END OF QUOTE.
Based on estimates...
The U.S. COVID-19 Vaccination Program at One Year: How Many Deaths and Hospitalizations Were Averted? https://www.commonwealthfund.org/publications/issue-briefs/2021/dec/us-covid-19-vaccination-program-one-year-how-many-deaths-and
Uncanny!!
Even though I receive emails notifying me of new articles, I receive so many Sub stack notifications that I generally do not look at them when I receive them but go through them when I have some spare time then I do some 'binge reading'. I had such a binge reading session on 15 April, prior to this article being published. I posted a comment on the 'Where Aare the Numbers' article about MHRA still not releasing data regarding pregnancies, etc. https://wherearethenumbers.substack.com/p/mhra-still-wont-release-critical
Today I am doing a similar binge read and have just read this article about data on the JHU dashboard. Whilst reading what it said, it resonated so much with what I had said in my post on 15 April, namely:
"Bill Malcolm
Apr 15
I'm lost for words...
I have 2 points I wish to make.....
My second point is that what can be so difficult about summarising and publishing data that is contained within the systems that they administer/ monitor? It is not rocket science!
From early 2020, every country in the world (allegedly) reported, ON A DAILY BASIS, the new number of cases and deaths linked to CV19. The logistics of doing such (worldwide) reporting always puzzled me.
When did the reporting system get put in place and how was it administered? Were additional staff employed just to do this daily reporting? The logistics of this are quite mind boggling. Such data was being fielded from every health centre/ doctors practice/ hospital within each NHS region and reported centrally every day!! Even if such daily reporting was done, which I very much doubt, who (if anyone) checked/ verified the figures reported?..."
My suspicion has always been that much of the data reported in real time (news media, Covid dashboards like JHU) must have been educated estimates (guesses) or, worse still, completely made up!! This article confirms my suspicions and, if anything, it seems more likely that it was the latter!
My go-to source for CV19 data during the 'Restrictions' or 'Period of Madness' (much more apt descriptions than 'pandemic') was Worldometer, which gets prominently & extensively mentioned in your article.
As you correctly point out this website provides information (on a world wide basis) in respect of populations. What is remarkable is that this website has just 2 main topics, Population & Coronavirus
https://www.worldometers.info/
As an aside, given the date of your article, there is a note that now appears in a banner displayed (from 14 April) at the top of of the Worldometer Covid page, which says:
"NOTE: As of April 13, 2024, the Coronavirus Tracker is no longer being updated..."
What is peculiar (gleaned from using the Wayback Machine) is that prior to 2019, the site only had one tab on its home page (Population). In May 2019, it added a "GDP" tab and in September it added a "CO2" tab. The peculiar thing about the CO2 tab is that it only shows data from 1971 up to 2016. The tables & graphs in the tab look like a test out of the tables & graphs that would used in the (still in the future) Coronavirus tab.
On 29 January 2020, the Coronavirus tab was added.
https://web.archive.org/web/20200131025422/https://www.worldometers.info/coronavirus/
Whoever did this must have had amazing forethought (like 'Mystic Meg'!!) to decide to set up a completely new section on the website, which according to the figures it reported that day, only had deaths (1,133) in one single country (China). The table this day has 20 countries listed. The other 19 countries, in aggregate, have zero deaths and only 101 cases (range 1 to 14), including USA with just 5 cases.
On this first day of Coronavirus reporting, Worldometer ditch their GDP tab. And the next day (30 Jan), Worldometer ditches its CO2 tab!!
Another remarkable thing about 30 January is that 963 previously dead people in China came back to life because the total deaths for China dropped from 1,133 to only 170!!!
Prior to 29 January 2020, The Worldometer website would not have required much administration because it was not providing real word up to date data. On the "About" page, the following is stated (December 2019) regarding how the website works:
"The live counters show the real-time estimate as computed by our proprietary algorithm, which processes the latest data and projections provided by the most reputable organizations and statistical offices in the world."
This begs the question, how did Worldometer magically have the resources to not only obtain real world live data (if it existed, which I very much doubt) from all countries around the world but also verify it on an ongoing 24 hour basis?
Below is what was stated on the About page of their website (again from December 2019):
"Trusted Authority
Worldometers was voted as one of the best free reference websites by the American Library Association (ALA), the oldest and largest library association in the world.
We have licensed our counters at the United Nations Conference on Sustainable Development (Rio+20), to BBC News, and to the U2 concert, among others.
Worldometers is cited as a source in over 3500 published books, in more than 2000 professional journal articles, and in over 1000 Wikipedia pages."
I am just throwing out a hypothesis. If, prior to 2020, Worldometer was widely perceived to be such a trusted source (similar probably to how JHU would have been perceived) then, if somebody, or a group or groups, wished to spread false information/ propaganda (and wanted it to be trusted as being true) spreading such information through a trusted source (like Worldometer) would be a good way of doing it.
Particularly so because journalists and politicians are widely regarded as not trustworthy (journalists, politicians, Govt ministers are all near the bottom, around 20% trusted, in the annual Ipsos Veracity Index). Therefore a long established, very well trusted and apparently "independent" website would be excellent for providing support for what the media/ governments report, should any non-trusting citizens want to verify/ try to fact-check what they are being told.
The trustworthiness of this website seems to have even improved since December 2019 as the About page now says the following. Note the 10+fold increase in citations in journal articles (2,000 to 25,000).
"Trusted Authority
Worldometer was voted as one of the best free reference websites by the American Library Association (ALA), the oldest and largest library association in the world.
Worldometer is a provider of global COVID-19 statistics for many caring people around the world. Our data is also trusted and used by the UK Government, Johns Hopkins CSSE, the Government of Thailand, the Government of Pakistan, the Government of Sri Lanka, Government of Vietnam, Financial Times, The New York Times, Business Insider, BBC, and many others.
Over the past 15 years, our statistics have been requested by, and provided to: Oxford University Press, Wiley, Pearson, CERN, World Wide Web Consortium (W3C), The Atlantic, BBC, Milton J. Rubenstein Museum of Science & Technology, Science Museum of Virginia, Morgan Stanley, IBM, Hewlett Packard, Dell, Kaspersky, PricewaterhouseCoopers, Amazon Alexa, Google Translate, the United Nations Conference on Sustainable Development (Rio+20), the U2 concert, and many others.
Worldometer is cited as a source in over 10,000 published books and in more than 25,000 professional journal articles."
Thanks for sharing this info.
Cheers!
Excellent investigation. It mentions that JHU used Worldometer data, and that this was computer modelled. Is there some source for this?
Worldometer uses computer simulations in general. That's a given. They say: "For the live counters on the home page, we elaborate instead a real-time estimate through our proprietary algorithm which processes the latest data and projections provided by the most reputable organizations and statistical offices in the world."
See : https://www.worldometers.info/about/
For Covid numbers, they claim otherwise: "Our sources include Official Websites of Ministries of Health or other Government Institutions and Government authorities' social media accounts. Because national aggregates often lag behind the regional and local health departments' data, part of our work consists in monitoring thousands of daily reports released by local authorities. Our multilingual team also monitors press briefings' live streams throughout the day. Occasionally, we can use a selection of leading and trusted news wires with a proven history of accuracy in communicating the data reported by Governments in live press conferences before it is published on the Official Websites."
Pay attention to what they fail to say.
Thank you Thomas V. It makes much but not all of our work on estimating infectious disease outbreaks and COVID-19 dependent on questionable data. We did use UK Office for National Statistics for the UK model not Worldometer. Published in the International Journal of General Medicine after near 3 years of rejections.
Cook M, Puri B, A Novel and Accurate Method for Estimating Deaths and Cases During Outbreaks of Infectious Diseases Including COVID-19.
Yup all fake modelling as reliable as Dr Pants Down Fergusons! All on purpose, to create the fear needed to roll out an injection based toxic synthetic weapon. Nothing biological about so called covid. Its not even made in a lab from bats etc. Its just a weaponised synthetic protein, or other chemical poison, crafted after years of prior also fake vaccines, for fake diseases, which are all just cover stories for industrial toxins, or sanitation/malnutrition issues.
Every State Health Authority reported their “covid” numbers daily. This data was then collated by various websites and dashboards like JHU.
It wasn’t that the data was false, it was just misinterpreted. The data actually proved everything they said and did was wrong.
Are you saying it was false? Which categories?
I have some questions for you, Human:
> Every State Health Authority reported their “covid” numbers daily. This data was then collated by various websites and dashboards like JHU.
To clarify what you are saying: are you asserting that you have personal, hands on technical(!) knowledge (as opposed to belief) of all(!) of the various data pipelines involved in this project, across all(!) states?
> It wasn’t that the data was false, it was just misinterpreted.
Please explain what you mean by this.
> The data actually proved everything they said and did was wrong.
How could you possibly know:
a) Everything that they said and did?
b) Whether that was proven out with perfection by the data? Can you even name the numerous extremely complicated disciplines involved in pulling something like that off?
Before you reply: I am quite familiar with your kind, a typical response will avoid addressing the questions, but rather engage in various forms of rhetoric, insults, evasion, etc. Please try to resist natural Allistic urges to engage in that behavior, this is a serious topic.
Reflecting on my early 2020 self/impressions, I did check the JHU dashboard but I did not think it was “ahead” of my state. The impression I and people I knew had re: JHU dashboard is that it was an aggregator.
The state health department was reporting daily “COVID” case, hosp., and death numbers in press releases and daily press conferences. In addition, my county medical examiner was publishing every COVID-attributed death in the county it’s public register. I could see each death (line by line w/age, causes, city of death, etc).
I initially looked at JHU to see what it ranked my county vs other counties and to see what it was saying about other countries. (Whole thing felt like something out of the 1980s movie “War Games” 🙄) But the novelty quickly wore off when I saw the local info wasn’t different from what the county/state was reporting.
The CDC provisional data pages had more info for my purposes. I don’t recall ever downloading data from JHU for analysis.
If the JHU dashboard was “leading” jurisdictions with modeled data, that would be a different impression than I had at the time re: what the dashboard was doing.
I understand your point: even taking covid numbers at face value, covid was not the big scary virus worthy of global lockdowns, like so many claimed.
The article is clear: covid case and death numbers as reported by JHU were meaningless for 3 reasons:
1. very few health authorities in the world are capable of producing daily data,
2. so JHU filled in the gaps with computer models.
3. JHU models were corrected with data from health authorities, but this ended up confounding the data.
So, it's not that the data is "false" like a PCR test might be. Rather, the data is meaningless in the same way faulty models are meaningless.
Case in point, only 9% of countries in the world could provide the WHO with weekly ICU numbers for covid, let alone daily numbers, let alone daily numbers of cases and deaths. See: https://t.co/Rs6RObeeL6
But the United States is one of the countries that theoretically could.
In addition to the questions I've already posted, I ask:
1. What are you saying/not saying about the JHU dashboard showed/represented when it comes to what it showed for the U.S./U.S. states and other countries which did have the ability to provide such data?
2. What do you mean by the models being "corrected" and how did it confound the data?
3. What did/do the "corrected" data reflect, and how do we know?
Your 2nd and 3rd questions are answered in the article.
As for your 1st question, if it took from 7 months to 2 years for the CDC to tabulate influenza cases and deaths, why do you think the US has the ability to track covid cases and deaths in real time?
I do not see the answers to my questions in the article. To be clear, I am referring to the U.S. and U.S. states, where state health department data was the source for the JHU dashboard. Are you saying that the JHU dashboard was consistently "ahead" of states?
I did not say that the U.S. has this ability. They do not and I said so earlier this week when discussing fraud in NYC. https://www.woodhouse76.com/p/the-f-word
But that's why I'm trying to gain clarity on what you think the JHU case and death numbers represented and their overall relationship to all-cause deaths.
Regarding flu, the time taken to "estimate" flu cases and deaths is part of the CDC's/WHO's longtime effort to pretend flu is deadlier than it is. IIRC, the page you cited was being updated in 2020 and 2021, as people kept comparing COVID deaths and flu deaths. But the purpose of flu surveillance is to give estimates of what is occurring in a flu season. Arguably, that's what governments were trying to "sell" the population on with the JHU dashboard.
The number of deaths with influenza on the death certificate is reported in the CDC WONDER Provisional database and finalized for a particular year ~12 months later.
I think we would agree an overall purpose & effect of the JHU dashboard early on was to show “global spread” and feed the “virus coming from afar to your hometown soon” story.
As far as I remember – like the data on the CDC’s COVID-19 pages and the respective states health department numbers – everything was presented as provisional, subject to change, etc. Federal, state, and local officials in the U.S., at least, were constantly saying that numbers reported (regardless of source) were an undercount due to delays, not enough testing, and so on. The claim that a disease was being or could be “tracked” in near real-time was absurd, but officials also weren’t saying all cases etc were being captured by health departments or dashboards. They were saying the opposite.
Thank you for deep and clear analysis of details underlying JHU dashboard. Indeed, at the beginning of 2020 I thought (as millions of people around the world) that it presented number of real cases (simply speaking- people suffering from severe pneumonia caused by novel virus).
To supplement above analysis- all C19 statistics are based on massive testing of people with highly unreliable tests. As a result- official C19 data are rubbish.
@Dr Piers Robinson couldn’t this be propaganda in its own way too though?
It is indeed, all persuasive writing is technically propaganda, but not all propaganda is bad. Figuring out which kind you're dealing with can be very difficult, but luckily the vast majority of Humans on either side are not able to care what is trueso don't get into such matters.
I commented earlier too. (https://open.substack.com/pub/pandauncut/p/the-dashboard-that-ruled-the-world?r=jjay2&utm_campaign=comment-list-share-cta&utm_medium=web&comments=true&commentId=54145142) I'm putting my more substantial comments/questions in a new spot, so they don't get buried in the thread where I was first asking about a source link.
I'm not following what is being shown regarding the probables, the difference, and what it necessarily shows or doesn't show. It may be because, for me, "Whence the probables?" has always been a critical question of the NYC mass casualty event.
I therefore offer the following context/comments, with questions:
1) You say, "In general, both NYCH and JHU included what they called 'probable deaths' in their daily numbers."
I think it’s important to note that inclusion of the probables began at a certain point in time.
NYC was the first U.S. jurisdiction to add such deaths to its running totals. They initially did this as a "dump" of 3,700+ deaths on April 14, 2020, all of which allegedly occurred between March 11 and April 13. https://www.nytimes.com/2020/04/14/nyregion/new-york-coronavirus-deaths.html?ugrp=m&unlocked_article_code=1.kk0.SU_2.O1-Kk3lN5iym&smid=url-share
The next day, the CDC told all states to follow suit. Whether this was done because of New York, I can’t say, but it very well could have been a strategy to “cover for” zealous modeling/dashboards. https://www.washingtontimes.com/news/2020/apr/15/cdc-tells-states-add-probable-coronavirus-cases-de/
I have not investigated the role of the timing/magnitude of “probables” in all states or in other countries.
Do you have a sense of how the JHU dashboard integrated the probables across the board?
2) The addition of the NYC probables was preceded by officials and media reports/experts quoted by media saying that "coronavirus"/COVID deaths in NYC were being undercounted because totals didn't include all potential coronavirus deaths at home, there wasn't enough testing, etc. Their claims were hogwash, in my opinion, but as relevant insofar as the contemporaneous government narrative is concerned.
Is there evidence that JHU’s dashboard was (at that point) stating a higher number than NYC was stating – or stating a very high case number and using a model that pushed the forecasting for deaths higher? (What I’m really asking is if we have reason to believe that federal or local officials were shown or saw something behind the scenes and had an “uh oh” moment in need of course-correction.)
3) Andrew Cuomo fueled the "Undercount!!" fire [intentionally IMO] when he said on April 8, 2020 that the state coronavirus death numbers were coming directly from hospitals. Because New York City, at least, launched mass testing in hospitals - and tested many existing hospital patients - I'm sure that was largely true, even if numbers were inflated. Of course, it raises questions about how numbers were getting from hospitals to the state/city health departments, whether those were model based, etc., but it may explain what both JHU and other dashboards were showing, i.e., positive tests and positive-test deaths in hospitals.
I'm not suggesting JHU was accurate, mind you - only suggesting that what appears to have been happening via hospital data in NYC could've been happening elsewhere.
Related excerpt from NYT article:
Mr. Cuomo said on Wednesday that the official death count numbers presented each day by the state are based on hospital data. Our most conservative understanding right now is that patients who have tested positive for the virus and die in hospitals are reflected in the state’s official death count.
The city has a different measure: Any patient who has had a positive coronavirus test and then later dies — whether at home or in a hospital — is being counted as a coronavirus death, said Dr. Oxiris Barbot, the commissioner of the city’s Department of Health.
“To date, we have only been recording on people who have had the test,' she said on Thursday morning.”
Do you think it's possible that hospital systems in some cities in the U.S. and elsewhere had any new or upgraded abilities were feeding data to health departments very quickly?
4) I don't see in the article where you defined or cited a reference for "probable death" or made/observed any distinctions between NYCDOHMH's definition and JHU's.
The city's vital statistics report for 2020 (published in April 2023) gives a definition and suggests that a good portion of the probable COVID deaths are NOT included in the COVID-19 mortality section. https://www.nyc.gov/assets/doh/downloads/pdf/vs/2020sum.pdf
I note that JHU page you linked doesn't say "probable COVID death" but rather "probable deaths."
What is your interpretation of the JHU “probable deaths”? Are those people who probably died, but may not have died at all?
5) Given the above background, I'm not seeing how "The similarities between the blue and green lines provide almost irrefutable proof that the 'probable death' numbers were artificially generated on a computer." Can you explain?
6) Finally, you said, "All of this suggests, and that rather strongly, that Covid death numbers for NYC were invented on a calculator rather than counted in a morgue."
As you may know, I believe and have written about how the New York City all-cause daily curve very likely represents a fantasy.
In your statement above, it's not clear whether you're implying a) that the deaths DID occur in real-time, as presented by official data, and were merely attributed to COVID-19 based on a model, b) some portion of the deaths did not occur in real-time, were fabricated, etc., or c) something else entirely.
Can you clarify?
Thanks.
Thanks for the comments. To answer your questions:
1) no.
2) yes and no. Yes because by their own testimony the dashboard was almost always higher than most health authorities numbers (they claimed this was because JHU was more accurate). No, because the repositories I used changed in time as per what is said in the article.
3) That info is unavailable, but it is quite unlikely they could do it in real time. They didn't have this ability in 2019, to be sure.
4) JHU defines probable deaths expressly in their Aug 31,2020 announcement about NYC by borough (link in the previous thread). I had no reason to invent a new definition.
5) I suggest you reread that section. I wouldn't know how to make it more clear.
6) my statement was solely about covid death numbers.
Thanks.
1) Okay, but I didn't get the impression from the article that you were aware of the overall probable/confirmed history.
2) and 5) I am not seeing how NYC example works for what you're trying to show.
I would like it to, because I believe the NYC death curve is fraudulent and was driven by a model - specifically, by a lower-bound estimate from a 2008 Pandemic Influenza Surge Plan published by the OCME of ~27,000 deaths occurring in 8 weeks.
It doesn't matter if the dashboard was usually higher in most places; I'm asking about New York City in particular.
A Wall Street Journal article published on May 9, 2020 gives an account of the "order" of events regarding the adding of the probables. https://www.wsj.com/articles/how-a-johns-hopkins-professor-and-her-chinese-students-tracked-coronavirus-11589016603?st=5o9ln4fzsvkzc6l&reflink=desktopwebshare_permalink
"Methods differ even within states. New York state only reports test-confirmed Covid-19 deaths in its official online tally. New York City, on the other hand, started adding probable cases to city-specific data in mid-April, conforming with CDC guidelines. Dr. Gardner’s team manually added thousands of probable deaths to the dashboard as a result. Dr. Gardner said it isn’t her team’s role to adjudicate these differences. Where authorities report probable cases, she’ll include it. 'How am I supposed to figure out how many probable deaths there are in all 4,000 dots on my dashboard?' Dr. Gardner says. 'It is an impossible thing for me to do. It is hard enough to just to pull in the data that’s been reported.'"
The JHU GitHub notes say "April 24 2020: New York City, NY | Back distribution of probable deaths, removal of probable deaths as probable cases | time_series_covid19_confirmed_us.csv, time_series_covid19_deaths_us.csv | This change is in line with CDC reporting guidelines.
The way I interpret all of this is that (at least in public), JHU was adjusting in response to NYC DOHMH.
You wrote, "In general, both NYCH and JHU included what they called “probable deaths” in their daily numbers. Since they kept track of these amounts separately and on a daily basis, we opted to include them in the NYCH daily values (blue line) but not for JHU (red line). In this way, the difference between the blue and red lines in the above graph is on account of “probable deaths.” The similarities between the blue and green lines provide almost irrefutable proof that the “probable death” numbers were artificially generated on a computer."
I'm saying a) it wasn't "in general" - NYCDOH did it first, the CDC quickly told all states to do it, JHU adjusted in response, b) we know what the probable death numbers asserted by NYCDOHMH (posted here) were https://substack.com/profile/32813354-jessica-hockett/note/c-54404599?utm_source=notes-share-action&r=jjay2
I'm not seeing why JHU making adjustments to what NYCDOHMH was reporting is "almost irrefutable proof that the probable death numbers were artificially generated on a computer." I would like to understand, because I have an interest in amassing as much evidence as possible about the NYC death event.
3) We agree they can't do it in with real-time. Arguably, even they weren't claiming they could do it IRT, because they were saying "undercount." In 2019, JHU had a measles outbreak dashboard https://www.arcgis.com/apps/dashboards/33c650a3740f478e99e308da9e5908ec This was cited as the "inspiration" for the coronavirus dashboard. https://www.technologyreview.com/2020/03/06/905436/best-worst-coronavirus-dashboards/
4) I did not say you needed to invent a new definition. I see JHU links to NYC pages but not an explicit definition on their page. After poling around, I do now see JHU's probable/confirmed definitions on a different page (which are the CSTE/CDC definitions).
5) [addressed above]
6) What about the COVID death numbers? If I'm not mistaken, like me, you don't think there was a sudden-spreading coronavirus or a new cause of death - only a new cause of death attribution/code. What is the underlying contention regarding Figure 2? We agree (and have previously discussed elsewhere) that the curve is too smooth and is a modeled event, not a real-time/disease spread event. What are the implications of the saying the COVID death numbers are invented on a calculator? How did that direct what is on death certificates? How does it relate to all deaths? Did those deaths occur on the days claimed, irrespective of cause?
I love this space. People who saw through the crap and kept telling the truth. Thank you one and all.
Chapter, verse and receipts on the Dempanic that somehow magically came and went with the 2020 election season.
The aggregate costs of the various models, “remediation” policies, economies collapsed, jobs lost, families split, government trust cratered, and children not educated is incalculable but certainly in the billions of dollars.
That is how much the globalists are willing - and can / have access to - to spend to crush the middle class and self-government.
Anyone thinking they won’t remove Trump from the board is not thinking.
Good work with the research on the dashboard.
Can you (Thomas) confirm that this is the source link for the confirmed/probables used in the NYC graph? https://github.com/nychealth/coronavirus-data/blob/master/archive/probable-confirmed-dod.csv The source link posted under the graph goes to the total deaths and deaths by borough data, as far as I can tell.
Thanks.
The sources listed are the ones used.
The probable deaths came from the JHU link, which was listed as "unassigned new York" (state) until May 17, which is why it was decided to include probable deaths in the NYCH blue line, but not the JHU red line of figure 1. It took the better part of 2 weeks to sort that out, but it was cleared up with this post on the JHU github: https://github.com/CSSEGISandData/COVID-19/issues/3084
In other words, the source for NYCH had the probable deaths included in the total deaths, but the JHU source had the probable deaths in a separate category.
This substack by Conspiracy Sarah is also an interesting and amusing take on things…
New York Times: We Simultaneously Won AND Lost Covid Because More People Died After the "Vaccine" That Also Managed to Save 300 Million Lives
https://conspiracysarah.substack.com/p/new-york-times-we-simultaneously
Thanks.
I’m not clear as to whether a dataset from NYCDOHMH was sought which showed confirmed and probables.
They are separated through early November 2020 here https://github.com/a/coronavirus-data/blob/master/archive/probable-confirmed-dod.csv, (purportedly) by date of death.
No, no effort was made to find a different NYCH source, nor to find their breakdown of confirmed and probable deaths. It wasn't felt to be necessary for this article, especially given that the difference between the NYCH numbers and those of JHU were explained by the probable death classification.
Continued here https://open.substack.com/pub/pandauncut/p/the-dashboard-that-ruled-the-world?r=jjay2&utm_campaign=comment-list-share-cta&utm_medium=web&comments=true&commentId=54174650
Beautiful.
I'm so sick of people being obsessed with data and not reasons, but I love this.
I wonder, was Mr. Bloombag involved?
Speaking of ‘modelling’…
Here’s a recent paper claiming “The Australian vaccination campaign was successful in reducing mortality over 2022, relative to alternative hypothetical vaccination scenarios.”
Assessing the impact of Australia’s mass vaccination campaigns over the Delta and Omicron outbreaks | PLOS ONE: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299844
Accompanying MSM articles:
- RACGP - Modelling reveals number of lives saved by COVID vaccine rollout: https://www1.racgp.org.au/newsgp/clinical/modelling-reveals-number-of-lives-saved-by-covid-v
- New research finds Covid vaccines prevented thousands of deaths in NSW during pandemic | news.com.au — Australia’s leading news site: https://www.news.com.au/lifestyle/health/health-problems/covid19-vaccines-likely-prevented-thousands-of-deaths-in-nsw/news-story/56ae19ef9f300763c0fe0dea2ffce5f6