reducing spread (aka lowering R0 which is the rate of viral spread at t=0 or Rt which is the rate of viral spread at time t>0) and/or
by lowering the risk of death if infected (i.e., they lower the infection fatality rate (IFR) which is typically measured as a change in the case fatality rate (CFR))
We can measure both impacts.
If the R0 was reduced, we’d flatten the curve. That didn’t happen. Areas that were boosted shortly before Omicron had a much higher peak than those that were less boosted. And the CFR was temporarily increased by over 2X post vaccine in large scale objective data.
Most importantly, nobody has been able to explain away the evidence I’ll show you below.
My argument
Let’s assume the vaccine saved lives.
Clearly, the greatest benefit of the vaccines will be in early 2021 because after that everyone is “naturally vaccinated” by the virus.
Secondly, vaccine efficacy wanes over time.
Therefore, if there is any benefit to be had, it would be most clearly demonstrable in early 2021.
There are only two ways a vaccine can provide a mortality benefit (and ChatGPT concurs):
reduce the R0 of subsequent variants (or the Rt of the current variant but it’s much less effective since it’s normally too late)
Reduce the IFR of the current or subsequent variants
The COVID vaccines did none of these. It increased both parameters.
The impact on R0 was to raise it, not lower it as promised
Remember “flatten the curve”? Vaccines are supposed to decimate R0 and crush it to be flat. They are NEVER supposed to amplify R0 which is what these vaccines clearly did and the evidence has been in plain sight for over 3 years now and nobody is paying attention.
The relative height of the Omicron outbreak in Israel and Denmark is undeniable evidence of an R0 increase.
If you compare alpha with Omicron, Omicron should be around 2X higher peak as we can see from the US wastewater data from all 192 sites:
Israel and Denmark were among the most vaccinated places on earth with strong recent boosters BEFORE Omicron hit. They both had the highest infection rate in the world. Look at this graph comparing the US, Denmark, and Israel for cases.
Right before the outbreak both Israel and Denmark had twice the booster coverage as the US. So Israel and Denmark should have the lowest peaks (a measure of R0) of any nation on earth, not the highest!
And the ratio of heights of alpha vs Omicron in our “vaccine enhanced” countries was over 10X, but the US wastewater data shows it should have been 2.2X.
The easiest way to clearly see the “amplified R0” effect is cumulative COVID cases. Look how the countries were tracking each other until Omicron. You can clearly see the disparity once Omicron hit:
And here’s the booster chart showing the boosted countries had the higher COVID cases in the wave after the booster. The “boosters” boosted cases. The graph above should have been completely opposite if vaccines worked.
So they lied about R0. It amplified R0.
There are many other studies confirming effects that increase R0 including the Cleveland Clinic (CC) study and the second CC study. There were 7 other studies which found the same effect as the first 2 CC studies: here, here, here, here, here, here, here.the recent confirmation in Japan finding the same thing, and various surveys.
New Japan study confirmed the CC results that more vaccines→more cases: “The odds of contracting COVID-19 increased with the number of vaccine doses: one to two doses (OR: 1.63, 95% CI: 1.08-2.46, p = 0.020), three to four doses (OR: 2.04, 95% CI: 1.35-3.08, p = 0.001), and five to seven doses (OR: 2.21, 95% CI: 1.07-4.56, p = 0.033).” This is consistent with Table 2 in the CC study.
And there are four different surveys showing in the real world, the vaccinated are much more likely to be infected. I couldn’t find any large company where the unvaccinated were out sick more than the vaxxed.
When the vaccine makes you more likely to be infected, R0 and Rt (the R during a wave) go up.
IFR
The most definitive data we have on IFR (infection fatality rate) is from the 15,366 US nursing homes which have reported to CMS on a weekly basis COVID cases and COVID deaths since mid-2020.
There is a reason that they never analyze this data. It’s career suicide for any epidemiologist. Can you guess why?
Yeah, that’s right. It’s because it shows the vaccines more than doubled the CFR (the case fatality rate which is an almost perfect estimate of IFR when you are using nursing home data) shortly after the vaccines were given for a limited period of time (OR 2.1, CI 1.9-2.3).
Nursing home data is dispositive because 80% of the people impacted by COVID were elderly and nursing homes are closed environments where the same people can be followed longitudinally and everyone with symptoms will get tested. There is no place to hide in a nursing home. They are required to report their stats weekly to CMS. And 15,366 is a big number so the Central Limit Theorem applies (i.e., the law of large numbers) which means the data can be noisy and we’ll still find the signal.
See the spike in the IFR right after vaccine rollout? See how the slope trends UP pre-omicron and post omicron? That’s the boosters. IFR, is normally supposed to go down or flatline over time. It can go up with a more deadly variant, but all the subsequent variants were less deadly so IFR should be monotonically decreasing. Whenever the IFR drops like a rock is at the start of a new variant wave. It does NOT drop when the vaccines roll out. And the peak after the initial rollout is insanely high as noted above. That’s statistically HIGHLY unlikely if the vaccines reduced IFR.
I included a list of the 9 positive and negative control conditions that this data meets that confirm the accuracy of the data (the “Pos neg controls” sheet).
There is no better data source for what happened to the elderly in the US after the vaccines rolled out. I don’t know of any data source that is more dispositive on the issue than this one.
Note that my earlier article on Santa Clara nursing homes (LTCFs) confirmed the effect (OR 2.6, CI 2.1-3.1) which means the CI’s overlapped. The best estimate (2.1) was inside the overlap of the CIs (2.1-2.3) of the two studies.
And finally, the Pfizer Phase 3 clinical trial confirmed the IFR increase as well. If you were vaccinated your IFR was over 10X higher than the unvaccinated making the 2X I found in the data look like rounding error. Thanks to Joe Fraiman for suggesting this. It’s not statistically significant because the numbers were too small in the trial to get a solid IFR signal, but it is extremely troubling that this was so lopsided. Nobody seemed to be bothered by a 10X higher IFR in the vaccinated. The FDA seemed so happy with this that they didn’t require any further data.
If you want to claim the IFR went down, you simply have to show more dispositive evidence than 15,366 nursing homes doing weekly tracking of 2.2M COVID cases and 150,889 COVID deaths and 1.34M all-cause deaths.
I’m all ears. What’s the better publicly available data source that meets all 9 positive and negative controls and why won’t anyone tell me what it is? Why is that a secret?
My favorite takedown articles is this research letter by Vinay Prasad which pointed out that a prominent study showing the vaccinated had 10X lower COVID mortality than the unvaccinated simply forgot to point out that the disparity happened during non-COVID periods as well.
In one of the most important revelations of the pandemic, Høeg et al. (2024) pointed out that in Arbel, 2021, the non-COVID mortality (NCACM) differences more than completely accounted for the 94.6% lower mortality benefit claimed in the study. The vaccine didn’t reduce mortality at all; it increased it!
In their reply, Arbel et al. acknowledged the failure, and then tried unsuccessfully to rescue their result with a ridiculous new analysis. They never corrected their original paper to note that the difference in mortality happened in non-COVID periods.
I spoke at length with one of the authors (Ram Duriseti) and he pointed out that a good way to tell it was ridiculous is to look in the table of mortality risks that the authors provided in their reply. The table shows that socioeconomic status has NO effect on death and that obesity is likely helpful to staying alive. And having a transient ischemic attack will improve your odds of living! You do trust the science don’t you?? :)
An organization in Israel sued Clalit Health Services to release the data used by Arbel, but they lost. So Arbel’s methods and data remain secret.
The Høeg authors are expecting their reply to be published in a month.
The nail in the coffin
Check out this post by Spiro Pantazatos. R2 is over .71 here and his new model is even higher at .75. The positive coefficient means vaccines have increased deaths.
I’m not aware of a multiple regression on the vaccination/death data with a higher R2 showing the opposite. Are you?
This is the final nail in the coffin for the people who claim vaccines didn’t increase deaths.
Is there evidence showing both R0 and IFR were lowered?
I’m not aware of any that is of equal or higher quality showing the reverse. Are you?
Summary
The COVID vaccines made things worse. It more than doubled the R0 and IFR (both temporarily before the “benefit” wore off).
If you can find a hole in any of these arguments, or identify data that is opposite and more dispositive, I’m all ears. I’ve got $1M riding on the outcome (we’re halfway through the arguments and will finish in around 3 weeks).
My parents lived to 85 and 91 and did not trust vaccines and never took them. They said the big improvements in peoples health was cleaner drinking water, sewage systems and less crowded houses etc.
Steve, the evidence has been there for a long time. “To those who refuse to accept, no amount of evidence will suffice.”
My parents lived to 85 and 91 and did not trust vaccines and never took them. They said the big improvements in peoples health was cleaner drinking water, sewage systems and less crowded houses etc.