Detailed Assessment of the Impact of the COVID "Vaccine" on Mortality in New York City.

Did it save lives or cause death?

In my last post, I extolled the utility of the cumulative excess deaths function in analysing mortality in England. However, my modified Gompertz function is just as useful in getting to the bottom of whether exogenous factors (external events) are exerting an influence on a mortality distribution.

COVID in NYC during the original spring epidemic is a perfect illustration. Irrespective of whether COVID the disease was really responsible for the excess deaths, we can easily determine if the measures to “flatten the curve” were successful or not.

In the chart above, we can see a perfect calibration to the empirical data between 07-Mar-20 when NYC reported its first COVID death and 04-Apr-20 when behavioural changes might have made a difference to COVID deaths, i.e. around three weeks after infection.

There is space between the actual COVID deaths (dark grey area) and the model (blue line with dot markers) which represents the deaths that were potentially mitigated.

In all, these amount to just shy of 1,000 deaths or about 5% of the expected tally derived from the continuous model.

In itself, according to usual cost/benefit analysis in the medical context, this is probably a long way off what would have been considered “worthwhile” pre-COVID. Sorry to sound callous but it’s a fact. With a finite amount of funds, not every life can be saved because Big Pharma has to make its dollar after all!

But then, when we consider that the non-COVID excess deaths during the same period amount to almost 5,000 (or 22% of the expected COVID deaths), we have what we might call a Pyrrhic victory?!

Anyway, the article is about the impact of the “vaccine” so let’s not dwell on that unfortunate fact. It was just to set the scene and see the model working in context.

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Moving on then, let’s test two hypotheses:

1. The COVID “vaccine” causes COVID (and therefore COVID death);

2. The COVID “vaccine” is effective in reducing COVID mortality.

A COVID death “surge” started again in NYC in late November. This means we can model this distribution just like we did in spring 2020 up until the point that the “vaccine” is introduced.

If hypothesis #1 holds then we should see an increase in COVID deaths outside of the calibration period. So, rather than a gap between the actual COVID deaths and the model, we should see COVID deaths outside the model line:

And so it is. It is evident in the chart above that we have substantial amount of COVID death over and above what we would expect using the exact same methodology as before.

Now, when we add another wave of death (wave 1) in the chart above, we still cannot accommodate all the COIVID deaths. Optimising the model (as in solving for the date range of data that produces the least model error when calibrating to the data), we naturally find a break in the distribution on 06-Feb-21.

We know that the “vaccine” campaign started in NYC on 14-Dec-20 so it does look like we have a smoking gun with this new wave of COVID deaths, emerging a couple of weeks after the campaign has gotten up to speed. This is consistent with the plethora of scientific evidence showing a two week window of immunosuppression whereby vaccinees are more likely to be infected and suffer worse outcomes.

However…

Let’s not jump to a premature conclusion. We have plenty of experience studying seasonal mortality patterns in places like NYC which is very similar to England and many other countries in Europe. And we know that we will often get two and sometimes even three or more distinct mortality distributions in a “mortality season” as different pathogens and susceptible populations compete with each other for viability.

So, we should wait and see if there is more evidence to support hypothesis #1.

Meanwhile, we can look at hypothesis #2 since it follows chronologically. A reasonable number of NYC citizens have been jabbed by 23-Jan-21 and given the alleged two weeks required before the “vaccine” becomes Effective™, we should expect to see a decline in COVID deaths from 06-Feb-21 onwards.

In fact, if you believe the narrative at the time that effectiveness was way up above 90%, we should see something much more spectacular than we saw in the spring. After all, that was the whole narrative - “flatten the curve until the “vaccine” comes to beat the virus!”

Except, it didn’t.

We still have a whole load of deaths outside the model curve, no gap underneath it.

Hmmmm…

So much for the narrative and hypothesis #2. Sorry, simply cannot support it without some mental gymnastics to the extent that all those deaths mitigated from wave 0 (pre-”vaccine”) are obfuscated by this new wave. Something’s got to give there I’m afraid. The two premises are simply incompatible. A bit like the argument that “my vaccine only works if you have it too” sort of premise.

I guess we’ll just have to return to hypothesis #1. Is there further evidence of the jab causing COVID rather than protecting against it?

In the interests of parsimony, I’m just going to say that there are lots more idiosyncratic waves of COVID deaths so we cannot maintain the notion that it’s what can happen in a mortality year. Here they all are when the bootstrapping of the model is complete:

In every case, the calibration was done to 1 week prior to the start of the new wave. Each waves starts around 5 weeks after the last which is a little “unnatural” too?

Anyway, we have four distinct waves that emerge after the introduction of the “vaccine” that is supposed to be a “dead end” for the virus. But is it just dead for the people taking it? Or are they just helping the virus stay a longer course that inevitably affects those who didn't sign up for the experiment as well?

I couldn’t tell you for sure but I can show you something rather curious.

If you plot the vaccination rates in NYC and overlay the idiosyncratic COVID death waves, you get some pretty clear “correlations”:

The relationship diminishes over time but that is not unexpected. If the “vaccine” is responsible for causing COVID, with each leg of the campaign targeting younger, more healthy participants, we should expect the relationship to diminish.

By the time we get to the final leg of the “vaccine” campaign, we don’t actually see a new COVID death wave at all. I’d say that’s due to a fortunate combination of seasonality (i.e. transmission is adversely affected just like it was at the end of spring and even continued jabbing can’t resurrect it) and depletion of the susceptible pool. There’s probably a good contribution from protective herd immunity too! Good old nature!!

Funnily enough, it’s exactly what we observed in England too which I reported at the start of the year in one of my first ever Substacks.

Hugging your gran didn't kill her but getting vaccinated probably did.

Curiouser and curiouser!

As usual, I’m interested to learn what you think of my interpretation of the data.