Where did the pandemic go?

A global pandemic that failed to nudge the world population numbers

This will definitely be a short one today and some apologies for a relative absence. A longer piece on something important is in the works, but today I thought this was important.

[DISCLAIMER: This post uses data from the Worldometer site, one of many sites “approved” by world governments and institutions. The data is not what you might think it is (see footnote at end, including a note on excess deaths¹).]

Currently doing the rounds is much infighting between members of the “freedom movement” and one focus of this infighting seems to be whether there was really a pandemic at all. The arguments seem to revolve around one of the following:

• There was a pandemic, but it wasn’t as bad as it was made out to be

• There was a virus but there wasn’t a pandemic

• There wasn’t even a virus

• Viruses don’t exist

• There was a virus, but the cure was worse than the disease

Of the above, “viruses don’t exist” is a false narrative seeded into discussion groups by chaos agents, primarily government-backed. This has been going on for decades.

The rest of the claims are variations on a theme. Well today I can definitively answer this part of the question: “was there even a pandemic?”

This would be a great time to remind you to revisit the following two very related articles if you haven’t yet:

But for today we are simply going to look at the official figures for the world’s population and see whether there was a significant drop in 2020-2021.

The population statistics are available at the Worldometer site here (archived here) and as you can see we have finally tipped over 8bn despite the best efforts of the CDC and WHO.

And as I’m in a modelling mood I decided to have a look at what the world population should have been if there hadn’t been a “pandemic” at all.

This is easy to do using the modelling function in R² (code is attached in the footnotes³). Essentially we are saying “OK let’s assume the growth in population is predictable based on the pattern over the 20 years before the pandemic”. We can then calculate the anticipated and actual global population and see what happened in 2020-2021.

This is what the fit of the prediction model looks like over the last 20 years (remember this is the percentage change of the population plotted, not the population itself). The red line is the fitted model (what we will use to predict the population).

Now obviously you can see a little deviation round 2021-2023 but it’s minimal. I think you can say just by looking at this that it’s a very close model and if we do a statistical test (such as the K-S test) you can’t tell any difference between the predicted and the actual figures. Excellent.

So what does the prediction tell us should be the population in 2023?

Now, I know it might be difficult to see but there is actually a difference in the bars.

I’ll show you by zooming in

You see? There is a slight difference between the actual and expected numbers over the 3 pandemic years, but unfortunately for the people that have made billions out of fearmongering a pandemic, the final tally is in the wrong direction.

2023 population Actual (reported) = 8,045,311,447

2023 population Predicted (modelled) = 8,042,755,243

That is, the population is 2.5 million more than it should be after 3 years of the “scariest pandemic ever”.

Of course the rise in population could be fuelled by the jump in birth rates in 2021 and the reduction in mortality seen in 2020 in many countries when the hospitals closed to make tiktok videos rather than seeing patients - apparently because of the stress of the pandemic.

Irrespective of the reason, what we can conclusively see from this data (the world governing bodies’ authorised data, no less).. is that there was no pandemic of lethality that made any statistically discernible blip on the world population.

Luckily, that’s not the current definition of pandemic, so the WHO, who declared a state of emergency sufficient to shut down the world and force people into a gene therapy treatment that they either didn’t want or didn’t need, get a free pass - again. You see, “pandemic” just means that it’s global.

What else is global? Cancer (10m deaths/year and increasing), health care mistakes (3m deaths/year) and opioid addiction (60m) to name a few. Oh and colds. Yep, they are definitely pandemic. Have been for centuries.

So, did the pandemic happen?

Technically, yes. But it wasn’t a pandemic of death.

What it does seem to have been is a pandemic of biomedical tyranny - and given that the countries with less media, less interventions and less control by WHO and UN bigwigs with vested interests in things other than the health of the population seem to have done a lot better than countries with the most interventions - it would appear that any pandemic of death that there was, wasn’t due to a virus at all.

1. The worldometer site is almost certainly curated by a group of people who use modelling to estimate the figures. During COVID we were told that even in the darkest crevices of Africa we would be getting live health information - this is simply not true (we have written about this on twitter and other places and I will likely do a substack on it in due course).

These sites cannot possibly curate live information on deaths up to the minute. To illustrate this please look at the archive for this worldomoter page.

On the 1st August 2023 the world population was recorded at 8,045,311,447 exactly the same as the world population recorded on 6th Jan 2024. Screenshots below.

This is likely to account for why there is no significant “excess deaths” recorded on worldometers for 2022-2023, following the vaccine rollouts, even though has been widely reported in individual countries’ statistics. There is no simple way of curating this information for all countries just as there was never any simple way of curating live COVID death figures, and why the person in charge of the Johns Hopkins dashboard was an expert in data modelling.

2. For this modelling I have used a 4-power (quartic) polynomial regression modelled on the population change percentage from 1999 to 2019 - 21 years.

The fit is very close to actual data from 2000 to 2020 and is robust to avoid major deviation from the 2015+ trajectory up to 2030 (pictured below for those interested)

wpop<-read.csv("world_pop_worldometer_2023.csv") 
##csv copy/pasted from worldometer site
wpop<-as.numeric(wpop)
wpop<-wpop[1:73,]
pchange<-data.frame(wpop$Year[1:72],(wpop$Pop[1:72]-wpop$Pop[2:73])/wpop$Pop[2:73])
colnames(pchange)<-c("year","pc.change")

lp<-lm(pc.change~poly(year,4),data=pchange[5:25,])
plot(pchange$pc.change*100~pchange$year,ylab="percent change",xlab="Year",pch=19,main="Percent change in population by year 1952-2023, worldometer data",cex=1.2)
lines(1988:2023,100*predict(lp,data.frame(year=1988:2023)),lty=2,col="red",lwd=3)
legend("topright",legend=c("actual","predicted"),pch=c(19,NA),lty=c(0,2),col=c("black","red"),cex=1.25)

ks.test(pchange[1:13,2],predict(lp,data.frame(year=2023:2011)))

pop_2010<-wpop$Pop[14]
p<-pop_2010
model<-data.frame(pchange[1:13,],predict(lp,data.frame(year=2023:2011)),0,0)
colnames(model)<-c("year","pc.change","predict.change","predicted.pop","actual.pop")
for (y in 13:1) {p<-p*(1+model$predict.change[y]); model[y,4]<-p}
model[,5]<-wpop$Pop[1:13]

m<-t(model[,c(1,4,5)])
colnames(m)<-m[1,]
m<-m[2:3,]

barplot(m[,13:1]/1e+06,beside=TRUE,col=c("red","grey"),main="Predicted vs Actual World Population 2011-2023 (millions)",xlab="Year",ylab="Population")
legend("topleft",legend=c("Actual","Predicted"),fill=c("grey","red"),cex=1.1,byt="n")

3.