THERE IS AN UPDATE AT THE BOTTOM OF THIS ARTICLE.

Short summary in layman's terms

COVID-19 cases and deaths per week decreased faster in regions where less boosters were administered since July 1st 2021. The more booster doses were administered in a region, the longer the virus prevailed there.

Summary

I present epidemiological evidence for modRNA boosters being the driving force behind the never-ending SARS-COV-2 pandemic.

The following observations were made looking at data for 248 regions, 130 of which deployed modRNA vaccines and have sufficient epidemiological data available:

Regionally administered modRNA booster doses per inhabitant correlate strongly with increases in COVID-19 cases

• p < 0.000001

• Adjusted R-Squared = 0.348

• Pearson correlation coefficient = 0.594 (update: 0.665 after removal of Sinopharm and Sputnik regions)

For every booster dose administered per inhabitant the time to reach 50% of the COVID-19 cases that were registered regionally regionally since July 1st 2021 increased by 18 weeks (95% CI: 13-22 weeks).

Regionally administered modRNA booster doses per inhabitant correlate strongly with increases in COVID-19 deaths

• p < 0.000001

• Adjusted R-Squared = 0.472

• Pearson correlation coefficient = 0.690 (update: 0.754 after removal of Sinopharm and Sputnik regions)

For every booster dose administered per inhabitant the time to reach 50% of the COVID-19 deaths that were registered regionally regionally since July 1st 2021 increased by 28 weeks (95% CI: 23-33 weeks).

Introduction

The pandemic that would not end

We can’t seem to shake off this virus. Shouldn’t we have developed herd immunity by now?

Virologists seemed to think so two years ago, yet the pandemic is dragging on forever. There were more cases of SARS-CoV-2 infections in 2022 than there were in 2020 and 2021 combined.

How modRNA boosters could relate to this

The term 'modRNA booster' refers to any dose of a modRNA-based vaccine against COVID-19 administered to a patient who has already received two doses of a COVID-19 vaccine.

ModRNA-based vaccines behave very differently from classical vaccines.

mRNA vs. modRNA

The terms modRNA and mRNA are commonly used interchangeably. This is incorrect and bears clinical significance.
While mRNA is an endogenous molecule with a very short half-life, modRNA is a codon-optimized version that has a much longer half-life.
Ribosomes fed with modRNA will translate the encoded sequence to produce a much larger number of protein molecules over a much longer timespan than ribosomes fed with the same protein encoded as mRNA.
You could say that modRNA is to mRNA what methamphetamine is to dopamine. It is not unreasonable to suspect a similar relationship between the harms caused by the two substances.

Antigen-based vaccine vs. nucleotide-based vaccine

Whereas after an immunisation with a classical antigen-based vaccine the body is only exposed to the antigen until it has been eliminated, the nucleotide-based modRNA vaccines lead to much longer exposure times of up to several weeks.

Immune tolerance and prolonged viral shedding

Evidence has surfaced, that immune tolerance could be developing after repeated administration of COVID-19 modRNA vaccines.
Boucal et. al wrote a letter to NEJM in which they present evidence that boosted patients shed virus particles for a longer periods of time than unvaccinated and fully vaccinated patients.

Antibody-dependent enhancement

Iwasaki et al. warned in early 2020 “higher titres of anti-S […] IgG and IgM correlate with worse clinical readouts and older age”.

Geert vanden Bossche

The virologist and vaccine research consultant Geert vanden Bossche has been doing nothing but warning us of these dangers.

Epidemiological observations

In order to figure out whether or not vaccines are extending the duration of the pandemic, I look at epidemiological data acquired through Bill Gates’ and Susanne Klatten’s pet project 'Our World in Data'.

Methods

I want to find out whether booster doses extend the pandemic by comparing the number of administered booster doses with the rates of new COVID-19 cases and deaths rates across all 248 regions 'Our World in Data' supplies figures for.

The problem of confounding factors

When comparing different regions with each other we face the problem that countries, their laws, cultures and demographic compositions can differ immensely in many ways.

If we wanted to find out whether or not boosters per inhabitant predict new COVID-19 cases or new COVID-19 deaths, then boosters per inhabitant is called our independent variable, while new cases per inhabitant and deaths per inhabitant are dependent variables.

The observed relationship between independent variables and a dependent variable can be distorted if it is affected by other factors. Statisticians call these factors confounding variables and their effects can sometimes cause us to see correlations where there are none.

An example of a confounding factor is the stringency at which diagnostic tests and vaccinations are enforced. Stricter legislation could lead to more registered COVID-19 cases through mandated testing and increases the booster rate through mandated vaccinations.
If we observed that regional booster doses are accompanied by higher regional case rates, we could not determine to what degree this effect is owed to the legislative stringency variance across regions.
While there are ways to control for confounding factors, in order to do so we would have to gather more data and determine the effect of each of the variables on our dependent variable.

Eliminating ALL region-specific confounding factors

If there are any regional confounding factors affecting COVID-19 incidence that we are either not aware of or can not measure, then comparing the COVID-19 case or death rates across regions should not yield any meaningful results.

So I thought of a way to eliminate this issue.

Instead of comparing case/death rates between regions, I am going to compare the changes in case/death rates within each region between regions.

I use this change in case rate and death rate over time as a measure of success of the region’s pandemic response.

Picking a timeframe and measuring change of rates

Since COVID-19 boosters were rolled out around the middle of the year 2021 and there is no data for the remainder of 2022 yet, I will look at what happened between July 1st 2021 and December 23rd 2022.

• I count all the administered booster doses, registered COVID-19 cases and registered COVID-19 deaths that occurred during this period.

• I determine the time it took for 50% of cases or deaths to have been registered.

• The less time it takes to reach 50% of cases or deaths, the higher the decrease in case rate or death rate is.

Two examples: Cuba and Finland (COVID-19 mortality)

Cuba

Cuba has yet to approve any nucleotide-based vaccines because it developed its own vaccine which received emergency use authorization on July 9th 2021.

It so happens the country is also a prime example of a region that ran a successful vaccination campaign.

The number of COVID-19 deaths registered in Cuba was similar during the first 7 and the last 69 weeks of our observed timeframe.

The rate at which COVID-19 deaths were registered decreased by 89% (7.5/69.5=0.11) between the first period (week 1-8) and the second period (week 8-77).

Cuba has nearly eradicated SARS-CoV-2.

Finland

According to 'Our World in Data' 95% of COVID-19 vaccine doses administered in Finland were modRNA-based vaccines, while the remaining 5% were adenovirus-based vaccines manufactured by AstraZeneca. Therefore, the number of booster doses administered should directly represent the number of modRNA booster doses administered.

While Finland and Cuba administered the same number of boosters per inhabitant, Finland’s vaccination campagin seems to be proving less successful than Cuba’s campaign.

The number of COVID-19 deaths registered in Finland was similar during the first 46 and the last 30 weeks of our observed timeframe.

The rate at which COVID-19 deaths were registered increased by 52% (46.5/30.5=1.52) between the first period (week 1-31) and the second period (week 31-77).

Finland’s COVID-19 mortality is now higher than before the booster rollout.

Eligible regions

'Our World in Data' supplies data for 248 regions. You can find a full list on my website.

Exclusion criteria:

• Countries which never approved any nucleotide-based vaccines (Cuba, Russia, China, Belarus, Venezuela, Myanmar, Iran, Turkmenistan, Afghanistan, Mauritania, Senegal, Mozambique, Madagascar, Zimbabwe, Angola, Ethiopia, Falkland Islands, South Sudan, Central African Republic, Chad, Mali, Niger, Burkina Faso, Sierra Leone, Liberia, Eritrea).

• Countries for which 'Our World in Data' lists no booster dose administrations in the past 4 months

• Countries which reported less than 10 weekly COVID-19 deaths at the peak of their local COVID-19 mortality

What remains is a list of 130 regions we are using for our analysis.

Analysis

In the results section I am presenting charts seen in the examples above for all of the 130 eligible regions.

I run two linear regression analyses via LibreOffice Calc for the timeframe of July 1st 2021 through December 23rd 2022.

• Our two dependent variables are:

  1. Weeks passed to reach 50% of the number of COVID-19 cases that occurred within the region in the 77 weeks between July 1st 2021 and December 23rd 2022

  2. Weeks passed to reach 50% of the number of COVID-19 deaths that occurred within the region in the 77 weeks between July 1st 2021 and December 23rd 2022

  Our independent variable is:

  • Cumulative number of booster doses administered per inhabitant of that region.

The results of the regression analyses are presented both textually and graphically.

Data sources

• New COVID-19 Cases

• New COVID-19 Deaths

• Boosters administered

Downloads

• List of all countries

• List of eligible countries

• All charts as svg and png

Results

You will only find the charts displaying COVID-19 deaths here. The charts displaying new COVID-19 cases can be downloaded.

Region-wise visualizations (new deaths)

Linear regression analysis (new cases)

Linear regression analysis (new deaths)

Conclusion

Regionally administered modRNA booster doses per inhabitant correlate strongly with increases in COVID-19 cases

• p < 0.000001

• Adjusted R-Squared = 0.348

• Pearson correlation coefficient = 0.594

For every booster dose administered per inhabitant the time to reach 50% of the COVID-19 cases that were registered regionally regionally since July 1st 2021 increased by 18 weeks (95% CI: 13-22 weeks).

Regionally administered modRNA booster doses per inhabitant correlate strongly with increases in COVID-19 deaths

• p < 0.000001

• Adjusted R-Squared = 0.472

• Pearson correlation coefficient = 0.690

For every booster dose administered per inhabitant the time to reach 50% of the COVID-19 deaths that were registered regionally regionally since July 1st 2021 increased by 28 weeks (95% CI: 23-33 weeks).

Update December 28th 2022

I took a look at those countries which success indicators were in the lower week-range despite high booster rates. All of the regions I looked at deployed Chinese or Russian vaccines.

In order to see how the relationship between 'time needed to reach 50% of cases/deaths' and 'boosters administered per inhabitant' would change, I removed all countries that deployed Sputnik V or Sinopharm BIBP and excluded all regions that are not countries.

There are 55 countries remaining which I ran a regression analysis on.

Linear regression analysis (new cases)

Regionally administered modRNA booster doses per inhabitant correlate strongly with increases in COVID-19 cases

• p < 0.000001

• Adjusted R-Squared = 0.432

• Pearson correlation coefficient = 0.665

For every booster dose administered per inhabitant the time to reach 50% of the COVID-19 cases that were registered regionally regionally since July 1st 2021 increased by 19 weeks (95% CI: 13-24 weeks).

Linear regression analysis (new deaths)

Regionally administered modRNA booster doses per inhabitant correlate strongly with increases in COVID-19 deaths

• p < 0.000001

• Adjusted R-Squared = 561

• Pearson correlation coefficient = 0.754

For every booster dose administered per inhabitant the time to reach 50% of the COVID-19 cases that were registered regionally regionally since July 1st 2021 increased by 31 weeks (95% CI: 23-38 weeks).