Boosters, Bias and Math
From vaccine math to biosafety preprints, recent studies reveal that unreported risks and hidden biases shape our understanding of COVID more than most headlines admit.
Remember when getting vaccinated against COVID felt simple? One shot, maybe two, and you were done. Fast forward a few years, and we’ve had first doses, second doses, boosters, updated boosters, and debates about whether you need a booster for your booster.
But a recent study crunching UK mortality data says: “Hold up. Maybe more shots aren’t adding as much protection as we think.” Add in your earlier research about antibody changes after repeated doses, and suddenly the COVID vaccine story feels less like “science is settled” and more like “science is a detective novel.”
Let’s break it down in plain language.
A.J. Oostenbrink’s new preprint looked at COVID and non-COVID death rates in the UK over two years. Instead of assuming COVID deaths rise proportionally with overall health risk, they used a formula:
If the exponent , risk is proportional (linear). But they found that in older adults, was closer to 1.5-2, meaning COVID risk skyrockets faster in frail people than other causes of death.
Why does this matter? Because people who stop vaccinating (or never start) are often the frailest. That makes them way more likely to die from anything, so their COVID death risk looks massive. This skews data, making boosters look superhuman even if most of the effect is just frailty bias.
An earlier work showed that after three mRNA doses, some people’s antibodies shifted toward IgG4 and IgG2, which are less aggressive virus fighters than IgG1 and IgG3. IgG4 is like a polite security guard - it notices the intruder but doesn’t want to start a fight. This switch correlated with more breakthrough infections, though these antibodies may help prevent severe inflammation.
The math showed that after correcting for this bias, vaccine effectiveness levels out at 60–90% after a single dose. Extra boosters? Not much visible improvement — at least in this dataset.
Here’s a real-world example:
-
A healthy elderly man gets his first AstraZeneca shot.
-
Within a day, his heart races over 100 bpm, his legs give out, and he can barely walk.
-
He recovers, but the experience scares him off future shots.
-
A month later, he catches COVID and dies.
This man did not get boosters because he’s “anti-vax" but because his body said, “Nope!” Stories like his fill the lower-dose and unvaccinated categories, making those groups look much riskier - and making extra boosters appear more protective than they may truly be.
An earlier work showed that after three mRNA doses, some people’s antibodies shifted toward IgG4 and IgG2, which are less aggressive virus fighters than IgG1 and IgG3. IgG4 is like a polite security guard - it notices the intruder but doesn’t want to start a fight. This switch correlated with more breakthrough infections, though these antibodies may help prevent severe inflammation.
Put that together with the new math:
-
More doses may shift the immune system into a quieter, less inflammatory mode (good for avoiding overreactions).
-
But that might also mean fewer antibodies ready to fight the virus head-on (bad for stopping infection).
This all ties into a bigger and more uncomfortable debate about what we don’t see in COVID science. A controversial 2025 preprint by Drs. Steven Massey and Steven Quay, titled “The Illusion of Biosafety During SARS-CoV-2 Research”, reported seven likely lab-acquired infections at the University of North Carolina between 2020–2021. These cases matched experimental strains rather than community variants, yet were never publicly reported.
Whether or not one agrees with the authors’ conclusions, the study underlines a central theme: hidden biases and unreported events can dramatically change how we interpret COVID risk, vaccine effectiveness, and even the origins of outbreaks. Just as uncounted lab infections could distort biosafety data, unmeasured frailty bias can distort vaccine data.
And note: all these papers are open-access preprints - because established publishers avoid anything controversial or critical of mainstream positions. Massey and Quay documented active suppression attempts: after contacting UNC lab staff, they were met with silence, then pressure from GISAID’s Washington DC office, reportedly relaying CDC and UNC complaints. GISAID even threatened to revoke access unless metadata and sequences were removed. This pressure campaign, combined with institutional opacity, only strengthens the suspicion that these infections were laboratory-acquired.
Comments
Post a Comment