Covid Inquiry’s Module 4 report - Lives Saved by the Vaccines: Part 3a

There is a moment in every public inquiry when confidence gives way to scrutiny—when the polished conclusions meet the awkward persistence of readers who ask a simple question: show us the evidence.

The Covid Inquiry’s Module 4 report, with its sweeping claims about lives saved, has now reached that moment. And it is not the academics or officials raising the sharpest questions, but the TTE readers—GPs, analysts, and ordinary citizens—who have begun to pick apart the foundations.

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Take the central claim: that vaccination saved vast numbers of lives. As docross, a retired GP, points out, the Inquiry itself admits that “directly measuring” this impact is difficult because the counterfactual—what would have happened without vaccines—was never observed. Yet there is a comparator, however imperfect: a substantial unvaccinated population. In the UK, this may have been as high as 20–25 per cent. Why, then, are we not shown detailed mortality data stratified by vaccination status, adjusted for age and risk? Why, asks docross, is such data withheld—reportedly on grounds of “commercial sensitivity”?

This is not a trivial omission. In evidence-based medicine, when direct observation is possible, one does not leap immediately to modelling. Yet that is precisely what has happened. Instead of presenting clear, adjusted mortality outcomes, the Inquiry leans heavily on estimates derived from models that depend on assumptions about infection rates, vaccine effectiveness, and behaviour in a hypothetical world without vaccination. As Richard Boughton puts it succinctly: “assumptions all the way down.”

The problem is that the modelling is presented with a confidence that belies its fragility. Several readers independently arrive at the same concern: that the “lives saved” figures are constructed backwards from assumptions rather than derived from observed data. Helen Mcardle highlights a striking example from Scotland. To justify the claimed number of lives saved, the underlying model would imply a 2021 mortality rate almost double that in 2020—a scenario so implausible that it exposes the estimate’s sensitivity to its assumptions.

Others point to what is missing. The models, notes Bilbo Baggins, assume fixed infection fatality rates, no waning immunity, and sustained infection-blocking effects—assumptions that have not held consistently in the real world. They also fail to incorporate potential harms or adverse events, effectively modelling only one side of the ledger. The result, he argues, is not a balanced assessment but a “counterfactual fantasy”—a simulation that risks creating the illusion of certainty.

Even more basic questions remain unanswered. As Dan Newell puts it: Where are the observed deaths? If the aim is to estimate deaths avoided, surely the starting point is to show deaths that actually occurred, broken down transparently by risk group and vaccination status. Without this, the exercise begins to resemble what one reader calls “grade school math”—a conclusion presented without the working.

There is also a deeper unease about transparency. Several commenters note that data releases have become less complete over time. Stephen Feldman points to earlier reports that included age-standardised comparisons, which were later discontinued. Whether justified or not, such changes erode confidence. In public health, trust depends not only on the strength of conclusions but on the openness of the process that leads to them.

The point is that the Inquiry’s methods—as presented—do not allow us to judge the scale of any effect with confidence. Instead, we are asked to accept large, precise numbers derived from models whose assumptions are only lightly interrogated.

Perhaps the most telling comment comes from Jo, who asks a deceptively simple question: if the vaccines had such a dramatic impact, why is that not clearly visible in the mortality trends of highly vaccinated countries? It is the sort of question that should be addressed head-on, not sidestepped with ever more elaborate modelling.

What emerges from these reader critiques is not a coordinated campaign, but something more interesting: a collective insistence on basic standards of evidence. Show us the data, explain the assumptions and distinguish between what is observed and what is inferred. These are not unreasonable demands; they are the foundations of good science.

And when large claims rest on opaque methods and untested assumptions, confidence inevitably gives way to doubt. Not because the public is misinformed, but because it is asking the right questions.

The two old geezers who wrote this post would like to thank our readers for their comments.