In the swirling aftermath of the COVID-19 pandemic, one phrase echoes like a mantra from public health officials, media outlets, and even Senate hearings, in the US and Australia: "The vaccines saved millions of lives." It's a bold assertion, often wielded to deflect criticism or silence sceptics. But this claim rests on shaky ground, not just because of ongoing debates about the vaccines' safety and effectiveness, but because it's fundamentally a hypothetical conditional, something where there is on-going philosophical debates about. It's a "what if" scenario: What would have happened if the vaccines hadn't been rolled out? How can we truly know something that never occurred? This isn't just a quibble over data; it's a profound logical problem that turns the claim into something more akin to speculation than settled science.
Let's unpack this step by step, starting with the philosophy, diving into the models, examining the critiques, and circling back to why this all feels like chasing shadows in a hall of mirrors. I'll draw on recent studies and discussions to illustrate, but remember: the core issue isn't whether vaccines worked or didn't; it's whether we can ever reliably quantify a counterfactual in a complex, real-world crisis.
The Philosophical Pitfall: Counterfactuals and the Limits of Knowledge
At its heart, the "millions saved" idea is a counterfactual claim. Philosophers and logicians have wrestled with these for centuries, think David Hume pondering causation or modern epistemologists debating alternate histories. In simple terms: We live in one timeline where vaccines were deployed. To say they "saved" X million lives, we need to compare it to an imaginary timeline where they weren't. But we don't have access to that parallel universe. No controlled experiment on a planetary scale. No do-over.
This isn't abstract navel-gazing. In practice, it means any estimate relies on assumptions about what "would have" happened without vaccines. What if natural immunity from infections played a bigger role? What if lockdowns, treatments, or behavioural changes altered the trajectory independently? What if variants evolved differently? These unknowns pile up, making the claim inherently unverifiable. Such assertions are often socio-political constructs, simulations engineered to create the illusion of benefit. Even proponents admit these are estimates based on models, not direct observations. So, how do we "know" regardless of safety debates? We don't; we infer, and inferences can be biased. But these models are only as good as input data, and here we have no such data. The inferences are therefore logically circular! It is surprising that this has not been pointed out before.
This logical snag isn't unique to COVID. It's why historians argue endlessly about "what if" questions, like whether World War II could have been avoided. But in public health, it becomes a tool for policy justification. The claim overrides evidence of harms by appealing to an unprovable greater good. It's rhetorically powerful but epistemically weak.
How the Numbers Are Conjured: The Modeling Machine
To bridge this hypothetical gap, scientists turn to mathematical models. These are sophisticated simulations that input data like infection rates, vaccine efficacy, and population demographics, then spit out "lives saved" figures. For instance, a 2022 study in The Lancet Infectious Diseases projected that vaccines averted 14–20 million deaths globally in the first year alone. Another from Eurosurveillance estimated 470,000 lives saved in Europe. More recently, a 2025 JAMA Health Forum analysis suggested over 2.5 million deaths prevented worldwide from 2020 to 2024, equating to one death averted per 5,400 doses.
These models often use "counterfactual baselines," imagined scenarios without vaccines, factoring in things like infection fatality rates (IFR) and assumed transmission blocking. A PubMed Central review, for example, calculated 14.4 million deaths prevented based on reported COVID fatalities and vaccine coverage. Another USC study pegged it at 2.4 million lives in 141 countries. SciTechDaily even framed it as preserving 14.8 million life-years.
Sounds convincing, right? But here's the rub: Models are only as good as their inputs. If you assume high IFRs that don't wane, perfect vaccine efficacy against transmission, and ignore variables like natural immunity or early treatments, you get inflated savings. As critics note, these are "assumptions stacked on assumptions." And since it's hypothetical, there's no way to falsify it directly. We can't rewind time and un-vaccinate the world to check.
The Cracks in the Foundation: Critiques and Real-World Data
Enter the sceptics, like yours truly, who argue that these models don't hold up against actual data. A 2025 paper titled A Step-by-Step Evaluation of the Claim That COVID-19 Vaccines Saved Millions of Lives, published in Fortune Journals and available on ResearchGate, dismantles the narrative piece by piece. Authors Yaakov Ophir, Yaffa Shir-Raz, Shay Zakov, Raphael Lataster, and Peter A. McCullough contend it's "scientifically baseless and manufactured through deception."
They highlight:
Flawed Assumptions on Transmission: Models like Watson et al. assumed vaccines "stopped the spread," but real-world breakthroughs and waves in highly vaccinated areas prove otherwise.
No Mortality Benefit in Trials: Pfizer's RCT showed more deaths in the vaccine arm (15 vs. 14). Observational studies? Biased by short-term infection prevention artifacts.
Dashboard Deception: Unadjusted national data suggested benefits, but controls for age and comorbidities erase them, sometimes reversing.
Manufacturing the Myth: Through cut-off tricks, misclassification (e.g., vaccine deaths as "unvaccinated"), and censorship of dissent.
Real-world data backs this up. One analysis found vaccinated mortality 14.5% higher than unvaccinated, with negative correlations between vax rates and deaths in 37 countries. Global deaths rose 6.08 million in 2021 over 2020, despite vaccination. A Brownstone Institute post echoes: "Did COVID vaccines really save millions? Our evaluation shows the narrative lacks empirical support."
Even some modellers are dialling back. A 2025 MedPage Today piece notes vaccines saved fewer than claimed when compared to expectations. And a medRxiv preprint on all-cause mortality post-vax questions net benefits.
On X discussions amplify this. Dr. Aseem Malhotra called the modelling "implausible," citing Oxford experts. Physicist Denis Rancourt argued it's "incompatible with all-cause mortality." Others, like Raphael Lataster, reduce estimates to "zero or less" using real data.
Why It Matters: Beyond the Numbers
The logical problem cuts both ways. Sceptics can't prove zero lives saved any more than proponents can prove millions. But the burden should be on those making extraordinary claims, especially when used to justify mandate madness or dismiss harms.
In the end, the "millions saved" trope is a rhetorical shield, not a logical fortress. It's problematic because it's unverifiable, a hypothetical dressed as fact. Regardless of where you stand on vaccine efficacy, we should demand better: transparent data, not models built on sand. Until we grapple with this epistemic humility, the debate will rage on, hypothetical as ever.
"A Step-by-Step Evaluation of the Claim That COVID-19 Vaccines Saved Millions of LivesAuthors: Yaakov Ophir, Yaffa Shir-Raz, Shay Zakov, Raphael Lataster, Peter A. McCullough
Concerns about potential harms of COVID-19 vaccines are often met with the widespread claim that the vaccines saved millions of lives. A recent U.S. Senate hearing on vaccine safety (May 21, 2025) even opened with the declaration that "there is no scientific question about that fact." This article offers a structured, step-by-step evaluation of the empirical basis for that claim, building on the authors' prior comprehensive investigation. Step 1 analyzes the mathematical models behind the 'millions saved' claim, including the one cited in the Senate hearing. Step 2 revisits the collapse of the initial narrative concerning vaccine efficacy against infection and transmission, which served as the cornerstone of the mass vaccination campaign. Step 3 examines the revised justification that followed: the claim that vaccines continued to protect against severe illness and death. This step draws on data from randomized trials (3.1), observational studies (3.2), and official public health dashboards (3.3). Taken together, this analysis shows that the 'millions saved' narrative lacks empirical support (readers are strongly encouraged to consult the full article and assess the evidence). To understand how such an unsupported narrative could emerge and dominate, Step 4 traces the direct mechanisms behind its rise: methodological flaws (4.1), misrepresentation and misinterpretation of findings (4.2, 4.3), and suppression of dissenting voices (4.4). By focusing on transient signals of success while overlooking concerns about efficacy and safety, a fragile assertion appears to have solidified into a widely accepted belief that shaped global health policy.
https://www.thefocalpoints.com/p/new-study-obliterates-the-million