This article is an analysis by Denis Rancourt of a report prepared by John Ioannidis et al (2025) asserting that millions of lives were saved by Covid injections. Rancourt criticizes Ioannidis' reliance on notoriously unreliable PCR test data, on Covid death statistics that include any deaths occurring after a positive PCR test, and on the manufacturers' claimed clinical trial outcomes. Additionally, Ioannidis failed to take into account the rise in all-cause deaths during 2022-23, after the international mass vaccination campaigns. Rancourt concludes that Ioannidis' data and analyses are demonstrably false. He also notes there is no evidence that the Covid injections saved any lives.
"Introduction
Recently (published online: 25 July 2025), Ioannidis et al. (2025) calculated that 1.4 to 4.0 million lives were saved by COVID-19 vaccination during 2020-2024.
It is important to assess such claims made by leading scientists in the leading scientific literature because they may have a disproportionate influence on global public health practices and may present distorted views of past presumed successes.
Here, I show that the calculations and conclusions of Ioannidis et al. (2025) are false...
Why it is false that 1-4M lives were saved by COVID-19 vaccination during 2020-2024
Ioannidis, with co-authors (2024, 2025), incorrectly projected that 1.4 to 4.0 million lives were saved by COVID-19 vaccinations worldwide, until October 2024. The underlying assumptions in their calculation are unjustified, as follows.
[T]heir analysis illustrates the core difficulties with all epidemiological counterfactual and forecast models based on presumed vaccine efficacy and estimated mortality if vaccination had not been implemented.
The said core difficulties are this. One must derive the number of deaths, D0, that should occur from the presumed pathogen in the absence of the intervention (i.e., without vaccination) and use an estimate of the vaccine efficacy, Ev, in preventing deaths...
Antibody tests (seroprevalence) approved for presumed COVID-19 and used in high-profile epidemiological studies have been shown to be invalid. In general, the seroprevalence tests used are non-specific, in effect not tested for specificity, not tested for false positives, not tested in the in vitro, animal model, clinical, and field environments, not based on fully validated pure standard analytes (none are available), and are manufactured for profit in global emergency approval circumstances, while being associated with a disease diagnosis (clinical symptoms or PCR or antibody test) which is itself ill-defined...
Conclusion
... The calculations and conclusions of Ioannidis et al. (2025) are false.
There are essentially no usable, relevant, and unbiased policy-grade clinical trials of COVID-19 vaccine efficacy, and COVID-19 vaccine efficacy has never been reliably demonstrated in observational or ecological studies free of design bias.
[T]here is no known example of a drop in measured all-cause mortality temporally associated with or following any rollout of a COVID-19 vaccination campaign. In fact, in Western jurisdictions, 2022 was generally the highest year of excess all-cause mortality in 2020-2024, following universal (all ages) vaccination and boosters in 2021.
The overwhelming cause of high mortality during the Covid period appears to be structurally imposed assaults by measures and responses against frail, elderly, and poor individuals...
In this context, the theoretical modelling papers of vaccine benefit are merely in effect part of the problem.