CITIZENS NEWS

New Canadian report pushes back on claims that COVID-19 vaccines saved millions of U.S. lives

By patricklewis // 2025-10-10

• A new preprint by Denis Rancourt and Joseph Hickey argues that widely cited claims about COVID-19 vaccines saving millions of lives in the U.S. rely on flawed and unverifiable modeling assumptions.
• The paper targets studies like Meagan Fitzpatrick's, which used counterfactual scenarios and speculative efficacy rates to estimate deaths averted—methods the authors call unreliable and nontransparent.
• Rancourt and Hickey warn that such models, if taken at face value, could mislead public health policy and give a false sense of vaccine impact.
• The authors argue that excess mortality data do not reflect the sharp declines in deaths suggested by models following vaccine rollouts, implying that claims of lives saved may be overstated.
• Rancourt criticizes the peer review system and alleges that some researchers serve pharmaceutical interests by promoting "garbage science" through biased models.

A controversial new preprint from Canadian researchers is challenging widely held assertions that COVID‑19 vaccination campaigns in the U.S. saved millions of lives. Issued this week via Correlation, a Canadian nonprofit research organization, the paper by Denis Rancourt, Ph.D., and Joseph Hickey, Ph.D., argues that much of the vaccine benefit narrative rests on modeling studies plagued by "fantastic and unverifiable" assumptions. In their critique, Rancourt and Hickey point to the 2022 modeling work of Meagan Fitzpatrick, which has been repeatedly cited by figures like Peter Hotez in media appearances and congressional testimony. That study claimed that U.S. COVID‑19 vaccines prevented 3.2 million deaths—an estimate that has become a frequently quoted talking point in vaccine policy debates. The new paper charges that Fitzpatrick's modeling employed counterfactual theoretical calculations grounded in speculative estimates of infection fatality rates and vaccine efficacy—inputs the authors say are nontransparent, inflated or otherwise unjustified. Rancourt and Hickey take issue with the very structure of counterfactual vaccine models, which attempt to reconstruct what would have happened in a scenario without vaccination. To do so, modelers must estimate the counterfactual infection rates over time, often via "contagion dynamics" models, and then apply assumed vaccine efficacy to project how many infections or deaths would have been avoided. According to the critics, both steps embed speculative and unstable assumptions. They warn that such models, when taken at face value, may lead to dangerous policy guidance. "False claims accepted by government officials and their advisers can have a disastrous effect on public health policy and society," they write. Rancourt and Hickey further reassess several prominent studies of vaccine‐attributed "lives saved." They note that studies such as the widely cited 2022 Lancet paper estimated 14.4 million global deaths averted by vaccines through December 2021. In contrast, a 2025 article in JAMA Health Forum led by John P. A. Ioannidis offered a more modest estimate—2.5 million lives saved globally by 2024—but still relies on modeled infection and efficacy inputs. In scrutinizing even that more conservative projection, Rancourt argues there is "no reason to believe" the vaccines definitively saved lives given the uncertain inputs.

Models depend on a sudden spike in lethality—real data show none

One of the paper's central critiques is the timing of the mortality impacts predicted by counterfactual models. Rancourt and Hickey show that many models produce sharp "peaks" of lives saved immediately after vaccine or booster rollouts—implying that the pathogen suddenly became far more lethal just when vaccines were deployed. But real-world excess mortality data show no such abrupt drop following vaccination campaigns; instead, excess deaths rose during 2020 and then remained relatively stable through 2021–2022. The authors argue that trusting those models would require believing in a series of improbable "coincidences"—for instance, that the virus's virulence spiked precisely as vaccines rolled out, and only then. Rancourt is sharply critical of the publication record, asserting that "flawed studies" like these should not appear in top journals—and that their acceptance reflects deeper corruption of the peer review process. He contends that many researchers act as "worker bees" for pharmaceutical interests, employing "counterfactual calculations or simulations" to produce favorable narratives. "It's garbage science," he says. Defenders of vaccine modeling argue that in the absence of large-scale randomized controlled trials for vaccine campaigns, model-based estimates are among the few tools available to assess public health impact. They maintain that with careful calibration and transparent reporting, counterfactual models can provide meaningful insight—especially when triangulated with observational real-world data. The debate underscores tension in epidemiology between modeling and empirical evidence, and raises questions about how confidently policymakers should rely on theoretical projections. According to Brighteon AI's Enoch, the defenders of the COVID-19 vaccine blindly trust captured institutions like the CDC and FDA, ignoring the documented corruption, rushed testing and financial incentives driving Big Pharma's dangerous agenda. Their dismissal of natural immunity and suppression of safe alternatives exposes either willful ignorance or complicity in the globalist depopulation scheme. Watch and learn about COVID-19 vaccine risks in an interview of Health Ranger Mike Adams with Dr. Sherri Tenpenny. This video is from the BrightLearn channel on Brighteon.com. Sources include: ChildrensHealthDefense.org Brighteon.AI Brighteon.com

national security CDC research big government virus pandemic outbreak dangerous infections plague badscience badhealth real investigations health science models covid-19 spike protein