A Research Paper Investigates Canada’s Covid-19 Response
Regina Watteel, a Canadian PhD in Statistics is taking on some of the academic frauds who fudged Statistics during the pandemic, to push for mandatory vaccination in Canada. To quote her Substack,
The key is in targeting influential researchers or organizations that have committed provably egregious acts of scientific dishonesty. The simpler it is to describe the fraudulent behavior and the more repugnant the act, the easier it will be to convince the public and institutions to hold them to account.
Here is her research paper (draft) on the same. It is unlikely to get published by any Canadian journal. Here is the full citation:
Watteel RN (2025) Unreliable Evidence: Flawed Vaccinated vs. Unvaccinated Comparisons in Canada’s COVID-19 Vaccine Mandates. doi: 10.31235/osf.io/uwtzn_v1
And here is the abstract (tl;dr: They fudged the statistics on an epic scale).
Background: Canada’s 2021–2022 COVID-19 vaccine mandates and passports were implemented with the stated aim of reducing transmission and hospital burden. In the absence of randomized controlled trial evidence for these endpoints, policymakers relied on vaccinated vs. unvaccinated comparisons from surveillance data and observational studies, as well as ungrounded simulations, despite known methodological limitations that rendered such evidence unreliable for public health policies with rights implications. Methods: We systematically critiqued Canadian public health surveillance reports and advisory briefs (Public Health Agency of Canada, Public Health Ontario, Ontario COVID-19 Science Advisory Table) together with related studies, identifying seven key biases classified by severity (critical/catastrophic), correctability, and scope. Distortions were quantified using published rate corrections, classification rules, time-series data, and population trends. Results: Critical biases included >40% misclassification of early post-vaccination cases as unvaccinated and an 80-fold overestimation of senior unvaccinated hospitalization rates due to denominator errors. Additional critical biases—age-standardization obfuscating low-risk youth trends, testing fluctuations understating breakthrough cases, and misattribution rendering COVID-19 hospitalizations an invalid metric for burden—further distorted policy evidence. Catastrophic biases—selection and cumulative methodology—rendered group comparability invalid. Population trends showed case and hospitalization surges despite >80% vaccination coverage, with vaccinated individuals dominating Omicron-era infections. Simulation studies retroactively justifying mandates contradicted real-world data with ungrounded counterfactuals used to estimate unproven benefits. Conclusions: Pervasive, uncorrected biases in observational comparisons invalidated causal claims of transmission or hospital burden reduction. Progressive reliance on weaker evidence—coupled with expert bodies’ lack of transparency in emphasizing unproven benefits while dismissing dissenting views—highlights a systemic failure to meet evidentiary standards for public health policies with rights implications. This analysis underscores the need for greater scientific rigor in interpreting observational data through real-time bias correction, transparent limitation reporting, and risk-stratified approaches to uphold evidentiary standards, ethical proportionality, and accountability. These lessons hold global relevance for evidence-based public health policy.
