Despite widespread anecdotal and research evidence of severe disease and death closely associated with COVID-19 injections, their link to COVID mortality has been dismissed as rare and coincidental, because comprehensive statistical evidence has not been obvious in official mortality data.
A recent paper [1] solves this puzzle by identifying a systemic data flaw in the reporting convention which obscures the immediate fatal impact of COVID-19 injections, where substantial “vaccine” deaths have been wrongly attributed to the “unvaccinated”.
Recently, Deborah Birx, coordinator of the White House Coronavirus Task Force (WHCTF), who set the strategies for early US COVID responses copied by much of the world, has publicly lamented the poor quality of US COVID data and said [2] “It was a pandemic driven by assumptions and perceptions, rather than data and science”.
On health agencies, she also said: “Data for publication, not data for implementation change.” That is, COVID data are collected, not to inform, guide and implement policy changes, but to manage public perception, which could mean that data may be manipulated to mislead the public, as will be shown below.
The official claim that “policy follows the science” is the opposite of reality: “science follows the policy” i.e., the policy is first supported later by fake science and manipulated data. Data analysts may not realize that they could be aiding and abetting misinformation by publishing misleading statistics of manipulated data. We provide evidence for the dramatic consequences of the flaw in COVID data reporting [3] specified by the US CDC.
The CDC defines “vaccination status” as a 14-day lag from the last COVID injection, with the rationale that it takes at least 14 days for the injection to take effect. For example, a “breakthrough case” of a person “vaccinated with a primary series” is specified by:
