The efficacy of COVID-19 vaccines can be viewed in multiple ways. These include metrics of infectivity, transmission, symptomatic disease, disease severity, hospitalization, death, and others. The most common ways are to view efficacies individual COVID-19 vaccines or COVID-19 vaccination programs as a whole. Many nations have used multiple vaccines both nationally and with individuals, which makes teasing apart individual data extremely problematic. However, observations on overall efficacy of national and international aggregate vaccination campaigns can still be revealing.
Pathologist Ryan Cole points out that the vaccinated still carry high viral loads of SARS-CoV-2 because the vaccines do not cause secretion of IgA antibodies and that the T cell infiltrates are tissue destructive.1) Dr. Sucharit Bhakdi and Dr. Arne Burkhardt concur, stating that the current COVID-19 vaccines elicit IgM antibodies and circulating IgA antibodies, but not the secretory IgA antibodies needed to protect the mucous membranes that line the respiratory and intestinal tract.2)
There are several forms of evidence with regard to vaccine efficacy, of which reports from trials and observational studies. It is worth noting that the trial reports are self-reported by the vaccine manufacturers with little outside oversight.
Evidence from both trial reports and retrospective analysis show similar results of increased infection and transmission among the recently vaccinated, though published efficacy computations seem to generally exclude these infections from efficacy computations. Including them dramatically changes efficacy results.3)
The first evidence for the efficacy of COVID-19 vaccines came from self-reported trials conducted by Pfizer and Moderna. These trials reported highly similar efficacy results for their mRNA vaccines. However, these trials are fraught with data exclusions, whistleblowers, and secrecy that has resulted in tense court battles.
See more complete details at COVID-19 Vaccine Trials.
* Mathew Crawford wrote at Rounding the Earth about how COVID-19 cases and deaths are going up in far more nations than in where they are going down since vaccine rollout. (Systemic COVID-19 Vaccine Efficacy Basics) The article reports that as of October 20, 2021, according to the Our World in Data COVID-19 dataset,
* Mathew Crawford wrote at Rounding the Earth about how there is no distinction in any COVID-19 vaccine trials data or subsequent retrospective analyses that is inconsistent with the hypothesis that the vaccines are ineffective. (Systemic Vaccine Efficacy, Part 4)
In February, 2022, the CDC admitted to withholding critical data on boosters, hospitalizations, and wastewater analyses.11)
Little difference in SARS-CoV-2 positive rates was observed among airport travelers in Israel. 12)
Despite a 99.5% double-vaccination rate, Co Waterford, Ireland had the highest case rate in all of Ireland in early November, 2021.13)
More recently, Rintrah wrote on the vaccine lack of effectiveness in Denmark:
https://www.rintrah.nl/the-complete-and-utter-failure-of-the-vaccines-illustrated-in-two-charts/
One group of researchers published a claim of 92% to 95% efficacy against COVID-19 among boosted Israelis.15)
In late 2021, a story of waning efficacy begin to appear, seemingly bolstered by several published papers.16)
There has been no effort to distinguish waning efficacy from statistical sieves.
Medpage Today reported that COVID vaccine booster efficacy may wane quickly.17)
According to Peter McCullough, the data from the UK Covid briefing of December 21, 2021,18) “shows that the most effective product against Delta and the legacy strains is now near zero efficacy over time against the milder and more brief Omicron strain.”
There are many reports and studies suggesting that the vaccines have a negative efficacy rate with respect to one or more of the SARS-omicron viruses.21)
Martin Neil et al wrote up a statistical analysis (in preprint as of December 14, 2021) suggestive of systematic mis-categorization of vaccination status making COVID-19 vaccine efficacy appear better than reality in retrospective analyses.
Professor Norman Fenton and colleagues noted numerous statistical sieves and paradoxes that make published studies unreliable.22) In a blog post, Fenton demonstrated that in a simulation, either delays in death reports or the counting of recently vaccinated as unvaccinated can fully explain claimed efficacy rates.23)
Pharmaceutical marketing expert, biotech analyst, and former Pfizer employee Karen Kingston argues that infections rose substantially after injection with COVID-19 vaccines.24)
The Canadian Covid Care Alliance published a critique of the safety and efficacy demonstrated in Pfizer's 6-month trial data published in the New England Journal of Medicine on January 10, 2022.25) 26)
Strangely, multiple research publications purport to show COVID-19 vaccines broadly reducing non-COVID-19 risk of mortality. The extremely unlikeliness of such results point to the hypothesis that data definitions and statistical sorting is behind most or all of the efficacy reported in some trials and studies.27)28)29)
In a retrospective analysis of New York state hospitals, researchers claim to see no real declining efficacy at preventing hospitalization from the various COVID-19 vaccines as the delta strain became predominant.30)
Official efficacy calculations often skip the first 10, 12, 14, or 21 days before computing efficacy. But those days cannot be skipped in a risk-benefit analysis (or in real life), and what happens during that span could be dangerous.31)
COVID-19 vaccine data discussions have been made confusing if not outright fraudulent by the unclarified or misclarified use of data categories such as intervals “first 14 days after dose 1”. It is unclear the extent to which such categorization has been used, intentionally or otherwise, to rig vaccine efficacy statistics, but the effect is undisputably large when such miscategorization takes place.
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)00448-7/fulltext
https://www.medrxiv.org/content/10.1101/2021.02.15.21251623v1
https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciab229/6167855
https://www.cdc.gov/mmwr/volumes/70/wr/mm7013e3.htm?s_cid=mm7013e3_w
https://www.nejm.org/doi/full/10.1056/NEJMoa2114228
https://www.nejm.org/doi/full/10.1056/NEJMoa2114583
https://www.nejm.org/doi/10.1056/NEJMoa2114255
https://www.nejm.org/doi/full/10.1056/NEJMoa2114114
https://pubmed.ncbi.nlm.nih.gov/32384142/
https://www.science.org/doi/10.1126/science.abm0620
https://archive.ph/EWnmO (Alex Berenson Alberta data)
https://doctorsandscientistsdeclaration.org/home/supporting-evidence/#children HUGE list of links here under three headings: