Were the COVID-19 Shots Good, Bad, or Just Ugly? Dispensing with the Only Reasonable Objection to the Empirical Fact that Each Dose of the COVID-19 Shots, on the Average, Shortened the Lives of Recipients
DOI:
https://doi.org/10.56098/0mv30n65Keywords:
abnormal clotting, autoimmune disease, bad and ugly, biowarfare, bioweapons, COVID-19 injectables, COVID-19 vaccines, days left-to-live, experimental gene therapies, intervals between doses, immunological damiage, Medicare participants, modified mRNA, number of doses, spike protein, synthetic nucleic acidsAbstract
The COVID-19 gene therapies substantially increased deaths from all-causes. The harmful impact of those therapies compounded the ordinary aging factor accounted for in the Gompertz Law of Mortality in the respective datasets we analyzed in 2022 and 2023. It did so with such strength and consistency that the harmful effects of 1 to 3 doses of the COVID-19 concoctions accounted for 18.6% of the total variance in 31,478 all-cause deaths reported to Public Health England in the 28-week period ending early in 2021 (yielding a huge F-ratio = 5127572, with p ≈ 0), and also accounted for 13.9% of the total variance in 53,061 all-cause deaths recorded in the US Medicare dataset from the announced “pandemic” in 2020 to the last day of 2022. The ordinary aging-effect summed up in the Gompertz Law accounted for 81.6% of the total reliable variance in the dataset from 28-weeks of 2020-2021 and it accounted for 74.6% of the total reliable variance in the 2020-2022 Medicare dataset. The harmful impact of every dose of COVID-19 “vaccine(s)” in the Medicare dataset ruled out chance at a vanishing p < 4.3454E—34. While it was confirmed, as suggested by Pantazatos and Seligman, also Stephanie Seneff, that the intervals between the successive doses of COVID-19 injectables in the Medicare dataset cumulatively increased the age of the persons taking each successive dose accounting for 74.6% of the total reliable variance in all-cause deaths for the entire dataset, the harmful impact of the cumulative doses of COVID-19 “vaccine(s)” accounted for an additional 13.9% of the variance in days-left-to-live after the last shot.
References
Alessandria M, Malatesta G, Di Palmo G et al. All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [original version. F1000Research 2024, 13:886. https://doi.org/10.12688/f1000research.154058.1
Alessandria M, Malatesta G, Di Palmo G et al. All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved]. F1000Research 2025, 13:886. https://doi.org/10.12688/f1000research.154058.2
Beattie, K. A. (2021). Worldwide Bayesian Causal Impact Analysis of Vaccine Administration on Deaths and Cases Associated with COVID-19: A BigData Analysis of 145 Countries. https://www.researchgate.net/publication/356248984_Worldwide_Bayesian_Causal_Impact_Analysis_of_Vaccine_Administration_on_Deaths_and_Cases_Associated_with_COVID-19_A_BigData_Analysis_of_145_Countries
Central Limit Theorem. (2021). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Central_limit_theorem&oldid=1014457764
Erdem, O., Erdem, S., & Monson, K. (2023). Children, vaccines, and financial incentives. International Journal of Health Economics and Management. https://doi.org/10.1007/s10754-023-09343-2
Franchi, F. (2025). Why the official theory about COVID-19 is not tenable. International Journal of Vaccine Theory, Practice, and Research, 4(1), 1541–1556. https://doi.org/10.56098/vdwyqd57
Kirsch, S. (2023a, January 17). Japan COVID shot data: Every single time you take another shot, it gets worse. EVERY. SINGLE. TIME. https://kirschsubstack.com/p/japan-covid-shot-data-every-single?utm_campaign=post&showWelcomeOnShare=false
Kirsch, S. (2023b, January 17). Introducing the Kirsch Cumulative Outcomes Ratio (KCOR) analysis: A powerful yet simple new technique for accurately assessing the impact of any intervention on any outcome. https://kirschsubstack.com/p/czech-data-clearly-shows-covid-vaccines?utm_medium=email
Kirsch, S. (2023c, February 9). EXCLUSIVE: Stunning new data pulled from the Medicare database shows how each shot increases your risk of death [Substack newsletter]. Steve Kirsch’s Newsletter. https://stevekirsch.substack.com/p/exclusive-stunning-new-data-pulled?publication_id=548354&isFreemail=true
Kirsch, S. (2023d, February 25). GAME OVER: Medicare data shows the COVID vaccines increase your risk of dying [Substack newsletter]. Steve Kirsch’s Newsletter. https://stevekirsch.substack.com/p/game-over-medicare-data-shows-the?isFreemail=true
Kirsch, S. (2025, September 8). KCOR/README.md at main · skirsch/KCOR. GitHub. https://github.com/skirsch/KCOR/blob/main/README.md
Le Cam, L. (1986). The Central Limit Theorem around 1935. Statistical Science, 1(1), 78–91.
Lyons-Weiler, J., & Thomas, P. (2021). Vaccine practice payment schedules create perverse incentives for unnecessary medical procedures – at what cost to patients? International Journal of Vaccine Theory, Practice, and Research, 2(1), 25–37. https://doi.org/10.56098/ijvtpr.v2i1.21
McIntyre, N., & Kirk, A. (2021, October 12). Explainer: Why has the UK’s vaccination rate slowed down? The Guardian. https://www.theguardian.com/world/2021/oct/12/explainer-why-has-the-uks-vaccination-rate-slowed-down
Mostert, S., Hoogland, M., Huibers, M., & Kaspers, G. (2024). Excess mortality across countries in the Western World since the COVID-19 pandemic: ‘Our World in Data’ estimates of January 2020 to December 2022. BMJ Public Health, 2(1). https://doi.org/10.1136/bmjph-2023-000282
Oller, J., & Santiago, D. (2022). All-cause mortality and COVID-19 injections: Evidence from 28 weeks of Public Health England “COVID-19 Vaccine Surveillance Reports.” International Journal of Vaccine Theory, Practice, and Research, 2(2), 301–319. https://doi.org/10.56098/ijvtpr.v2i2.42
Oller, J. (2023). Supplementary Files for Santiago & Oller: Source Data for Statistical Analyses. International Journal of Vaccine Theory, Practice, and Research, 3(1). https://doi.org/10.56098/ijvtpr.v3i1.75
Pantazatos, S. P. & Hervé Seligmannn. (2025). COVID vaccination and age-stratified all-cause mortality risk. International Journal of Vaccine Theory, Practice, and Research, 4(1), 1583–1608. https://doi.org/10.56098/rbvsmw07
Pólya, G. (1920). Über den zentralen Grenzwertsatz der Wahrscheinlichkeitsrechnung und das Momentenproblem [About the central limit theorem of the probability calculation and the moment problem]. Mathematische Zeitschrift, 8(3–4), 171–181. https://doi.org/10.1007/BF01206525
Rapley, B. (2025). From innovation to inquiry—COVID-19 mRNA vaccination and its global consequences—With special reference to New Zealand. International Journal of Vaccine Theory, Practice, and Research, 4(1), 1487–1516. https://doi.org/10.56098/hqjnk479
Santiago, D., & Oller, J. W. (2023). Abnormal clots and all-cause mortality during the pandemic experiment: Five doses of COVID-19 vaccine are evidently lethal to nearly all Medicare participants. International Journal of Vaccine Theory, Practice, and Research, 3(1), 847–890. https://doi.org/10.56098/ijvtpr.v3i1.73
Schwalbe, N., Hanbali, L., Nunes, M. C., & Lehtimaki, S. (2022). Use of financial incentives to increase adult vaccination coverage: A narrative review of lessons learned from COVID-19 and other adult vaccination efforts. Vaccine: X, 12, 100225. https://doi.org/10.1016/j.jvacx.2022.100225
Shaw, C. A. (2021). The age of COVID-19: Fear, loathing, and the “new normal.” International Journal of Vaccine Theory, Practice, and Research, 1(2), 98–142. https://doi.org/10.56098/ijvtpr.v1i2.11
Welch’s t-test. (2023). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Welch%27s_t-test&oldid=1132254179
Zabell, S. L. (1995). Alan Turing and the Central Limit Theorem. The American Mathematical Monthly, 102(6), 483–494. https://doi.org/10.1080/00029890.1995.12004608
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