COVID-19 and All-Cause Mortality Data by Age Group Reveals Risk of COVID
Vaccine-Induced Fatality is Equal to or Greater than the Risk of a COVID death
for all Age Groups Under 80 Years Old as of 6 February 2022.
Kathy Dopp, MS Mathematics and Stephanie Seneff, PhD
13 February 2022.
Abstract
As of 6 February 2022, based on publicly available official UK and US data, all age groups under 50
years old are at greater risk of fatality after receiving a COVID-19 inoculation than an unvaccinatedperson is at risk of a COVID-19 death. All age groups under 80 years old have virtually no benefit fromreceiving a COVID-19 inoculation, and the younger ages incur significant risk. This analysis isconservative because it ignores the fact that inoculation-induced adverse events such as thrombosis,
myocarditis, Bell’s palsy, and other vaccine-induced injuries can lead to shortened life span. When one
takes into consideration the fact that there is approximately a 90% decrease in risk of COVID-19 death
if early treatment is provided to all symptomatic high-risk persons, one can only conclude that mandatesof COVID-19 inoculations are ill-advised. Considering the emergence of antibody-resistant variants like
Delta and Omicron, for most age groups COVID-19 vaccine inoculations result in higher death rates than
COVID-19 does for the unvaccinated.
1. Introduction
To rationally determine COVID vaccination policy, it is essential to determine whether the COVID-19
vaccines are beneficial or harmful. COVID vaccine manufacturers’ studies claimed benefit based on therelative risk reduction of testing positive and having symptoms of a SARS-CoV-2 viral infection in
their vaccinated versus unvaccinated study participants. However, vaccine manufacturers neglected to
calculate absolute risk reductions based on the prevalence or likelihood of a person developing
symptomatic COVID illness, which varies by age; and they failed to take into consideration thepotential costs of COVID vaccine-induced serious illness and death outcomes [1]. Furthermore, vaccine
manufacturers quickly allowed the control groups to be vaccinated, thus eliminating the opportunity forlong-term safety analysis. In other words, the COVID vaccine manufacturers failed to provide any
cost/benefit analyses for their products.

1

To conduct a risk-benefit analysis of COVID vaccine inoculations by age cohort from publicly
available official sources requires comparing vaccine fatality rates to COVID-19 fatality rates forunvaccinated and vaccinated populations, by age group.
A helpful starting point is provided by the clever analyses of Pantazatos and Seligmann [2]. who
computed vaccine fatality rates by age cohort (aVFR) using official age-stratified all-cause mortality
data, vaccination doses, and other pertinent data from US Census and Center for Disease Control(CDC) sources for the 50 US states for monthly time periods from 1 January through 31 August in 2020
and 2021. They used linear regression models to calculate aVFRs for months where the logtransformed number of vaccination doses significantly predicted the number of all-cause deaths for anage cohort from 1 February through 31 August 2021. Pantazatos and Seligmann's methods are
explained in more detail in Appendix A.
For comparison, we needed to compute COVID-19 mortality and all-cause mortality risks for
vaccinated and unvaccinated populations. Since the United Kingdom keeps more detailed records of
COVID-19 mortality by vaccination status and age cohort than the US does and provides weekly
updates of its prior month’s data, we use UK COVID-19 data to compute this term. This strategy
assumes that death rate from COVID-19 in the US and the UK are approximately the same. We useddata for a single month’s time spanning between week 2 2022 (week ending 16 January 2022) andweek 5 2022 (week ending 6 February 2022). Recent UK COVID-19 fatality rates by vaccination status
and age cohort were chosen to best reflect COVID-19 death rates due to the current mix of prevalent
SARS-CoV-2 variants Omicron and Delta. Calculations were performed in a spreadsheet using datafrom UK Health Security Agency reports containing the same one month’s data ending as of 6 February
2022 [3,4].

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Note that our analysis considers only vaccine fatality rates within one month of the injection and
COVID-19 fatality rates within 28 days of a positive COVID-19 test. There is some non-zeroprobability that either the disease or the injection could cause death beyond the one-month interval, but
the likelihood of this is much lower than the impact within the first month, and the precise numbers aremuch more difficult to determine.
2. Methods Tables 1, 2, and 3 below provide the data we have used in our calculations. Relevant entries
in the tables are color-coded to reflect the source from which they were retrieved, as explained below.
The risk/benefit analysis was calculated from data from the following three (3) sources:•

Source 1: UK COVID-19 Health Security Agency. Vaccine Surveillance Report - Week 6,
Table 12b, [4,p.43]
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/fil
e/1050721/vaccine-surveillance-report-week-6.pdf Data from this source is highlighted in green
in Table 1, and covers week 2 2022 through the end of week 5 2022, as shown in Appendix B.

•

Source 2: UK COVID-19 Health Security Agency. Weekly national Influenza and COVID-19
surveillance report Week 6 (up to week 5 data), 6 February 2022, Table 9 [3, p.81] Data fromthis source is highlighted in pale gray in Table 1 herein, and is obtained directly from official
UK weekly national influenza and COVID-19 surveillance report for the same weeks as the datafrom source 1, shown and is shown in Appendix B.
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/fil
e/1050508/Weekly_Flu_and_COVID-19_report__w6.pdf•

Source 3: COVID vaccination and age-stratified all-cause mortality risk. By Spiro Pantazatos,
Columbia University and Herve Seligmann, Israel. Data from this source, highlighted in blue inTable 2 and Table 3, is estimated from a study of excess all-cause mortality from official US
CDC and census data during or subsequent to the month of vaccinations [2,p.20].
https://www.researchgate.net/profile/SpiroPantazatos/publication/355581860_COVID_vaccination_and_age-stratified_allcause_mortality_risk/

3

The formulas for calculating relative risk reduction (RRRD) and absolute risk reduction (ARRD) for
deaths due to COVID-19 are:
DV = number of vaccinated deaths due to COVID-19
DU = number of unvaccinated deaths due to COVID-19
PV = number of vaccinated persons in the population
PU = number of unvaccinated persons in the population!!!! = 1 −&!!! =

!"
#"
!$
#$

! "

= 1 − !" "$

$ "

'# '$
−
(# (%

Notice, the formula for RRRD is 1 minus the relative ratio of COVID-19 death rates of thevaccinated to the unvaccinated and fails to reflect actual risk due to real-life prevalence of COVID-19
death among vaccinated and unvaccinated populations. The ARRD is the actual real-life risk reduction
from vaccination, the difference in risk of COVID-19 death in the unvaccinated minus the same risk inthe vaccinated. The number needed to treat or vaccinate (NNT) to prevent one COVID-19 death times%
the absolute risk reduction per person equals one (1), i.e., ))* ∗ &!! = 1, so the NNT is = '((.
!

The vaccine inoculation fatality rate (VFR) for each age cohort is the number of vaccineinduced deaths within one month per (divided by) the number of persons vaccinated in that age cohort.
Thus, the number of expected vaccine-induced fatalities to prevent one COVID-19 death in each age
cohort = ))* ∗ ,-!. The relative rate of vaccine-induced deaths compared to COVID-19 deaths in
the unvaccinated for each age cohort is the VFR divided by the COVID-19 death rate in theunvaccinated = ")(
!$
#$

4

Table 1: UK Data: COVID Deaths by Age Group & COVID Vaccination Status in weeks between Week 51, 2021 and Week 2, 2022.

2.1 Calculation of Age-Stratified COVID-19 Fatality Rates by Vaccination Status in one month: UK Data

Table 1 data sources are the UK COVID-19 Vaccine Surveillance Report and the UK Weekly National Influenza and COVID-19
Surveillance Report. The data includes: Total COVID-19 Deaths by Age Group (column B) and total COVID-19 Deaths in the
Unvaccinated (column E) and in the Vaccinated (column K) by Age Groups; The number of total people in the study (column D) andthe percentage of the people in the study group who were vaccinated (column F). The two columns highlighted in light gray werecombined from more detailed age groups in the Weekly national Influenza and COVID-19 surveillance report Week 4 (containing

5

data through Week 3, 2022) using simple arithmetic sums and weighted averages. The rightmost column
K is the sum of vaccinated deaths from columns G through J for persons who received one or two
COVID inoculation doses.
2.2 Calculation of Age-Stratified COVID vaccine Fatality Rates from US Data

Vaccine fatality rates by age cohort (aVFRs) were estimated from official US data by Pantazatos andSeligmann in COVID vaccination and age-stratified all-cause mortality risk [2] using age-stratified allcause mortality data, vaccination doses, and other pertinent data from US census and Center for Disease
Control (CDC) data for the 50 US states for high vaccination rate monthly periods from 1 January
through 31 August for 2020 and 2021 to predict the number of monthly all-cause deaths from 1 February
through 31 August 2021. Please see APPENDIX A for a more detailed summary description of their
methods. aVFRs are expected to remain constant over time unless the vaccine itself is modified.
3. Results and Discussion
Absolute real-life risk reductions (ARRs) of a COVID death obtained from the COVID vaccine
inoculation are shown below in Table 2, column P. Risk reductions from COVID inoculations vary froma low of negative 0.00007% (an increased risk of a COVID death from inoculation) for children underage 18 to a positive 0.183% (0.00183) risk reduction of a COVID death for persons over age 80.
However, column R in Table 2 shows the vaccine fatality rate (VFR) for each age cohort, derived by
Pantazatos and Seligmann from US Census and CDC data, as described in Appendix A. COVID vaccine
inoculations increase risk of death and produce a net negative benefit, aka increased risk of death (shownby the negative numbers in column S = ARR-VFR) for all age groups younger than 60 years old. In
other words, the COVID inoculations cause a net increase, rather than decrease, in the likelihood of death
for all persons under 60 years old. For those over 60 years old, the benefit of COVID inoculations is
negligible, ranging from a 0.0016% (16/1,000,000th) reduction in likelihood of death for 60- to 69-year-6old persons to a 0.125% (125/100,000th) reduction in likelihood of death for those over 80 years old.
Because preventative treatments are often given to well persons, a vaccine is supposed to provide verysmall risk compared to benefit. Thus, such high fatality risks (VFRs) versus low benefit of risk reduction(ARRs) from the COVID inoculations are not acceptable, especially considering that low-cost, effective
treatments are available that would additionally reduce COVID-19 death rates by as much as 90% ormore if provided as soon as symptoms appear in high-risk persons [5,9]. Pantazatos and Seligmann’s
estimates of overall US vaccine fatality rates (VFRs) agree with credible analyses of the US CDC’s
vaccine adverse events reporting system (VAERS) showing US death reports to VAERs are underreported by a factor of 20 [6].

7Table 2: Calculations for Absolute Risk Reduction, Risk/Benefit, Number Needed to Treat/Vaccinate,
Number of Vaccine-Induced Fatalities to Prevent One (1) Covid Death, Relative Risk of Death by Vaccination Status.

Calculations in Table 2 are calculated from the data columns in Table 1 columns. Formulas are shown at the bottom of column
headings. Results include: Percentages of COVID Deaths in the Surveilled UK NIMS population by vaccination status and age
cohort, in columns M and N; Relative and Absolute Risk Reduction benefits of the COVID vaccine, in columns O and P; Number
needed to treat or vaccinate to prevent one COVID death, 1/ARR in column Q; Vaccine Fatality Rates in column R (See Appendix A
for calculations); Cost/benefit in column S are the result of subtracting vaccine-induced fatality rate (VFR) from absolute risk
reduction (ARR). If negative, risks exceed benefits; if positive, benefit exceeds risk of the COVID inoculations; Column T is thenumber of expected vaccine-induced deaths to prevent one COVID-19 death; column U is the relative risk of death by vaccine status.
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Table 3. Number of Expected Vaccine Fatalities to Prevent One COVID Deathand Number of Expected Vaccine Fatalities for each One COVID Fatality

Data in Table 3, from the columns of Table 2, summarize the cost of vaccine fatality derived from US
Census and CDC data in 2021 vaccination rollouts, compared to vaccination benefit or risk reduction in
COVID fatality rates, derived from UK data on COVID death rates from weeks ending 16 January to 6
February 2022. Altogether, the data show a COVID inoculated person is more likely to die within 30 to
60 days of “vaccination” than an unvaccinated person is to die of COVID-19 within 60 days of a positive
COVID test, in all age cohorts under 60 years old. The results by age cohort are that within the same orsubsequent month of receiving a COVID “vaccine” inoculation:•

For those under age 18, vaccination increases their COVID death rate, and those under 18 are 51
times more likely to die from the inoculation than to die from COVID if not vaccinated.

•

Those aged 18 to 29, are 16 times more likely to die from COVID vaccination than to prevent one
COVID death and are 8 times more likely to die from vaccination than to die from COVID if not
vaccinated.

9•Those aged 30 to 39, are 15 times more likely to die from COVID inoculation than to prevent one
COVID death, and 7 times more likely to die from the inoculation than to die from COVID
COVID if not vaccinated.

•

Those aged 40 to 49, are 9 times more likely to die from the COVID inoculation than likely toprevent one COVID death in this age group, and 5 times more likely to die from the inoculation
than to die from COVID if not vaccinated.

•

Those aged 50 to 59, are twice (2 times) more likely to die from the COVID inoculation than toprevent one COVID death and are slightly more likely to die from the inoculation than to die fromCOVID if not vaccinated.

•

Those aged 60 to 79, are virtually equally likely to die from the COVID inoculation as to prevent
one COVID death or die from COVID if not vaccinated.

•

Those aged 80+ are 0.13% less likely to die from the COVID inoculation than to die from COVID
if not vaccinated.

5. Conclusion
The benefits of vaccination against COVID-19 have not lived up to expectations. There has been a rapid
drop in vaccine-induced antibody levels over time [7] and the rapid emergence of SARS-CoV-2 variants
that are resistant to the vaccinal antibodies to the spike protein [8]. When COVID-19 death data by
vaccination status from early 2022 are analyzed to estimate the degree of protection from mortality
afforded to the vaccinated population, the protection from COVID-19 death falls far short of the risk ofdying from the vaccine, for anyone below 50 years old.
With Omicron now the dominant strain, the vaccinated population are still catching the disease inlarge numbers and spreading it. The mRNA vaccines were designed to target the original SARS-CoV-2
strain, and the arrival of variants like Delta and Omicron have changed the risk/benefit ratio. With such alarge percentage of the population catching omicron and recovering, we now have a much larger base of10

a naturally resistant population, whose immunity is much longer lasting and robust than that achieved
with the vaccine [10]. With Omicron being both less deadly and more resistant to vaccine antibodies, thebenefits of vaccination are further weakened, while the risks of dying from the vaccine remain unaltered.
Even if we roll out new versions of the vaccines, the virus will continue to mutate in a futile cat-andmouse game.
According to the data analysis presented in this paper, all age cohorts under 50 years old are at
greater risk (from 5 to 51 times higher) of vaccine-induced fatality within the same or subsequent monthof receiving a COVID-19 inoculation than they are at risk of a COVID-19 death within 60 days of apositive test if unvaccinated. All age cohorts have less than ¼ of 1% benefit of absolute risk reduction of
a COVID-19 death from receiving a COVID-19 inoculation. Children under age 18 years have 51 times
higher chance of fatality after a COVID inoculation than risk of dying from COVID if unvaccinated.
Vaccinations in the under 18 age group are more likely to increase the number of COVID deaths in thisage group rather than prevent any. Young adults age 18 to 29 have an 8 times higher risk of fatality fromthe inoculation than from COVID if not inoculated. This analysis is conservative because it ignores the
inoculation-induced risk increases of later fatalities and shortened life spans from thrombosis,
myocarditis, Bell’s palsy, and other known vaccine-induced injuries and ignores the 90% or moredecreases in risk of COVID-19 death if early, effective treatments were provided to all symptomatic
high-risk persons [5,9]. Mandates of COVID inoculations are ill-advised because the alleged vaccines
result in higher death rates than COVID itself.

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References
1. Canadian COVID Care Alliance. More Harm than Good. January 2022.
https://www.canadiancovidcarealliance.org/wp-content/uploads/2021/12/The-COVID-19Inoculations-More-Harm-Than-Good-REV-Dec-16-2021.pdf and J. Bart Classen, MD, Classen
Immunotherapies, Inc, 3637 Rockdale Road, Manchester, MD. US COVID-19 Vaccines Proven to
Cause More Harm than Good Based on Pivotal Clinical Trial Data Analyzed by Using the Proper
Scientific Endpoint, “All Cause Severe Morbidity”. https://www.scivisionpub.com/pdfs/us-covid19vaccines-proven-to-cause-more-harm-than-good-based-on-pivotal-clinical-trial-data-analyzed-usingthe-proper-scientific--1811.pdf
2. COVID vaccination and age-stratified all-cause mortality risk. By Spiro Pantazatos, Columbia
University and Herve Seligmann, Israel. https://www.researchgate.net/profile/SpiroPantazatos/publication/355581860_COVID_vaccination_and_age-stratified_all-cause_mortality_risk/
3. UK COVID-19 Health Security Agency. Weekly national Influenza and COVID-19 surveillance
report Week 6 (up to week 5), ending 6 February 2022.
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/105
0508/Weekly_Flu_and_COVID-19_report__w6.pdf
4. UK COVID-19 Health Security Agency. Vaccine Surveillance Report - Week 6, w/e 6 February 2022
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/105
0721/vaccine-surveillance-report-week-6.pdf
5. Brownstein D, Ng R, Rowen R, Drummond J-D, Eason T, Brownstein H, Brownstein J. A Novel
Approach to Treating COVID-19 Using Nutritional and Oxidative Therapies. Science, Public Health
Policy, and The Law 2020; 2: 4-22. https://thepowerofozone.com/wp-content/uploads/2020/07/Dr.David-Brownstein-Covid.pdf
6. Rose J, Crawford M. Estimating the number of COVID vaccine deaths in America [Internet].
Available from: https://downloads.regulations.gov/CDC-2021-0089-0024/attachment_1.pdf
7. Naaber P, Tserel L, Kangrod K. et al. Dynamics of antibody response to BNT162b2 vaccine after six
months: a longitudinal prospective study. The Lancet Regional Health - Europe 2021; 10: 100208.
8. Krüger N, Schulz S, Cossmann A, et al. The Omicron variant is highly resistant against antibodymediated neutralization: Implications for control of the COVID-19 pandemic. Cell 2022, 185(3) 447456.
9. 20-Week Study of Clinical Outcomes of Over-the-Counter COVID-19 Prophylaxis and Treatment.
Leon Margolin, MD, PhD, Jeremy Luchins, PhD, Daniel Margolin, Bsc, ... Journal of EvidenceBased Integrative Medicine July 6, 2021.
https://journals.sagepub.com/doi/full/10.1177/2515690X211026193
10. Alejo JL, Mitchell J, Chang A, et al. Prevalence and Durability of SARS-CoV-2 Antibodies Among
Unvaccinated US Adults by History of COVID-19. JAMA February 3, 2022 [Epub ahead of print]
doi:10.1001/jama.2022.1393.https://jamanetwork.com/journals/jama/fullarticle/2788894
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APPENDIX A: Method Used to Calculate Age-Stratified Vaccine Fatality Rates
COVID vaccination and age-stratified all-cause mortality risk. By Pantazatos and Seligmann

2.2 This section explains the derivation of age-stratified Vaccine Fatality Rates (aVFRs) that were
calculated by Pantazatos and Seligmann shown in the table tothe left. Pantazatos and Seligmann’s goal was to use publicly
available data sources to test whether variation in vaccination
dosage rates in the same or previous month predicts or correlateswith regional variation in age-stratified mortality. US Census
and CDC data allows for such estimates at a monthly temporal
resolution. Data from January 1, 2021 through August 31, 2021
was used to predict the total # deaths in the months of February
through August 2021.
Data Sources: All data were obtained from the US CDC or US
Census Bureau. The authors have made the preprocessed tablesof data available on https://github.com/spiropan/CoVFR .
Vaccination rates across time in US states were obtained from COVID-19 Vaccinations in the UnitedStates, Jurisdiction | Data | Centers for Disease Control and Prevention [Internet]. [cited 2021 Oct 1].
Available from: https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-StatesJurisdi/unskb7fc
Total deaths per month by age group for each US state were extracted from Provisional COVID-19
Deaths by Sex and Age | Data | Centers for Disease Control and Prevention [Internet]. [cited 2021 Oct 1].
Available from: https://data.cdc.gov/NCHS/Provisional-COVID-19-Deaths-by-Sex-and-Age/9bhg-hcku
Age stratified total populations per US state in 2019 were obtained from Bureau UC. 2019 Population
Estimates by Age, Sex, Race and Hispanic Origin [Internet]. The United States Census Bureau. [cited
2021 Oct 1]. Available from:https://www.census.gov/newsroom/press-kits/2020/population-estimates-detailed.html

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2.3 Estimation of Models by Pantazatos and Seligmann
For 7 months (February through August) and 8 age groups (0-17, 18-29, 30-39, 40-49, 50-64, 65-74, 7584, >85 years) 56 models were estimated using data as available from all US states.

Where Y21_deaths and Y20_deaths are the number of total deaths for that month in year 2021 and2020, respectively, and Vax is the number of vaccine doses administered in the previous or current
month. The model is an ANCOVA model that adjusts for baseline outcomes using Y20 monthly deaths
and predicting post-vaccination Y21 monthly deaths using robust regression corrected for multiple
comparisons using the Benjamini-Hochberg False Discovery Rate (FDR) correction. For the 56
regressions and resulting p-values in 15 of the models the beta coefficient (!! ) for the vaccine term forthat month and age group was positive and significant at p < 0.05, FDR corrected. Two (2) of thesesignificant regressions predicted deaths in the 75-84 and 85+ age categories using vaccination dosagesfor >65 year old; seven (7) of these significant regressions predicted deaths in the same months asvaccinations for younger groups age 18 to 49. There were no significant coefficients with negative(!! ) beta coefficients. Thus, all significant results showed a positive relationship of prior or samemonth vaccine doses with predicted monthly all-cause deaths. In older age groups, the correlations
were strongest in the beginning of the year when the vaccination campaign first targeted nursing homes
and older age groups. In younger ages correlations were stronger later in the year when the younger
age groups became eligible for vaccination. The image shown to the left shows the table of significant
FDR corrected beta coefficient (!! ) highlighted in yellow. By adjusting for the number of deaths in thesame month during the previous year, age-stratified population size differences, seasonal effects on
mortality and state-to-state variability in mortality due to other factors were controlled for. Standard
least-squares linear regression using MATLAB’s tool, glmfit, was also applied for cases where resultsfrom robust regression could not be determined. To control for deaths due to COVID, regressions
were also run to predict non-COVID deaths and an additional set of analyses that include COVID casenumbers in previous month as a nuisance regressor yielded largely similar results (Supplementary Table
S5).

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15

2.4 Method: Estimate the Number of Deaths Attributed to COVID Vaccination from Regression:
The estimated models having beta (!! ) weights that were significant at p < 0.05, FDR
corrected, were used to estimate death counts for months and age groups. For each state,model predictions for increases in deaths due to a 10% increase in vaccination doses wasestimated using each state’s Y20 death data and vaccine doses administered in the samestate. The predicted increases in deaths were summed across all states and then divided by
1/10th or 10% of the sum of the vaccine doses administered across those states to estimate
an age-specific vaccine-attributed fatality rate (aVFR). The formulas are shown here:

The aVFR was multiplied by the total number of vaccinations used in each regression model thatsurvived the applied significance threshold in the US during the month to arrive at an estimated deathcount attributed to vaccines for each month and age group. The values are then used to populate the cells
in Table 3.

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COVID vaccination and age-stratified all-cause mortality risk. p.
20, table 3. Pantazatos and Seligmann1

1

https://www.researchgate.net/profile/SpiroPantazatos/publication/355581860_COVID_vaccination_and_age-stratified_all-cause_mortality_risk/

17

2.5 A causal link to vaccination was established by:(1) Temporal precedence or Granger causality analysis;(2) Total deaths due to vaccine status are similar to independent estimates using data-driven, credible
assumptions about VAERS underreporting bias [6];(3) COVID case rates in previous months do not explain the findings; results were largely similar
when cases were included in the regressions;(4) Vaccination rates predict mortality in younger age groups where COVID deaths are much rarer;(5) Existing vaccine surveillance studies showing contrary results contain critical errors and issues;(6) Results show an age-related temporal pattern consistent with vaccination campaigns that firsttargeted nursing homes and older age groups; and(7) Results comport with the volume and nature of responses to social media posts, FDA dockets forsolicited public comments and websites created to give voice to vaccine injured.

2.6 Findings from COVID vaccination and age-stratified all-cause mortality risk. By Spiro
Pantazatos and Herve Seligmann
Regional variation in vaccination rates predicts mortality in subsequent periods. COVID vaccination
rates predict higher mortality. Estimates are based only on significant effects, and hence likely represent alower bound on vaccine-associated all-cause fatality. Severe adverse reactions to the COVID vaccines
appear to be mediated in part by cytotoxicity of the spike protein and its cleaving from transfected cells
and biodistribution in organs outside the injection site. Vaccination may also contribute to higher
COVID infection fatality rates before vaccination protection kicks in and again after full protection wears
off due to antibody dependent enhancement (ADE). These calculations do not rely on VAERS data andsuggest the CDC reported VFR of 0.002% means vaccine-associated deaths are under-reported to
VAERS by a factor of 20. The majority of deaths in < 18 years age occur in infants < 1 years, and asignificant effect of vaccination on infant mortality was detected. The observed effects may relate toabnormally high mortalities around delivery in pregnant women. There is no evidence that vaccines
reduce community spread and transmission. Thus, vaccine mandates are not based on sound science andare ill-advised.
18

Existing safety and surveillance studies relying on Vaccine Trial Data and Vaccine Safety Datalink
are not designed to reliably estimate COVID vaccine-induced death risks. For example, data has been
withheld; its authors received funding from vaccine manufacturers; control groups were vaccinatedwithin a few months eliminating long-term safety data; adverse events were compared between two
vaccinated groups rather than between vaccinated and unvaccinated groups; and the data, e.g. Vaccine
Safety Datalink, is not publicly accessible. The taboo on discussing vaccine-induced injury and deathneeds to be lifted.

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APPENDIX B: UK OFFICIAL SOURCES
Source 1: UK COVID-19 Health Security Agency. Vaccine Surveillance Report - Week 6 ending 6 February 2022, Table 12b,[4,p.43]

20

Source 2: Weekly national Influenza and COVID-19 surveillance report Week 6 (up to week 5 data) 10
Febuary 2022, table 9, p. 81

21