2.1. Pandemic History

In December 2019, a viral outbreak was reported in Wuhan, China, and the responsible coronavirus was termed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) (Docea et al., 2020, Arsene et al., 2020). The associated disease was called Coronavirus Disease 2019, or COVID-2019. The virus spread worldwide, and a global pandemic was declared by the WHO in March 2020 (Goumenou et al., 2020, Islam et al., 2021b). Restrictive measures of differing severity were implemented by countries globally, and included social distancing, quarantining, face masks, frequent hand sanitation, etc. (Sidiropoulou et al., 2021, Farsalinos et al., 2021). In the USA, these measures were taken as well, differing from state-to-state (Tsatsakis et al., 2020). At the same time, vaccine development was initiated to control COVID-19 (Calina et al., 2020c). In the USA, non-vaccine treatments were not encouraged at the Federal level, but different treatment regimens were pursued by some healthcare practitioners on an individual level (Islam et al., 2021; Sharifi-Rad et al., 2020b, Islam et al., 2021b).

By the end of May 2021, the official CDC death count attributed to COVID-19 was approaching 600,000, as stated previously. This number has been disputed for many reasons. First, before COVID-19 testing began, or in the absence of testing, after it was available, the diagnosis of COVID-19 (in the USA) could be made by the presumption of the healthcare practitioner that COVID-19 existed (CDC, 2021b, Kostoff et al., 2020c). Second, after testing began, the main diagnostic used was the RT-PCR test. This test was done at very high amplification cycles, ranging up to 45 (Neagu et al., 2021, Mandavilli, 2020, Mercola, 2020). In this range, very high numbers of false positives are possible (Kostoff et al., 2020a).

Third, most deaths attributed to COVID-19 were elderly with high comorbidities (Kostoff et al., 2018, Calina et al., 2021a). As we showed in a previous study (Kostoff et al., 2018), attribution of death to one of many possible comorbidities or toxic exposures is highly arbitrary and can be viewed as a political decision more than a medical decision. The CDC recently admitted that about 94% of the deaths attributed to COVID-19 could just as easily have been attributed to one of the comorbidities (CDC, 2021 j). Thus, the actual number of COVID-19-based deaths in the USA may have been on the order of 35,000 or less, characteristic of a mild flu season.

Even the 35,000 deaths may be an overestimate. Comorbidities were based on the clinical definition of specific diseases, using threshold biomarker levels and relevant symptoms for the disease(s) of interest (Torequl Islam et al., 2020, Pott-Junior et al., 2021). But many people have what are known as pre-clinical conditions. The biomarkers have not reached the threshold level for official disease diagnosis, but their abnormality reflects some degree of underlying dysfunction. The immune system response (including pre-clinical conditions) to the COVID-19 viral trigger should not be expected to be the same as the response of a healthy immune system (Calina et al., 2020a). If pre-clinical conditions had been taken into account and coupled with the false positives as well, the CDC estimate of 94% misdiagnosis would be substantially higher.

2.2. Clinical Trials

3.1. Adverse events reported for adults

This section describes the adverse effects that followed COVID-19 mass inoculation in the USA. The main source of adverse effects data used was VAERS. Because VAERS is used to estimate adverse event information by many other countries as well, a short overview of VAERS and its intrinsic problems is summarized in Appendix 1.

The period in the present study covered by the reported inoculations is mid-December 2020 to the end of May 2021. The population inoculated during this period is mainly adults. Child inoculations did not begin until mid-May. Because the different age groups were inoculated starting at different times based on priority, the elapsed times after inoculation will be different, and any adverse event comparisons across age groups will require some type of elapsed post-inoculation time normalization.

We examined VAERS-reported deaths by age group, normalized to:

• 1)

• the number of inoculations given

• 2)

• the period within seven days after inoculation.

This allows a credible comparison of very short-term adverse effects post-inoculation for all age groups. During this period, which is eight days post-inoculation (where day zero is the day of inoculation), ˜sixty percent of all post-inoculation deaths are reported in VAERS.

Fig. 2 below shows the results circa late May 2021 (CDC, 2021 l). The age band ranges are different from those in Fig. 1 because the CDC provides inoculation after-effect age bands differently from COVID-19 death age bands. In general, the inoculation deaths by age per inoculant roughly parallel the COVID-19 deaths by age per capita (the curve structures are very similar), with one exception: the 0-17 demographic. In the normalized COVID-19 death graph (Fig. 1), the deaths per capita in the 0-17 demographic are negligible, while in the normalized inoculant death graphs (Fig. 2) the normalized deaths are small, but not negligible. The members of the 65+ demographic, where the bulk of deaths are occurring in Fig. 1, Fig. 2, have been receiving inoculations for ˜five months, whereas the members of the youngest demographic have been receiving inoculations only for a few weeks. More time needs to pass before more definitive conclusions can be drawn about the youngest demographic, and how its members are impacted adversely following the inoculations.

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The high death rates from both COVID-19 and the inoculations in the 65+ demographic should not be surprising. In both cases, the immune system is challenged, and in both cases, a dysfunctional immune system characteristic of many elderly people with multiple comorbidities cannot respond adequately to the challenge.

3.2. Novel best-case scenario cost-benefit analysis of COVID-19 inoculations for most vulnerable

Traditional cost-benefit analyses are typically financial tools used to estimate the potential value of a proposed project. They involve generating cost streams over time, benefit streams over time, and then comparing the net present value of these two streams (including risk) to see whether the risk-adjusted discounted benefits outweigh the risk-adjusted discounted costs. Appendix D presents a detailed non-traditional best-case scenario pseudo-cost-benefit analysis of inoculating people in the 65+ demographic in the USA. In this incarnation of a cost-benefit analysis, the costs are the number of deaths resulting from the inoculations, and the benefits are the lives saved by the inoculations. The time range used was from December 2019 to end-of-May 2021. No discounting was done; an inoculation-based death occurring immediately post-inoculation was given the same importance/weighting as an inoculation-based death months after inoculation.

Why was this non-traditional approach selected for a cost-benefit analysis? In a traditional non-financial cost-benefit analysis relative to inoculations, the adverse events prevented by the inoculations would be compared with the adverse events resulting from the inoculations. Presently, in the USA, definitions, test criteria, and reporting incentives for COVID-19 and its inoculants have shifted over time, and we believe a standard approach could not be performed credibly. Appendix Da presents some of the problems with the COVID-19 diagnostic criteria on which the above statements are based.

In contrast to the pandemic buildup phase, where many who died with COVID-19 were assumed to have died from COVID-19 by the medical community and the CDC, the post-inoculation deaths reported in VAERS are assumed by the CDC to be mostly from causes other than the inoculations. We wanted to use a modified cost-benefit analysis that would have less dependence on arbitrary criteria and subjective judgments.

The approach selected can be viewed as a best-case scenario pseudo-cost-benefit analysis. We assume the inoculations prevent all the deaths truly attributable to COVID-19 (these are the total deaths attributed to COVID-19 officially minus 1) the number of false positives resulting from the PCR tests run at very high amplification cycles and 2) the number of deaths that could have been attributed to one of the many comorbidities that were typical of those who succumbed, as shown in our results section) over the period December 2019 to end-of-May 2021, and relate that number to the deaths truly attributable to the inoculation (from January 2021 to end-of-May 2021) based on our computations in the results section. The results show conservatively that there is five times the number of deaths truly attributable to each inoculation vs those truly attributable to COVID-19 in the 65+ demographic. As age decreases, and the risk for COVID-19 decreases, the cost-benefit increases. Thus, if the best-case scenario looks poor for benefits from the inoculations, any realistic scenario will look very poor. For children the chances of death from COVID-19 are negligible, but the chances of serious damage over their lifetime from the toxic inoculations are not negligible.

EXPECTED DEATHS IN 65+ DEMOGRAPHIC VS COVID-19 INOCULATION DEATHS

The goal of this appendix is to estimate the number of actual deaths from the COVID-19 inoculation based on the number of deaths following inoculation reported in VAERS (CDC, 2021 l, CDC, 2021d). The approach used will:

• 
  

  
  

• 1)

• identify the number of deaths following COVID-19 inoculation that would have been expected without COVID-19 inoculation (i.e., pre-COVID-19 death statistics);

• 2)

• relate the VAERS expected death data to the actual number of deaths expected based on historical death statistics; and

• 3)

• apply this ratio to scale-up the deaths attributed to COVID-19 inoculation reported in VAERS to arrive at actual deaths attributable to COVID-19 inoculation.

For example, if ten deaths could be shown in VAERS to reflect expected pre-COVID-19 deaths, and the actual number of expected pre-COVID-19 deaths from historical data was 100, the scaling factor of deaths would be ten to translate VAERS-reported deaths to actual deaths. Then, the deaths reported in VAERS that can be attributed to the COVID-19 inoculation will be multiplied by the expected deaths scaling factor, ten, to arrive at the actual number of deaths resulting from the COVID-19 inoculation. Thus, if VAERS shows fifty deaths that can be attributed to the COVID-19 inoculation, then the actual number of deaths attributed to COVID-19 will be 500 with these assumptions (CDC, 2021 l).

The basis for our approach is the following statement from the USA Federal government: “Healthcare providers are required to report to VAERS the following adverse events after COVID-19 vaccination (FDA, 2021) and other adverse events if later revised by FDA (CDC, 2021h) (FDA, 2020b). Serious AEs regardless of causality. (CDC, 2021 l)

If there had been full compliance with this requirement in VAERS, then the VAERS-reported deaths would have equaled the sum of

• 1)

• actual expected deaths (based on past statistics)

• 2)

• actual deaths over and above expected deaths that could be attributed to the COVID-19 inoculations.

Based on this requirement, we will generate a rough estimate (in the simplest form possible) of the number of deaths that would have occurred in the 65+ demographic if there had been no COVID-19 “pandemic”. Then, we will relate this number to the number of deaths reported to VAERS following COVID-19 inoculations in the 65+demographic. This would provide a “floor” for estimating the fraction of actual deaths reported to VAERS. This will be followed by parameterizing potential deaths attributable to the COVID-19 inoculations and displaying the effects on ratio of reported deaths to actual deaths. We will perform a global analysis and a local analysis, to see whether major or minor differences occur. The local analysis (Section A1-a2) may be somewhat easier to comprehend than the global analysis, but both come to similar conclusions.

A1-a. Deaths Following COVID-19 Inoculations Reported to VAERS Compared to Expected Deaths

A1-a2. Global analysis

We used 2019 death statistics from CDC to start the analysis. According to search results from CDC Wonder (CDC, 2021a) obtained 11 June 2021, there were 2,117,332 deaths from all causes for people aged 65+ in the United States in 2019. Assuming uniformity throughout the year, there would have been ˜882,000 deaths occurring the first five months of the year, and that number will be used as the expected deaths for the first five months of 2021. From the same source, the population estimate is ˜54,000,000 for the 65+ age range. From CDC COVID-19 data tracker, the number of people 65+ vaccinated with at least one dose is ˜44,000,000 (CDC, 2021g)

For those who were inoculated somewhere in the time frame 1 January 2021 to 31 May 2021, the number who would have been expected to die in the period from inoculation to 31 May will be a function of the duration of this period. For example, if all 44,000,000 people had been fully inoculated on 1 January 2021, then the number expected to die post-inoculation from non-COVID-19 inoculation causes would be simply (44,000,000/54,000,000) x 882,000, or ˜723,000 deaths. Conversely, if all 44,000,000 people had been fully inoculated on 31 May 2021, then the number expected to die post-inoculation from non-COVID-19 inoculation causes would be extremely small (CDC, 2021g).

For an accurate estimation of the number expected to die post-inoculation from non-COVID-19 causes, one would need to integrate the time between inoculation and 31 May over the inoculation temporal distribution function. For present purposes, we will do a very rough approximation by modeling the inoculation distribution function as a delta function occurring at a mean temporal location. In other words, we compress all inoculations an individual receives into one, identify the mean temporal location from the actual inoculation distribution function, and compute the expected deaths based on the distance from 31 May to the temporal mean point.

From a graph of inoculation trends in the CDC data tracker (CDC, 2021d) the distribution appears to be non-symmetrical pyramidal, rising to a peak in mid-April. This is slightly over the 2/3 point in the five-month range of interest. We will approximate the mean time point as 2/3 of the distance.

Table A1 displays the mean time normalized to the five-month study window vs potential deaths from COVID-19 inoculation (not expected from prior census data) normalized to the deaths expected from prior census data. Each cell represents the percent of deaths reported in VAERS following inoculation relative to total deaths (number of deaths expected from prior census data plus number of deaths following COVID-19 inoculation not contained in the expected death group). The model on which the table is based is as follows: there are two classes of deaths for the period following COVID-19 inoculation. One is the deaths expected from prior census data, and the other is deaths attributable mainly to COVID-19 inoculation. There would be potentially substantial overlap between the two in this age group (and perhaps other age groups as well). We assume that we can tag those individuals who would be expected to die based on prior census data. The remaining deaths attributable to COVID-19 inoculation not contained within the tagged group are classified as potential COVID deaths in Table A1.

Table A1. EXPECTED DEATHS FROM NON-COVID-19 CAUSES FOR INOCULEES (THOUSANDS)

Consider the cell (2/3,0). The mean time is about mid-April 2021 and the only deaths occurring are those expected (some may have died because of the inoculation, but they were sufficiently ill that they would have died during that period without the inoculation). There were 723,000 expected deaths and ˜3560 reported, yielding a ratio of deaths reported in VAERS to actual deaths of ½%.

Consider the cell (1/2,1). The mean time would have been about mid-March 2021 and the inoculation distribution would have resembled an isosceles triangle. The total deaths occurring are those expected and an equal number whose deaths were attributed to COVID-19 inoculation but did not overlap with those in the tagged expected group (there still could have been some/many in the latter group that may have died because of the inoculation, but they were sufficiently ill that they would have died during that period without the inoculation). There were 724,000 total deaths that occurred during that period and ˜3560 reported, yielding a ratio of deaths reported in VAERS to actual deaths of ½%. (CDC, 2021l)

So, according to Table A1, focusing on the parameter most closely reflecting the actual inoculation distribution (2/3), the reporting percentages of actual to total are about 1%. This mirrors the Harvard Pilgrim study results referenced in our vaccine safety study which were obtained through an entirely different empirical approach (Kostoff et al., 2020c). At least for deaths reporting, there appears to be an approximately two order of magnitude difference between actual and reported deaths in VAERS.

Table A1 used two parameters to examine a broad spectrum of possible results, the mean time and the number of deaths solely attributable to COVID-19 inoculation. The mean time parameter was fairly well known and constrained in interpretation, because it was based on an empirical inoculation distribution function. The number of deaths solely attributable to COVID-19 inoculation is completely unknown.

As will be shown in the next section, the numbers of deaths reported in VAERS are strongly related to the inoculation date by clustering, but those who died might also have been those who would have died anyway because they were expected to die. There were probably some of each in that group reported. But we have no idea of the total number whose death could be directly attributed to COVID-19 inoculation and who were not in the group expected to die. For all we know, there could have been ten million people in that group, and only an extremely small fraction of that total group was reported in VAERS.

Suppose, for example, that the actual number of deaths reported in VAERS came from two groups: 90% were from the inoculation-attributable death group and 10% were from the expected death group. Assume there is no overlap between the two groups. In that case, what VAERS shows is not that 1% of actual expected deaths were reported, but rather that 1/10 of one percent of the expected deaths were reported. If that metric is used as the standard to scale up to total deaths, then the number in the actual inoculation-attributable death group is not 100 times the VAERS reported deaths, but rather 1000 times the VAERS-reported deaths! The point is we can’t “reverse-engineer” the reported VAERS death numbers to get the actual inoculation-attributable deaths because it depends on the unknown contribution of each of the two groups (expected deaths and inoculation-attributable deaths) to the VAERS reported deaths, and we can’t separate those out.

All this analysis shows is that, at best, only about 1% of the number expected to die was reported, and because the number reported in VAERS included deaths from both groups, the fraction from each actual group of deaths could not be determined. Realistically, we may have to wait until mid-2022, when the 2021 total deaths for each age group are finalized, to ascertain whether we can see increases in all-cause mortality that could have come from the inoculation-attributable deaths.

A1-a3. Local Analysis

Another way of estimating VAERS reporting efficiency is to perform a local analysis, focused on clustering about date of COVID-19 inoculation. For the 65+demographic, the post-inoculation deaths cluster near the vaccination date, providing evidence of a strong link to the inoculation.

Following the approach in the first section of this appendix, we calculate the deaths expected in any ten-day period based on 2019 pre-COVID-19 death statistics. For the inoculated group, the number of deaths expected for any ten-day period are (2,117, 332 deaths/per year) x (44,000,000/54,000,000 fraction of population in age range inoculated) x (10/365 fraction of year), or ˜47,270 deaths.

Suppose in that ten-day interval there had been 10,000 deaths that could be directly attributed to COVID-19 inoculation in addition to the expected deaths. This would have given a ratio of 2,000/57,270 actual total deaths, or 3.5% reported in VAERS. This latter approach requires less assumptions than the former approach, but still yields results of only a few percent actual deaths reported in VAERS.

The Harvard Pilgrim electronic tracking study of post-vaccination events reported to VAERS performed in 2010 (Kostoff et al., 2020c) showed a 1% reporting rate for a thirty-day period. In the present case, ˜2900 post-inoculation deaths were reported to VAERS within thirty days of inoculation, or ˜82% of total deaths for the 65+demographic. Substituting thirty days for ten in the above computation yields 141,810 expected non-COVID-19 post-inoculation deaths for the thirty-day period, or 2% that are reported in VAERS. The Harvard study used an electronic system that automatically tracked every event that occurred, no matter how small. Because of the effort (time and cost) required to submit event reports to VAERS, we suspect that only the more serious events, such as death, would be reported, and even in this case, the numbers reported are miniscule.

We also did an analysis for sixty days post-inoculation. In the present case, ˜3300 post-inoculation deaths were reported to VAERS within sixty days of inoculation, or ˜93% of total deaths for the 65+demographic. Substituting sixty days for ten in the above computation yields 283620 expected non-COVID-19 post-inoculation deaths for the thirty-day period, or 1.2% that are reported in VAERS. Remember, this normalization is based only on expected deaths. If 100,000 deaths attributable mainly to the COVID-19 inoculation beyond those that overlapped with the expected group occurred during this period, then the denominator would have to be increased by 100,000, yielding a VAERS reporting rate of 0.86%.

Thus, both the global and local analyses, and the Harvard Pilgrim empirical analysis, are converging on the same two orders-of-magnitude difference between the actual number of deaths that occurred in the USA and those reported in VAERS. Depending on how many people have really died as a result of the COVID-19 inoculation, this reporting rate could well be a fraction of a percent!

A1-a3a. Local Clustering Analysis

We end this appendix with one more example from the local analysis. Some background perspective is required. In the buildup to the pandemic (putting aside the issue of high false positives from PCR tests run at high numbers of amplification cycles), almost anyone who died with COVID-19 was assumed to have died from COVID-19, irrespective of the number of potentially lethal comorbidities they had. The CDC admitted later that about 94% of the deaths attributed to COVID-19 would ordinarily have been attributed to one of the comorbidities.

For this example, we adopt a similar philosophy for the COVID-19 inoculations. People in the 65+ demographic who have died following inoculation are divided into two groups: those who died from the inoculation and those who died as expected based on pre-COVID-19 death data. The two groups range from being entirely separate to completely overlapping. We will examine two cases: entirely separate and completely overlapping.

How are the members of each group determined? The death from inoculation group consists of those whose deaths cluster significantly around the date of inoculation. The deaths expected group are the number who would have died in the absence of COVID-19. We allow for overlap, where each person who died can be double-valued (a member of both groups), but not double-counted

To obtain a relatively precise estimate of expected deaths, we would want to select a region of time where the distribution function has substantially leveled off. From Fig. A1, the thirty-sixty-day range appears reasonable. However, there is a time issue here. Given the lag time in data reported by VAERS, most of the data in this range will probably have come from inoculations in January and February, and early-mid March, approximately 35 percent of the total inoculations. Therefore, we could multiply the thirty-sixty-day average number of deaths by ˜3 to obtain ˜40 expected deaths per day. An even simpler way to estimate the expected deaths reported in VAERS is to use the 15-30-day average shown, which will represent most of the range. This value is ˜37, which is close to the ˜40 obtained with the above approximation. This analysis should be re-run in three-four months, when more of the long-range data has been filled in.

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Table A2 shows the results of our analysis. As stated previously, two separate cases were analyzed: completely separate groups and completely overlapping groups. Two values of daily expected deaths were used: the 37 as described above, and 20 to account for potentially lower expected death reporting when the VAERS data has filled in more completely.

Thus, based on the deaths reported in VAERS following COVID-19 inoculation, and assuming the inoculation-related deaths are reported in the same ratio as expected deaths, the actual number of deaths strongly related to the COVID-19 inoculation should be scaled up by factors of 100-200. For the broadest definition of VAERS coverage provided by CDC Wonder, which includes the USA and all territories, protectorates, and possessions, the total deaths following COVID-19 were ˜5200 in early June 2021. Using our scaling factors, this translates into somewhere between one-half million and one-million deaths, and this has not taken into account the lag times associated with entering data into VAERS. Compared with the ˜28,000 deaths the CDC stated were due to COVID-19 and not associated morbidities for the 65+ age range, the inoculation-based deaths are an order-of-magnitude greater than the COVID-19 deaths! It should be remembered these are only the very-short-term inoculation-based deaths, and could increase dramatically if mid- and long-term adverse effects come to fruition.

We end this appendix with an even more unsettling possibility. The main assumption upon which the results in Table A2 were based is that the post-inoculation temporal distribution function shown in Fig. A1 could be divided into two regions. The strongly varying region originating from the inoculation date reflected deaths from the inoculation, and the essentially flat region that followed reflected expected deaths (that flat region also started at the inoculation date, and formed the base on which the highly varying region is positioned). This model excludes the possibility that deaths from the inoculation extend well beyond the limits of the highly varying region.

Table A2. ACTUAL COVID-19 INOCULATION-BASED DEATHS

We know in general this is not true. There can be lag effects such as ADE in the Fall viral season, and longer-term effects such as autoimmune diseases. We postulate that there are other effects from the inoculation that could result in the same flat death profile as that for expected deaths.

Consider the following. Some of the damage we have seen following the inoculations in VAERS includes coagulation/clotting effects and neurological effects of all types (CDC, 2021c). If these effects are not lethal initially, they raise the level of dysfunction. Thus, platelet aggregation has increased to a new base level, and micro-clots have raised the probability of serious clots forming from other lifestyle factors (Wojciechowski et al., 2017). Death of specific neurons can increase the risk of Alzheimer’s disease or Parkinson’s disease, and can accelerate the onset of these and many other diseases. Thus, the adverse impacts of the COVID-19 inoculations could be viewed as raising the level of expected deaths in the future. Any deaths of this nature reported in VAERS would need to be viewed as inoculation-driven, and the expected deaths used in the computations would be reduced accordingly.

Consider Table A3 below. The “expected deaths reported” have been reduced below their counterparts in Table A2 to illustrate parametrically how the total inoculation-based deaths would change from VAERS reporting if this baseline effect is operable. While Table A2 used values of 37 and 20 for expected deaths, Table A3 uses values of 10 and 15.

Table A3. POSSIBLE COVID-19 INOCULATION-BASED DEATHS

Thus, if the baseline of the host for coagulation/clotting, inflammation, hypoxia, neurodegeneration, etc., has been raised by the inoculations, translating into an increase in expected deaths and accelerated deaths, then it is entirely plausible that the VAERS death numbers reflect over a million deaths from COVID-19 inoculations so far. These are very short-term-effects only, and time will tell whether the large potential waves of ADE-driven deaths and autoimmune-driven deaths come to pass.

DETAILED ANALYSIS OF MAJOR COVID-19 INOCULANT CLINICAL TRIALS

A2-b. Ongoing Clinical Trials in the Pediatric Population

In a recent Phase III study performed in the pediatric population, Comirnaty (Pfizer) was tested on a group of 2,260 children, aged 12 to 15, years who had no previous clinical signs of SARS-CoV-2 infection. They were divided into two groups, one placebo (978 children) and the other with Comirnaty (1005 children). In the Comirnaty group, of the 1005 children in whom the serum was administered, none developed COVID-19 disease, compared with the placebo group in which 16 children in 978 had clinical signs of the disease. The Pfizer study showed that the children's immune response was comparable to the immune response in the 16-25 age group (measured by the level of antibodies against SARS-CoV-2). It could be concluded that in this study, Comirnaty was 100% effective in preventing SARS-CoV-2 infection, although the actual rate could be between 75% and 100%. (CDC, 2021c). The results will be evaluated by the FDA and EMA.

The predictive value (for mass inoculation results) of the Comirnaty trial for the children aged 12 to 15 years is questionable. There were 1005 children who were inoculated with Comirnaty. Using the rule of three in statistics, where to obtain a predictive result of 1/x with high confidence (e.g., 1 in a thousand), 3x participants are required for the test sample. For the Comirnaty test sample of 1005, an adverse event of about 1/340 could be detected with high confidence.

What does this mean in the real world? In the USA, there are approximately 4,000,000 children in each age year for adolescents. Thus, there are ˜16,000,000 children in the 12-15 age band. A serious adverse event, including death, that occurred at a 1/800 rate would not be detectable with high confidence in a sample of 1005 people. Thus, the results of the trials for 1005 children would allow for 20,000 children to suffer a non-trial-detected serious adverse event, including death, when extrapolated to potential inoculation of all children in the 12-15 age group! Given that the risk of contracting COVID-19 with serious outcomes is negligible in this population, proceeding with mass inoculation of children 12-15 years old based on the trials that were conducted cannot be justified on any cost-benefit ratio findings.

Also, the evaluation of efficacy in children aged 6 months to 11 years has recently begun and continues (CDC, 2021j). Pfizer began enrolling children under 12 to evaluate the COVID-19 mRNA inoculant. Also, Comirnaty will be evaluated in a new clinical trial for children aged 6 months to 11 years. In the first phase, the study will enroll 144 people and will identify the required dose for 3 age groups (6 months - 2 years, 2-5 years and 5-11 years). After a 6-month follow-up period, the parents/guardians of children in the placebo group will have the option of allowing their children to receive the inoculation. The results are expected in the second half of 2021.

Moderna also began a study to evaluate the mRNA inoculation in children aged 6 months to 12 years. Both companies have already started testing vaccines in 14-year-olds. In the US, children make up 23% of the population (McCarty, 2016).

Data on the risks and benefits of possible inoculation in children and adolescents are currently insufficient and no recommendation can be made. Specifically, mass child inoculations cannot be recommended until the benefits and minimal projected risks have been demonstrated in a sufficiently large trial to provide confidence that mass inoculation will have an acceptable level of adverse effects relative to the demonstrated benefits. On the other hand, children often experience COVID-19 asymptomatically, and the SARS-CoV-2 infection progresses harmlessly. Currently, in the context of limited inoculation capacities, there is no indication of urgent inoculation of children. In the context of declining incidences of SARS-CoV-2 infections and demonstrated low serious adverse effects from COVID-19 infections for children and adolescents, the issue of inoculating children and adolescents is no longer paramount. Authorized forums must calculate what prevails for children and adolescents: the benefits or risks.

A2-c. Clinical Trial Issues for Other Categories

Although people with severe comorbidities such as obesity or oncological conditions were not initially included in the clinical trials that led to obtaining EUA, they were included in subsequent studies, some even ongoing. In their case, it seems that the efficacy was lower compared to the results obtained initially with healthy adults.

The interim analysis of data from a prospective observational study indicates the need to prioritize cancer patients for timely (respectively 21-day) booster administration in the case of administration against COVID-19 with Comirnaty. According to the study, the effectiveness of a single dose of Comirnaty among cancer patients is low, but the immunogenicity of patients with solid cancers increased at 2 weeks after receiving the second dose of inoculant 21 days after the first dose. Because the study was conducted in the UK, participants inoculated before December 29, 2020 received two doses of Comirnaty 21 days apart, and those who started the regimen after this date were scheduled to receive a second dose of Comirnaty 12 weeks apart. first administration. Thus, the study continues to collect data from participants receiving Comirnaty 12 weeks after the first dose.

Approximately 21 days after a single dose of Comirnaty, the proportion of study participants who tested positive for anti-S IgG antibodies was (Monin et al., 2021):

94% among healthy participants;

38% among patients with solid cancers;

18% among patients with hematological cancers.

Among participants who received the 21-day booster and for whom biological samples were available two weeks after the second dose, the following proportions of confirmation as seropositive for anti-S IgG antibodies were reported (Monin et al., 2021).

100% of healthy participants, compared to 86% of the same group of participants who did not receive the second dose;

95% of patients with solid cancers, compared with 30% of the same group of participants who did not receive the second dose;

60% of patients with hematological cancers, compared with 11% of the same group of participants who did not receive the second dose.

Two other studies suggest low immunogenicity in the context of Comirnaty administration in patients with hematological cancers. In one study, patients with chronic lymphocytic leukemia (CLL) had significantly reduced immune response rates to COVID-19 inoculation compared to healthy participants of the same age. Considerable variations in post-administration immune response have been reported among patients with CLL depending on their stage of treatment

The effectiveness of Comirnaty administration was also evaluated in elderly patients with multiple myeloma (Gavriatopoulou et al., 2021). 21 days after administration of the first dose of Comirnaty inoculation (before receiving the second dose), 20.5% of patients with multiple myeloma compared to 32.5% of control participants had neutralizing antibodies against SARS-CoV-2. One possible explanation could be that the therapy negatively affects the production of antibodies. However, the administration of the second dose is important for the development of the immune response in these patients (Gavriatopoulou et al., 2021).

Preliminary data from the v-safe surveillance system, the v-safe pregnancy registry and the Vaccine Adverse Event Reporting System (VAERS) do not indicate obvious safety signals regarding pregnancy or the associated neonatal implications with mRNA injections against COVID-19 in the third trimester of pregnancy (CDC, 2021l). The study included 35,691 pregnant women (Shimabukuro et al., 2021). Compared to non-pregnant women, pregnant women reported more frequent pain at the injection site as an adverse event associated with mRNA COVID-19 vaccination, and headache, myalgia, chills, and fever were reported less frequently. In the context where initial clinical trials of messenger RNA-based inoculants have not evaluated the efficacy and safety of innovative technology among pregnant women, these preliminary data from the third trimester only help to inform both pregnant women and health professionals in making the inoculation decision. However, continuous monitoring through large-scale longitudinal studies remains necessary to investigate the effects associated with maternal anti-COVID-19 inoculation on mothers, pregnancies, the neonatal period and childhood.

On the other hand, the inoculation landscape has become even more complex due to new circulating viral variants. Authorities recommend genomic surveillance and adaptation in order to be effective against new variants (different from the initial strain that was detected at the end of 2019). The efficacy data of Comirnaty against circulating viral variants are highlighted in a very recent study in Israel which showed that the protection offered by the Pfizer inoculant against variant B.1.351 (first identified in South Africa) is lower (Madhi et al., 2021).

The results have not yet been submitted to the expertise of specialists. The study compared nearly 400 adults who were diagnosed with COVID-19 at least 14 days after receiving one or two doses of the inoculant to the same number of uninoculated people. It was found that B.1.351 represents approximately 1% of the COVID-19 cases studied. But among patients who received two doses of inoculant, the prevalence rate of the variant was eight times higher than in those not inoculated - 5.4% compared to 0.7%. This suggests that Comirnaty is less effective against variant B.1.351, compared to the original variant and variant B.1.1.7. The limitation of the study comes from the small number of adult people studied, but it is an alarm signal for a closer study of these cases. In addition, it seems that at present, the prevalence of this variant is low. On the other hand, in early April, Pfizer announced that according to the results of the Phase III study in the adult population, Comirnaty also demonstrated 100% efficacy in the prevention of Covid-19 disease caused by SARS-CoV-2 variant B.1.351 (9 cases of Covid-19 were recorded, all in the placebo group, and after sequencing it was found that 6 had been determined by B.1.351) (Yan et al., 2021).

MID- AND LONG-TERM ADVERSE EFFECTS FROM PRIOR VACCINES

A 2020 study emphasizing mid- and long-term adverse effects from prior vaccines (Kostoff et al., 2020c) identified the following sixteen mid- and longer-term potential issues concerning vaccines. These include:

3.1. Antibody-Dependent Enhancement (where enhanced virus entry and replication in a number of cell types is enabled by antibodies);

-1a. Intrinsic Antibody-Dependent Enhancement (where non-neutralizing antibodies raised by natural infection with one virus may enhance infection with a different virus);

-1b. Immune Enhancement (enhancement of secondary infections via immune interactions);

-1c. Cross-Reactivity (an antibody raised against one specific antigen has a competing high affinity toward a different antigen.);

-1d. Cross-Infection Enhancement (infection enhancement of one virus by antibodies from another virus);

3. 2. Vaccine-Associated Virus Interference (where vaccinated individuals may be at increased risk for other respiratory viruses because they do not receive the non-specific immunity associated with natural infection);

3. Vaccine-Associated Imprinting Reduction (where vaccinations could also reduce the benefits of ‘imprinting’, a protection conferred upon children who experienced infection at an early age)

4. Non-Specific Vaccine Effects on Immune System (where previous infections can alter an individual's susceptibility to unrelated diseases);

5. Impact of Infection Route on Immune System (where immune protection can be influenced by the route of exposure/delivery);

6. Impact of Combinations of Toxic Stimuli (where people are exposed over their lifetime to myriad toxic stimuli that may impact the influence of any vaccine);

7. Antigenic Distance Hypothesis (negative interference from prior season’s influenza vaccine (v1) on the current season’s vaccine (v2) protection may occur when the antigenic distance is small between v1 and v2 (v1 ≈ v2) but large between v1 and the current epidemic (e) strain (v1 ≠ e).);

8. Bystander Activation (activation of T cells specific for an antigen X during an immune response against antigen Y);

9. Gut Microbiota (Impact of gut microbial composition on vaccine response);

10. Homologous Challenge Infection Enhancement (the strain of challenge virus used in the testing assay is very closely related to the seed virus strain used to produce the vaccine that a subject received);

11. Immune Evasion (evasion of host response to viral infection);

12. Immune Interference (interference from circulating antibody to the vaccine virus);

-12a. Original Antigenic Sin (propensity of the body's immune system to preferentially utilize immunological memory based on a previous infection when a second slightly different version of that foreign entity (e.g. a virus or bacterium) is encountered.);

13. Prior Influenza Infection/Vaccination (effects of prior influenza infection/vaccination on severity of future disease symptoms);

14. Timing between Viral Exposures (elapsed time between viral exposures);

15. Vaccine-Associated Enhanced Respiratory Disease (where vaccination enhances respiratory disease); and

16. Chronic Immune Activation (continuous innate immune responses).

Most of these events are not predictable, and most, if not all, would be possible for the COVID-19 inoculant in the mid- and long-term for adults and children.

3.3. Mid- and Long-Term Serious Illnesses for Adults and Children from Past Vaccines

As stated in the aforementioned 2020 study on vaccine safety: “The biomedical literature is very sparse with studies on long-term vaccine effects, especially long-term adverse effects. Large numbers of people and long periods of time are required to identify such adverse events, and draw statistically-valid connections between vaccinations and disease. These efforts would be very resource-intensive, and there appears to be little motivation among the vaccine producers and regulators to make these resources available for such studies. Thus, the following examples reflect the extremely small tip of an extremely large iceberg of long-term adverse vaccine effects.” (Kostoff et al., 2020c)

“The two main categories of diseases reported in the biomedical literature triggered by past vaccinations are “Autoimmune (e.g., Systemic Lupus Erythematosus, Psoriasis, Arthritis, Multiple Sclerosis, Hepatitis, Uveitis, Pseudolymphoma, Guillain-Barre Syndrome, Thrombocytopenic Purpura, etc.) and Neurological (e.g., Central Demyelinating Diseases, Developmental Disability, Febrile seizures, Narcolepsy, Encephalomyelitis, Autonomic Dysfunction, etc.). Others include Diabetes, Gastrointestinal, Joint-related, Necrobiotic Granuloma, Neutropenia, Pulmonary Fibrosis, etc.”

“Vaccinations may also contribute to the mosaic of autoimmunity (Vadalà et al., 2017). Infrequently reported post-vaccination autoimmune diseases include systemic lupus erythematosus, rheumatoid arthritis, inflammatory myopathies, multiple sclerosis, Guillain-Barre syndrome, and vasculitis”.

“Studies have demonstrated a latency period of years between HiB vaccination and diabetes mellitus, and between HBV vaccination and demyelinating events (Vadalà et al., 2017) latency periods can range from days to years for postinfection and postvaccination autoimmunity”.

“Most of the extra cases of IDDM appeared in statistically significant clusters that occurred in periods starting approximately 38 months after immunization and lasting approximately 6–8 months. Immunization with pediatric vaccines increased the risk of insulin diabetes in NOD mice.Exposure to HiB immunization is associated with an increased risk of IDDM.” (Kostoff et al., 2020c)

Thus, even the sparse past vaccine studies that went beyond the short-term showed latency effects of serious diseases occurring three years or more post-vaccination.

COST-BENEFIT ANALYSIS OF COVID-19 INOCULATIONS

This appendix presents a non-traditional best-case scenario pseudo-cost-benefit analysis of the COVID-19 inoculations for the 65+ demographic in the USA. In this incarnation of a cost-benefit analysis, the costs are the number of deaths resulting from the inoculations, and the benefits are the lives saved by the inoculations. The time range used was from December 2019 to end-of-May 2021.

It is assumed, in this best-case scenario, that all the deaths truly attributable to COVID-19 only could have been eliminated by the inoculations given (about half the USA population has been inoculated at this time) (Islam et al., 2021a). It can be conceptualized as the vaccines having been available in Summer 2019, and subsequent administration having eliminated all the deaths experienced that were truly attributable to COVID-19. If the cost-benefit ratio is poor for this best-case scenario, it will be very poor for any real-world scenario (Islam et al., 2020a).

We will use Fig. 1, Fig. 2 as starting points to conduct a cost-benefit analysis of COVID-19 inoculations for the most vulnerable demographic, those 65 + . We start with the official government numbers for COVID-19 and post-inoculation deaths, and modify them to arrive at actual deaths resulting from COVID-19 and the inoculations. We compare the two numbers (appropriately normalized) to ascertain costs vs benefits (Islam et al., 2021a).

As Fig. 1 shows, there are three age bands that comprise the 65+ demographic. We weight the COVID-19 deaths per capita in each band by the band’s population, and divide the sum of these three products by the total 65+ population to arrive at an average COVID-19 deaths per capita of 0.0087 for the total 65+ demographic.

Fig. 2 contains two normalizations. First, the deaths were normalized by total inoculations given, not by people inoculated or people who had completed the full series of inoculations. We will retain the normalization by total inoculations given, since it will provide the most conservative results (largest denominator) for estimation purposes. Second, the deaths were normalized/restricted to those occurring within seven days post-inoculation. This normalization was done to compare across age bands, where the inoculations started at very different points in time. For the present cost-benefit purpose, where we are concentrating on the 65+ band, we remove this latter normalization, and include all post-inoculation deaths. Removing this normalization increases deaths per inoculation by about 40% to a value of 0.000032, and offers a more credible comparison to the numbers from Fig. 1.

Thus, based on the CDC’s official numbers, there are an average COVID-19 deaths per capita of 0.0087 and an average deaths per inoculation of 0.000032 for the 65+ demographic. The chances of a person 65+ dying from an inoculation relative to their chances of dying from COVID-19 are approximately .0037, or about 1/270, based on these official CDC figures.

However, as we have shown previously, three corrections to these numbers are required to convert them to real-world effects. First, as the Harvard Pilgrim study has shown and as our results in Appendix 1 confirm, VAERS is underreporting actual deaths by about two orders of magnitude. Applying this correction alone to the above 1/270 ratio changes the risk benefit to about 1/3. Second, as the CDC has stated, approximately 94% of the COVID-19 deaths could have been attributed to any of the comorbidities these patients had, and only 6% of the deaths could actually be attributed to COVID-19. As we pointed out, if pre-clinical comorbidities had been included, this number of 6% would probably be decreased further. For conservative purposes, we will remain with the 6%. Applying this correction to the 1/3 risk-benefit ratio changes it to 5/1! Third, as a comprehensive survey of false positives from RT-PCR tests concluded: “evidence from external quality assessments and real-world data indicate enough a high enough false positive rate to make positive results highly unreliable over a broad range of scenarios” (Cohen A.N., Kessel B., Milgroom M.G., 2020). Because of the myriad RT-PCR tests performed in the USA to screen for/diagnose COVID-19 using different values for Ct and different procedures, a specific number for false positives cannot be obtained at this point in time. Again, these false positives would reduce the 6% number, perhaps substantially. And again, for conservative purposes, we will remain with the 6% number.

Thus, our extremely conservative estimate for risk-benefit ratio is about 5/1. In plain English, people in the 65+ demographic are five times as likely to die from the inoculation as from COVID-19 under the most favorable assumptions! This demographic is the most vulnerable to adverse effects from COVID-19. As the age demographics go below about 35 years old, the chances of death from COVID-19 become very small, and when they go below 18, become negligible.

It should be remembered that the deaths from the inoculations shown in VAERS are short-term only (˜six months for those inoculated initially), and for children, extremely short-term (˜one month) (CDC, 2021l). Intermediate and long-term deaths remain to be identified, and are possible from ADE, autoimmune effects, further clotting and vascular diseases, etc., that take time to develop. Thus, the long-term cost-benefit ratio under the best-case scenario could well be on the order of 10/1, 20/1, or more for all the demographics, increasing with decreasing age, and an order-of-magnitude higher under real-world scenarios! In summary, the value of these COVID-19 inoculations is not obvious from a cost-benefit perspective for the most vulnerable age demographic, and is not obvious from any perspective for the least vulnerable age demographic.

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