Digit ratios and their asymmetries as risk factors of developmental instability and hospitalization for COVID-19

Authors: A. Kasielska-TrojanJ. T. ManningM. JabłkowskiJ. Białkowska-WarzechaA. L. Hirschberg & B. Antoszewski  Scientific Reports volume 12, Article number: 4573 (2022) Cite this article Article Open Access Published: 


COVID-19 presents with mild symptoms in the majority of patients but in a minority it progresses to acute illness and hospitalization. Here we consider whether markers for prenatal sex hormones and postnatal stressors on developmental instability, i.e. digit ratios and their directional and unsigned asymmetries, are predictive of hospitalization. We focus on six ratios: 2D:3D; 2D:4D; 2D:5D; 3D:4D; 3D:5D; 4D:5D and compare hospitalized patient and control means for right, and left ratios, directional asymmetries (right–left) and unsigned asymmetries [|(right–left)|]. There were 54 patients and 100 controls. We found (i) patients differed in their digit ratios from controls (patients > controls) in all three ratios that included 5D (2D:5D, 3D:5D and 4D:5D) with small to medium effect sizes (d = 0.3 to 0.64), (ii) they did not differ in their directional asymmetries, and (iii) patients had greater |(right–left)| asymmetry than controls for 2D:4D (d = .74) , and all ratios that included 5D; 2D:5D (d = 0.66), 3D:5D (d = .79), 4D:5D (d = 0.47). The Composite Asymmetry of the two largest effects (2D:4D + 3D:5D) gave a patient and control difference with effect size d = 1.04. All patient versus control differences were independent of sex. We conclude that digit ratio patterns differ between patients and controls and this was most evident in ratios that included 5D. Large |(right–left)| asymmetries in the patients are likely to be a marker for postnatal stressors resulting in developmental perturbations and for potential severity of COVID-19.


Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory and systemic illness (COVID-19) which may present as a severe pneumonia in 10–15% of patients. Severe disease can lead to acute respiratory distress and multi-organ failure often followed by intravascular coagulopathy1,2. Due to this variety and unknown severity and death risk factors, many studies and analyses have focused on identifying biomarkers of severe disease or poor outcomes in COVID-19 infections. Recent studies have shown that the clinical progress could be severe in cases of increased: neutrophil-lymphocytes ratio, C-reactive protein (CRP), troponin I, lactate dehydrogenase and that the troponin I, elder age and SO2 values are linked to in-hospital mortality. Across nations, there is variation in case fatality rates and in predictors of mortality3. For example, data from Belgium indicated severity was associated with older age, renal insufficiency, higher lactate dehydrogenase and thrombocytopenia and obesity4. Patterns of severity from Chinese studies included higher age, male sex, higher Body Mass Index, hypertension, lower T lymphocyte and B lymphocyte count, higher white blood cell count, higher D2 dimer, procalcitonin, CRP and aspartate aminotransferase. Among these variables age and weight appeared to be independent risk factors for disease severity5. Importantly, identifying these risk factors did not significantly change our understanding of the COVID-19 pandemic nor did it facilitate a reduction in mortality.

In many populations the severity of COVID-19 is sex dependent (males > females)6. The excess of male deaths has led to two opposing suggestions: (i) The androgen-driven COVID-19 pandemic theory7,8, and (ii) the male hypogonadism theory9. With regard to support for the former, viral entry to cells is androgen dependent, involving priming of the spike proteins and cleaving of angiotensin converting enzyme 2 (ACE2). Both processes are facilitated by trans-membrane protease, serine 2 (TMPRSS2)10. Androgen receptor activity is a requirement for the transcription of the TMPRSS2 gene, suggesting that testosterone facilitates SARS-CoV2 cell entry11. Thus the androgen-driven COVID-19 pandemic theory postulates that high mortality from SARS-CoV2 in men is related to hypergonadism. In contrast, proponents of the male hypogonadism theory point to theory-inconsistent relationships between testosterone and COVID-19 in males9. Thus, in men COVID-19 mortality rates increase with age but testosterone levels decrease12. The male hypogonadism theory gave a rationale for the analyses conducted by Manning and Fink9 who considered national values of digit ratios, in this case 2nd to 4th digit ratio, in relation to Covid-19 case fatality rates (CFR’s). The relative lengths of the second digit and fourth digit (digit ratio or 2D:4D) is sexually dimorphic (2D:4D males < 2D:4D females). It has been suggested that 2D:4D is a biomarker of prenatal sex steroids exposure, i.e. low 2D:4D correlates with high prenatal testosterone and low oestrogens, while high 2D:4D correlates with low foetal testosterone and high oestrogen. There is considerable support for the link between 2D:4D and prenatal sex steroids13 but for a contrasting view see McCormick & Carre, 202014. Manning and Fink found that nations with high CFR’s had high mean male 2D:4D9, thus supporting the hypogonadal theory (in support see Sahin, 2020 and for a critical view see Jones et al., 202015,16). With regard to right–left asymmetry of 2D:4D, i.e. directional asymmetry of 2D:4D, (Δ r–l 2D:4D) is also thought to be a negative correlate of high prenatal testosterone and low prenatal oestrogen9,17,18,19,20,21. In general unsigned asymmetries (such as that of single digit R–L asymmetries) may reflect developmental instability related to postnatal stressors including sex steroids and to correlates of low socio-economic status such as poor nutrition22,23.

Sex differences in digit ratios, with males < females, are present across a number of digits24,25,26,27. Here we focus on six ratios from digits 2 to 5, i.e. 2D:3D, 2D:4D, 2D:5D, 3D:4D, 3D:5D and 4D:5D (digit 1 is difficult to measure accurately). There is considerable evidence that prenatal sex steroids have an effect on 2D:4D. However, effect sizes for 2D:4D are likely to be linked to other ratios that share 2D or 4D. Right 2D:4D is stable during growth in children and adolescents supporting the contention that it retains information pertaining to prenatal sex steroids. However, other ratios, such as those that include 5D (and in particular 3D:5D), show sex differences (males < females) but are highly unstable during growth in children and adolescents. This instability is present in both hands but is expressed most intensely in the left hand24,27. The difference in stability across right and left hands suggests that R–L differences in ratios may contain important information which pertains to developmental instability rather than effects of prenatal sex steroids. Therefore, for each ratio we consider values from the right hand, left hand, Δ right–left (directional asymmetry) and |(right–left)|(FA). The purpose of this preliminary report was to focus on associations between digit ratios and severity of COVID-19, as evidenced by hospitalization of patients.

Following from the across-nation correlations between digit ratio and CFR’s we suggest that, in comparison to controls, patients hospitalized for COVID-19 will have: (i) high right and left hand digit ratios, and high Δ right–left directional asymmetry, indicating exposure to low prenatal testosterone and high prenatal oestrogen, and (ii) high |(right–left)| unsigned asymmetry (FA), indicating heightened levels of developmental instability arising from stressors such as pubertal sex steroids. With regard to these predictions we emphasize that there is potential for considerable inter-correlation between digit ratios. In this regard, 2D:4D has been shown to exhibit developmental stability while 3D:5D is particularly unstable during development24,27. Therefore, the patterns associated with 2D:4D and 3D:5D are least likely to be affected by inter-correlations between digit ratios. Thus, 2D:4D may contain information concerning prenatal influences and 3D:5D information concerning postnatal effects of developmental instability. Consequently, we suggest these two digit ratios should be the focus of greatest attention.


Participants were recruited from a Department of Infectious Diseases and Liver Diseases of a Medical University. All consecutive patients with diagnosed COVID-19 who were hospitalized in the Department due to the severe or high risk of severe COVID-19 were included. During a first wave of the Covid-19 pandemic (March–August 2020) there were 54 (28 men and 26 women) patients who met the study criteria (Inclusion Criteria: 1. admitted to hospital because of Covid-19, 2. positive PCR test, 3. conscious and able to give informed written consent for participation; Exclusion Criteria: 1. unconscious, unable to give written consent for participation, 2. Covid-19 positive patients hospitalized because of other than Covid health issues, pregnant women, children (< 18 years), patients after transplantations, during immunotherapy and with renal failure requiring dialyses). Of these, there were 51 for whom the right hand ratios could be measured, 52 for the left hand and 49 for whom R–L measurements were possible (one hand only was available for measurement for 5 patients due to a hand injury and/or finger contractures). The protocol of the study included a clinical questionnaire based on medical records (age, symptoms) severity of the disease (scale 0–4; 0 -no symptoms, 1—mild, 2—medium, 3—severe, 4—critical), length of hospitalization and oxygen therapy, days in intensive care unit, concomitant diseases, history of smoking and occupational exposure, and laboratory test results (white blood count, fibrinogen, d-dimers, platelets count, oxygen saturation, procalcitonin) and anthropometric measurements.

Controls, 47 women (mean age 51.3 ± 16.1 years) and 53 men (mean age 52.2 ± 14.4 years) were recruited from a Plastic Surgery Out-patient Clinic (approximately 80% of the sample) and among other volunteers after the first wave of COVID-19. We consider our sample to be representative of the general population. Thus, the Out-patient Clinic is state-funded, attendees are from a variety of backgrounds and ages, and they present with a variety of needs such as removal of scars, moles and eyelid disorders (ptosis, ectropion, entropion). We did not include women after breast cancer who come for breast reconstruction, patients with skin cancer, post-bariatric patients, any patient who has immunosuppression. Controls were included based on a negative history of COVID-19 (non-infected or non-symptomatic subjects). One woman reported injury of the 3rd finger of the left hand and was included in the study after exclusion of this finger measurement. All the participants were White (based on patients’ medical data and controls recruitment).

Ethical statement

The protocol was agreed by the Bioethical Committee of the Medical University of Lodz (RNN/152/20/KE). All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all participants.

Hand images

With regard to the measurement of digit ratio, our preference would have been for direct measurement of fingers. However, it was difficult to measure digit length directly from the hands of the patients because many of them were very ill and measurers were hampered by personal protective equipment. Moreover, direct digit measurement requires a period of time during which the patient and measurer are in close proximity. This is to be avoided with an infectious viral agent. Indirect methods such as the use of photocopies or scanners, give a permanent record of digit lengths. Against this, it was felt that the use of photocopiers or scanners was not appropriate as repeated use of such machines may result in cross infection resulting from virus particles being left on surfaces. Moreover, in comparison to directly measured digits, indirect images yield lower 2D:4D ratios24,28,29 with magnitudes that may vary by sex and hand30. These effects may extend to asymmetries also, and the accuracy of asymmetries measured from photocopies has been questioned31. Therefore, it was decided to photograph the hands of the patients. Typically, the patient was sitting up in bed and he/she was instructed to place their hands horizontally with the palms uppermost, the digits straight and together. In order to minimize inconvenience to the patient it was decided not to use a tripod with the camera. Rather, the experimenter held the camera approximately 30 cm above the patient’s hand. This protocol was felt to be appropriate as it would minimise the amount of proximity necessary between experimenter and patient. Moreover, it gives a permanent image of the supine hand which did not involve potential distortions resulting from digit contact with glass surfaces. It is to be noted that the relative lengths of digits within a hand can be obtained in this way but R–L contrasts of absolute measures of digit length are likely to be unreliable as they will be influenced by small vertical differences in distance between hand and camera. Photographs were checked for definition at the tips of the digits and at the metacarpophalangeal crease at the base of the digits. A second photograph was taken if the first was not deemed to be of sufficient quality.


Eight measurements were taken from patients’ and controls’ hand photographs: second, third, fourth and fifth digits’ lengths (2D, 3D, 4D and 5D) (right (R) and left hand (L)). On the basis of the these parameters the following ratios were calculated: 2D:3D, 2D:4D, 2D:5D, 3D:4D, 3D:5D, 4D:5D for the right (R) and left (L) hand (D length [mm]/D length [mm]) in addition to the ratios’ directional asymmetries (right ratio–left ratio: Δ2D:3D, Δ2D:4D, Δ2D:5D, Δ3D:4D, Δ3D:5D, Δ4D:5D) and unsigned asymmetries (FAs) (|(right–left)|). We also calculated two composite asymmetries by summing (i) all six (|(right–left)|) asymmetries and (ii) and asymmetries for the “independent” ratios of 2D:4D and 3D:5D. We refer to the latter as the “Clinical Composite Asymmetry” in the Results section. All measurements (in patients and controls) were made twice by AKT using the GNU Image Manipulation Program (GIMP) version 2.10.20. For a subset of measurements, a sliding calliper was used directly on the image of the fingers on the computer screen (by JTM). Measurements were performed on the palmar side of the hand using anthropometric points lying on the digit axis: pseudophalangion—the most proximal point in the finger metacarpophalangeal crease, dactylion—the most distal point on the fingertip32. There was high repeatability of digit ratios within and between observers. The final ratios were calculated as a mean of two ratios obtained from the GIMP program. These ratios were used in the further analysis of the data.

Statistical analysis

Analysis was conducted on the differences in the digit ratios and their directional (right–left) and unsigned [|(right–left)|] asymmetries between patients hospitalized due to Covid-19 and controls. The normality of distribution of the tested variables was examined (using Shapiro–Wilk test) and the homogeneity of variances was checked (using the Bartlett test). With both assumptions met we applied univariate t-tests for differences between means in addition to two-way analysis of variance (ANOVA). If any of these assumptions were not met then non-parametric tests were used. Logistic regression was used to evaluate the relationship between the asymmetry index being the sum of the unsigned asymmetries of the ratios of the largest effect sizes estimated with omega-squared for ANOVA (“Clinical Composite Asymmetry”) and the risk of hospitalization due to Covid-19. Finally, logistic regression model included the following variables: the sum of asymmetries of 2D:4D and 3D:5D (dependent variable) and the group (patient vs. control) and sex (independent variables). Effect size for inter-group differences was evaluated with Cohen’s d for t-tests and omega-squared (ω2) for ANOVA. The interpretation of descriptors of magnitude for the former were small 0.20, medium 0.50 and large 0.80 and for ω2 > 0.01 —weak, > 0.06—medium, > 0.14—strong effect. The probability of p < 0.05 was accepted as a level of significance.


Characteristics of Covid-19 patients

Among 54 patients there were 28 men (mean age 54.7 ± 14.7 years) and 26 women (mean age 59.3 ± 18.2 years). The group of patients did not differ in age and frequency of males and females from the controls (F = 1.085; p = 0.299). Specific characteristics (i.e. BMI, comorbidity, smoking status) and Covid-19 symptoms and severity are shown in Table 1.Table 1 Characteristics of patients hospitalized because of Covid-19.Full size table

Reliability of measurements

First we checked intra-observer reliability for all twelve ratios (ratio 1 versus ratio 2) for observer AKT. The coefficient of reliability for raw measurements (R) ranged from 96.07% (for 3D:4D L) to 99.66% (for 2D:5D R). Intra-class correlation coefficients were also very high Table 2). Repeatability of signed asymmetries can be low because they contain the measurement error of four digits. However, for the signed asymmetries (R–L) and the unsigned asymmetries (|R–L|) the R ranged from 99.86% (for 2D:3D |R–L| to 99.97% (for 2D:4D R–L) also with high ICC’s (Table 2). Further analysis included mean values of ratio 1 and 2. Then, inter-observer reliability was checked (observer AKT versus observer JTM), for two ratios: 2D:4D R and 2D:4D L and their signed and unsigned asymmetries. Due to the high reliability between observers (2D:4D R: TEM = 0.0089, R = 99.66%, ICC = 98.09%; 2D:4D L: TEM = 0.0118, R = 98.91%, ICC = 97.93%; R–L: TEM = 0.0076, R = 98.66%, |R–L|: TEM = 0.0076, R = 96.13%, ICC = 96.43%) final analysis included data from AKT.Table 2 Technical error measurement (TEM) and the coefficient of reliability for raw measurements (R) for ratios and for R–L and |R–L| of six ratios for observer 1.Full size table

Digit ratios: patients vs. controls

There were no relationships between age and digit ratios in any of the twelve tests (values of r varied from − 0.14 for right 2D:3D to 0.1 for right 4D:5D, all p > 0.05).

Patient and control means and SD’s for six ratios and 12 effects (right and left ratios) are given in Table 3. Values of p and Cohen’s d are included from t-tests. There were five significant effects ranging from small to medium in magnitude. Four of these showed higher values in the patients compared to the controls, i.e. 3D:5D right d = 0.55, left d = 0.37; 4D:5D right d = 0.64, left d = 0.58. One effect showed mean patient < control (right 2D:5D d = 0.38). We note that all five significant effects were present in ratios that included 5D. Correction for multiple tests is inappropriate across Table 3 as the variables are not independent, i.e. the length of each digit is present in three ratios. We considered the effect of sex on these patient/control differences by performing two-factor ANOVA’s (independent variables: group [patients, controls], sex [males, females] with dependent variable digit ratio). All five remained significant (see effect sizes [ω2]), There were no effects of sex and no significant interactions (Table 4).Table 3 Patient and control means and SD’s for six digit ratios (2D:3D; 2D:4D; 2D:5D;3D:4D; 3D:5D; 4D:5D) and their signed and unsigned asymmetries.Full size tableTable 4 Differences in digit ratios and their asymmetries between patients and controls—(ANOVA) controlled for sex.Full size table

Digit ratio asymmetries: patients vs. controls

Two associations between age and asymmetry were significant (|R–L| 2D:4D, R = 0.17, p = 0.03 and |R–L| 4D:5D, R = 0.24, p = 0.03). However, there were no relationships between age and asymmetries in ten of the twelve tests (values of R varied from − 0.13 for R–L 2D:3D to 0.16 for |R–L| 2D:3D, all p > 0.05).

There were no significant differences in directional asymmetries (R–L) between patients and controls (Tables 3 and 4). There is some evidence in the literature that directional asymmetry of 2D:4D shows sex differences (males < females). Therefore we checked for directional asymmetry (deviations from a mean of zero) in (R–L) in patients and controls for all six ratios split by sex. For male patients (n = 23) one-sample t-tests with mean set at zero showed there were no significant deviations from zero in any ratio (means varied from 0.002 for 3D:4D to 0.049 for 2D:5D, all p > 0.05). For female patients (n = 26), for five ratios means varied from − 0.014 for 2D:4D to 0.031 for 3D:5D, all p > 0.05. For female 4D:5D there was directional asymmetry with mean of 0.034, t = 2.20, p = 0.04. With regard to controls (males n = 53, females n = 47) there was a similar pattern with evidence of directional asymmetry in 4D:5D (males: mean = 0.018, t = 2.44, p = 0.02 and females: mean = 0.016, t = 2.31, p = 0.03). For the remaining ratios means varied from − 0.006 to 0.010, all p > 0.05. Therefore, there was no evidence of significant directional asymmetry in male and female mean (R–L) ratios with the exception of 4D:5D which showed some evidence of higher ratios in the right hand compared to left hand. This suggests that the ratios we consider here (with the exception of 4D:5D) have a mean that does not significantly deviate from zero, i.e. they have the properties of ideal fluctuating asymmetry.

With regard to unsigned asymmetries (|R–L|), the distributions are “half-normal”. It may be that t-tests of means for patients versus controls are robust enough to give meaningful p values. However, in order to consider such differences in a conservative manner we applied Mann–Whitney U tests. There were four significant effects (2D:4D, d = 0.74; 2D:5D, d = 0.66; 3D5D, d = 0.79; 4D:5D, d = 0.47) and all showed patients > controls. We note that three effects are for variables that include digit 5D. Summing the unsigned asymmetries across all six ratios we found this composite measure of asymmetry was higher in the patients compared to controls (d = 0.8). We then focused on |R–L| in the two “independent” ratios with the highest effect size (i.e. 2D:4D and 3D:5D) and found they were not correlated (r = − 0.047). A composite of these two variables, a “Clinical Composite Asymmetry showed the highest effect size of all with patients > controls, d = 1.04 (Fig. 1, Table 3).

figure 1
Figure 1

We further considered the effect of sex on these patient/control differences by performing two-factor ANOVA’s (independent variables: group [patients, controls], sex [males, females] with dependent variable digit ratio). There were high effect sizes (ω2) for |Δ2D:4D| = 0.115; |Δ2D:5D| = 0.105; |Δ3D:5D| = 0.155; |Δ4D:5D| = 0.055. The effect size for the “Clinical Composite Asymmetry” of 2D:4D and 3D:5D was 0.231. Logistic regression indicated that the “Clinical Composite Asymmetry”, regardless of sex, correlates with the risk of hospitalization due to Covid-19. The area under an ROC curve (AUC) is 0.787, which shows that this classifier is better than a random classifier (AUC = 0.5) with the cut-off point of 0.087. A “Clinical Composite Asymmetry” that is higher than 0.087 discriminates hospitalized patients (sensitivity—71% and specificity 75%) (Fig. 2). The risk of hospitalization in case of the index > 0.087 is 3.5 times higher than in those with lower “Clinical Composite Asymmetry” (OR 3.667).

figure 2
Figure 2

“Clinical Composite Asymmetry” did not correlate with Covid-19 severity (R = − 0.075, p = 0.61) or with length of hospitalization (R = 0.137, p = 0.35).


This study focused on associations between digit ratios and severity of COVID-19, as evidenced by hospitalization of patients. Our results indicate that digit ratios, and their asymmetries may be regarded as simple clinical markers of the possible risk of hospitalization due to Covid-19. Additionally, the study aimed to examine the role of prenatal sex steroids and that of postnatal developmental instability on the course of Covid-19. We have found evidence for digit ratio and digit ratio asymmetry differences between hospitalized patients with COVID-19 and controls. For digit ratios the magnitude of the effect sizes was small to medium (d = 0.3–0.64) and involved all ratios that included 5D, i.e. 2D:5D, 3D:5D and 4D:5D (patients > controls). There were no significant differences between patients and controls for directional (right-left) asymmetry. The largest effect sizes (medium to large) were found for measures of developmental instability, i.e. differences in unsigned asymmetries between patients and controls (patients > controls). These included 2D:4D (d = 0.74), and all ratios that involved 5D, i.e. 2D:5D (d = 0.66), 3D:5D (d = 0.79) and 4D:5D (d = 0.47). There are likely to be inter-correlations between these asymmetry effect sizes, for example 2D is present in two of them, as is 4D and 5D is present in three. The two largest effect sizes were found in ratios that may be independent of each other in the sense that they do not share digits (i.e. 2D:4D and 3D:5D). Summing the unsigned asymmetries of 2D:4D and 3D:5D gave a composite asymmetry with a large effect size (patient > control) of d = 1.04. Removing the effect of sex in a two-factor ANOVA had little effect on the magnitude of the effect size which remained large (ω2 = 0.231). We suggest that the unsigned composite asymmetry of 2D:4D and 3D:5D may have utility in identifying individuals that are of high risk for hospitalization resulting from COVID-19. Therefore, we have referred to it as a „Clinical Composite Asymmetry”. The utility of Clinical Composite Asymmetry as a classifier was characterized by AUC = 0.787 (good classifier). In addition, the optimum cut off point ≤ 0.087 was determined, for which sensitivity and specificity were 71% and 75% respectively with OR over 3.5. Regression analysis showed that the index > 0.087 may be a prognostic factor for hospital care for patients with Covid-19. However, to verify the prognostic value of the suggested index further studies based on larger populations in different ethnic groups are needed.

Much of the work concerning effects of prenatal sex steroids on digit ratio has concentrated on 2D:4D. However, effect sizes for 2D:4D are likely to be linked to other ratios that share 2D or 4D (i.e. 2D:3D; 2D:5D; 3D:4D; 4D:5D). The 3D:5D ratio has also been described as sexually dimorphic (males < females) and may show effects that are independent of 2D:4D24,25,26. Importantly, 3D:5D is not stable during development across age ranges from 2 to 18 years. Rather it shows a reduction with age which suggests that it may be influenced by postnatal production of androgens24. Comparisons between digit ratios of hospitalized patients versus controls gave small to medium effect sizes for 2D:5D, 3D:5D and 4D:5D. In so far as these digit ratios are influenced by sex steroids, this may be evidence for a link between severity of COVID-19 and prenatal (2D:4D) and postnatal (3D:5D) testosterone and oestrogen. Studies in humans and with an animal model (Golden Hamsters) have reported that SARS-CoV2 upregulates the enzyme CYP19A1 (oestrogen synthetase) leading to a profound reduction in testosterone and an increase in oestrogen in the lungs and other organs. Dysregulated sex hormones and interferon gamma (IFN-γ) levels are associated with critical illness in Covid-19 patients. In this regard, both male and female Covid-19 patients, present elevated oestradiol levels which positively correlates with IFN-γ levels (for humans33, for an animal model34). Manning and Fink9 reported that national values of male 2D:4D are positively related to national COVID-19 CFR’s. This led them to suggest that nations with high COVID-19 mortality have male populations that have experienced low prenatal testosterone relative to oestrogen.

However, it is more likely that the differences between patients and controls have arisen as the result of elevated levels of developmental instability in the former compared to the latter. Manning24 has considered the stability of all six digit ratios during growth between the ages of 2 years and 18 years. Right 2D:4D was stable but left 2D:4D was not. All ratios that included 5D showed growth-linked instability for both the right and left hands. We suggest that ratios that include 5D are „hotspots” for developmental instability that may be triggered by stressors that include rapid growth22,24. Recently a syndemic approach, which includes biological and social interactions for prognosis, treatment, and health policy, has been proposed. Interaction between infection with SARS-CoV-2 and an array of non-communicable diseases strongly associated with poverty, including obesity, hypertension, diabetes, cardiovascular and chronic respiratory diseases, and cancer is now considered. Moreover, syndemics are characterised by biological and social interactions between conditions and states, which increase one’s susceptibility to poor health outcomes35. In this respect, considering morphological signs of exposure to prenatal sex steroids and developmental instabilities (interaction between rapid early growth and stressors such as poor maternal and childhood nutrition) in patients with severe or fatal course of COVID-19 may give insight into the syndemic nature of Covid-19.

In contrast to right and left digit ratios, differences in the magnitude of digit ratio asymmetries between the right and left hand gave medium to large effect sizes. This was not apparent in directional asymmetries (R–L), perhaps because they comprise subtle deviations from perfect symmetry. Such asymmetries have been described as weakly sexually dimorphic for right-left 2D:4D (or Dr-l: with male Dr-l < female Dr-l36). Removing the signs from directional asymmetry (|R–L|) gave us variables that showed medium to high effect sizes in comparisons between patients and controls. Digit ratio (|R–L|) is a measure which is equivalent to asymmetry differences in digit length22. However, in this case we are dealing with R–L differences in morphological patterns involving two digits rather than differences between single digits of the right and left hands. It is not known whether |R–L| is sexually dimorphic across the six digit ratios. There is evidence that the phenotype of Dr-l is influenced by variation in the gene for the enzyme CYP19A1. Thus, Zhanbing et al. (2019) have reported a CYP19A1 single-nucleotide polymorphism (rs4775936) is related to variation in Dr-l in a Chinese sample36. CYP19A1 is important in the conversion of testosterone to oestrogen and SNP rs4775936 has been linked to the incidence of breast cancer. It may not be coincidental that up-regulation of CYP19A1 occurs in the lungs and other organs of COVID-19 patients leading to dysregulation of sex hormones (acute reduction in testosterone and an increase in oestrogen) and a marked increase in interferon gamma (IFN-γ) levels. Both are associated with critical illness in Covid-19 patients33,34. Further work is indicated to investigate patterns of age in digit ratio asymmetry (|R–L|). However, if it takes the form of single trait asymmetry, such as that of digit length asymmetry, it may show high levels in children which reduce with increasing age22. Associated with these age changes in digit asymmetry we find age dependent instability of all digit ratios that include 5D24. Such a pattern would suggest unsigned asymmetries of digit ratios involving 5D are sensitive correlates of developmental instability which may be negatively associated with immune system function. Such an interpretation is consistent with the view that increased asymmetry is the result of a combination of deleterious genetic and environmental factors and is defined as small, random deviations from perfect bilateral symmetry regarded as a measure of the developmental stability of the individual and phenotypic and genetic quality23.

We acknowledge our study has limitations: (i) Our sample size of 54 hospitalized patients is small. Obtaining good quality photographs of patients’ hands during hospitalization was sometimes challenging. A larger number would have been possible if the patient’s hands had been photocopied or scanned at discharge. However, with this latter methodology one risks missing severe cases of COVID-19 that are never discharged. We plan to extend our study, adding numbers of hospitalized patients and remeasuring digit lengths at discharge. With an increase in sample size we will consider relationships between clinical variables and digit ratios and their asymmetries. It is to be noted that within Table 4 we control for sex and report effect sizes for patients versus controls. With regard to the latter differences the effect sizes are medium to strong but none of the former are significant. This may be because sex differences in digit ratios have small to medium effect sizes. If we are correct in this we would expect that larger samples to show significant effects for sex in addition to differences between patients and controls. (ii) Additionally a further confounder may be the unknown infection status of the controls. They were recruited during the same time frame as patients hospitalized due to Covid-19 among other out-patient patients age-matched and based on their negative history of any symptoms of Covid-19 infection. Such appointment of controls may have resulted in a heterogeneity of this group such that they may have included non-infected individuals as well as non-symptomatic but infected or past-infected individuals. However, this does not invalidate the idea of this study which was focused on “markers” of symptomatology related to hospitalization. It would be beneficial to perform such analysis comparing symptomatic versus asymptomatic (or mildly symptomatic treated out-patients) but infected patients. However, this was not possible during the first waves of Covid-19 as there was no nation-wide testing in Poland (in general only symptomatic individuals were tested). In this regard our results may be biased by behavioral factors—i.e. the way participants prevented infection. (iii). We were not able to make comparisons between individuals who had been vaccinated and those who have not. This was because our data were obtained during the first wave of Covid-19, so none of the participants (patients and controls) had been vaccinated. In this regard, it would be of great interest to compare the efficacy of the vaccine in individuals with high and low values of the „Clinical Composite Asymmetry”. The prediction would be that vaccine efficacy would be low when „Clinical Composite Asymmetry” is high and high when „Clinical Composite Asymmetry” is low.

In conclusion, we have found differences in digit variables between patients hospitalized for COVID-19 and controls. Overall, our findings point to high levels of developmental instability in the former compared to the latter. Our focus was on six digit ratios and for each we considered right and left ratios and their asymmetries (signed and unsigned). We found differences in digit ratios between patients and controls that were focused on ratios that included 5D. The effect sizes were small to medium. Unsigned asymmetries of four digit ratios, including three that involved 5D, yielded medium to large effect sizes with patients > controls. The largest of these asymmetries were for 2D:4D and 3D:5D. A „Clinical Composite Asymmetry” for these two variables gave an effect size which may have some utility in identifying individuals who have experienced high developmental instability. Thus, this „Clinical Composite Asymmetry” may enable us to identify individuals who are likely to experience severe COVID-19 and those who may not.


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Author information


  1. Plastic, Reconstructive and Aesthetic Surgery Clinic, Institute of Surgery, Medical University of Lodz, Kopcinskiego 22, 90-153, Lodz, PolandA. Kasielska-Trojan & B. Antoszewski
  2. Applied Sports, Technology, Exercise, and Medicine (A-STEM), Swansea University, Swansea, UKJ. T. Manning
  3. Department of Infectious and Liver Diseases, Medical University of Lodz, Lodz, PolandM. Jabłkowski & J. Białkowska-Warzecha
  4. Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, Stockholm, SwedenA. L. Hirschberg


Conception or design of the work: J.T.M., A.K.T., B.A., A.H. Data acquisition: J.B.W., M.J. Data analysis: J.T.M., A.K.T. Interpretation of data: J.T.M., A.K.T., B.A. Drafting and revising the ms: J.T.M., A.K.T., B.A., A.H., J.B.W., M.J. All authors have approved the submitted version.

The length of your fingers may determine how sick you get from COVID-19

Authors: Chris Melore Studyfinds.org March 28, 2022

Your risk of ending up in the hospital with COVID-19 may literally be in your own hands. A new study finds finger length displays a link to a person’s sex hormone levels. What does this have to do with COVID-19? Researchers at Swansea University say a patient’s testosterone levels play a key role in how sick they get after infection.

Previous studies show that having a longer ring finger is a sign of higher testosterone levels in the womb. On the other hand, a longer index finger signals higher levels of estrogen. Typically, men have longer ring fingers and women have longer index fingers.

The new study examined this link between the sex hormones before birth and during puberty and the rate of COVID hospitalizations. Their findings reveal that people with “feminized” short little fingers in comparison to their other digits end up suffering more severe cases of COVID-19. Moreover, people who have larger size differences between the fingers on their left and right hands are at even greater risk.

The link between testosterone and coronavirus

Although most people only experience mild COVID symptoms, the elderly and men are more likely to have a severe case that requires urgent care. This has led scientists to wonder if a man’s testosterone levels play a role in disease severity.

One theory is that high testosterone levels cause COVID to worsen. However, another study links low levels in elderly men to a severe case of the virus.

To figure out which is right, the team examined the size ratios of the 2nd, 3rd, 4th, and 5th digits on the hands of over 150 people. Fifty-four of these individuals were COVID-19 patients, while the others served as a healthy control group.

Specifically, the results show bigger differences between the 2D:4D and 3D:5D ratios on each person’s hands had a connection to a more severe case of COVID-19.

“Our findings suggest that COVID-19 severity is related to low testosterone and possibly high estrogen in both men and women,” says Professor John Manning in a university release.

“’Feminized’ differences in digit ratios in hospitalized patients supports the view that individuals who have experienced low testosterone and/or high estrogen are prone to severe expression of COVID-19. This may explain why the most at-risk group is elderly males,” the researcher continues. “This is significant because if it is possible to identify more precisely who is likely to be prone severe COVID-19, this would help in targeting vaccination. Right-Left differences in digit ratios (particularly 2D:4D and 3D:5D) may help in this regard.”

Could testosterone drugs defeat the pandemic?

Currently, study authors say there are several trials examining anti-androgen (testosterone) drugs which may help treat COVID-19. At the same time, scientists are also looking at testosterone as a possible anti-viral medication against COVID.

“Our research is helping to add to understanding of Covid-19 and may bring us closer to improving the repertoire of anti-viral drugs, helping to shorten hospital stays and reduce mortality rates,” Prof. Manning adds. “The sample is small but ongoing work has increased the sample. We hope to report further results shortly.”

This isn’t the first study to link finger length to seemingly unrelated topics. A previous study connected children’s finger length to their mother’s income as well as vulnerability to childhood illnesses.

The study is published in the journal Scientific Reports.

Different SARS-CoV-2 variants may give rise to different long COVID symptoms, study suggests

Italian study of long-COVID patients suggests those infected with the Alpha variant experienced different neurological and emotional symptoms compared to those who contracted the original form of SARS-CoV-2



New research to be presented at this year’s European Congress of Clinical Microbiology & Infectious Diseases (ECCMID) in Lisbon, Portugal (23-26 April), suggests that the symptoms connected to long COVID could be different in people who are infected with different variants. The study is by Dr Michele Spinicci and colleagues from the University of Florence and Careggi University Hospital in Italy.

Estimates suggest that over half of survivors of SARS-CoV-2 infection experience post-acute sequelae of COVID-19 (PASC), more commonly known as ‘long COVID’ [1]. The condition can affect anyone—old and young, otherwise healthy, and those with underlying conditions. It has been seen in people who were hospitalised with COVID-19 and those with mild symptoms. But despite an increasing body of literature, long COVID remains poorly understood.

In this study, researchers did a retrospective observational study of 428 patients—254 (59%) men and 174 (41%) women—treated at the Careggi University Hospital’s post-COVID outpatient service between June 2020 and June 2021, when the original form of SARS-CoV-2 and the Alpha variant were circulating in the population. The patients had been hospitalised with COVID-19 and discharged 4-12 weeks before attending a clinical visit at the outpatient service and completing a questionnaire on persistent symptoms (an average [median] of 53 days after hospital discharge). In addition, data on medical history, microbiological and clinical COVID-19 course, and patient demographics were obtained from electronic medical records.

At least three-quarters 325/428 (76%) of patients reported at least one persistent symptom. The most common reported symptoms were shortness of breath (157/428; 37%) and chronic fatigue (156/428; 36%) followed by sleep problems (68/428; 16%), visual problems (55/428; 13%), and brain fog (54/428; 13%).

Analyses suggest that people with more severe forms, who required immunosuppressant drugs such as tocilizumab, were six times as likely to report long COVID symptoms, while those who received high flow oxygen support were 40% more likely to experience ongoing problems. Women were almost twice as likely to report symptoms of long COVID compared with men. However, patients with type 2 diabetes seemed to have a lower risk of developing long COVID symptoms. The authors say that further studies are needed to better understand this unexpected finding.

Researchers performed a more detailed evaluation comparing the symptoms reported by patients infected between March and December 2020 (when the original SARS-COV-2 was dominant) with those reported by patients infected between January and April 2021 (when Alpha was the dominant variant) and discovered a substantial change in the pattern of neurological and cognitive/emotional problems.

They found that when the Alpha variant was the dominant strain, the prevalence of myalgia (muscle aches and pain), insomnia, brain fog and anxiety/depression significantly increased, while anosmia (loss of smell), dysgeusia (difficulty in swallowing), ad impaired hearing were less common (figure 2 in notes to editors).

“Many of the symptoms reported in this study have been measured, but this is the first time they have been linked to different COVID-19 variants”, says Dr Spinicci. “The long duration and broad range of symptoms reminds us that the problem is not going away, and we need to do more to support and protect these patients in the long term. Future research should focus on the potential impacts of variants of concern and vaccination status on ongoing symptoms.”

The authors acknowledge that the study was observational and does not prove cause and effect, and they could not confirm which variant of the virus caused the infection in different patients—which may limit the conclusions that can be drawn.

Whole genome sequencing reveals host factors underlying critical Covid-19

Authors: Athanasios KousathanasErola Pairo-CastineiraJ. Kenneth BaillieArticle

Published:  nature  articles  article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.


Critical Covid-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalisation2–4 following SARS-CoV-2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from critically-ill cases with population controls in order to find underlying disease mechanisms. Here, we use whole genome sequencing in 7,491 critically-ill cases compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical Covid-19. We identify 16 new independent associations, including variants within genes involved in interferon signalling (IL10RBPLSCR1), leucocyte differentiation (BCL11A), and blood type antigen secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in critical disease. Mendelian randomisation provides evidence in support of causal roles for myeloid cell adhesion molecules (SELEICAM5CD209) and coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of Covid-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication, or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between critically-ill cases and population controls is highly efficient for detection of therapeutically-relevant mechanisms of disease.

Author information

Author notes

  1. These authors contributed equally: Athanasios Kousathanas, Erola Pairo-Castineira
  2. These authors jointly supervised this work: Sara Clohisey Hendry, Loukas Moutsianas, Andy Law, Mark J Caulfield, J. Kenneth Baillie
  3. A list of authors and their affiliations appears in the Supplementary Information


  1. Genomics England, London, UKAthanasios Kousathanas, Alex Stuckey, Christopher A. Odhams, Susan Walker, Daniel Rhodes, Afshan Siddiq, Peter Goddard, Sally Donovan, Tala Zainy, Fiona Maleady-Crowe, Linda Todd, Shahla Salehi, Greg Elgar, Georgia Chan, Prabhu Arumugam, Christine Patch, Augusto Rendon, Tom A. Fowler, Richard H. Scott, Loukas Moutsianas & Mark J. Caulfield
  2. Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, UKErola Pairo-Castineira, Konrad Rawlik, Clark D. Russell, Jonathan Millar, Fiona Griffiths, Wilna Oosthuyzen, Bo Wang, Marie Zechner, Nick Parkinson, Albert Tenesa, Sara Clohisey Hendry, Andy Law & J. Kenneth Baillie
  3. MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UKErola Pairo-Castineira, Lucija Klaric, Albert Tenesa, Chris P. Ponting, Veronique Vitart, James F. Wilson, Andrew D. Bretherick & J. Kenneth Baillie
  4. Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh, UKClark D. Russell & J. Kenneth Baillie
  5. Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, UKTomas Malinauskas, Katherine S. Elliott & Julian Knight
  6. Institute for Molecular Bioscience, The University of Queensland, Brisbane, AustraliaYang Wu
  7. Biostatistics Group, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, ChinaXia Shen
  8. Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, Teviot Place, Edinburgh, UKXia Shen, Albert Tenesa & James F. Wilson
  9. Edinburgh Clinical Research Facility, Western General Hospital, University of Edinburgh, Edinburgh, UKKirstie Morrice, Angie Fawkes & Lee Murphy
  10. Intensive Care Unit, Royal Infirmary of Edinburgh, 54 Little France Drive, Edinburgh, UKSean Keating, Timothy Walsh & J. Kenneth Baillie
  11. Department of Critical Care Medicine, Queen’s University and Kingston Health Sciences Centre, Kingston, ON, CanadaDavid Maslove
  12. Clinical Research Centre at St Vincent’s University Hospital, University College Dublin, Dublin, IrelandAlistair Nichol
  13. NIHR Health Protection Research Unit for Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences University of Liverpool, Liverpool, UKMalcolm G. Semple
  14. Respiratory Medicine, Alder Hey Children’s Hospital, Institute in The Park, University of Liverpool, Alder Hey Children’s Hospital, Liverpool, UKMalcolm G. Semple
  15. Illumina Cambridge, 19 Granta Park, Great Abington, Cambridge, UKDavid Bentley & Clare Kingsley
  16. Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, USAJack A. Kosmicki, Julie E. Horowitz, Aris Baras, Goncalo R. Abecasis & Manuel A. R. Ferreira
  17. Geisinger, Danville, PA, USAAnne Justice, Tooraj Mirshahi & Matthew Oetjens
  18. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USADaniel J. Rader, Marylyn D. Ritchie & Anurag Verma
  19. Test and Trace, the Health Security Agency, Department of Health and Social Care, Victoria St, London, UKTom A. Fowler
  20. Department of Intensive Care Medicine, Guy’s and St. Thomas NHS Foundation Trust, London, UKManu Shankar-Hari
  21. Department of Medicine, University of Cambridge, Cambridge, UKCharlotte Summers
  22. William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UKCharles Hinds
  23. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, UKPeter Horby
  24. Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, ChinaLowell Ling
  25. Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland, UKDanny McAuley
  26. Department of Intensive Care Medicine, Royal Victoria Hospital, Belfast, Northern Ireland, UKDanny McAuley
  27. UCL Centre for Human Health and Performance, London, UKHugh Montgomery
  28. National Heart and Lung Institute, Imperial College London, London, UKPeter J. M. Openshaw
  29. Imperial College Healthcare NHS Trust: London, London, UKPeter J. M. Openshaw
  30. Imperial College, London, UKPaul Elliott
  31. Intensive Care National Audit & Research Centre, London, UKKathy Rowan
  32. School of Life Sciences, Westlake University, Hangzhou, Zhejiang, ChinaJian Yang
  33. Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, ChinaJian Yang
  34. Great Ormond Street Hospital, London, UKRichard H. Scott
  35. William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, UKMark J. Caulfield


GenOMICC Investigators


Covid-19 Human Genetics Initiative

Corresponding authors

Correspondence to Mark J. Caulfield or J. Kenneth Baillie.

Supplementary information

Supplementary Information

This file contains Supplementary Figures; Supplementary Tables and Supplementary References

Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome

Nature Communications volume 13, Article number: 446 (2022) 


Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS.


Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause asymptomatic or symptomatic coronavirus disease 2019 (COVID-19). As of October 25, 2021, more than 244 million SARS-CoV-2 infections have been confirmed worldwide that have caused at least 5 million deaths. Symptoms of acute SARS-CoV-2 infection can include fever, fatigue, myalgia, weakness, headache, rhinorrhea, dry cough, shortness of breath (dyspnea), change in smell or taste, nausea, vomiting, and diarrhea1. Following infection, a rapid systemic immune response is mounted against SARS-CoV-2, characterized by increased serum concentrations of chemokines and pro-inflammatory cytokines, such as interleukin (IL)-6 and tumor necrosis factor (TNF), and the appearance of activated monocytes, followed by SARS-CoV-2-specific immunoglobulin M (IgM), IgA, and IgG antibodies and interferon-γ-producing T cells2,3,4,5,6,7. This concerted action of the immune system controls the replication of SARS-CoV-2, and infectious SARS-CoV-2 cannot be isolated from the respiratory tract after 3 weeks8. This typically coincides with the recovery of most individuals with symptomatic COVID-19.

However, about one-third of individuals report one or more COVID-19-related symptoms that last for more than 4 weeks (i.e. 29 days and more) after the onset of the first COVID-19-related symptom9,10, a condition termed post-acute COVID-19 syndrome (PACS) or long COVID. Community prevalence of PACS has been estimated in most studies to lie between 10% and 60%, which depends on the definition of PACS used and patient care level10,11. PACS can be further subdivided into subacute COVID-19 when COVID-19-related symptoms last 12 weeks (84 days) or less versus post-COVID-19 syndrome, which defines patients with COVID-19-related symptoms persisting for more than 84 days after onset of their first symptoms of COVID-1912. The most frequent symptoms of PACS are reported to be fatigue, dyspnea, and cognitive impairment (also termed “brain fog”, which includes loss of concentration and memory), as well as pain and aches at different sites (including headache), cough, change in smell or taste, and diarrhea12,13. As PACS is increasingly recognized as a serious consequence of SARS-CoV-2 infection, early identification of individuals at risk of developing PACS is needed.

A recent study analyzed PACS in individuals who self-reported their symptoms by using an app. The authors found PACS to correlate with increased hospitalization rate and comorbidities, such as lung disease, asthma bronchiale, and heart disease, and they concluded that age, female sex, and number of symptoms during the first week of disease could be used to estimate an individual’s risk for PACS14. However, self-reported data and telehealth are at risk for bias, and risk factors associated with a severe course of primary SARS-CoV-2 infection complicate the detection of underlying risk factors for PACS independent of disease severity. To address these issues, we have characterized a prospective cohort of 215 individuals by clinical visits and laboratory analyses up to one year of follow-up. We found distinct patterns of total immunoglobulin (Ig) levels in patients with COVID-19 and integrated these in a clinical prediction score, which allowed early identification of both outpatients and hospitalized individuals with COVID-19 that were at high risk for PACS.


Characteristics of COVID-19 patients with and without PACS

Our multicentric cohort included 175 individuals with reverse-transcriptase quantitative polymerase chain reaction-confirmed SARS-CoV-2 infection as well as 40 healthy controls without acute symptoms and negative SARS-CoV-2-specific immunoassays. A total of 134 individuals were followed up, including 123 patients at about 6 months and 50 patients at one year after primary SARS-CoV-2 infection (Fig. 1). Based on the classification by the World Health Organization15, we distinguished 89 mild and 45 severe COVID-19 cases attending follow-up and further subclassified them into four cases of asymptomatic disease, 76 mild illness, nine mild pneumonia, 20 severe pneumonia, and 25 acute respiratory distress syndrome (ARDS), including five mild, 10 moderate, and 10 severe ARDS cases (Table 1).

figure 1
Fig. 1: Flow chart of COVID-19 patients and healthy controls enrolled in the study.

Table 1 Participant characteristics at inclusion and 6-month follow-up.Full size table

53.9% of mild COVID-19 cases and 82.2% of patients that developed severe COVID-19 had PACS, defined—as aforementioned—by the persistence of one or more COVID-19-related symptoms for more than four weeks (i.e. 29 days and more) after the onset of the first COVID-19-related symptom (Table 1). Conversely, only 8.6% of healthy controls experienced one or more symptoms for more than 28 days during the one-year follow-up period (Table 1). The most common prolonged symptoms were fatigue, dyspnea, a change in smell or taste, and anxiety or depression. Symptoms of PACS were about 2–6.5-fold more frequent in severe compared to mild COVID-19 cases overall, with the exception of smell or taste disorders (Table 1).

In patients with severe disease, laboratory values taken at primary infection showed signs of lymphopenia and systemic inflammation, including increased concentrations of C-reactive protein (CRP), IL-6, and TNF, and some of these inflammatory markers remained perturbed at 6-month follow-up (Table 1).

When studying the above-mentioned demographic characteristics, comorbidities, and laboratory values at primary SARS-CoV-2 infection in individuals experiencing PACS, we observed several differences (Table 2). Compared to individuals without PACS, the group of patients experiencing PACS contained a larger percentage of severe COVID-19 cases (odds ratio 3.87; p = 0.001), showed more COVID-19-related symptoms during primary infection (odds ratio 1.81; p = 0.001), were of higher age (odds ratio 1.67; p = 0.008), and more often required hospitalization (odds ratio 2.55; p = 0.014) (Table 2). Sex distribution between the groups of our cohort with and without PACS was similar (p = 0.840). Moreover, we observed an association of risk of developing PACS with a history of lung disease (odds ratio 6.29; p = 0.004) and, particularly, asthma bronchiale (odds ratio 9.74; p = 0.003) (Table 2). Furthermore, CRP and TNF concentrations were slightly higher at primary SARS-CoV-2 infection in individuals later developing PACS, although the inflammatory parameters did not have largely increased odds ratios (odds ratios 1.01 and 1.07; p = 0.022 and 0.049, respectively) (Table 2). Collectively, we observed that several determinants of severe COVID-19, including age, hospitalization, and an increase of certain inflammatory markers, present during primary infection correlated with an increased risk of developing PACS.Table 2 Characteristics of patients during primary SARS-CoV-2 infection correlating with post-acute COVID-19 syndrome (PACS).Full size table

Distinct immunoglobulin signature correlating with development of PACS

We assessed serum concentrations of IgA and IgG antibodies specific for the SARS-CoV-2 spike protein subunit 1 (S1) and of total Igs. Compared to healthy controls, we detected increased serum titers of SARS-CoV-2 S1-specific IgA and IgG, in both mild and severe COVID-19 cases, with higher titers found in severe COVID-19 cases (Table 1), confirming the previous findings6. Comparison of individuals with and without PACS revealed that at primary infection S1-specific IgA and IgG values were similar between these two groups (Table 2).

On measuring total serum concentrations of different Igs, we made several interesting findings. Compared to healthy controls, IgM and IgG1 were indifferent in COVID-19 patients, whereas IgG3 was significantly increased in COVID-19 patients (Fig. 2a). Differentiating mild versus severe COVID-19, IgM was lower in severe compared to mild COVID-19 patients and healthy controls, both at primary infection and 6-month follow-up. IgG1 was indifferent, whereas IgG3 was higher in both mild and severe COVID-19 cases, compared to healthy controls (Fig. 2b and Supplementary Fig. 1a). IgM levels negatively correlated with age, whereas none of the IgG subclasses showed a significant trend with age (Fig. 2c).

figure 2
Fig. 2: Specific and total immunoglobulins at primary infection and follow-up.

In individuals developing PACS, we detected decreased IgM, both at primary infection and 6-month follow-up (Fig. 2d). Whereas IgG1 was unaltered, IgG3 tended to be lower in patients with PACS (Fig. 2d), which was contrary to the increased IgG3 concentrations in both mild and severe COVID-19 cases (Fig. 2a). IgA, IgG2, and IgG4 were neither significantly different in patients with PACS compared to without PACS nor did they show a trend that differed from the one observed in mild and severe COVID-19 cases (Supplementary Fig. 1b–e). Assessment of temporal changes in COVID-19 patients, of whom we had blood samples at primary infection, 6-month, and 1-year follow-up, showed that these total serum Ig concentrations remained stable over time (Fig. 2e and Supplementary Fig. 1f).

In notable contrast to the increased IgG3 concentrations in both mild and severe COVID-19 cases (Fig. 2b), IgG3 showed a trend to being lower in patients developing PACS (Fig. 2d, f). This discrepancy in IgG3 was also evident when analyzing the proportion of IgG3 within total IgG during primary infection, with severe COVID-19 patients without PACS demonstrating increased IgG3, whereas severe COVID-19 patients developing PACS failed to show such increase in IgG3 (Fig. 2g). Other IgG subclasses did not show such changes (Supplementary Fig. 1g).

Notably, individuals with either low IgM or low IgG3 had an increased risk of developing PACS, whereas patients with both high IgM and high IgG3 were less likely to develop PACS (Fig. 2h). In line with this finding, we observed in healthy controls that contracted COVID-19 during the course of this study (Supplementary Table 1), those developing PACS had low IgM prior to SARS-CoV-2 infection, which remained low during the observation period (Supplementary Fig. 1h).

Building of an immunoglobulin signature-based score predicting PACS

We extended the identified Ig signature to comprise additional parameters readily available during primary infection. Building on a previously published prediction model14, we considered patient age and number of symptoms during primary infection. For all continuous variables a linear relationship with the outcome PACS was accepted (Supplementary Fig. 2a). We found patient age and number of symptoms were increased in patients developing PACS (Fig. 3a and Supplementary Fig. 2b), whereas sex was not (Table 2). The symptom count during primary infection correlated with the maximal followed-up disease severity of COVID-19 patients (Fig. 3b). Vice versa, disease severity was associated with an increased risk of PACS (Supplementary Fig. 2c).

figure 3
Fig. 3: Prediction of post-acute COVID-19 syndrome (PACS) based on clinical features and immunoglobulin signature.

Regardless of their COVID-19 severity, 94% of individuals with a history of asthma bronchiale developed PACS and 71% developed post-COVID-19 syndrome defined as prolonged symptoms for more than 12 weeks after symptom onset. This is in stark contrast to 59% of individuals without a history of asthma bronchiale developing PACS and 42% developing post-COVID-19 syndrome (Fig. 3c). Interestingly, healthy controls and COVID-19 patients with a history of asthma bronchiale had lower serum IgG3 concentrations compared to their counterparts (Fig. 3d).

We applied our data obtained during primary infection to test different models predicting PACS. Use of a symptom-based score14, reliant on age, sex, and a number of symptoms during primary infection revealed an area under the curve (AUC) value of the receiver operating characteristic curve of 68% (CI 59–78%) and moderately underestimated the risk for PACS with a calibration-in-the-large of 1.76, a calibration slope of 0.57 and a Brier score of 0.328 (Fig. 3e). Based on our findings, we assessed previously identified predictors, such as patient age, sex, number of symptoms, body-mass-index, comorbidities, disease severity, and level of care as well as different combinations of serum Ig concentrations during primary infection to support development of a model predicting PACS (Supplementary Table 2). By combining patient age, number of symptoms during primary infection, history of asthma bronchiale, and an Ig signature consisting of IgM and IgG3 during primary infection, we were able to calculate a risk score—which we termed PACS score—that resulted in an AUC value of 77% (CI 69–85%) with a calibration-in-the-large of 0, a calibration slope of 1 and a Brier score of 0.185. To minimize overfitting, we modified the PACS score by shrinkage of the estimated coefficients. In a sensitivity analysis, the PACS score demonstrated, using the corresponding 6-month follow-up Ig measurements of our COVID-19 patients, a preserved calibration and ability to identify individuals with PACS with an AUC of 74% (CI 65–84%), a calibration-in-the-large of 0, a calibration slope of 1.2 and a Brier score of 0.191 (Fig. 3f). The addition of an interaction term between IgM and IgG3 significantly improved our PACS score (ANOVA; p = 0.02) compared to a model without interaction of IgM and IgG3 (Fig. 3g and Supplementary Tables 2 and 3).

Comparison of our PACS score to a recently published symptom-based score by Sudre et al.14 showed optimal performance of our PACS score in hospitalized patients of our cohort (Fig. 3h, i). We used our PACS score in an independent validation cohort of 395 individuals with confirmed COVID-19, including a small subgroup of hospitalized COVID-19 cases. This validated the improved predictive performance of our PACS score in the subgroup of hospitalized patients, resulting in an AUC of 99%, while the use of our PACS scores in the entire validation cohort resulted in an AUC of 64% (CI 58–69%) with a calibration-in-the-large of –0.3, a calibration slope of 0.8, and a Brier score of 0.239 (Fig. 3j and Supplementary Table 2). The PACS score performed well in the validation cohort, which consisted mainly of outpatients that showed a tendency to low IgG3 in individuals that had not recovered after 6 months (Supplementary Fig. 3a, b). Consistent with optimal performance in hospitalized patients, when applied to mild and severe COVID-19 patients, the PACS score performed better in the latter across all grades of severe COVID-19 (Supplementary Fig. 4a). Moreover, sensitivity analysis using different definitions of PACS showed a maintained ability of the PACS score to identify patients developing post-COVID-19 syndrome with symptoms lasting for more than 12 weeks and patients of the validation cohort that had not recovered after 6 months (Supplementary Fig. 4b).

Finally, we performed decision curve analyses, thus weighing the relative harms of false-positive and false-negative predictions. These decision curve analyses assessed the clinical utility of our PACS score and identified a range of threshold probabilities, in which the model could support clinical decision making compared to alternative intervention strategies, e.g. treating nobody or treating everybody with COVID-1916. The PACS score showed the best clinical utility within threshold probability ranges of 40% and 100% and an independently validated utility in ranges between 40% and 60% (Fig. 3k). Subgroup analysis in hospitalized patients revealed best clinical utility within probability threshold ranges of 35–100% and 55–100% in the derivation and validation cohort, respectively (Fig. 3l). Next, we calculated two probability thresholds as rule-in cut-offs for different clinical settings with the disparate prevalence of PACS. One threshold (0.52) was selected as optimal cut-off maximizing both sensitivity and specificity in the validation cohort. A second threshold (0.75) was calculated as the optimal cut-off for hospitalized patients of both derivation or validation cohorts independently (Supplementary Table 4). With a positive predictive value (PPV) of 0.88 in the derivation cohort and 0.90 in hospitalized patients, the upper threshold of 0.75 identifies with high specificity patients at very high risk for developing PACS. Conversely, with a PPV of 0.76 in the derivation cohort and 0.67 in outpatients, the lower threshold differentiates between patients at moderate versus high risk for developing PACS, while maintaining high sensitivity and negative predictive value (NPV) (Fig. 3m and Supplementary Table 4).


Collectively, we demonstrate that the development of PACS correlates with a distinct Ig signature as well as patient age, history of asthma bronchiale, and a number of symptoms, all measured during primary infection. We translated these findings into a model, termed PACS score. When applied to our cohort comprising 134 followed-up and extensively characterized COVID-19 patients, the PACS score performed better than a symptom-based score14, was independent of timepoint of testing and sex, and only required broadly available Ig measurements rather than specialized tests, such as SARS-CoV-2-specific immunoassays. Despite previous reports on female sex as a risk factor for PACS, male sex is associated with a worse outcome of acute COVID-19, and a sex-independent prediction score benefits from improved applicability to different healthcare settings1,14.

Compared to symptom-based prediction scores, the measurement of an Ig signature allows the identification of patients at risk for developing PACS, particularly, in hospitalized patients. This finding suggests a possible pathomechanism distinct from merely increased inflammation and immune activation. Moreover, unspecific Ig levels are stable over time, unlike inflammatory markers that only transiently increase early in the disease course. This biological stability of Igs further increases their utility as biomarkers, as independence of sampling timepoint facilitates clinical application and Ig signatures can be determined already before infection.

Limitations of our study comprise a non-excludable selection bias of patients enrolled in our study affecting the transferability of our findings to all SARS-CoV-2 RT-qPCR-positive patients, a non-excludable selection bias of patients agreeing to follow-up despite a high follow-up rate of 77%, as well as a limited number of hospitalized patients and differences in study design of the validation cohort. Moreover, our study included only a small number of participants of non-white ethnicity due to Central European demographics, potentially affecting the transferability of our findings.

Based on decision curve analyses we determined the highest clinical benefit of our PACS score to lie between threshold probability ranges of 40–60%, and above 55% in hospitalized patients, meaning that a clinician would advise preventive measures against PACS if the probability of PACS were above 55%. Thus, depending on future intervention strategies, associated side effects, and costs, our PACS score can be applied in a setting where false-positive predictions are of greater harm than false negatives. This would enable clinical studies and prevention strategies targeting high specificity patients at very high risk for developing PACS. We, therefore, suggest our PACS score can be applied to identify outpatients at risk, high-risk asthmatic patients, and hospitalized patients, the latter of which are already at high risk for developing PACS. Reliable identification of high-risk patients not only allows precise recommendation of early medical consultations but also facilitates the study of preventive treatment strategies, such as the use of inhaled corticosteroids in asthmatic and non-asthmatic patients and possibly intravenous Ig therapies17,18. Early measurement of Ig titers upon hospitalization of COVID-19 patients can support clinical decision-making and personalized treatment strategies.

In reflecting on the association of the identified Ig signature correlating with increased risk of PACS, the following aspects are worth considering. IgM and, particularly, IgG3 secretion by B cells is induced by interferons and antagonized by IL-4 signals19,20,21. Thus, reduced production of type I interferons, as proposed to occur in poorly controlled SARS-CoV-2 infection22,23, or a predisposition to secreting increased IL-4 concentrations, as present in asthma bronchiale24, may contribute to a failure to efficiently induce Ig isotype switching to IgG3. This hypothesis is consistent with our finding of low IgG3 in asthma bronchiale patients. Conversely, immune responses dominated by IgG3 can occur with similar temporal dynamics as IgM responses and have been associated with viral infections at mucosal tissues25,26. Thus, the reduced IgG3 concentrations in patients with PACS might support a role for IgG3 in Fc receptor-dependent viral control. Low IgG3 levels have also been linked to chronic fatigue syndrome, a debilitating condition resembling certain symptoms of PACS, as well as an increased rate of respiratory infections18,27.

PACS has been proposed to result from tissue damage due to direct effects of the virus, excessive inflammation, or thrombotic events; alternatively, PACS could be the consequence of bystander or virus-mediated activation of autoreactive T and B cells28. Recent observations of PACS resolution after SARS-CoV-2 vaccination might hint at the depletion of persisting viral reservoirs29. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which in turn is crucial for understanding the pathomechanisms of PACS and identification of preventive measures for treatment and care.


Experimental study design

Adult individuals were included in the study and visited between April 2020 and August 2021. The study was approved by the Cantonal Ethics Committee of Zurich (BASEC #2016-01440). The majority of participants were of white ethnicity.

Coronavirus disease 2019 (COVID-19) patients

Following written informed consent, 175 patients with quantitative reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR)-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were recruited for clinical evaluation and sampling of blood. Patients were included based on the selection criteria of SARS-CoV-2 PCR positivity and experiencing acute COVID-19. The multicentric study design comprised patient recruitment at four different hospitals in the area of Zurich, Switzerland, including the University Hospital Zurich (n = 111), the City Hospital Triemli Zurich (n = 35), the Limmattal Hospital (n = 15), and the Uster Hospital (n = 14). No exclusion criteria were applied on the analysis of the 175 COVID-19 patients, with the aim of generating a broadly applicable prediction score. Thus, SARS-CoV-2-specific RT-qPCR-positive individuals were included independently of comorbidities and medication. COVID-19 patients were sampled a first time during their primary SARS-CoV-2 infection (termed “primary infection”), a second time 6 months, and a third time one year after the initial blood sampling (Fig. 1). 39 patients declined follow-up or were not reachable and two patients were deceased. Eight patients only declined laboratory testing at 6-month follow-up and 12 patients at 1-year follow-up, whereas their medical history could be obtained by phone. In all followed-up COVID-19 patients (n = 134) medical history was obtained at least 3.5 months (105 days) after symptom onset to detect the presence or absence of PACS. Blood samples of COVID-19 patients were obtained during primary infection, around six months after symptom onset (n = 115) at an average of 199 days after symptom onset (interquartile range 185–216) and around one year after symptom onset (n = 38) at an average time point of 383 days (interquartile range 371–397) after symptom onset (Supplementary Fig. 5). The follow-up rate was 77% and followed-up patients are considered representative of the larger population of patients initially enrolled in the study (Supplementary Table 5).


COVID-19 patients were grouped according to the World Health Organization classification criteria into (a) mild cases, comprising asymptomatic and symptomatic cases of mild illness and mild pneumonia, versus (b) severe cases, including severe pneumonia and acute respiratory distress syndrome (ARDS). Mild illness was defined as patients with uncomplicated respiratory tract infection and/or non-specific symptoms. Pneumonia was defined as the presence of respiratory symptoms, abnormal vital signs such as fever, and pathological lung examination findings, whereas patients with mild pneumonia showed no signs of severe pneumonia and did not require supplemental oxygen therapy. Severe pneumonia was defined as respiratory infection or fever with an observed respiratory rate greater than 30 breaths per minute, severe respiratory distress, and/or a SpO2 ≤ 93% on room air. Patients with severe pneumonia mostly required supplemental oxygen therapy. ARDS classification relied on measured oxygenation impairments (PaO2/FiO2a in mild ARDS ≤ 300 mmHg, moderate ARDS ≤ 200 mmHg, and severe ARDS ≤ 100 mmHg)15,30,31. Our COVID-19 derivation cohort did not contain any patients with sepsis or septic shock. For the validation cohort, patients with severe COVID-19 were identified as hospitalized patients reporting supplemental oxygen therapy during hospitalization. If not otherwise specified, all analyses were performed using the maximal followed-up disease severity of COVID-19 patients. We defined patients with PACS as individuals with PCR-confirmed COVID-19 experiencing one or more symptoms associated with COVID-19 that lasted for more than 4 weeks (i.e. 29 days and more) after the onset of the first COVID-19-related symptom12. Symptoms were assessed in a standardized manner by trained study physicians, both during primary infection (acute COVID-19) and at follow-up visits. During primary infection, five symptoms (fever, fatigue, cough, dyspnea, and gastrointestinal symptoms) were recorded systematically, which were subsequently used for our PACS prediction model. All five symptoms were patient-reported symptoms and, based on a standardized questionnaire, individually asked by a trained physician whether they were present during primary infection. Patient-reported temperature can be inaccurate for various reasons, including individual body temperature norms that vary with patient age as well as method and timepoint of measurement. Therefore, the following was considered as patient-reported “fever”: (i) reported increase of body temperature, (ii) fever chills, or (iii) sweating32,33. Gastrointestinal symptoms were counted as one symptom, also when multiple gastrointestinal symptoms were reported, including nausea, loss of appetite, heartburn, abdominal pain, flatulence, diarrhea, and obstipation. The severity of symptoms was not assessed. During follow-up visits, patients were asked whether and when they recovered from COVID-19 and which symptoms persisted. A total of nine symptoms were recorded systematically at follow-up visits (fever, cough, dyspnea, fatigue, gastrointestinal symptoms, headache, chest pain, anxiety and/or depression, and disorders of smell and/or taste; Table 1). Additional patient-reported prolonged symptoms were also recorded. Symptom severity was not assessed. When using the more stringent definition of PACS as symptoms lasting for more than 12 weeks, termed post-COVID-19 syndrome12, we found the preserved performance of our PACS prediction model (Supplementary Fig. 4b). For our validation cohort, we used the same definition of PACS as for our derivation cohort, i.e. patient-reported COVID-19-related symptoms lasting longer than four weeks, and we performed a sensitivity analysis showing preserved model performance using a different outcome based on whether patients had recovered after six months (Supplementary Fig. 4b).

Healthy controls

Following written informed consent, we additionally included 40 healthy controls who had no history of SARS-CoV-2 infection-associated symptoms, such as fever, rhinorrhea, respiratory symptoms (e.g. dry cough or shortness of breath), change in smell or taste, nausea, vomiting, and diarrhea1 and had a negative SARS-CoV-2 spike S1 protein-specific immunoassay, whereby individuals with borderline and positive values were excluded. Moreover, our healthy controls had no acute or active illness prior to or at blood sampling and no history of autoimmune disorder. We obtained a medical history from all 40 healthy controls at their blood sampling and at least 6 months thereafter in 35 healthy controls. Five healthy controls got infected with SARS-CoV-2 during the follow-up period and were therefore excluded from clinical follow-up (Supplementary Table 1). Participants were not compensated.

Validation cohort

Prognostic models were validated in a separate cohort of 395 PCR-confirmed COVID-19 patients that were enrolled at diagnosis between 06 August 2020 and 19 January 2021 and prospectively followed-up for 6 months after infection34. All serum samples were obtained during primary infection and in 98% of patients at 2 weeks after the diagnosis of COVID-19 with a median sampling time point of 19 days (interquartile range 17–22 days) after the onset of the first COVID-19-related symptom (Table 1). Pre-existing comorbidities and COVID-19 symptoms were recorded at baseline using standardized, self-administered, electronic questionnaires. Details regarding relevant medical history were clarified via phone by trained study personnel. Patient-reported symptoms were reassessed 1, 3, and 6 months after diagnosis. After 6 months, patients were asked whether they had recovered from COVID-19 or not.


All laboratory tests were performed in accredited laboratories at the University Hospital Zurich. Blood samples were collected in BD Vacutainer CAT serum tubes (Becton Dickinson; Cat# 367896). Different serum immunoglobulins subsets and IgG subclasses were measured using the commercially available turbidimetric Optilite® assays using an Optilite® analyzer (The Binding Site Group Ltd; Cat# NK004.OPT, NK006–NK010.OPT, NK012.OPT). Laboratory reference values are as follows (g/l): IgM (0.4–2.8), IgA (0.7–4.0), IgG (7.0–16.0), IgG1 (2.8–8.0), IgG2 (1.15–5.70), IgG3 (0.24–1.25), IgG4 (0.052–1.25). SARS-CoV-2-specific IgA and IgG antibodies were measured, as previously established6, by using a commercial enzyme-linked immunosorbent assay (ELISA) specific for the SARS-CoV-2 spike S1 protein (Euroimmun SARS-CoV-2 IgA and IgG immunoassay; Cat# EI 2606-9601A and G). Interleukin IL-6 and tumor necrosis factor (TNF) were quantified using R&D Systems assays (Cat# S6050 and LHSCM210, respectively). Antibody dilutions were prepared according to the manufacturer’s instructions. Blood samples obtained after SARS-CoV-2 vaccination were excluded from comparisons of SARS-CoV-2-specific Ig titers. We observed no sex differences in the measured total Igs and S1-specific antibody titers (Supplementary Fig. 6).

Clinical prediction model

The sample for the development of our prediction model was obtained by including and following up all consecutive patients between April 2020 and August 2021 and resulted in a total of 134 followed-up patients. The number of outcome events was 85, which corresponds to the number of patients experiencing PACS. The required sample size for the development of clinical prediction models is a matter of active discussion and research. Our PACS score was developed using 14.2 events per predictor parameter, which is in line with the rule of thumb of 15 events per predictor parameter as well as several other recommendations on the required number of events per predictor parameter for accurate modeling in logistic regression analysis35. More precise estimates of the required sample size could be calculated based on published parameters of the previous studies36. However, as only one previous model for PACS prediction was available at the time of our study, and as definitions and prevalence of PACS in different populations vary significantly, we were unable to calculate precise requirements for model development. This might be reflected by some optimism in predictor effect estimates of our model (yielding a global shrinkage factor of 0.72) and a small overestimation of the overall risk for PACS (after shrinkage) in the validation cohort, that might be promoted by shrinkage of predictor effect estimates37.

Thus, the sample size was considered adequate to develop a prediction model with six predictor variables. These predictor variables were based on previous publications (age + number of symptoms during primary infection + history of asthma bronchiale)14,38 and include two new variables (total IgM + total IgG3) as well as one interaction term (total IgM * total IgG3), yielding 14.2 events per predictor parameter35,39,40,41,42,43. The validation cohort amounted to a sample size of 395 and counted 216 events, which was in line with a suggested sample size of 400 and an outcome event size of 200 in order to obtain precise calibration curves44.

The symptom-based prediction score was calculated using a previously published model14 and modified by applying it on five recorded symptoms instead of 14. The following five symptoms were recorded during primary infection: fever, fatigue, cough, shortness of breath (dyspnea), and gastrointestinal symptoms.

Our prediction model (PACS score) was built on a published prediction model14 that was based on “age + sex + number of symptoms during primary infection”. We have evaluated the three suggested predictors, together with other reported risk factors for PACS, such as asthma bronchiale14,38. Selection of new variables was a hypothesis-driven process based on the observation that total immunoglobulins are altered in COVID-19 patients experiencing long-term symptoms (Fig. 2), and previous studies connecting low total IgG3 levels to chronic fatigue syndrome and increased susceptibility to infection18,45. As some variables such as “age” represent risk factors for severe COVID-19 disease, a risk factor for PACS itself38, we further explored the influence of COVID-19 disease severity as well as associated risk factors (Table 2, Supplementary Table 2, and Supplementary Fig. 2c).

Moreover, we modified the estimated coefficients of the PACS score by shrinkage. As prognostic models tend to describe optimally the evaluated dataset but may perform less well in other datasets, we addressed this phenomenon of overfitting by applying the statistical method of shrinkage. Estimated coefficients of the generalized linear model were multiplied with a global shrinkage factor (0.72) that was calculated using the dfbeta-method46,47. Original and regression coefficients after shrinkage are summarized in Supplementary Table 3 with 95% confidence intervals (CI) and corresponding p values. Areas under the curve (AUC) of receiver operating characteristic (ROC) curves and calibration plots were calculated as previously described48,49. The PACS score was validated in a separate validation cohort using the same patient-centered outcome definition for PACS as in the derivation cohort. The PACS score (after shrinkage) can be calculated and the logistic regression model reproduced using the following R code: PACS_score < - glm(PACS_score ~ –1 + offset(–0.981011 + 0.2616998*scale(age)+0.3307986*number of symptoms during primary infection + 1.896502*history of asthma bronchiale + 0.8429766*total IgM (g/l) + 1.3716198*total IgG3 (g/l)–1.5316550*IgM*IgG3), family = binomial, data = patient_data_to_test), with the variables “age” in years, “number of symptoms during primary infection” ranging from zero to five, and “history of asthma bronchiale” as number zero if absent and number one if present. Individual risk for PACS can further be predicted using the following R code: predictions < −predict(PACS_score, patient_data_to_test, type = “response”). The number of symptoms can be determined by counting the occurrence of the following five symptom categories in tested COVID-19 patients (all self-reported): fever, fatigue, cough, shortness of breath (dyspnea), and gastrointestinal symptoms.


Descriptive statistics for the followed-up healthy controls, COVID-19 patients, and validation cohort are presented as numbers and percentages of the total for categorical variables, as well as the median and interquartile range (IQR) for continuous variables. Comparison of variables was performed using non-parametric Wilcoxon’s rank-sum test if not otherwise specified. Evidence was quantified on a continuous scale, as results were considered exploratory. Thus, p values are to be interpreted as quantified evidence of the hypothesis without specified significance thresholds. In Table 2, odds ratios of categorical variables were calculated by median-unbiased estimation and odds ratios of continuous variables were calculated using univariate, unadjusted regression models for the outcome PACS. Horizontal lines in split violin plots indicate median values. Wedge sizes of radar plots visualize median values of measured immunoglobulins in patients with or without PACS, normalized by dividing the respective median with the overall median measured in COVID-19 patients. Microsoft Office Excel (version 2102) was used for data collection. Statistical analyses were performed with R (version 4.1.2) and using the packages “biostatUZH” (version 1.8.0), “CalibrationCurves” (version 0.1.2), “dcurves” (version 0.2.0), “epitools” (version 0.5-10.1), “interactions” (version 1.1.5), “gbm” (version 2.1.8), “ggstatsplot” (version 0.9.0), “interactions”, “pROC” (version 1.18.0), “mgcv” (version 1.8-38), “shrink” (version 1.2.1), and “sjPlot” (version 2.8.9), and missing values were omitted. The present study is reported according to the STROBE (Statement for reporting cohort studies) and TRIPOD (Statement for reporting clinical prediction models) guidelines50,51.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All relevant data generated in this study are provided in the Supplementary Information. A PACS score calculator is accessible online (www.pacs-score.com). Source data are provided with this paper.

Code availability

R code for immunoglobulin signature analysis and prediction model development is provided in the Supplementary Software 1.


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Download references


We thank the diagnostic laboratories of the University Hospital Zurich, Alessandra Guaita, Claudia Meloni, Jennifer Jörger, Barbara Turi, Alberto Turi, and the members of the Boyman laboratory for helpful discussions and support. Swiss National Science Foundation (NRP 78 Implementation Program to C.C. and O.B.; #4078P0-198431 to O.B. and J.N.; and #310030-200669 to O.B.), Digitalization Initiative of the Zurich Higher Education Institutions (#2021.1_RAC_ID_34 to C.C.), Clinical Research Priority Program CYTIMM-Z of University of Zurich (UZH) (to O.B.), Pandemic Fund of UZH (to O.B.), Innovation grant of USZ (to O.B.), UZH Forschungskredit Candoc (#FK-20-022 to S.A.), Swiss Academy of Medical Sciences (SAMW) fellowships (#323530-191220 to C.C.; #323530-191230 to Y.Z.; #323530-177975 to S.A.), Young Talents in Clinical Research Fellowship (YTCR 32/18) by SAMW and Bangerter Foundation (to M.R.).

Author information


  1. Department of Immunology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandCarlo Cervia, Yves Zurbuchen, Patrick Taeschler, Sara Hasler, Sarah Adamo, Miro E. Raeber, Jakob Nilsson & Onur Boyman
  2. Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, SwitzerlandTala Ballouz, Dominik Menges, Ulrike Held & Milo A. Puhan
  3. Clinic for Internal Medicine, Uster Hospital, Uster, SwitzerlandEsther Bächli
  4. Department of Medicine, Limmattal Hospital, Schlieren, SwitzerlandAlain Rudiger
  5. Clinic for Internal Medicine, City Hospital Triemli Zurich, Zurich, SwitzerlandMelina Stüssi-Helbling & Lars C. Huber
  6. Faculty of Medicine, University of Zurich, Zurich, SwitzerlandOnur Boyman


Conceptualization: O.B.; Methodology: C.C., J.N., U.H., O.B.; Investigation: C.C., Y.Z., P.T., D.M., T.B., S.H., E.B., A.R., M.S.H., L.C.H., U.H., M.A.P., O.B.; Visualization: C.C., O.B.; Funding acquisition: C.C., Y.Z., S.A., M.E.R., J.N., O.B.; Project administration: S.H., M.E.R.; Supervision: O.B.; Writing: C.C., O.B.

Corresponding author

Correspondence to Onur Boyman.

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Competing interests

The authors declare no competing interests.

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Description of Additional Supplementary Files

Supplementary Software 1

Reporting Summary

Source data

Source Data

Multiple Early Factors Anticipate Post-Acute COVID-19 Sequelae


  • Longitudinal multiomics associate PASC with autoantibodies, viremia and comorbidities
  • Reactivation of latent viruses during initial infection may contribute to PASC
  • Subclinical autoantibodies negatively correlate with anti-SARS-CoV-2 antibodies
  • Gastrointestinal PASC uniquely present with post-acute expansion of cytotoxic T cells


Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Article Info

Publication History

Accepted: January 19, 2022Received in revised form: December 14, 2021Received: September 29, 2021

Click on Link Below for Complete Study:


Why are we vaccinating children against COVID-19?

Authors: Ronald N.Kostoffa DanielaCalinab DarjaKanducc Michael B.Briggsd Panayiotis Vlachoyiannopoulose Andrey A.Svistunovf AristidisTsatsakisg


• Bulk of COVID-19 per capita deaths occur in elderly with high comorbidities.

•Per capita COVID-19 deaths are negligible in children.

•Clinical trials for these inoculations were very short-term.

•Clinical trials did not address long-term effects most relevant to children.

•High post-inoculation deaths reported in VAERS (very short-term).


This article examines issues related to COVID-19 inoculations for children. The bulk of the official COVID-19-attributed deaths per capita occur in the elderly with high comorbidities, and the COVID-19 attributed deaths per capita are negligible in children. The bulk of the normalized post-inoculation deaths also occur in the elderly with high comorbidities, while the normalized post-inoculation deaths are small, but not negligible, in children. Clinical trials for these inoculations were very short-term (a few months), had samples not representative of the total population, and for adolescents/children, had poor predictive power because of their small size. Further, the clinical trials did not address changes in biomarkers that could serve as early warning indicators of elevated predisposition to serious diseases. Most importantly, the clinical trials did not address long-term effects that, if serious, would be borne by children/adolescents for potentially decades.

A novel best-case scenario cost-benefit analysis showed very conservatively that there are five times the number of deaths attributable to each inoculation vs those attributable to COVID-19 in the most vulnerable 65+ demographic. The risk of death from COVID-19 decreases drastically as age decreases, and the longer-term effects of the inoculations on lower age groups will increase their risk-benefit ratio, perhaps substantially.

Graphical abstract


COVID-19SARS-CoV-2InoculationmRNA vaccines Viral vector vaccines Adverse events Vaccine safety

1. Introduction

Currently, we are in the fifteenth month of the WHO-declared global COVID-19 pandemic. Restrictions of different severity are still in effect throughout the world [1]. The global COVID-19 mass inoculation is in its eighth month. As of this writing in mid-June 2021, over 800,000,000 people globally have received at least one dose of the inoculation and roughly half that number have been fully inoculated [2]. In the USA, about 170,000,000 people have received at least one dose and roughly 80 % of that number have been fully inoculated [2].

Also, in the USA, nearly 600,000 deaths have been officially attributed to COVID-19. Almost 5,000 deaths following inoculation have been reported to VAERS by late May 2021; specifically, “Over 285 million doses of COVID-19 vaccines were administered in the United States from December 14, 2020, through May 24, 2021. During this time, VAERS received 4,863 reports of death (0.0017 %) among people who received a COVID-19 vaccine.” [3] (the Vaccine Adverse Events Reporting System (VAERS) is a passive surveillance system managed jointly by the CDC and FDA [3]. Historically, VAERS has been shown to report about 1% of actual vaccine/inoculation adverse events [4]. See Appendix 1 for a first-principles confirmation of that result). By mid-June, deaths following COVID-19 inoculations had reached the ˜6000 levels.

A vaccine is legally defined as any substance designed to be administered to a human being for the prevention of one or more diseases [5]. For example, a January 2000 patent application that defined vaccines as “compositions or mixtures that when introduced into the circulatory system of an animal will evoke a protective response to a pathogen.” was rejected by the U.S. Patent Office because “The immune response produced by a vaccine must be more than merely some immune response but must be protective. As noted in the previous Office Action, the art recognizes the term “vaccine” to be a compound which prevents infection” [6]. In the remainder of this article, we use the term ‘inoculated’ rather than vaccinated, because the injected material in the present COVID-19 inoculations prevents neither viral infection nor transmission. Since its main function in practice appears to be symptom suppression, it is operationally a “treatment”.

In the USA, inoculations were administered on a priority basis. Initially, first responders and frontline health workers, as well as the frailest elderly, had the highest priority. Then the campaign became more inclusive of lower age groups. Currently, approval has been granted for inoculation administration to the 12–17 years demographic, and the target for this demographic is to achieve the largest number of inoculations possible by the start of school in the Fall. The schedule for inoculation administration to the 5–11 years demographic has been accelerated to start somewhere in the second half of 2021, and there is the possibility that infants as young as six months may begin to get inoculated before the end of 2021 [7].

The remainder of this article will focus on the USA situation, and address mainly the pros and cons of inoculating children under eighteen. The article is structured as follows:

Section 1 (the present section) introduces the problem.

Section 2 (Background):1) provides the background for the declared COVID-19 “pandemic” that led to the present inoculations;2) describes the clinical trials that provided the justification for obtaining Emergency Use Authorization (EUA) from the FDA to administer the inoculations to the larger population;3)

shows why the clinical trials did not predict either the seriousness of adverse events that have occurred so far (as reported in VAERS) or the potential extent of the underlying pre-symptomatic damage that has occurred as a result of the inoculations.

Section 3 (Mass Inoculation) summarizes the adverse events that have occurred already (through reporting in VAERS) from the mass inoculation and will present biological evidence to support the potential occurrence of many more adverse effects from these inoculations in the mid-and long-term.

Section 4 (Discussion) addresses these effects further

Section 5 (Summary and Conclusions) presents the conclusions of this study.

There are four appendices to this paper.

Appendix A provides some idea of the level of under-reporting of post-inoculation adverse events to VAERS and presents estimations of the actual number of post-inoculation deaths based on extrapolating the VAERS results to real-world experiences.

Appendix B provides a detailed analysis of the major clinical trials that were used to justify EUA for the inoculants presently being administered in the USA.

Appendix C summarizes potential adverse effects shown to have resulted from past vaccines, all of which could potentially occur as a result of the present inoculations.

Appendix D presents a novel best-case scenario cost-benefit analysis of the COVID-19 inoculations that have been administered in the USA.

2. Background

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) [8,9]. 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 [10,11]. Restrictive measures of differing severity were implemented by countries globally, and included social distancing, quarantining, face masks, frequent hand sanitation, etc. [12,13]. In the USA, these measures were taken as well, differing from state-to-state [14]. At the same time, vaccine development was initiated to control COVID-19 [15]. 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 [11,16,17].

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 [4,18]. 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 [[19][20][21]]. In this range, very high numbers of false positives are possible [22].

Third, most deaths attributed to COVID-19 were elderly with high comorbidities [1,22]. As we showed in a previous study [22], attribution of death to one of many possible comorbidities or especially toxic exposures in combinations [23] is highly arbitrary and can be viewed as a political decision more than a medical decision. For over 5 % of these deaths, COVID-19 was the only cause mentioned on the death certificate. For deaths with conditions or causes in addition to COVID-19, on average, there were 4.0 additional conditions or causes per death [24]. These deaths with comorbidities could equally have been ascribed to any of the comorbidities [22]. 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 [25,26]. 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 [27]. 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

2.2.1. Clinical trials to gain FDA Emergency Use Authorization (EUA) approval

The unprecedented accelerated development of COVID-19 vaccines in the USA, dubbed Operation Warp Speed, resulted in a handful of substances available for clinical trials by mid-2020 [28]. These clinical trials were conducted to predict the safety and efficacy of the potential vaccines (which have turned out to be treatments/inoculations as stated previously), and thereby gain approval for inoculating the public at large [29]. An overview of the Pfizer clinical trials is presented in this section, and a more detailed description of the main clinical trials is shown in Appendix B.

Two types of inoculants have gained FDA EUA in the US: mRNA-based inoculants and viral vector-based inoculants, with the mRNA inoculants having the widest distribution so far. Comirnaty is the brand name of the mRNA-based inoculant developed by Pfizer/BioNTech, and Moderna COVID-19 Vaccine is the brand name of the mRNA-based inoculant developed by Moderna [30]. Both inoculants contain the genetic information needed for the production of the viral protein S (spike), which stimulates the development of a protective immune response against COVID-19 [31]. Janssen COVID-19 Vaccine is the brand name of the viral vector-based inoculant developed by Johnson and Johnson. Janssen COVID-19 vaccine uses an adenovirus to transport a gene from the coronavirus into human cells, which then produce the coronavirus spike protein. This spike protein primes the immune system to fight off potential coronavirus infection [32].

The results of these trials that allowed granting of EUA by the FDA can be found in the inserts to the inoculation materials. For example, the Pfizer inoculation trial results are contained in the fact sheet for healthcare providers administering vaccine (vaccination providers) [33].

There were two clinical trials conducted to gain FDA EUA for Pfizer: a smaller Phase 1/2 study, and a larger Phase 1/2/3 study. The age demographics for the larger clinical study are as follows (from the Pfizer insert): “Of the total number of Pfizer-BioNTech COVID-19 Vaccine recipients in Study 2 (N = 20,033), 21.4 % (n = 4,294) were 65 years of age and older and 4.3 % (n = 860) were 75 years of age and older.” Additionally: “In an analysis of Study 2, based on data up to the cutoff date of March 13, 2021, 2,260 adolescents (1,131 Pfizer-BioNTech COVID-19 Vaccine; 1,129 placebo) were 12 through 15 years of age. Of these, 1,308 (660 Pfizer-BioNTech COVID-19 Vaccine and 648 placebo) adolescents have been followed for at least 2 months after the second dose of Pfizer-BioNTech COVID-19 Vaccine. The safety evaluation in Study 2 is ongoing.”

The relevant demographics are presented in Table 7 on p.31 of the Pfizer insert. The age component of those demographics is shown below in Table 1.

Table 1. Demographics (population for the primary efficacy endpoint). The number of participants who received vaccine and placebo, stratified by age.

AGE GROUPPfizer-BioNTech COVID-19 Vaccine (N = 18,242) n (%)Placebo (N = 18,379)
n (%)
≥12 through 15 yearsb46 (0.3 %)42 (0.2 %)
≥16 through 17 years66 (0.4 %)68 (0.4 %)
≥16 through 64 years14,216 (77.9 %)14,299 (77.8 %)
≥65 through 74 years3176 (17.4 %)3226 (17.6 %)
≥75 years804 (4.4 %)812 (4.4 %)

Symbols: b: “100 participants 12 through 15 years of age with limited follow-up in the randomized population received at least one dose (49 in the vaccine group and 51 in the placebo group). Some of these participants were included in the efficacy evaluation depending on the population analyzed. They contributed to exposure information but with no confirmed COVID-19 cases, and did not affect efficacy conclusions.”, N: number of test subjects, n: number of controls.

There are very minor differences between most of the data in the above table and the preceding narrative shown, and they are probably due to different time horizons. The major difference is the number of adolescents used and appears to result from a much later reporting time.

Fig. 1 uses the official large CDC numbers (coupled with USA census data estimates from CDC Wonder) to show the COVID-19 deaths per capita as a function of age, circa early June 2021. Unfortunately, the most critical range, 85+, has the least resolution. It is obvious that most of the deaths occurred in the 55 to 100+ range, and the remaining individuals in the other ranges (especially under 35) have negligible risk of dying from the disease.

Fig. 1

The age distribution in Fig. 1 differs substantially from the age distribution in Table 1. Why is this important? When designing a trial for the efficacy and safety of a potential treatment, the focus should be on the target population who could benefit from that treatment. There is little rationale for including participants in a trial for whom the treatment would not be relevant or warranted.

For the COVID-19 Pfizer trials, based on the data from Fig. 1, the trial population should have been limited at most to the 45−100+ age segment, appropriately weighted toward the higher end where the deaths per capita are most frequent. That was almost the exact opposite of what was done in the Pfizer clinical trials. In Fig. 1, approximately 58 % of the deaths occurred in the age range 75+, whereas 4.4 % of the participants in the Pfizer clinical trial were 75 + . Thus, the age range most impacted by COVID-19 deaths was minimally represented in the Pfizer clinical trials, and the age range least impacted by COVID-19 deaths was maximally represented in the Pfizer clinical trials. This skewed sampling has major implications for predicting the expected numbers of deaths for the target population from the clinical trials.

Besides age, the other metric of importance in determining COVID-19 deaths is the presence of comorbidities. The more comorbidities, and the more severe the comorbidities, the greater the chances of death or severe adverse outcomes from COVID-19. It is not clear how well the number and severity of comorbidities in the clinical trial sample matched those reflected in Fig. 1, but the insert does mention the large number of conditions that excluded participation in the trials. In sum, the results from the clinical trials could not be expected to reflect the results that could occur (and have occurred) from mass inoculation of the public, given the unaffected nature of the bulk of the trial population from SARS-CoV-2 exposure.

The prior discussion on the clinical trials has focused on the efficacy and safety of the inoculants, and the relationship of the trial test population to the total target population. We have limited the focus so far to the safety and efficacy issues since these constituted the core of what was presented to the FDA for EUA approval. We have not focused on the trials from an early warning indicator perspective.

We will address summarily the science/early warning indicator issues associated with the Pfizer trials, and how the neglect of these issues has translated into disastrous consequences during the mass inoculation rollout. Standard practice for determining and understanding the impact of new technology (such as mRNA “vaccines”) on a system involves measuring the state and flux variables of the system before the new technology intervention, measuring the state and flux variables of the system after the new technology intervention, and identifying the types and magnitudes of changes in the state and flux variables attributable to the intervention. This would be in addition to evaluating performance metrics before and after the intervention.

In Pfizer’s proposed clinical trials for the mRNA “vaccine” (Study to Describe the Safety, Tolerability, Immunogenicity, and Efficacy of RNA Vaccine Candidates Against COVID-19 in Healthy Individuals – https://clinicaltrials.gov/ct2/show/NCT04368728), the focus was on determining 1) adverse events/symptoms, 2) SARS-CoV-2 serum neutralizing antibody levels, 3) SARS-CoV-2 anti-S1 binding antibody levels and anti-RBD binding antibody levels, and 4) effectiveness. These metrics are all related to safety at the symptom level and performance.

However, symptoms/diseases are typically end points of processes that can take months, years, or decades to surface. During that symptom/disease development period, many biomarker early warning indicators tend to exhibit increasing abnormalities that reflect an increasing predisposition to the eventual symptom/disease. Thus, serious symptoms/diseases that ordinarily take long periods to develop would be expected to be rare events if they occurred shortly following an inoculation. If the clinical trials that were performed by Pfizer and Moderna were designed to focus on efficacy and only adverse effects at the symptom level of description as an indicator of safety, the trial results would be limited to the identification of rare events, and the trial results would potentially under-estimate the actual pre-symptom level damage from the inoculations.

Credible safety science applied to this experiment would have required a much more expansive approach to determining effects on a wide variety of state and flux metrics that could serve as early warning indicators of potentially serious symptoms/disease, and might occur with much higher frequencies at this early stage than the rare serious symptoms. The only mention of these other metrics in the above proposal is in the Phase I trial description: “Percentage of Phase 1 participants with abnormal haematology and chemistry laboratory values”, to be generated seven days after dose 1 and dose 2.

A paper published in NEJM in December 2020 [34] summarized the Phase 1 results. The focus was on local and systemic adverse events and efficacy metrics (antibody responses). The only metrics other than these reported were transiently decreased lymphocyte counts.

We view this level of reporting as poor safety science for the following reasons. Before the clinical trials had started, many published articles were reporting serious effects associated with the presence of the SARS-CoV-2 virus such as hyperinflammation, hypercoagulation, hypoxia, etc. SARS-CoV-2 includes the S1 Subunit (spike protein), and it was not known how much of the damage was associated with the spike protein component of SARS-CoV-2. A credible high-quality safety science experiment would have required state measurements of specific biomarkers associated with each of these abnormal general biomarkers before and after the inoculations, such as d-dimers for evidence of enhanced coagulation/clotting; CRP for evidence of enhanced inflammation; troponins for evidence of cardiac damage; occludin and claudin for evidence of enhanced barrier permeability; blood oxygen levels for evidence of enhanced hypoxia; amyloid-beta and phosphorylated tau for evidence of increased predisposition to Alzheimer’s disease; Serum HMGB1, CXCL13, Dickkopf-1 for evidence of an increased disposition to autoimmune disease, etc. A credible high-quality safety science experiment would have required flux measurements of products resulting from the mRNA interactions, from the LNP shell interactions, from dormant viruses that might have been stimulated by the mRNA-generated spike protein, etc., emitted through the sweat glands, faeces, saliva, exhalation, etc.

Most importantly, these types of measurements would have shown changes in the host that did not reach the symptom level of expression but raised the general level of host abnormality that could predispose the host to a higher probability of serious symptoms and diseases at some point in the future. Instead, in the absence of high-quality safety science reflected in these experiments, all that could be determined were short-term adverse effects and deaths. This focus on symptoms masked the true costs of the mRNA intervention, which would probably include much larger numbers of people whose health could have been degraded by the intervention as evidenced by increased abnormal values of these biomarkers. For example, the trials and VAERS reported clots that resulted in serious symptoms and deaths but gave no indication of the enhanced predisposition to forming serious clots in the future with a higher base of micro-clots formed because of the mRNA intervention. The latter is particularly relevant to children, who have a long future that could be seriously affected by having an increased predisposition to multiple clot-based (and other) serious diseases resulting from these inoculations.

3. Mass inoculation

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 given2)

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 [3]. 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. 1Fig. 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.

Fig. 2

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.

3.1.1. Specific short-term adverse events reported in VAERS

The most comprehensive single evaluation of VAERS-reported adverse events (mainly for adult recipients of the COVID-19 “vaccines”) we have seen is a non-peer-reviewed collection of possible side effects by Dr. Ray Sahelian [35]. We recommend reading this short data-rich summary of the broad types of events reported already, in the context that these events are very short-term. Dr. Sahelian identifies five mechanisms he believes are responsible for most of these events, with research potentially uncovering other mechanisms. These five mechanisms include:1

“An overreacting inflammatory response is known as systemic inflammatory response syndrome (SIRS). This SIRS reaction, perhaps a cytokine storm, can range from very mild to very severe. It can begin the very first day of the shot or begin days or weeks later as a delayed reaction.”2

“Interaction of the spike proteins with ACE2 receptors on cell membranes. Such cells are found widely in the body including the skin, lungs, blood vessels, heart, mouth, gastrointestinal tract, kidneys, and brain.”3

“Interaction of spike proteins with platelets and/or endothelial cells that line the inside of blood vessels. This can lead to clotting or bleeding (low number of circulating platelets in the bloodstream). Some of the clots, even if tiny, cause certain neurological symptoms if the blood supply to nerves is compromised.”4

“Immediate or delayed release of histamine from mast cells and basophils (mast cell activation syndrome, MCAS).”5

5. “Swelling of lymph nodes in various areas of the body could interfere with blood flow, put pressure on nerves causing pain, or compromise their proper function.”

These reactions can be classified as Hyperinflammation, Hypercoagulation, Allergy, and Neurological, and can contribute to many symptoms and diseases, as VAERS is showing.

An excellent review of acute and potential long-term pathologies resulting from the COVID-19 inoculations [36] showed potential relationships to blood disordersneurodegenerative diseases and autoimmune diseases. This review discussed the relevance of prion-protein-related amino acid sequences within the spike protein.

3.1.2. Potential mid- and long-term events and serious illnesses for adults and children from past vaccines

A detailed description of potential mid- and long-term events and serious illnesses for adults and children from past vaccines is presented in Appendix C. Most of these events and illnesses are not predictable, and most, if not all, would be possible for the COVID-19 inoculations in the mid- and long-term for adults and children.

3.1.3. Potential short-, mid-, and long-term risks of mass COVID-19 inoculation for children Intrinsic inoculant toxicity

Children are unique relative to COVID-19. They have negligible risks of serious effects from the disease, as shown in Fig. 1. Given that the COVID-19 inoculants were only tested for a few months, and mid-or long-term adverse effects are unknown, any mid- or long-term adverse events that emerge could impact children adversely for decades.

We believe that mid-or long-term adverse effects are possible based on the recent emergence of evidence that would support the probability of mid-and long-term adverse effects from the COVID-19 inoculants, such as:1)

The spike protein itself can be a toxin/pathogenic protein:2)

S protein alone can damage vascular endothelial cells (ECs) by downregulating ACE2 and consequently inhibiting mitochondrial function [37].3)

it is concluded that ACE2 and endothelial damage is a central part of SARS-CoV2 pathology and may be induced by the spike protein alone [38].4)

the spike protein of SARS-CoV-1 (without the rest of the virus) reduces ACE2 expression, increases angiotensin II levels, exacerbates lung injury, and triggers cell signaling events that may promote pulmonary vascular remodeling and Pulmonary Arterial Hypertension (PAH) as well as possibly other cardiovascular complications [39].5)

the recombinant S protein alone elicits functional alterations in cardiac vascular pericytes (PCs) [40]. This was documented as:6)

increased migration7)

reduced ability to support EC network formation on Matrigel8)

secretion of pro-inflammatory molecules typically involved in the cytokine storm9)

production of pro-apoptotic factors responsible for EC death. Furthermore, the S protein stimulates the phosphorylation/activation of the extracellular signal-regulated kinase 1/2 (ERK1/2) through the CD147 receptor, but not ACE2, in cardiac PCs, the S protein may elicit vascular cell dysfunction, potentially amplifying, or perpetuating, the damage caused by the whole coronavirus [40].10)

“even in the absence of the angiotensin-converting enzyme 2 receptors, the S1 subunit from SARS-CoV-2 spike protein binding to neutral phospholipid membranes leads to their mechanical destabilization and permeabilization. A similar cytotoxic effect of the protein was seen in human lung epithelial cells.” [125].11)

The LNP layer encapsulating the mRNA of the inoculant is highly inflammatory in both intradermal and intranasal inoculation [41] and “Polyethylene glycol (PEG) is a cause of anaphylaxis to the Pfizer/BioNTech mRNA COVID-19 vaccine” [42]. “Humans are likely developing PEG antibodies because of exposure to everyday products containing PEG. Therefore, some of the immediate allergic responses observed with the first shot of mRNA-LNP vaccines might be related to pre-existing PEG antibodies. Since these vaccines often require a booster shot, anti-PEG antibody formation is expected after the first shot. Thus, the allergic events are likely to increase upon re-vaccination” [43].

There is also the possibility that the components of the LNP shell could induce the ASIA Syndrome (autoimmune/inflammatory syndrome induced by adjuvants), as shown by studies on post-inoculation thyroid hyperactivity [44] and post-inoculation subacute thyroiditis [45].12

The spike protein has been found in the plasma of post-inoculation individuals, implying that it could circulate to, and impact adversely, any part of the body [46].13

The spike protein of SARS-CoV-2 crosses the blood-brain barrier in mice [47], and “the SARS-CoV-2 spike proteins trigger a pro-inflammatory response on brain endothelial cells that may contribute to an altered state of BBB function” [48].14

The spike proteins manufactured in vivo by the present COVID-19 inoculations could potentially “precipitate the onset of autoimmunity in susceptible subgroups, and potentially exacerbate autoimmunity in subjects that have pre-existing autoimmune diseases”, based on the finding that anti-SARS-CoV-2 protein antibodies cross-reacted with 28 of 55 diverse human tissue antigens [49].15

“The biodistribution of ChaAdOx1 [Astra Zeneca’s recombinant adenovirus vaccine candidate against SARS-CoV-2] in mice confirmed the delivery of vaccine into the brain tissues [50]. The vaccine may therefore spur the brain cells to produce CoViD spike proteins that may lead to an immune response against brain cells, or it may spark a spike protein-induced thrombosis. This may explain the peculiar incidences of the fatal cerebral venous sinus thrombosis (CVST) observed with viral vector-based CoViD-19 vaccines” [51,52].

A complementary perspective to explain adenovirus-based vaccine-induced thrombocytopenia is that “transcription of wildtype and codon-optimized Spike open reading frames enables alternative splice events that lead to C-terminal truncated, soluble Spike protein variants. These soluble Spike variants may initiate severe side effects when binding to ACE2-expressing endothelial cells in blood vessels.” [100].16

A Pfizer Confidential study performed in Japan showed that “modRNA encoding luciferase formulated in LNP comparable to BNT162b2″ injected intramuscularly concentrated in many organs/tissues in addition to the injection site [53]. The main organs/sites identified were adrenal glands, liver, spleen, bone marrow, and ovaries. While damage to any of these organs/sites could be serious (if real for humans), adverse effects on the ovaries could be potentially catastrophic for women of childbearing or pre-childbearing age.

The main objective of credible biodistribution studies (of inoculants for eventual human use) is to identify the spatio-temporal distribution of the actual inoculant in humans; i.e., how much of the final desired product (in this case, expressed protein antigen/spike protein) is produced in different human tissues and organs as a function of time. That’s not what was reported in the Pfizer Confidential study.

Rats were used for the in vivo studies; the relationship of their biodistribution to that of humans is unclear. They were injected in different locations (hindpaw/intramuscular); the relationship to human injections in the deltoid muscle is unclear. They were injected with “modRNA encoding luciferase formulated in LNP comparable to BNT162b2″; it is unclear why they weren’t injected with BNT162b2, it is unclear why spike protein expression wasn’t evaluated rather than LNP concentration, and it is unclear how well the biodistribution from the actual inoculant used in the experiments compares to the biodistribution from BNT162b2.

They were injected once per rat. Given that a second injection would not be in the same exact location as the first, and that the circulatory system might have changed due to clotting effects from the first injection and other potential vascular complications, it is unclear how the biodistribution change with the second injection would compare with the first. If a booster injection is given to counter variants, it is unclear how its biodistribution would be altered as a consequence of the preceding two injections.

Clotting will occur with the highest probability where the blood flow is reduced (and more time is available for LNP-endothelial cell interaction). It is unclear whether the clotting process would show positive feedback behaviour where the initial inoculation constricts the flow in low-velocity regions even further by enhanced clotting, and subsequent inoculations further amplify this reduced flow-enhanced clotting cycle.

The rats were injected under pristine conditions; how that compares with humans, who have been, are being, and will continue to be exposed to multiple toxic substances in combination, is open to question. We know these combinations can act synergistically to adversely impact myriad organs and tissues throughout the body [23]. We don’t know how these toxic exposures in humans affect the permeability of the blood/tissue barriers, and especially the ability of the injected material to diffuse into the bloodstream (and also the ability of the manufactured spike proteins to diffuse from the bloodstream into the surrounding tissue).

Higher-level primates should have been used for these short-term experiments, to obtain a more realistic picture of the biodistribution of inoculant in human organs and tissues. In other words, these laboratory experiments may be just the tip of the iceberg of estimating the amount of inoculant that concentrates in critical organs and tissues of human beings.

The many studies referenced above indicate collectively that the mRNA-based COVID-19 inoculations (the most prolific inoculations used in the USA for COVID-19 so far) consist of (at least) two major toxins: the instructions for the spike protein (mRNA) and the mRNA-encapsulating synthetic fat LNP. The vaccine is injected into the deltoid muscle, at which time it contributes to inflammation at the injection site due in part to the LNP and potentially to anaphylaxis from the LNP PEG-2000 component. Some of the injected material stays at the injection site, where it combines with cells through endocytosis to express spike protein on the cell surface, stimulating the adaptive immune system to eventually produce antibodies to the spike protein [54].

The remainder of the injected material enters the lymphatic system and the bloodstream, and is distributed to tissues and organs throughout the body: e.g., “Drugs administered by the intramuscular (IM) route are deposited into vascular muscle tissue, which allows for rapid absorption into the circulation” [55]. The basis of this process is that the bulky muscles have good vascularity, and therefore the injected drug quickly reaches the systemic circulation and thereafter into the specific region of action, bypassing the first-pass metabolism [56]. The widespread distribution is greatly enhanced by the LNP PEG-2000 coating as follows: building from the success of PEGylating proteins to improve systemic circulation time and decrease immunogenicity [57]. PEG coatings on nanoparticles shield the surface from aggregation, opsonization, and phagocytosis, prolonging systemic circulation time. [57]. PEG coatings on nanoparticles have also been utilized for overcoming various biological barriers to efficient drug and gene delivery associated with other modes of administration. [57]

In the bloodstream, one possible outcome is that the LNPs coalesce with the endothelial cells on the inner lining of the blood vessels and transfer the mRNA to the cells through endocytosis. The endothelial cells would then express the spike protein on their surface. Platelets flowing by the spike protein express ACE2 receptors on their surface; therefore, one possible outcome would be activation of the platelets by the spike protein and initiation of clotting. Another possible outcome would be the modified endothelial cells being recognized by innate immune system cells as foreign. These immune killer cells would then destroy parts of the endothelium and weaken the blood-organ barriers. The LNPs would inflame the endothelium as well, both increasing barrier permeability and increasing the blood vessel diameter. This weakening of the blood-organ barriers would be superimposed on any inflammation due to the myriad toxic contributing factors operable [4]. The newly-formed cells with spike proteins would penetrate the blood-organ barriers and bind to tissue with expressed ACE2 receptors. Any LNPs that did not coalesce with the endothelial cells, but remained intact, could also pass through the permeable blood-organ barrier, and coalesce directly with the organ cells. This could lead to an attack by innate immune system cells, and be a precursor to autoimmunity [4].

In the preceding discussion of the Pfizer biodistribution studies, the issue of multiple inoculations on changes in biodistribution was raised. Similarly, the alteration of effects as described above by multiple inoculations must be considered. Each inoculation will have positive aspects and negative aspects. The positive aspects are the formation of antibodies in the muscle cells and lymphatic system. The negative aspects include, but are not limited to, the potential clotting effects and permeability increases for that fraction of the inoculant that enters the bloodstream. The first inoculant dose can be viewed as priming the immune system. The immune response will be relatively modest. The second inoculant dose can be expected to elicit a more vigorous immune response. This will enhance the desired antibody production in the muscle cells and lymphatic system, but may also enhance the immune response to both the blood vessel-lining endothelial cells displaying the spike protein and the platelets, causing more severe damage. If a booster(s) inoculation is also required, this may further enhance both the positive and negative immune responses resulting from the second inoculation. While the positive effects are reversible (antibody levels decrease with time), adverse effects may be cumulative and irreversible, and therefore injury and death rates may increase with every additional inoculation [58].

These effects can occur throughout the body in the short term, as we are seeing with the VAERS results. They can occur in the mid- and long-term as well, due to the time required for destructive processes to have full effect and the administration of further inoculations. For example, micro-clots resulting from the inoculation that were insufficient to cause observable symptoms could in effect raise the baseline for thrombotic disease [92]. Lifestyle activities that contribute to enhanced blood clotting would have less distance to travel to produce observable symptoms, and thus the serious effects of clotting would have been accelerated [59,60]. As an example: the risk of venous thrombosis is approximately 2- to 4-fold increased after air travel [61]. How much this rate would increase after the inoculations, where microthrombi have formed in some recipients, is unknown. These potential baseline-raising effects could impact the interpretation of the VAERS results, as we show at the end of Appendix 1. Adverse inoculant effects on children

What are the potential mid- and long-term adverse health effects from the COVID-19 inoculation on children specifically, taking into account that they will be exposed not only to the spike protein component of the SARS-CoV-2 virus but also to the toxic LNP encapsulating-shell? This toxic combination will have bypassed many defensive safeguards (typically provided by the innate immune system) through direct injection [62]. As we have shown, the main reasons why we believe the spike protein could be harmful to children even though they don’t seem to get sick from exposure to SARS-CoV-2 are 1) the bypassing of the innate immune system by inoculation, 2) the larger volume of spike protein that enters the bloodstream, and 3) the additional toxic effects of the encapsulating LNP layer. Potential mid-term adverse health effects

Examination of the myriad post-COVID-19 inoculation symptoms/biomarker changes for the 0–17 age demographic reported to VAERS circa mid-June 2021 provides some indication of very early damage [84]. Main regions/systems affected adversely (VAERS symptoms/biomarkers shown in parentheses) include:•

Cardiovascular (blood creatine phosphokinase increased, cardiac imaging procedure abnormal, echocardiogram abnormal, electrocardiogram abnormal, heart rate increased, myocarditispalpitationspericarditistachycardiatroponin I increased, troponin increased, fibrin D-Dimer increased, platelet count decreased, blood pressure increased, bradycardiabrain natriuretic peptide increased, ejection fraction decreased, migraine)•

Gastrointestinal (abdominal pain, diarrhoea, vomiting, alanine aminotransferase increased, aspartate aminotransferase increased.)•

Neural (gait disturbance, mobility decreased, muscle spasms, muscle twitching, seizure, tremor, Bell’s Palsy, dyskinesia)•

Immune (C-Reactive Protein increased, red blood cell sedimentation rate increased, white blood cell counts increased, inflammation, anaphylactic reaction, pruritis, rash, lymphadenopathy)•

Endocrine (heavy menstrual bleeding, menstrual disorder)

In addition, there were large numbers of different vision and breathing problems reported.

All the major systems of the body are impacted, and many of the major organs as well. Given the lag times in entering data into VAERS and the fact that inoculations of children started fairly recently, we would expect the emphasis to be immediate symptomatic and biomarker reactions. More time is required for organ and system damage to develop and emerge. Cardiovascular problems dominate, as our model for spike protein/LNP circulation and damage predicts, and it is unknown how reversible such problems are. Many of the VAERS symptoms listed above were also found in COVID-19 adult patients [64].

Consider the example of Multisystem Inflammatory Syndrome in Children (MIS-C). It has emerged in VAERS with modest frequency so far, and it also occurred about a month after COVID-19 infection [65]. In both cases, the presence of the spike protein was a common feature. Many of its characteristic symptoms are those listed above from VAERS. MIS-C has similarities with known disease entities like Kawasaki Disease (KD), toxic shock syndrome (TSS) and macrophage activation syndrome (MAS)/secondary hemophagocytic lymphohistiocytosis (HLH) [66]. One presentation of MIS-C is in adolescents with a high disease burden as evidenced by more organ systems involved, almost universally including cardiac and gastrointestinal systems, and with a higher incidence of shock, lymphopenia, and elevated cardiac biomarkers indicating myocarditis [67]. Since the first reports of children developing MIS-C, it was evident that others presented with some of the classic symptoms of the well-recognized childhood illness KD [68]. Further, despite KD being ordinarily incredibly rare in adults, patients with MIS-A have also been reported with KD-like features. [68] Thus, an examination of the adverse effects from COVID-19 as evidenced through these diseases might shed some light on what can be expected further down the line from the inoculations.

The following section addresses Kawasaki disease (KD) and Multisystem Inflammatory Syndrome in Children (MIS-C) [65].

KD is an acute vasculitis and inflammation that predominantly affects the coronary arteries and can cause coronary artery aneurysms. Other KD manifestations include systemic inflammation of arteries, organs, and tissues, with consequent hepatitis and abdominal pain; lung interstitial pneumonitisaseptic meningitis due to brain membrane inflammations; myocarditis, pericarditis, and valvulitis; urinary tract pyuriapancreatitis; and lymph-node enlargement [69]. In general, although almost all children fully recover, some of them later develop coronary artery dilation or aneurysm [70]. Etiologically and pathologically, numerous studies indicate that KD is triggered by an abnormal autoimmune response caused by an infection [71]. The infection hypothesis is supported by epidemiology data showing that an infectious disease is involved at least as a starting point. Previously proposed infectious agents include Herpesviridaeretroviruses, Parvovirus B19, bocavirus, and bacterial infections such as staphylococci, streptococci, Bartonella, and Yersinia infections [72].

SARS-CoV-2 adds to these infectious agents by eliciting autoantibodies likely via molecular mimicry and cross-reactivity with autoantigens [72,73].

Then, the formation of antigen–antibody immune complexes can lead to KD symptoms via activation of the receptors of mast cells, neutrophils, and macrophages with consequent release of pro-inflammatory cytokines and increase of blood vessel permeability; activation of the complement system, stimulation of neutrophils and macrophages to secrete proteases and more proinflammatory cytokines [74], thus merging into the “cytokine storm” that characterizes MIS-C [75]. Indeed, features of KD are raised levels of Interleukin (IL)-6, IL-8, IL-15, and IL-17, with the cytokine level predicting coronary aneurysm formation in KD patients [76,77] Potential long-term adverse health effects

In the long-term, SARS-CoV-2-induced KD vasculitis can lead to severe pathologies. Vasculitis has a predilection for coronary arteries with a high complication rate across the lifespan for those with medium to large coronary artery aneurysms [78]. The cytokine-induced inflammation produces endothelial dysfunction and damage to the vascular wall, leading to aneurysmal dilatation. Successively, vascular remodeling can also occur, but this does not imply resolution of the disease or reduction of risk for future complications. A rigorous follow-up to detect progressive stenosis, thrombosis and luminal occlusion that may lead to myocardial ischemia and infarction becomes mandatory [78]. Of equal importance, among other long-term outcomes, children with KD may have increased risks not only for ischemic heart disease, but also for autoimmune disorders, cancer as well as an increased all-cause mortality [71].

Additional questions regarding mass inoculation of children and adolescents include:


Do children, being asymptomatic carriers of SARS-CoV-2, transmit the virus?b)

Do recently vaccinated people, infected with SARS-CoV-2, transmit the virus?

There is evidence of children transmitting SARS-CoV-2 in community settings, but the existing literature is heterogeneous with regards to the relative rate at which they do so compared to adults [79].

Studies from South Korea and Thailand found a very limited number of secondary cases [80,81]. On the contrary, a large contact tracing study from India concluded that the highest probability of transmission was between case-contact pairs of similar age and that this pattern of enhanced transmission risk was highest among children 0–4 years of age as well as adults 65 years of age and older [80]

With regard to the second question, it was shown that household members of healthcare workers inoculated with a single dose of either Pfizer or Astra Zeneca COVID-19 inoculant were at significantly reduced risk of PCR-confirmed SARS-CoV-2 infection but at non-statistically significant reduced risk of hospitalization, compared to household members of uninoculated healthcare workers, fourteen days after inoculation [82]. This finding again underlines the association of severe disease to the characteristics of the infected person and not directly to the transmission, implying that the elderly should be inoculated and not the children.

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 are 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.

4. Discussion

Two issues arise from these results.

First, where is the data justifying inoculation for children, much less most people under forty? It’s not found on Fig. 1, where the most vulnerable are almost exclusively the elderly with many comorbidities [83]. Yet, in the USA, Pfizer has been approved to inoculate children 12–17, and the goal is to accomplish this by the start of the school year in the Fall. As stated previously, there are plans to inoculate children as young as six months starting before the end of 2021.

What is the rush for a group at essentially zero risks? Given that the inoculations were tested only for a few months, only very short-term adverse effects could be obtained. It is questionable how well even these short-term effects obtained from the clinical trials reflect the short-term effects from the initial mass inoculation results reported in VAERS.

Fig. 1Fig. 2 reflect only these very short-term results. A number of researchers have suggested the possibility of severe longer-term autoimmune, Antibody-Dependent Enhancement, neurological, and other potentially serious effects, with lag periods ranging from months to years. If such effects do turn out to be real, the children are the ones who will have to bear the brunt of the suffering. There appear to be no benefits for the children and young adults from the inoculations and only Costs!

The second issue is why the deaths shown on Fig. 2 were not predicted by the clinical trials. We examined the Pfizer trial results (based on a few months of testing) and did not see how (potentially) hundreds of thousands of deaths could have been predicted from the trials’ mortality results. Why this gap?

As we showed in the clinical trials section, 17.4 % of the Pfizer sample members were over 65, and 4.4 % were over 75. When the later phases of the trials started in late July 2020, the managers knew the COVID-19 age demographics affected from the July 2020 analog of Fig. 1. Rather than sampling from the age region most affected, they sampled mainly from the age region least affected! And even in the very limited sampling from the oldest groups, it is unclear whether they selected from those with the most serious comorbidities. Our impression is that the sickest were excluded from the trials, but were first in line for the inoculants.

It is becoming clear that the central ingredient of the injection, the recipe for the spike protein, will produce a product that can have three effects. Two of the three occur with the production of antibodies to the spike protein. These antibodies could allegedly offer protection against the virus (although with all the “breakthrough” cases reported, that is questionable), or could suppress serious symptoms to some extent. They could also cross-react with human tissue antigen, leading to potential autoimmune effects. The third occurs when the injected material enters the bloodstream and circulates widely, which is enabled by the highly vascular injection site and the use of the PEG-2000 coating.

This allows spike protein to be manufactured/expressed in endothelial cells at any location in the body, both activating platelets to cause clotting and causing vascular damage. It is difficult to believe this effect is unknown to the manufacturer, and in any case, has been demonstrated in myriad locations in the body using VAERS data. There appears to be modest benefit from the inoculations to the elderly population most at risk, no benefit to the younger population not at risk, and much potential for harm from the inoculations to both populations. It is unclear why this mass inoculation for all groups is being done, being allowed, and being promoted.

5. Overall conclusions

The people with myriad comorbidities in the age range where most deaths with COVID-19 occurred were in very poor health. Their deaths did not seem to increase all-cause mortality as shown in several studies. If they hadn’t died with COVID-19, they probably would have died from the flu or many of the other comorbidities they had. We can’t say for sure that many/most died from COVID-19 because of: 1) how the PCR tests were manipulated to give copious false positives and 2) how deaths were arbitrarily attributed to COVID-19 in the presence of myriad comorbidities.

The graphs presented in this paper indicate that the frail injection recipients receive minimal benefit from the inoculation. Their basic problem is a dysfunctional immune system, resulting in part or in whole from a lifetime of toxic exposures and toxic behaviors. They are susceptible to either the wild virus triggering the dysfunctional immune system into over-reacting or under-reacting, leading to poor outcomes or the injection doing the same.

This can be illustrated by the following analogy. A person stands in a bare metal enclosure. What happens when the person lights a match and drops it on the floor depends on what is on the floor. If the floor remains bare metal, the match burns for a few seconds until extinguished. If there is a sheet of paper on the floor under the match, the match and the paper will burn for a short time until both are extinguished. If, however, the floor is covered with ammonium nitrate and similar combustible/explosive materials, a major explosion will result! For COVID-19, the wild virus is the match. The combustible materials are the toxic exposures and toxic behaviors. If there are no biomarker ‘footprints’ from toxic exposures and toxic behaviors, nothing happens. If there are significant biomarker ‘footprints’ from toxic exposures and toxic behaviors, bad outcomes result.

Adequate safety testing of the COVID-19 inoculations would have provided a distribution of the outcomes to be expected from ‘lighting the match’. Since adequate testing was not performed, we have no idea how many combustible materials are on the floor, and what the expected outcomes will be from ‘lighting the match’.

The injection goes two steps further than the wild virus because 1) it contains the instructions for making the spike protein, which several experiments are showing can cause vascular and other forms of damage, and 2) it bypasses many front-line defenses of the innate immune system to enter the bloodstream directly in part. Unlike the virus example, the injection ensures there will always be some combustible materials on the floor, even if there are no other toxic exposures or behaviors. In other words, the spike protein and the surrounding LNP are toxins with the potential to cause myriad short-, mid-, and long-term adverse health effects even in the absence of other contributing factors! Where and when these effects occur will depend on the biodistribution of the injected material. Pfizer’s own biodistribution studies have shown the injected material can be found in myriad critical organs throughout the body, leading to the possibility of multi-organ failure. And these studies were from a single injection. Multiple injections and booster shots may have cumulative effects on organ distributions of inoculant!

The COVID-19 reported deaths are people who died with COVID-19, not necessarily from COVID-19. Likewise, the VAERS deaths are people who have died following inoculation, not necessarily from inoculation.

As stated before, CDC showed that 94 % of the reported deaths had multiple comorbidities, thereby reducing the CDC’s numbers attributed strictly to COVID-19 to about 35,000 for all age groups. Given the number of high false positives from the high amplification cycle PCR tests, and the willingness of healthcare professionals to attribute death to COVID-19 in the absence of tests or sometimes even with negative PCR tests, this 35,000 number is probably highly inflated as well.

On the latter issue, both Virginia Stoner [85] and Jessica Rose [86] have shown independently that the deaths following inoculation are not coincidental and are strongly related to inoculation through strong clustering around the time of injection. Our independent analyses of the VAERS database reported in Appendix 1 confirmed these clustering findings.

Additionally, VAERS historically has under-reported adverse events by about two orders-of-magnitude, so COVID-19 inoculation deaths in the short-term could be in the hundreds of thousands for the USA for the period mid-December 2020 to the end of May 2021, potentially swamping the real COVID-19 deaths. Finally, the VAERS deaths reported so far are for the very short term. We have no idea what the death numbers will be in the intermediate and long-term; the clinical trials did not test for those.

The clinical trials used a non-representative younger and healthier sample to get EUA for the injection. Following EUA, the mass inoculations were administered to the very sick (and first responders) initially, and many died quite rapidly. However, because the elderly who died following COVID-19 inoculation were very frail with multiple comorbidities, their deaths could easily be attributed to causes other than the injection (as should have been the case for COVID-19 deaths as well).

Now the objective is the inoculation of the total USA population. Since many of these potential serious adverse effects have built-in lag times of at least six months or more, we won’t know what they are until most of the population has been inoculated, and corrective action may be too late.

All the authors contributed equally and approved the final version of the manuscript.

Author’s contribution

Kostoff RN contributed to this paper with conception, data analysis, and writing the manuscript; Calina D contributed to data analysis, writing the manuscript, and editing; Kanduc D participated in data analysis and writing the manuscript; Briggs MB participated in data analysis, results validation, and graphics development; Vlachoyiannopoulos P participated in writing the manuscript; Svistunov AA participated in editing and reviewing the manuscript; Tsatsakis A participated in editing and reviewing the manuscript; all the authors contributed equally and approved the final version of the manuscript.

Ethical approval

Not applicable.

Declaration of Competing Interest

The authors declare that they have no competing interests. Aristides Tsatsakis is the Editor-in-Chief for the journal but had no personal involvement in the reviewing process, or any influence in terms of adjudicating on the final decision, for this article.


Not applicable.

Appendix A


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 [93,94,101]. 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; and3) 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 [3].

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 [33] and other adverse events if later revised by FDA” [96,102,103]. “Serious AEs regardless of causality.”, including death [3,95].

If there had been full compliance with this requirement in VAERS, then the VAERS-reported deaths would have equaled the sum of1)

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-a . Problems with VAERS

Before we discuss numbers of adverse events reported by VAERS, we need to identify potential shortcomings of, and problems with, VAERS, so these numbers of adverse events can be understood in their proper context. As stated previously, VAERS is a passive surveillance system managed jointly by the CDC and FDA, and historically has been shown to report about 1% of actual vaccine/inoculation adverse events (confirmed by the first principles analysis that follows in this appendix). There is no evidence that even the 1% reported have been selected randomly.

Some of this gross underreporting of adverse events reflects a major conflict-of-interest of CDC with respect to VAERS. CDC provides funding for administration of many vaccines, including the COVID-19 inoculations. Prior to COVID-19, the CDC provided about five billion dollars annually to the Vaccines for Children Program alone [102].

For COVID-19, the CDC has received many billions of dollars in supplemental funding for myriad activities, including vaccine distribution. It is difficult to separate out the CDC funding available for vaccine distribution from other CDC COVID-19 related activities, but one budget item (of many) should illustrate the magnitude of the effort: “Coronavirus Response and Relief Supplemental Appropriations Act, 2021 (P.L. 116–260): P.L. 116–260 provided $8.75 billion to CDC to plan, prepare for, promote, distribute, administer, monitor, and track coronavirus vaccines to ensure broad-based distribution, access, and vaccine coverage.” [3]. Low reporting rates of actual adverse events in VAERS should not be surprising, since the same organization that receives multi-billions of dollars in funding annually for promoting and administering vaccines also has responsibility for monitoring the safety of these products (whose liability has been waived).

In addition, the 1% reporting rates came from a thirty-day tracking study [22], and therefore are strictly applicable to very near-term adverse events. For mid-term and especially long-term events, the reporting rates would be much lower, since the links between inoculation and adverse events would be less obvious. That doesn’t mean these non-very-short-term adverse events don’t exist; it just means they haven’t been tracked. Absence of evidence is not evidence of absence. Thus, the VAERS numbers should be viewed as a very low “floor’ of the numbers and types of adverse events from COVID-19 inoculations that exist in the real-world.

A1-a2 Global analysis

We used 2019 death statistics from CDC to start the analysis. According to search results from CDC Wonder [104] 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 [24]

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 [24].

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 [101] 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).

Potential covid deaths/#
non-covid expected
Mean time location/five months

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%. [3]

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 [4]. 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.

Consider the ten days following inoculation (including day of inoculation). Approximately 2,000 deaths were reported in VAERS. Assume hypothetically that all these deaths were in the expected category; this can be viewed as a best-case scenario. In this ˜best-case scenario, where the concentration of deaths is the highest and is normalized to the expected number of non-COVID-19 inoculation deaths (excluding deaths due solely to COVID-19 inoculation), 2,000/47,270 % of actual deaths (inoculation-related or not), or 4.23%, are reported in VAERS. Thus, at best, VAERS is underreporting by a factor of ˜20.

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 [4] 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.

Fig. A1

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.

Actual COVID-19 inoculation-based deaths from vaers reporting
Separate GroupsOverlapping Groups
Expected Deaths Reported37203720
Range Of Days Inoculation Deaths0−300−300−300−30
Total Reported Deaths Over Range2901290129012901
Total Expected Deaths Over Range11476201147620
Inoculation-Based Deaths Reported1754228129012901
Expected Deaths Reported/Total Expected.0077.0041.0077.0041
Total Actual Inoculation-Based Deaths Using Expected Ratio (Above)227792556341376753707561

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 [63]. 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 [105]. 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.

Possible COVID-19 inoculation-based deaths from vaers reporting
Separate GroupsOverlapping Groups
Expected Deaths Reported10151015
Range Of Days Inoculation Deaths0−300−300−300−30
Total Reported Deaths Over Range2901290129012901
Total Expected Deaths Over Range310465310465
Inoculation-Based Deaths Reported2591243629012901
Expected Deaths Reported/Total Expected.0021.0031.0021.0031
Total Actual Inoculation-Based Deaths Using Expected Ratio (Above)12338107858061381429935806

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.

Appendix B


A2-a Clinical Trials in the Mainly Adult Population


Efficacy is the degree to which a vaccine prevents disease, and possibly also transmission, under ideal and controlled circumstances – comparing a vaccinated group with a placebo group [106].

Effectiveness refers to how well a vaccine performs in the real world [107]

Relative Risk (RR) is computed by dividing the percentage of patients that contracted disease in the vaccine arm by the percentage of patients that contracted disease in the placebo arm.

Relative Risk Reduction (RRR) is computed by subtracting the RR from 1.

Absolute Risk Reduction (ARR) is computed by subtracting the percentage that contracted disease in the vaccine arm from the percentage that contracted disease in the placebo arm.

Absolute Risk = probability = incidence.

Cumulative Incidence represents the number of new cases in a period of time / population at risk.

Incidence Density is the number of new cases of a given disease during a given period in specified population; also, the rate at which new events occur in a defined population.

Immunogenicity is the ability of a molecule or substance to provoke an immune response or the strength or magnitude of an immune response. It can be a positive (wanted) or negative (unwanted) effect, depending on the context.

Immune Response is an integrated systemic response to an antigen (Ag), especially one mediated by lymphocytes and involving recognition of Ags by specific antibodies (Abs) or previously sensitized lymphocytes [108]

Safety data for Pfizer and Moderna trials:

There were two major COVID-19 inoculant clinical trials: Pfizer/BioNTech and Moderna.

The Pfizer clinical trials were titled officially “a phase 1/2/3, placebo-controlled, randomized, observer-blind, dose-finding study to evaluate the safety, tolerability, immunogenicity, and efficacy of sars-cov-2 rna vaccine candidates against covid-19 in healthy individuals” [98]. The “Actual Study Start Date” was 29 April 2020, the “Estimated Primary Completion Date” was 2 November 2020, and the “Estimated Study Completion Date” is 2 May 2023. Thus, the mass inoculation rollout so far has been conducted in parallel with the Pfizer Phase III Clinical Trial. For all practical purposes, the mass global inoculation of the Pfizer inoculant recipients can be considered Phase III 2.0 of the Clinical Trials! The inclusion criteria for the official Phase III Clinical Trials incorporated (as stated in the title and in the protocol document) healthy individuals, while the criteria for mass inoculation went well beyond healthy individuals. In essence, we have an official Phase III Clinical Trial with ˜43,000+ healthy individuals, and an unofficial Phase III Clinical Trial with billions of individuals covering a wide spectrum of health levels [98].

The Pfizer Phase III trials were initiated July 2020, the efficacy data were submitted to the FDA for EUA approval in November 2020, and FDA approval was granted in December 2020. Six deaths occurred in the Pfizer trial, two in the inoculated group and four in the placebo group (which received saline) [33]. The two inoculated, both over the age of 55, died of cardiovascular causes. One died three days after inoculation and the other died 62 days after inoculation [109]. These two deaths were comparable (in frequency and cause) to placebo group deaths and perhaps more importantly, similar to the general population at that age. In the case of Moderna, there were 13 deaths, six in the inoculated group, seven in the placebo group (normal saline placebo, a mixture of sodium chloride in water 0.90 % w/v) at 21–57 days after the inoculation ([103]b).

In a report by the Norwegian National Medicines Association, published on 15 January 2021, there were 23 elderly people (all over the age of 75 and frail) in nursing homes, who died at various intervals from the time of inoculation with mRNA inoculant The report then suggested that, following the assessment, 13 of the 23 deaths would have been a direct result of the side effects of inoculation. It is possible that the other 10 deaths were post-inoculation, but not directly related to side effects, so not necessarily related to the inoculant itself [109].

It is no surprise that frail elderly people can be fatally destabilized by adverse reactions associated with post-inoculation inflammation, which in a young adult would have been considered minor. It is also no surprise that frail elderly people with comorbidities can be fatally destabilized from COVID-19 infection, which in a young adult or child would have been considered minor. A frail elderly person can be fatally destabilized by a simple coughing fit! This does not mean that these deaths are not events that need to be taken very seriously; on the contrary, if confirmed, they should guide inoculation policies in this category of patients from now on. Specifically, each case should be carefully assessed and an inoculation decision made based on the risk-benefit ratio [110].

In light of these data, the question may arise as to why there were no inoculant-attributed deaths in clinical testing of inoculants. The answer is that neither Pfizer nor Moderna included frail patients and included only a small number of very elderly patients – those over 75 accounted for 4.4 % of the total tested for Pfizer and 4.1 % for Moderna. While they could not in fact determine a causal relationship between inoculation and death, they also could not rule out that the inoculations had accelerated the deterioration of the condition of those patients [33].

Effectiveness data

As defined previously, the effectiveness of a vaccine lies in its ability to prevent a particular disease. If designed, tested, and administered correctly, authorized vaccines are effective in preventing disease and protecting the population. Like medicines, vaccines are not 100 % effective in all vaccinated people. Their effectiveness in a person depends on several factors. These include: age; other possible diseases or conditions; time elapsed since vaccination; previous contact with the disease.

To be declared safe and effective, a vaccine against COVID-19 infection must pass a series of tests and must meet regulatory standards, like any other vaccine or drug approved on the pharmaceutical market [111].

Regarding Pfizer and Moderna trials: The first important note is that maximum efficiency does not come immediately, because the immune response needs time.

In the case of Pfizer, the chance of developing COVID-19 becoming virtually the same between the inoculated and placebo groups increases up to 12 days after the first inoculation, then gradually decreases for those inoculated. The inoculum efficiency between the first and second doses is 52 % [106], but it is unclear what long-term protection a single dose provides. After the second dose, the effectiveness rises to 91 % and only beyond 7 days after the second dose is 95 % reached. However, the ARR for the latter case is only 0.7 % [112]. In other words, within 12 days after the first dose we can get COVID-19 as if we had not been inoculated. Another important aspect is that we still do not know if the Pfizer inoculant prevents severe cases. Seven days after the second dose, there were four severe cases of COVID-19, one in the inoculated group and three in the placebo group, which is far too low for us to make a statistical assessment. There are as yet no data on the inoculant’s ability to prevent community transmission. Realistically, the effectiveness of the inoculant in preventing asymptomatic cases has not been tested.

For Moderna, the effectiveness is only 50 % in the first 14 days after the first dose and reaches a maximum of 92.1 % on the edge of the second dose (ARR of 1.1 %, which is 28 days, not 21 as in the case of Pfizer) [46]. Moderna also did not test the long-term efficacy of a single dose. Then, 14 days after the second dose, the effectiveness rises to 94.1 %, with the amendment being an average. Thus, in people over 65 it was 86.4 %, compared to 95.6 % in the 18–65 age range ([103]). It is a minor difference from Pfizer, which declares equal efficiency in all age groups. An important observation is the statement by Moderna that their inoculant prevents severe cases, but only more than 14 days after both doses [126]. All 30 severe cases were in the placebo group, suggesting 100 % efficacy. After a single dose, there were two severe cases among those inoculated and four in the placebo group [33]. Last, but not least, unlike Pfizer, Moderna tested the presence of asymptomatic infection by RT-PCR before the second dose: there were 39 asymptomatic cases in the placebo group and 15 in the inoculated group. It is difficult to draw definitive conclusions due to the small number of cases. These data suggest that the inoculant reduces, but does not prevent, asymptomatic transmission [126].

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 12–15, 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 %. [63]. 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 12–15 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 [24]. 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 [113].

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 [114]:

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 [114].

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 [115]. 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 [115].

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 [3]. The study included 35,691 pregnant women [116]. 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 [112].

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) [117].

Appendix C


A 2020 study emphasizing mid- and long-term adverse effects from prior vaccines [4] 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.” [4]

“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, UveitisPseudolymphoma, Guillain-Barre Syndrome, Thrombocytopenic Purpura, etc.) and Neurological (e.g., Central Demyelinating Diseases, Developmental Disability, Febrile seizuresNarcolepsyEncephalomyelitisAutonomic Dysfunction, etc.). Others include Diabetes, Gastrointestinal, Joint-related, Necrobiotic GranulomaNeutropeniaPulmonary Fibrosis, etc.”

“Vaccinations may also contribute to the mosaic of autoimmunity [118]. Infrequently reported post-vaccination autoimmune diseases include systemic lupus erythematosus, rheumatoid arthritisinflammatory 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 [118] 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.” [4]

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.

Appendix D


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) [88,119]. 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 [120].

We will use Fig. 1Fig. 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 .

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 0.0037, 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” [127]. 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) [3]. 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.

Appendix Da


Consider the criteria for determining whether an RT-PCR test result is positive for SARS-CoV-2. The CDC instruction (until 1 May 2021) specifies running the RT-PCR tests for 45 amplification cycles. Then, to interpret the data: when all controls exhibit the expected performance, a specimen is considered positive for SARS-CoV-2 if all SARS-CoV-2 marker (N1, N2) cycle threshold growth curves cross the threshold line within 40.00 cycles (< 40.00 Ct). The RNase P may or may not be positive as described above, but the SARS-CoV-2 result is still valid ([103]a).

Many false positives are possible in the upper part of this cycle threshold range, especially in areas of low prevalence. In particular, virus culture has been found to be unfeasible in cases with a Ct value exceeding 33. A prospective cohort study involving the first 100 COVID-19 patients in Singapore also showed that attempts to culture the virus failed in all PCR-positive samples with a Ct value >30” [121]. During mass testing in Germany, it was found “that more than half of individuals with positive PCR test results are unlikely to have been infectious” [122]. Another study found that tests with low specificity (deriving from use of many cycles) cannot provide strong evidence for the presence of an infection [123]. A systematic review of PCR testing concluded “Complete live viruses are necessary for transmission, not the fragments identified by PCR. Prospective routine testing of reference and culture specimens and their relationship to symptoms, signs and patient co-factors should be used to define the reliability of PCR for assessing infectious potential. Those with high cycle threshold are unlikely to have infectious potential.” [89].

As skeptics have argued, in the buildup of the pandemic, the rapid increase in numbers of COVID-19 cases was due in part to the high values of cycle threshold used in the tests. Unfortunately, the true numbers of false positives will probably be unobtainable if an audit were performed, since these values are not reported with the test results: all currently-available nucleic acid tests for SARS-CoV-2 are FDA-authorized as qualitative tests, and Ct values from qualitative tests should never be used to direct or inform patient management decisions. Therefore, it is not good for laboratories to include Ct values on patient reports [124].

After mass inoculations started, a large number of “breakthrough” cases emerged, and a total of 10,262 SARS-CoV-2 vaccine breakthrough infections had been reported from 46 U.S. states and territories as of April 30, 2021 [18]; the number of reported COVID-19 vaccine breakthrough cases is likely a substantial undercount of all SARS-CoV-2 infections among fully vaccinated persons. The national surveillance system relies on passive and voluntary reporting, and data might not be complete or representative. Many persons with vaccine breakthrough infections, especially those who are asymptomatic or who experience mild illness, might not seek testing [18].

This negative outcome of increased “breakthrough” cases motivated the CDC to change a number of reporting and test procedures and issue new regulations for identifying and investigating hospitalized or fatal vaccine breakthrough cases starting 1 May 2021, stating: “For cases with a known RT-PCR cycle threshold (Ct) value, submit only specimens with Ct value ≤28 to CDC for sequencing. (Sequencing is not feasible with higher Ct values.)”. Thus, the Ct values for sequencing were lowered from the high false positive range allowed during the pandemic buildup to a limit that would eliminate many of these false positives in the ‘breakthrough case’ identification phase [101].


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Scientists Identify Factors That Appear Linked to Long Covid

Authors: Marthe Fourcade 04:35 PM IST, 26 Jan 2022 06:47 PM IST, 26 Jan 2022

Scientists seeking to find out which patients are most at risk of developing long Covid offered partial answers in a study.

People who have circulating fragments of the coronavirus, specific antibodies directed against their own tissues or organs — known as auto-antibodies — and a resurgence of the Epstein-Barr virus appear more at risk, researchers said in an article in the scientific journal Cell.

Scientists are racing to better understand and predict long Covid, in which patients still confront a wide range of health problems months after recovery. The team of more than 50 researchers found some markers that could be identified early and appeared to correlate with lasting symptoms, regardless of whether the initial infection was severe. 

The researchers followed 309 Covid patients from initial diagnosis to convalescence two or three months later and compared them to healthy control subjects. They analyzed blood samples and nasal swabs, integrating the data with patients’ health records and self-reported symptoms.

After three months, more than half of patients reported fatigue and a quarter reported a lingering cough. Others suffered gastro-intestinal symptoms.

The study results were complex, with different profiles associated with different symptoms. Overall, the scientists pointed to a reactivation of the Epstein-Barr virus — which usually remains dormant in the body — and circulating fragments of SARS-CoV-2 at diagnosis as factors that could anticipate long Covid. So did a handful of auto-antibodies, including some associated with lupus.

They also found that patients with respiratory symptoms had low levels of the hormone cortisol.  The researchers found a correlation between type 2 diabetes and cough; that women tended to suffer neurological symptoms; and that patients with heart disease or pre-existing cough tended to experience loss of smell or taste.  The authors said their findings pointed to potential treatment strategies that include antiviral medicines, since they have an effect on viral load, and cortisol-replacement therapy, for patients who are deficient.

For More Information: https://www.cell.com/COVID-19

Read more at: https://www.bloombergquint.com/onweb/scientists-identify-factors-that-appear-linked-to-long-covid
Copyright © Bloomberg Quint

These symptoms and risk factors may predict whether you could become a ‘COVID-19 long hauler,’ study suggests

Authors: Adrianna Rodriguez USA TODAY March 11, 2021

A new study suggests coronavirus symptoms felt in the first week of infection may be a predictor of how long they will last.

Patients with COVID-19 who felt more than five symptoms in their first week of illness were more likely to become a “COVID-19 long hauler,” which researchers qualified as having symptoms for longer than 28 days, according to the study published Wednesday in the peer-reviewed journal Nature Medicine.

The five symptoms experienced during the first week that were most predictive of becoming a long hauler were fatigue, headache, hoarse voice, muscle pain and difficulty breathing.

Researchers from King’s College London, Massachusetts General Hospital and Boston Children’s Hospital asked COVID-19 patients from the U.K., U.S. and Sweden to report their symptoms through a smartphone application from March to September 2020.

Out of more than 4,000 participants, about 13% of patients reported symptoms lasting more than 28 days, 4% for more than 8 weeks and 2% more than 12 weeks.

Out of the patients who reported symptoms for more than four weeks, “a third of those will have symptoms at 8 weeks and then a third of those at 12 weeks,” said study co-author Dr. Christina Astley, a physician scientist at Boston Children’s Hospital. “If you think about it, 1 in 20 people who have COVID-19 will have symptoms lasting 8 weeks or more.” 

The likelihood of having persistent symptoms was significantly associated with increasing age, rising from 9.9% of individuals 18 to 49, to 21.9% in those above 70. Anosmia, or the loss of smell, was the most common symptom in older age groups.

Women also were more likely to have long COVID-19 than men, with 14.9% of female study participants reporting symptoms 28 days after initial infection, compared with 9.5% of men.

While the study attempted to identify risk factors and markers that may indicate long COVID-19, doctors are finding it can happen to anyone at any age, said Dr. Michael Wechsler, a pulmonologist at National Jewish Health.

“It can happen in any age group, but it’s most alarming to younger people who are otherwise healthy and not used to these symptoms,” he said.

COVID long haulers:Dr. Anthony Fauci aims to answer ‘a lot of important questions’ in new nationwide initiative

The study found two main patterns among study participants. One group of COVID-19 long haulers exclusively reported fatigue, headache and upper respiratory issues, such as shortness of breath, sore throat, cough and loss of smell. However, a second group of long haulers had persistent multi-system complaints, such as a fever or gastrointestinal symptoms.

Weschler sees a wide array of symptoms in the clinic that caters to COVID-19 long haulers at National Jewish Health. Similar clinics have popped up in hospitals across the country to accommodate the growing number of COVID-19 patients who report symptoms months after recovery.

“Long COVID is common. It affects a large proportion of patients and has a wide distribution of symptoms,” he said. “It’s important to make people aware that all these different side effects and symptoms can occur.”

The study comes a few weeks after Dr. Anthony Fauci announced the U.S. government was launching nationwide initiative to study long COVID-19, which he called Post Acute Sequelae of SARS-CoV-2 (PASC).

A study published in JAMA Network Open on Feb. 19 found that about 30% of COVID-19 patients reported persistent symptoms as long as nine months after illness.

“(There are) a lot of important questions that are now unanswered that we hope with this series of initiatives we will ultimately answer,” he said during a White House briefing Feb. 24.

Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis




We aimed to systematically identify the possible risk factors responsible for severe cases.


We searched PubMed, Embase, Web of science and Cochrane Library for epidemiological studies of confirmed COVID-19, which include information about clinical characteristics and severity of patients’ disease. We analyzed the potential associations between clinical characteristics and severe cases.


We identified a total of 41 eligible studies including 21060 patients with COVID-19. Severe cases were potentially associated with advanced age (Standard Mean Difference (SMD) = 1.73, 95% CI: 1.34–2.12), male gender (Odds Ratio (OR) = 1.51, 95% CI:1.33–1.71), obesity (OR = 1.89, 95% CI: 1.44–2.46), history of smoking (OR = 1.40, 95% CI:1.06–1.85), hypertension (OR = 2.42, 95% CI: 2.03–2.88), diabetes (OR = 2.40, 95% CI: 1.98–2.91), coronary heart disease (OR: 2.87, 95% CI: 2.22–3.71), chronic kidney disease (CKD) (OR = 2.97, 95% CI: 1.63–5.41), cerebrovascular disease (OR = 2.47, 95% CI: 1.54–3.97), chronic obstructive pulmonary disease (COPD) (OR = 2.88, 95% CI: 1.89–4.38), malignancy (OR = 2.60, 95% CI: 2.00–3.40), and chronic liver disease (OR = 1.51, 95% CI: 1.06–2.17). Acute respiratory distress syndrome (ARDS) (OR = 39.59, 95% CI: 19.99–78.41), shock (OR = 21.50, 95% CI: 10.49–44.06) and acute kidney injury (AKI) (OR = 8.84, 95% CI: 4.34–18.00) were most likely to prevent recovery. In summary, patients with severe conditions had a higher rate of comorbidities and complications than patients with non-severe conditions.


Patients who were male, with advanced age, obesity, a history of smoking, hypertension, diabetes, malignancy, coronary heart disease, hypertension, chronic liver disease, COPD, or CKD are more likely to develop severe COVID-19 symptoms. ARDS, shock and AKI were thought to be the main hinderances to recovery.

For More Information: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250602