CDC’s VAERS safety signal analysis based on reports from Dec. 14, 2020 – July 29, 2022 for mRNA COVID-19 vaccines shows clear safety signals for death and a range of highly concerning thrombo-embolic, cardiac, neurological, hemorrhagic, hematological, immune-system and menstrual adverse events (AEs) among U.S. adults.
There were 770 different types of adverse events that showed safety signals in ages 18+, of which over 500 (or 2/3) had a larger safety signal than myocarditis/pericarditis.
The CDC analysis shows that the number of serious adverse events reported in less than two years for mRNA COVID-19 vaccines is 5.5 times larger than all serious reports for vaccines given to adults in the US since 2009 (~73,000 vs. ~13,000).
Twice as many mRNA COVID-19 vaccine reports were classified as serious compared to all other vaccines given to adults (11% vs. 5.5%). This meets the CDC definition of a safety signal.
There are 96 safety signals for 12-17 year-olds, which include: myocarditis, pericarditis, Bell’s Palsy, genital ulcerations, high blood pressure and heartrate, menstrual irregularities, cardiac valve incompetencies, pulmonary embolism, cardiac arrhythmias, thromboses, pericardial and pleural effusion, appendicitis and perforated appendix, immune thrombocytopenia, chest pain, increased troponin levels, being in intensive care, and having anticoagulant therapy.
There are 66 safety signals for 5-11 year-olds, which include: myocarditis, pericarditis, ventricular dysfunction and cardiac valve incompetencies, pericardial and pleural effusion, chest pain, appendicitis & appendectomies, Kawasaki’s disease, menstrual irregularities, vitiligo, and vaccine breakthrough infection.
The safety signals cannot be dismissed as due to “stimulated,” exaggerated, fraudulent or otherwise artificially inflated reporting, nor can they be dismissed due to the huge number of COVID vaccines administered. There are several reasons why, but the simplest one is this: the safety signal analysis does not depend on the number of reports, but whether or not some AEs are reported at a higher rate for these vaccines than for other non-COVID vaccines. Other reasons are discussed in the full post below.
In August, 2022, the CDC told the Epoch Times that the results of their safety signal analysis “were generally consistent with EB [Empirical Bayesian] data mining [conducted by the FDA], revealing no additional unexpected safety signals.” So either the FDA’s data mining was consistent with the CDC’s method—meaning they “generally” found the same large number of highly alarming safety signals—or the signals they did find were expected. Or they were lying. We may never know because the FDA has refused to release their data mining results.
Finally! Zachary Stieber at the Epoch Times managed to get the CDC to release the results of its VAERS safety signal monitoring for COVID-19 vaccines, and they paint a very alarming picture (see his reporting and the data files here, or if that is behind a paywall then here). The analyses cover VAERS reports for mRNA COVID vaccines from the period from the vaccine rollout on December 14, 2020 through to the end of July, 2022. The CDC admitted to only having started its safety signal analysis on March 25, 2022 (coincidentally 3 days after a lawyer at Children’s Health Defense wrote to them reminding them about our FOIA request for it).
[UPDATE: T Coddington left a link in comments to a website where he made the data in the Excel files more accessible.]
Like me, you might be wondering why the CDC waited over 15 months before doing its first safety signal analysis of VAERS, despite having said in a document posted to its website that it would begin in early 2021—especially since VAERS is touted as our early warning vaccine safety system. You might also wonder how they could insist all the while that the COVID-19 vaccines are being subjected to the most rigorous safety monitoring the world has ever known. I’ll come back to that later. First I’m going to give a little background information on the analysis they did (which you can skip if you’re up to speed) and then describe what they found.
BACKGROUND ON SAFETY SIGNAL ANALYSIS
Back in June 2022, the CDC replied to a Freedom of Information Act (FOIA) request for the safety signal monitoring of the Vaccine Adverse Events Reporting System (VAERS)—the one it had said it was going to do weekly beginning in early 2021. Their response was: we never did it. Then a little later they said they had been doing it from early on. But by August, 2022, they had finally gotten their story straight, saying that they actually did do it, but only from March 25, 2022 through end of July. You can get up to speed on that here.
The analysis they were supposed to do uses what’s called proportional reporting ratios (PRRs). This is a type of disproportionality analysis commonly used in pharmacovigilance (meaning the monitoring of adverse events after drugs/vaccines go to market). The basic idea of disproportionality analysis is to take a new drug and compare it to one or more existing drugs generally considered safe. We look for disproportionality in the number of adverse events (AEs) reported for a specific AE out of the total number of AEs reported (since we generally don’t know how many people take a given drug). We then compare to existing drugs considered safe to see if there is a higher proportion of particular adverse events reported for the new drug compared to existing ones. (In this case they are looking at vaccines, but they still use PRR even though they generally have a much better sense of how many vaccines were administered.)
There are many ways to do disproportionality analysis. The PRR is one of the oldest. Empirical Bayesian data mining, which was supposed to be done on VAERS by the FDA, is another. The PRR is calculated by taking the number of reports for a given adverse event divided by the total number of events reported for the new vaccine or the total number of reports. It then divides that by the same ratio for one or more existing drugs/vaccines considered safe. Here is a simple formula:
So for example, if half of all adverse events reported for COVID-19 vaccines and the comparator vaccine(s) are for myocarditis, then the PRR is 0.5/0.5 = 1. If one quarter of all AEs for the comparator vaccine are for myocarditis, then the PRR is 0.5/0.25 = 2.
Traditionally, for a PRR to count as a safety signal, the PRR has to be 2 or greater, have a Chi-square value of 4 or greater (meaning it is statistically significant) and there has to be at least 3 events reported for a given AE. (This also means that if there are tons of different AEs reported for COVID vaccines that have never been reported for any other vaccine, it will not count as a safety signal. I found over 6,000 of those in my safety signal analysis from 2021.
Ah yes, shared with the public — after first refusing to share the results and months of foot-dragging following repeated FOIA requests! We will see that the CDC has not done a more focused study on almost any of adverse events with “new patterns” (AKA safety signals).
SO WHAT DID THE CDC ACTUALLY DO?
The Epoch Times obtained 3 weeks of safety signal analyses from the CDC for VAERS data updated on July 15, 22 and 29, 2022. Here I will focus on the last one, since there is very little difference between them and it is more complete. The safety signal analysis compares adverse events1 reported to VAERS for mRNA COVID-19 vaccines from Dec. 14, 2020 through July 29, 2022 to reports for all non-COVID vaccines from Jan 1, 2009 through July 29, 2022.
PRRs are calculated separately for 5-11 year-olds, 12-15 year-olds and 18+ separately. For each age group, there are separate tables for AEs from all reports, AEs from reports marked serious and AEs from reports not marked as serious.2 Recall that a serious report is one that involves death, a life-threatening event, new or prolonged hospitalization, disability or permanent damage, or a congenital anomaly. I will focus on the reports for all AE’s.
They also have a table that calculates PRRs by comparing reports for the Pfizer COVID-19 vaccine to reports for the Moderna vaccine and vice versa, again for all reports, serious reports only and non-serious reports. There were no remarkable findings in those tables, so I will not discuss them. [Edit: I forgot what Norman Fenton noted in his analysis: the overall proportion of reports with serious adverse events is 9.6% for Modern compared to 12.6% for Pfizer.] This isn’t that surprising since both vaccines are very similar and so should present relatively similar adverse events when compared to each other, and any differences are likely not large enough to be picked up by a PRR analysis. [Though the difference in the overall rate of serious adverse events, which are not specific to a particular type of event only how serious it is, was significant.]
The CDC seems to have calculated PRRs for every different type of adverse event reported for all the COVID vaccines examined – though it’s possible they only analyzed a subset. What seems clear is that, among the AEs they examined, the only ones included in the tables satisfy at least one of two conditions: a PRR value of at least 2 and a Chi-square value of at least 4 (Chi is the Greek letter χ and is pronounced like ‘kai’). When both conditions were met, they highlighted the adverse event in yellow, which appears to indicate a safety signal. There were no COVID vaccine AEs listed with fewer than 3 reported events, though for non-COVID vaccines there were many AEs listed that had only 1 or 2 reported since 2009. The CDC tables still include these and highlight them in yellow when the PRR is greater than 2 and the Chi-square value is great than 4, indicating these events are counted as safety signals.
WHAT SAFETY SIGNALS DID THE CDC FIND?
I’m going to divide this up by age groups and the Pfizer v. Moderna comparison. Let’s start with the 18+ group.
There are 772 AEs that appear on the list. Of these, 770 are marked in yellow and have PRR and Chi-square values that qualify them as safety signals. Some of these are new COVID-19 related codes, and we would expect those to trigger a signal since they didn’t exist in prior years to be reported by other vaccines. So if we take those off, we are left with 758 different types of non-COVID adverse events that showed safety signals.
I grouped these 758 safety signals into different categories. The figure below shows the total number of AEs reported for each of the major categories of safety signals:
Let’s dig into some of these categories to look at what types of AEs generated the most number of reports:3
You can peruse the adverse events using the Excel tables provided by the CDC, which were posted by The Epoch Times and Children’s Health Defense at the links at the top of this post.
What about The Children?
If there is anything that looks remotely like a bright spot in all of this is that the list of safety signals for 12-17 and 5-11 year-olds is much shorter than for 18+. There are 96 AEs that qualify as a safety signal for the 12-17 group and 67 for the 5-11. When we take out the new COVID-era AEs, there are 92 safety signals for 12-17 year-olds and 65 for 5-11 year-olds. Here are the most alarming ones:
I don’t know why the list of AE’s is so much shorter for these age groups. It could be that the list of AE’s for other vaccines for these age groups is much shorter, so in a case where AEs have been reported for the mRNA COVID vaccines but not for other vaccines, it will not be counted as a safety signal by definition.
COMPARISONS TO MYOCARDITIS & PERICARDITIS
We are told that the existence of a safety signal doesn’t necessarily mean the AE is caused by the vaccine, and I accept that premise. But the current practice seems to be to ignore safety signals, dismiss them as noise without any evidence, and stall any investigation into them as long as possible. The precautionary principle, however, dictates we should presume that a safety signal indicates causality, until proven otherwise. Since, it has been acknowledged that the mRNA COVID vaccines can cause myocarditis and pericarditis (often referred to as myo-pericarditis), we can take those AEs as a kind of benchmark, and propose that, at minimum, any AE with a signal of equal or greater size should be considered potentially causal and investigated more thoroughly.4
After dropping the new COVID-era AEs, there are 503 AEs with PRRs larger than myocarditis (PRR=3.09) and 552 with PRRs larger than pericarditis (PRR=2.82).5This means that 66.4% of the AEs had a bigger safety signal than myocarditis and 77.3% were larger than pericarditis. You can see what those were by use this Excel file provided by the CDC and sorting the 18+ tab by the 12/14-07/29 PRR column (Column E). Then just look at which AEs have PRRs larger than the ones for pericarditis and myocarditis.
For 12-17 year-olds, there is 1 safety signal larger than myocarditis (it’s ‘troponin increased’) and 14 safety signals larger than pericarditis (excluding myocarditis), which include: mitral valve incompetence, bell’s palsy, heavy menstrual bleeding, genital ulceration, vaccine breakthrough infection, and a range of indicators of cardiac abnormalities.
For 5-11 year-olds, the comparison to myo/pericarditis is less germane, as they seem to suffer less from this side effect. But we can still make the comparison: there are 7 safety signals larger than pericarditis, including bell’s palsy, left ventricular dysfunction, mitral valve incompetence, and ‘drug ineffective’ (presumably meaning they still got COVID). There are 16 safety signals larger than myocarditis (excluding pericarditis), which in addition to those listed above also include: pericardial effusion, diastolic blood pressure increase, tricuspid valve incompetence, and vitiligo. Sinus tachycardia (high heart rate), appendicitis, and menstrual disorder come in just below myocarditis.
Now if we think of a safety signal as having both strength and clarity, then the PRR can be thought of as an indicator of how strong the signal is, while the Chi-square is a measure of how clear or unambiguous the signal is, because it gives us a sense of how likely the signal is due to chance alone: the larger the Chi-square value, the less likely the signal is due to chance. A Chi-square of 4 means there is only a 5% chance the observed signal is due to chance. A Chi-square of 8 means there is only a 0.5% chance of it being due to chance.6
For the 18+ group, there are 57 AEs with a Chi-square larger than myocarditis (Chi-square=303.8) and 68 with a Chi-square larger than pericarditis (Chi-square=229.5). Again, you can see what these are by going the Excel file linked above and sorting on Column D.
For the 12-17 group, there are 4 AEs with a larger Chi-square than myocarditis (Chi-square=681.5) and 6 larger than pericarditis (Chi-square=175.4).
For the 5-11 group, there are 22 AEs with a Chi-square larger than myocarditis (Chi-square=30.42) and 34 AEs with a Chi-square larger than pericarditis (Chi-square=18.86).
RESPONDING TO OBJECTIONS
Let’s dispense with some of the criticisms used to dismiss VAERS data, which will undoubtedly be raised if you try to bring the CDC’s analysis to people’s attention.
Objection: Anybody can report to VAERS. The reports are unreliable. Anti-vaxxers made lots of fraudulent reports. Nobody was aware of VAERS in the past, but now they are. So many people were afraid of the vaccine so they blamed all their health problems on it. Health workers were required by law to report certain adverse events, like deaths and anaphylaxis. Etc. Etc.All of these objections ultimately rely on the notion that VAERS reports for COVID-19 vaccines have been artificially inflated over previous years for one reason or another. The thing of it is, though, that the CDC has a method for distinguishing between artificial inflation and real signal. The idea is simple: if adverse events are artificially inflated, they should be artificially inflated to the same degree. Meaning, the PRRs for all of these safety signals should be about the same. But even a casual glance at the PRRs in the Excel file show they vary widely, from as low at 2 to as high as 105 for vaccine breakthrough infection or 74 for cerebral thrombosis. This method does not on the number of reports, but the rate of reporting for certain events out of all events reported. If anything, this method would tend to hide safety signals in a situation where a new vaccine generates a very large number of reports.The CDC has even done us the favor of calculating upper and lower confidence intervals, meaning that we can be at least 95% confident that two PRRs are truly different if their confidence intervals don’t overlap. So for example the lower confidence interval for pulmonary thrombosis is 19.7, which is higher than the upper confidence interval for 543 other signals. Artificially inflated reporting cannot explain why so many different adverse events have large PRRs that are statistically distinct from one another.
Objection: The safety signals are due to the huge number of COVID vaccines given out. Never before have we given out so many vaccine doses. By the end of July, the US had administered something like 600 million vaccine doses to people aged 18+. But the CDC analysis compares VAERS reports for these doses to all doses for all other vaccines for this age group since Jan. 1, 2009. But from 2015-2020 there were over 100 million flu doses administered annually to this age group alone. In previous work, I estimated 538 million doses of flu given to people 18+ from July 2015-June 2020. The number of flu and other non-COVID vaccines for this age group administered from Jan 1., 2009 through July 29, 2022 must be well over double this number, meaning VAERS reports for COVID vaccines are being compared to reports for at least double the number of doses for other vaccines. In addition to this, as already noted, the PRR methodology does not depend, strictly speaking, on the number of doses, but rather the rate of reporting of a specific AE out of all AEs for that vaccine.
Objection: the vaccines are mainly being given to older people who tend to have health problems, whereas other vaccines are given to younger people. This objection is dealt with, since the analyses are stratified by age groups. It might be still be somewhat valid for the 18+ group, except that in the safety signal analysis I did in the fall of 2021, I stratified by smaller age bands and still found safety signals. In any case, this objection is not enough to dismiss the safety signal analysis out of hand, but rather calls for better and more refined research.
Objection: The VAERS data is not verified and cannot be trusted. I’ll be the first person to agree that VAERS is not high quality data, but if it is completely untrustworthy, then how is it that the CDC uses these data to publish in the best medical journals such as JAMA and The Lancet? If the data were worthless, then these journals shouldn’t accept these papers. In that JAMA paper, they reported that 80% of the myocarditis reports met their definition of myocarditis and were included in the analysis. Many other reports simply needed more details for validation. Furthermore, the CDC has the ability and budget to follow-up on every report VAERS receives to get more details and even medical records to verify the report.So if myocarditis shows a clear signal in the CDC’s analysis, and 80% of those reports were apparently high quality enough to be included in a paper published in one of the world’s top medical journals, how is it possible that all the rest of the reports are junk? That all of the other safety signals are meaningless? Answer: it isn’t.And since we’re on the topic of safety signals that turned out to be real, it’s instructive to find appendicitis turn up as a safety signal in all 3 age groups, since a study published in NEJM based on medical records of over a million adult Israelis found an increased risk of appendicitis in the 42 days following Pfizer vaccination (but not following a positive SARS-CoV-2 PCR test). That study also found an increase in lymphadenopathy (swollen lymph nodes) after vaccination, but not after positive COVID test. Lymphadenopathy was another safety signal.
And that brings us to our last objection to be dispensed with: all of these AEs were due to COVID. There was an epidemic and so people were falling ill due to COVID and having all of these problems that were then blamed on the vaccine. Well to begin with, as we just saw, at least two of them (appendicitis and lymphadenopathy) do not appear to have increased risk ratios following a positive SARS-CoV-2 test, and we know that the mRNA vaccines increase risk of myo/pericarditis independent of infections. So how can we assume the rest of these are and dismiss them with the wave of a hand? We can’t. At minimum, they need further investigation. Furthermore, in the safety signal analysis I did in 2021, I dropped all VAERS reports where any sign of a SARS-CoV-2 exposure or infection was indicated on the report, and I still found large, significant safety signals.
PUTTING IT ALL INTO PERSPECTIVE
The Epoch Times article quotes my esteemed colleague and friend, Norman Fenton, Professor of Risk Management and an world renowned expert in Bayesian statistical analysis: “from a Bayesian perspective, the probability that the true rate of the AE of the COVID-19 vaccines is not higher than that of the non-COVID-19 vaccines is essentially zero…. The onus is on the regulators to come up with some other causal explanation for this difference if they wish to claim that the probability a COVID vaccine AE results in death is not significantly higher than that of other vaccines.” (See his post on the CDC analysis here.) The same is true for all the safety signals they found.
The CDC’s VAERS SOP analysis document lists 18 Adverse Events of Special Interest says they are going to pay close attention to. In their 2021 JAMA paper (and similar presentations to ACIP), the researchers responsible for analyzing the millions of medical records in the CDC’s Vaccine Safety Datalink (VSD) using the ‘Rapid Cycle Analysis’ only studied 23 outcomes. A Similar analysis in NEJM from Israeli researchers focused on only 25 outcomes. Compare this to over 700 safety signals found by the CDC when they finally decided to look—and that’s not even counting all the adverse events that have never been reported for other vaccines so cannot ever show a safety signal by definition. How can the CDC say that these safety signals are meaningless if almost none of them have been studied any further? And yet we are assured that these vaccines have undergone the most intensive safety monitoring effort in history. It’s complete and utter hogwash!
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Josh Guetzkow is a senior lecturer at The Hebrew University of Jerusalem. Subscribe to his Substack here.
1) To be precise, the ‘adverse events’ are for ‘preferred terms’ (PTs) which is a type/level of classification used in the Medical Dictionary for Regulatory Activities (MedDRA), which is the classification system used by VAERS and in other pharmacovigilance systems and clinical research for coding reported adverse events. Not all preferred terms are a symptom or adverse event per se. Some refer to a specific diagnostic test that was done or a treatment that was given.
2) It’s not entirely clear how they divided these up, since there are clearly AEs that should be considered serious that don’t show up in the serious Excel table — though maybe they don’t come up simply because they are looking within serious reports. I believe that they just filtered the reports to include only serious reports or non-serious reports, then did the safety signal analysis on all the AE’s coded in those reports. The reason I think this is that I used the MedAlerts Wayback Machine, selected just the serious COVID-19 vaccine reports, and the numbers of total reports was very close to the one in the table provided by the CDC (MedAlerts actually had a bit less). The files obtained by the Epoch Times do not include much in the way of a description as to how the analyses were done, so I had to infer some details, which might be incorrect. I will try to note when I am drawing an inference about how the analysis was done.
3) Generally speaking, these figures show the top ten AEs in each category. In some cases I combined AEs that indicated the same thing, such as combining ‘heart rate irregular’ with ‘arrythmia.’ [UPDATE: Note that the charts of all categories, cardiac and thrombo-embolic events were updated on Jan 7, 2023. The reason is that I had previously categorized acute myocardial infarction as a cardiac issue and myocardial infarction as thrombo-embolic. To be consistent, I have now combined myocardial infarction and acute myocardial infarction into one AE category in the thrombo-embolic events (which made the total AEs reported for that category larger than for pulmonary ones) and then added a different cardiac AE to the cardiovascular AE category, ventricular extrasystoles, AKA premature ventricular contraction (PVC), which dependent on frequency and the presence of other cardiomyopathies is associated with sudden cardiac arrest.]
4) Note that using the myo-pericarditis signal as a yardstick doesn’t mean that these are the only signals that matter. To give one example, anaphylactic reactions don’t even show up in the list of safety signals, even though that was one of the very first risk of the vaccine that became apparent from day one of the vaccine rollout.
One potential objection to this benchmark is that it is too low of a bar, since myo-pericarditis appears to disproportionately affect younger men and so a proper safety signal should be stratified by age and gender then compared with myocarditis similarly stratified. I agree, and it is the CDC’s job to do that. But the fact is that any adverse reaction might disproportionately affect some subgroup of people, in which case the safety signal for that group would be similarly faint or diluted when we look at everyone together. So objection overruled.
5) In their Standard Operation Procedures document, the CDC said they would combine these and related codes together to assess a safety signal, but never mind – at least they finally got around to doing something.
6) In this context, the Chi-square is largely driven by the sheer number of adverse events: the more adverse events reported, including for the comparator vaccine, the larger the Chi-square. For example, the PRR for pericarditis and subdural haematoma is the same (2.82), but there were 1,701 incidents of pericarditis reported for mRNA COVID vaccines versus 221for the comparator vaccines, with Chi-square of 229.5. For subdural haematoma, these numbers are 162 verus 21, for a Chi-square of 21.2.
England’s COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories.
This mathematical modelling study was done to assess the UK Government’s four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions.
The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69–83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500–5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700–1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness.
Our findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures.
National Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.
Despite the UK being the first country to start nationwide vaccination campaigns,1 the emergence of the alpha (B.1.1.7) variant of concern drove the severe second wave over the 2020–21 winter leading to a third lockdown in England from Jan 5, 2021.2 Informed by mathematical modelling, the UK Government published a roadmap out of lockdown policy for England, setting out the conditions for and expected timeline of a stepwise lifting of non-pharmaceutical interventions (NPIs).3 Between March 8 and July 19, 2021, NPIs were incrementally lifted as vaccination coverage increased. By July 19, 87·5% of the adult population in England had received at least one dose of vaccine, and 68·2% had received two doses.1 The impact of each roadmap step was assessed in real time before further interventions were lifted, against the Government’s four tests as follows: continued success of the vaccine programme; evidence of the effectiveness of vaccines against hospitalisation; no risk of overwhelming the National Health Service (NHS); and new variants of concern do not change the risk assessment.3
Research in context
Evidence before this study
We searched PubMed up to July 23, 2021, with no language restrictions using the following search terms: (COVID-19 or SARS-CoV-2 or 2019-nCoV or “novel coronavirus”) AND (vaccine or vaccination) AND (“non pharmaceutical interventions” OR “non-pharmaceutical interventions”) AND (model*). We found nine studies that analysed the relaxation of controls with vaccination roll-out. However, none explicitly analysed real-world evidence, balancing lifting of interventions, vaccination, and emergence of the delta variant.
Added value of this study
Our data synthesis approach combines real-world evidence from multiple data sources to retrospectively assess how relaxation of COVID-19 measures have been balanced with vaccination roll-out. We explicitly capture the emergence of the delta variant, its transmissibility over alpha, and quantify its impact on the roadmap. We show the benefits of maintaining non-pharmaceutical interventions while vaccine coverage continues to increase and capture key uncertainties in the epidemic trajectory after NPIs are lifted.
Implications of all the available evidence
Our study shows that lifting interventions must be balanced carefully and cautiously with vaccine roll-out. In the presence of a new, highly transmissible variant, vaccination alone might not be enough to control COVID-19. Careful monitoring of vaccine uptake, effectiveness, variants, and changes in contact patterns as restrictions are lifted will be crucial in any exit strategy.
The emergence of variants of concern, notably the lineages alpha, beta (B.1.351), gamma (P.1), and delta (B.1.617.2) first detected in the UK,4 South Africa,5 Brazil,6 and India,7 respectively, has posed recurring challenges for pandemic control efforts globally. The delta variant was designated a variant of concern on May 6, 2021, by Public Health England (PHE) and quickly became the dominant variant in the UK.8 Delta is substantially more transmissible than alpha,8, 9 a variant already 50–80% more transmissible than previously circulating variants,4, 10 and it is estimated to have a 1·85–2·6-fold increase in the risk of hospital admission.11, 12 It is also associated with partial immune escape and consequent reductions in vaccine effectiveness and cross-protection from previous non-delta infections.13, 14
Delta’s emergence in the UK in mid-April, 2021,15 drove a rapid increase in cases and hospital admissions across all areas of England,8 prompting the final roadmap step (step four) to be delayed by a month to July 19.16 Daily case numbers in England started increasing from mid-May, 2021, and grew exponentially from mid-June to mid-July, reaching a peak of 56 282 on July 15. Numbers then unexpectedly and synchronously fell to half that value over the following 2 weeks, before plateauing and beginning to rise slowly again in August.17
In this study, we quantify the impact of each of the four steps of the roadmap—school reopening; outdoor hospitality and non-essential retail reopening; indoor hospitality reopening; and lifting of all remaining restrictions.3 Our model framework was developed and adapted throughout the COVID-19 epidemic to provide quantitative evidence and epidemiological insights to the UK Government. As an update to this work, we show what the impact of the full roadmap would have been in the absence of delta, and summarise the real-time modelling of policy options that informed the delaying of the final step four.18, 19, 20 We also assess the potential epidemic magnitude, timing, and main sources of uncertainty after step four under different vaccine effectiveness and immune escape assumptions of delta and the level of transmissibility after NPIs are lifted,21 taking into account recent trends in case incidence and hospital admissions.
This epidemiological mathematical modelling study was done to assess the UK Government’s four-step process to easing lockdown restrictions in England, UK.
Ethics permission was sought for the study via Imperial College London’s (London, UK) standard ethical review processes and was approved by the College’s Research Governance and Integrity Team (ICREC reference 21IC6945).
Epidemiological model and fitting
We extended a previously described stochastic SARS-CoV-2 transmission model to include vaccination and to capture multiple variants.22 We used Bayesian methods to fit a single strain transmission model (capturing alpha and pre-alpha variants, referred to as alpha hereafter) to multiple data sources, including daily deaths, hospital admissions and bed occupancy, serological data, and population-level PCR tests, from each English NHS region up to March 8, 2021. By fitting a piecewise linear time-varying transmission rate with change points aligned to policy change dates, the model reliably captures the age-specific scale and timing of the first two waves in England.22 To model vaccine roll-out, we assumed an 11-week interval between first and second vaccine doses with the distribution of doses by vaccine type and uptake by age informed by detailed NHS data on vaccine administration. To explicitly model the emergence of the delta variant, we then fitted a two-strain version of the same model to data from March 8 to July 31, 2021, using information propagated from the first inference step and additionally fitting to the PHE variant and mutation dataset. The variant and mutation dataset lists all genotyped or sequenced SARS-CoV-2 cases in England by region and date of specimen. By fitting to the frequency of delta among alpha and delta cases over time, we were able to estimate the transmission advantage of delta over alpha.
We then used the fitted two-strain model to project the epidemic trajectory after July 19 under different scenarios. For these forward projections, we accounted for the effect of school holidays on transmission (except July 23–Sept 1, which overlaps with the period directly after step four where we assume a gradual increase in contacts) and seasonality in SARS-CoV-2 transmission (for full model description and data sources see appendix pp 3–6).
Characteristics of the delta variant
In our model fitting and forward projections (note that in our forward projections, our central projected trend is the median across all daily projections and is not a single trajectory), we explored plausible ranges of key epidemiological characteristics of delta. First, we used estimates of the transmission advantage of delta over alpha, which were informed by fitting the two-strain model to variant and mutation data. Second, we allowed for imperfect cross-immunity between alpha and delta: infection with a pre-delta variant (eg, alpha) provides only 75–100% protection against infection with delta (appendix p 7). This cross-protection was modelled independently from vaccine effectiveness. Third, we explored central, optimistic, and pessimistic assumptions for vaccine effectiveness against delta, including protection against death, severe disease, mild disease or infection, and onward infectiousness, informed by recent studies (table).12, 28, 34 Fourth, we assumed that vaccine-induced and infection-induced immunity were independent, with vaccines inducing long-lasting immunity. We examined varying assumptions about the duration of infection-induced immunity: lifelong or average of 6-year or 3-year duration (appendix pp 38, 42).35, 36, 37 Furthermore, we accounted for the increased severity of delta relative to alpha by assuming a 1·85-fold increased risk of hospital admission.12
Vaccine effectiveness assumptions for three two-dose vaccines licensed for use in England
We assumed that MD had the same vaccine effectiveness as PF for first and second doses. AZ=Oxford–AstraZeneca ChadOx1 nCov-19 AZD1222. MD=Moderna mRNA-1273. PF=Pfizer–BioNTech COVID-19 vaccine BNT162b2.
†Vaccine effectiveness against infection was assumed equal to vaccine effectiveness against mild disease.
*Assumed greater than mild disease.
Assessing the impact of delta on the roadmap
We explored counterfactual scenarios of the impact of the roadmap on the epidemic trajectory in the presence and absence of delta. We compared the projected number of infections, hospital admissions, and deaths for scenarios with and without delta, varying cross-protection, vaccine effectiveness, and waning immunity as described above. To capture the easing of restrictions at step four, we sampled from a range of values for the reproduction number R, the average number of secondary infections generated by one case, in the absence of naturally induced and vaccine-induced immunity (Rexcl_immunity) that could occur at that stage. We also estimated, for each scenario, the resulting effective reproduction number (Rteff), which accounts for naturally induced and vaccine-induced immunity (appendix p 36). Our baseline scenario assumes contacts increase gradually over an 11-week period after step four to a maximum of 40% (low mixing), 70% (moderate mixing), or 100% (high mixing) greater than contact rates estimated for the period that step three was in place (average Rexcl_immunity; appendix p 43),38 central vaccine effectiveness and cross-immunity (appendix pp 9, 42), and a 3-year average duration of infection-induced immunity. We also assessed the impact of delaying step four, planned initially for not before June 21, but delayed to July 19.
To understand the main drivers of uncertainty of the magnitude of the third wave, in the forward projections we systematically varied four factors as follows: vaccine effectiveness against delta; cross-protection against delta from previous infection with non-delta variants; the duration of natural immunity; and the level of transmissibility after step four (appendix pp 47–48). Shapley values are used to quantify the relative importance of each parameter (appendix p 71). We explored the impact of waning vaccine-induced protection in a separate sensitivity analysis (appendix pp 12, 76). We also assessed a further counterfactual scenario in which contacts increased to maximum levels immediately after the step four date, rather than gradually as in the baseline scenario.
Role of the funding source
The funders of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
The model effectively reproduces national (figure 1) and regional (appendix p 49) trends in the SARS-CoV-2 epidemic in England between Dec 1, 2020, and July 19, 2021, including the July dip in case numbers, hospital admissions (figure 1A, B), and hospital deaths (figure 1C, D), and the emergence and eventual dominance of the delta variant (figure 1E, F). In our model validation (figure A–D), we show that our original model projections match the subsequently observed hospital admissions and deaths between July 19 and Sept 16, 2021.
Trajectory of the COVID-19 epidemic in England and the emergence of the delta variant
Observed (grey points) daily hospital admissions (A), log hospital admissions (B), hospital deaths (C), and log hospital deaths (D). The blue line shows the model fit up to July 19, 2021, and the red and purple lines show the projected admissions and deaths assuming a central vaccine effectiveness, cross-immunity, and immunity duration and a gradual increase in contacts and a return to high (dark red), moderate (light red), or low (purple) transmissibility (contact rates) after non-pharmaceutical interventions are lifted. The shaded area is the 95% credible interval. Note that the central projected trend is the median across all daily projections and is not a single trajectory. The yellow points in panels A–D show the most recent data (July 19–Sept 16, 2021), which were not fitted to. (E) Model fit to the daily proportion of cases due to the delta variant (variant and mutation data) over time from March 8–July 19, 2021, in London as an example (appendix p 54 shows other regions). Points show the data, bars are 95% CIs, the blue line is the model fit, and the shaded area is the 95% credible interval. (F) Estimated delta seeding date by UK National Health Service region. Points show the median estimate and horizontal bars show the 95% credible interval.
Figure 2 shows estimates of how the reproduction number and population immunity, under our central immunity and moderate transmissibility scenario, changed over time since December, 2020. Overall, while Rexcl_immunity increased markedly from March to July, 2021, as a result of the relaxation of lockdown and then the emergence of the delta variant, the rapid roll-out of vaccines progressively increases the gap between Rexcl_immunity and Rteff. The Christmas school holidays and accompanying near-lockdown physical distancing in December, 2020, followed by the third national lockdown in January, 2021, successfully brought Rteff below the critical threshold of one. This period coincided with a rapid expansion of the national vaccination programme. By March 8 (step one of the roadmap) when educational institutions reopened, 43% of eligible adults (>18 years) had received their first vaccine dose and 2% had their second dose.1 Cases, hospital admissions, and deaths continued to decrease and remained at low levels even after schools reopened (figure 1A, C). Although we estimated a slight increase in Rteff after step one, Easter holidays (from April 1, 2021) and the roll-out of vaccination maintained Rteff below one (figure 2A) when non-essential retail opened (61% first dose and 15% second dose coverage by April 12, step two).1 The delta variant, detected in early April, 2021, predominantly in London and the North West NHS regions (figure 1F), was designated as a variant under investigation on April 15, after increasing numbers of locally acquired infections with that variant were detected (figure 1E).15
Prevalence-weighted effective R(t) and R(t) excluding infection-induced or vaccine-induced immunity (A) and proportion of the population in England protected after infection or vaccination against infection, severe disease, or death (B), over time
(A) Estimated values from the end of the second national lockdown up to July 19, 2021, and assumed values thereafter. The solid line shows the median R(t) and the shaded area shows the 95% credible interval. Shaded area shows school holidays—note we do not explicitly model the impact of school closures for the period July 23–Aug 31 in order to capture the overall gradual increase in contacts from July 19 to Oct 1 (appendix p 44). The forward projection section of the figure corresponds to the central immunity and gradual return to moderate mixing scenario. Note that our central projected trend is the median across all daily projections and is not a single trajectory. (B) Proportion of the English population, from Jan 1, 2021, protected after infection or vaccination over time against infection, severe disease, or death. The vertical dashed line shows the separation between the observed vaccination schedule up to July 31, followed by the simulated schedule assuming central immunity and a gradual return to moderate transmissibility after non-pharmaceutical restrictions are lifted. The white space in the plot corresponds to individuals who have neither been vaccinated against nor infected with SARS-CoV-2. R(t)=reproduction number. *Euro 2020 football tournament.
We estimated that the Rteff for alpha remained less than one (appendix p 54) after step two because of increasing population immunity from vaccination, with uptake increasing to 69% for first dose and 39% for second dose by May 17 (step three, resumption of indoor hospitality and inter-household mixing1). However, after initial seeding between late March and early April (figure 1F) the proportion of delta variant cases increased rapidly across all regions in this period (figure 1E; appendix p 54). This rapidly drove Rteff to greater than one by mid-May, reflecting the 76% (95% credible interval [CrI] 69–83) transmission advantage of delta compared with alpha that we estimate (figure 2A).
The increase in contacts after step three continued to be offset by the increasing vaccine-induced population immunity (figure 2); 82% had a first dose and 60% had a second dose by June 21).1 However, the net impact of infection-induced and vaccine-induced immunity differed qualitatively by variant. We estimated that Rteff for alpha remained less than one through to mid-July, whereas Rteff for delta remained greater than one (appendix p 54).
A further sharp increase in Rteff was then seen in the first half of July. At the time, we and other modelling groups advising the UK Government concluded that the increase was a belated result of step three, but the rapid drop after July 11 (and other data indicating a sex imbalance in case incidence),39 suggest that the increase in transmission rates seen in that period resulted from a transient increase in population contact rates, particularly in young men, probably associated with the Euro 2020 football tournament. At the time of writing, that decrease has reversed, with a gradual increase in case incidence in August.17
Despite high vaccine coverage in adults, sufficient population susceptibility remains for a third wave to occur as contact rates rise (figure 2). The proportion protected against a delta infection after vaccination is substantially lower than for alpha. Additionally, most individuals younger than 18 years have neither been vaccinated nor infected. In this context, delta’s high transmissibility means that population immunity, whether vaccine induced or infection induced, is insufficient to keep Rteff below one.
With the emergence of delta, our projections show that had step four occurred on June 21, as initially planned, it might have caused a substantial third wave of hospital admissions and deaths, but with wide uncertainty regarding the magnitude and trajectory of that wave (figure 3). Projected total deaths between June 21, 2021, and June 1, 2022, ranged from 13 400 (95% CrI 8300–22 700) in the most optimistic scenario (high vaccine effectiveness, high cross-protection, slower waning of natural immunity, and 40% increase in contacts) to 40 600 (33 000–49 500) in the most pessimistic scenario (low vaccine effectiveness, low cross-protection, faster waning of natural immunity, and 100% increase in contacts; appendix pp 9, 38, 43).
England COVID-19 daily infections (A), hospital admissions (B), deaths (C), and total additional deaths between June 21, 2021, and Jan 1, 2022 (D)
Assumptions were that all remaining NPIs were lifted on June 21 (blue) or July 19 (red and purple) with a gradual increase in contacts over 11 weeks thereafter, and a return to low (light blue or purple), moderate (medium blue or red), or high (dark blue or red) transmissibility (contact rates). The grey points show the fitted data and yellow the most recent trends (July 19–Sept 16, not fitted). Each column shows projections assuming delta variant with optimistic (left column); central (middle column); and pessimistic (right column) vaccine effectiveness, cross-protection, and waning immunity assumptions (appendix pp 9, 38). The plots are truncated on Jan 1, 2022, but model results in the main text are based on simulations up to June 1, 2022. The central projected trend is the median across all daily projections and is not a single trajectory. Shading shows 95% credible intervals.
We found that delaying step four until July 19, was beneficial for all scenarios considered, with a much smaller projected wave, although still highly uncertain and sensitive to assumptions around vaccine effectiveness against delta and mixing after NPIs are lifted (figure 3). The delay allowed the distribution of an additional 2·8 million first doses and 3·8 million second doses between June 21 and July 19,1 reducing the projected future peak of daily hospital admissions by three fold from 3900 (95% CrI 1500–5700) to 1400 (700–1700) in the baseline scenario of central immunity and moderate mixing (figure 3B). It also reduced the total predicted deaths between June 21, 2021, and June 1, 2022, by about 20% (appendix pp 62–70). Had contact rates increased more abruptly after step four, the reductions in peak hospital admissions and deaths caused by delaying until July 19, would have been substantially greater (see appendix p 60). However, trends in cases, hospital admissions, and death data in the months up to mid-August, 2021, suggest that contacts have increased gradually since step four, in line with the assumptions of our baseline scenario (figure 1B, D).
In sensitivity analyses, we found that the level of mixing of people after NPIs are lifted was the main driver of uncertainty of the magnitude of the third wave, followed by vaccine effectiveness, the level of cross-immunity, and waning immunity. The rate at which previous infection-induced immunity waned had a greater impact on expected total infections but only minimally impacted expected hospital admissions or deaths (appendix pp 72–73). As expected, allowing for waning of vaccine-induced immunity resulted in a higher number of hospital admissions and deaths. However, this sensitivity analysis did not include the potential mitigating impact of planned booster doses (appendix pp 12, 76).
We project future deaths predominantly in fully vaccinated individuals aged 75 years or older because of the high vaccine uptake of a highly effective, but not 100% effective, vaccine in the most vulnerable age groups; it does not imply a poor vaccine effectiveness against death. We also anticipate a substantial number of deaths among fully vaccinated adults aged 50–74 years (appendix p 61).
Mathematical models are valuable tools to inform the design, monitoring, and evaluation of vaccination programmes.40 In this study, we retrospectively assessed steps one to three of England’s roadmap out of lockdown policy, and prospectively explored the impact of step four. By extending our evidence synthesis framework to account for vaccinations and multiple variants, we reliably captured the past epidemic and quantified the impact of each phase of lifting interventions and the emergence of the delta variant on transmission.22
We show that the roadmap was successful in mitigating the increase in mixing due to lifting NPIs, by increasing population-level immunity through the mass vaccination programme. Our projections show that in the absence of the delta variant, lifting NPIs at the planned earliest date of the final step (June 21) would not have resulted in a substantial third wave. This emphasises the importance of carefully aligning the lifting of interventions with immunity levels in the population. Israel rapidly vaccinated a high proportion of their population throughout their reopening plan, and then implemented vaccination passes for entry to high-risk settings.41, 42 Conversely, case incidence and hospital admissions are currently surging in several US states, due to restrictions being relaxed when vaccination coverage was too low to give sufficient population immunity.43
Our analyses emphasise the significant impact that the emergence of delta had on the planned roadmap. Similar to previous studies, we estimated that delta has an average 76% (95% CrI 69–83) transmissibility advantage over alpha.9 Together with reductions in vaccine effectiveness for delta and waning of natural immunity, this drove a rapid increase in cases and hospital admissions from mid-May that was not offset by the vaccination programme.28, 34 A similar displacement of alpha and a surge in cases was observed in India where delta was first detected.44 The dominance of delta has now led to a tightening of restrictions in many countries, including in Israel, the country with the fastest vaccination roll-out.45
Across all the scenarios we examined, delaying step four until July 19 was beneficial because it allowed more individuals to be vaccinated. In the baseline scenario, this delayed and reduced the peak of hospital admissions by three fold and reduced total deaths between June, 2021 and June, 2022 by about 20%.
We projected that a substantial autumn wave of transmission is possible, at least in the absence of substantial additional vaccination (eg, booster doses and full vaccination of teenagers), but with large uncertainty around the resulting peak number of hospital admissions and total deaths. This uncertainty is driven by uncertainty around levels of mixing after NPIs are lifted and by imperfect knowledge of vaccine effectiveness against delta. Transmission intensity (Rteff) in the coming months will depend on how high and how quickly population contact rates will increase, the continued use of face coverings,46 physical distancing, and adherence to case isolation.47, 48
The rapid exponential growth in case incidence in the first half of July, 2021, illustrates the high transmission levels that could be reached if contact rates approach prepandemic levels in the coming months. Fortunately, that increase in contact rates, probably caused by the Euro 2020 football tournament,39 proved transient, and was then followed by the synchronous self-isolation of contacts alerted through NHS test and trace49 and the start of school holidays a week later, driving transmission down for the last 2 weeks of July. Polls showed that 57% of UK adults were worried about the removal of legal restrictions and 66% would continue to wear a face covering after July 19.50 The average number of contacts in early September, 2021, was still much lower than prepandemic levels,51 and our baseline scenario, which assumes a gradual increase in contact rates, most closely reflects current trends. We estimate that this slow increase in contact rates after July 19 will reduce the peak number of hospital admissions and total deaths compared with an abrupt increase.
Uncertainty around vaccine effectiveness means it is difficult to accurately estimate the overall level of population immunity accounting for waning and imperfect cross-protection. At high vaccine coverage, even a difference of 98% versus 95% in vaccine effectiveness against mortality translates to a doubling of projected deaths.52 The duration of infection-induced and vaccine-induced immunity for all SARS-CoV-2 lineages remains another key unknown that will determine long-term transmission dynamics. We explored the impact of waning vaccine-induced protection in a simple sensitivity analysis. Including waning of vaccine protection had no impact on our retrospective assessment of steps one to three of the roadmap, but resulted in a substantially higher number of projected hospital admissions and deaths. Better characterising the duration of natural and vaccine-induced protection against infection and severe disease will be important for informing booster vaccination programmes. We have focused on deaths and hospital admissions as primary outcomes in our analysis, given the impact of hospital admissions on NHS capacity. However, an estimated 1·5% of individuals in the UK had symptoms of so-called long COVID (symptoms lasting >4 weeks) on July 4, 2021.53 Although our estimates of infections and cases over time might capture this wider burden of disease,54 we did not explicitly quantify this. Overall infection levels also determine the risks of new variants of concern emerging within the UK with the risk increasing with transmission levels.
Our analysis has a number of limitations. We did not consider reintroduction of NPIs, vaccination of people younger than 18 years, nor booster doses in our projections. These measures might at least partially mitigate the third wave. A first dose of vaccine has now been advised for all eligible 12–17-year-olds and booster doses for individuals aged 50 years or older is being rolled out.55, 56 However, given the age profile of projected hospital admissions and deaths, we anticipate expansion of vaccine eligibility to children might only have a moderate effect. Although we modelled heterogeneity by age in mixing patterns and capture changes in the overall level of mixing over time, we assumed that age-related mixing patterns remained constant over time. We also modelled age-dependent vaccine uptake, which we assumed was independent of mixing patterns or viral transmission, but did not explicitly model other types of heterogeneity (eg, by occupation, sociodemographic, and ethnic groups57, 58) which might affect both the risk of infection and vaccine uptake. Groups of individuals who are both at high risk of infection and less likely to take the vaccine might lead to continued outbreaks among vulnerable populations and reduce the overall impact of vaccination. Furthermore, our analysis focused only on outcomes directly related to COVID-19: we did not consider the impact on health services, other diseases, mental health, or the economic impact of measures.
In summary, our study shows how the phased lifting of NPIs in England, coordinated with vaccine roll-out, has been largely successful at keeping hospital admissions and deaths at low levels since March, 2021. However, our projections show that the high transmissibility of delta, imperfect vaccine effectiveness, and future increases in contact rates are likely to lead to a substantial wave of transmission in the autumn, albeit of highly uncertain magnitude. Overall, our analysis highlights the clear benefit of early and accessible national vaccination programmes that allow population immunity to increase to high levels before NPIs are lifted. Furthermore, we have shown that vaccination alone in the absence of NPIs might not be sufficient to control delta, even with high vaccination coverage. We quantified how the emergence of delta affected the progress of the roadmap and the benefit of delaying step four of that roadmap by 1 month. The experience of delta highlights the threat posed by any future variants of concern and underscores the need for global collaborative efforts to control transmission and mitigate the risk of emergence of new variants of concern through equitable global access to vaccines.
AC has received payment from Pfizer for teaching of mathematical modelling of infectious diseases. KAMG has received honoraria from Wellcome Genome Campus for lectures and salary support from the Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance, through Imperial College London for work outside this study. All other authors declare no competing interests.
We thank all colleagues at PHE and front-line health professionals who have not only driven and continue to drive the daily response to the COVID-19 epidemic in England but also provided the necessary data to inform this study. This work would not have been possible without the dedication and expertise of said colleagues and professionals. The use of pillar-2 PCR testing data, vaccination data, and the variant and mutation data was made possible thanks to PHE colleagues, and we extend our thanks to N Gent and A Charlett for facilitation and insights into these data. The use of serological data was made possible by colleagues at PHE Porton Down, Colindale, and the NHS Blood Transfusion Service. We are particularly grateful to G Amirthalingam and N Andrews for helpful discussions around these data. We thank all the REal-time Assessment of Community Transmission (REACT) Study investigators for sharing PCR prevalence data. We also thank the entire Imperial College London COVID-19 response team for support and feedback throughout. The views expressed are those of the authors and not necessarily those of the UK Department of Health and Social Care, the NHS, the National Institute for Health Research (NIHR), PHE, UK Medical Research Council (MRC), UK Research and Innovation, or the EU. This work was supported jointly by the Wellcome Trust and the Department for International Development (DFID; 221350]. We acknowledge joint Centre funding from the UK MRC and DFID (MR/R015600/1). This work was also supported by the NIHR Health Protection Research Unit in Modelling Methodology, a partnership between PHE, Imperial College London, and London School of Hygiene & Tropical Medicine (NIHR200908), the Abdul Latif Jameel Foundation, and the EDCTP2 programme supported by the EU.
RS, PNP-G, ESK, LKW, NI, EMV, ACG, NMF, MB, and AC were involved in conceptualisation of the study, including formulation or evolution of overarching research goals and aims. KAMG, WH, BAD, NI, RGF, PNP-G, and ESK curated the data, including management activities to annotate (produce metadata), scrub data, and maintain research data. RS, LKW, PNP-G, ESK, KAMG, MB, and AC did formal analysis, including application of statistical, mathematical, computational, or other formal techniques to analyse or synthesise study data. MB, NMF, and AC acquired funding. RS, LKW, PNP-G, ESK, TR, MB, and AC did the investigation, including conducting a research and investigation process, specifically performing the experiments, or data or evidence collection. RS, LKW, PNP-G, ESK, MB, NMF, and AC developed or designed the methodology, including creation of models. NI, RS, NMF, MB, and AC were involved in project administration, including management and coordination responsibility for the research activity planning and execution. JAL and RGF were responsible for provision of computing resources or other analysis tools. RS, LKW, PNP-G, ESK, TR, WH, JAL, RGF, MB, and AC were responsible for software, including programming and software development; designing computer programs; implementation of the computer code and supporting algorithms; and testing of existing code components. NMF, MB, and AC supervised the study, including oversight and leadership responsibility for the research activity planning and execution, and mentorship external to the core team. RS, LKW, NI, PNP-G, ESK, TR, NMF, MB, and AC were responsible for verification, whether as a part of the activity or separate, of the overall replication or reproducibility of results or experiments and other research outputs. RS, LKW, PNP-G, TR, and WH prepared, created, or presented the published work, specifically regarding visualisation and data presentation. NI, TR, RS, ESK, LKW, MB, and AC wrote the original draft, including preparation, creation, or presentation of the published work. NI, RS, LKW, TR, ESK, PNP-G, DTK, KAMG, JAL, ACG, MB, AC, and NMF reviewed and edited the manuscript, including preparation, creation, or presentation of the published work by those from the original research group, and gave critical review, commentary, or revision, including stages before and after publication. All authors had access to the underlying data and KAMG, WH, BAD, and NI accessed and verified the data.
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People appear to be more likely to suffer a Covid rebound if they use Pfizer‘s antiviral drug Paxlovid, a study suggests.
In the past few months, President Joe Biden, Dr Anthony Fauci and CDC director Dr Rochelle Walensky have all tested positive again quickly after they stopped taking the drug.
Scientists from Scripps Research Translational Institute in La Jolla, California, compared 127 infected people who used Paxlovid to 43 others who beat the virus without the drug.
They found that 14 per cent of Paxlovid users tested positive for the virus in the weeks after recovering. Meanwhile, only nine per cent tested positive again in the group that didn’t use the antiviral.
The study was small and the researchers don’t feel confident that the results weren’t chance, but they aim to stand it up in a future trial involving 800 people.
The exact causes of the rebound are unknown, but doctors suspect it is because of the how the drug functions. Rather than killing the virus outright, Paxlovid stop its replication within the body.
Experts theorize that, having been suppressed by Paxlovid, Covid bounces back when the drug vanishes from the body, leading to high viral levels and potent immune responses that can cause symptoms to reappear.
Notable examples of the Covid rebounds include President Joe Biden, who suffered a rebound after being infected with the virus in June and receiving Paxlovid
Two of America’s leading health officials, Dr Fauci, the nation’s top infectious disease expert, and Dr Walensky, director of the Centers for Disease Control and Prevention (CDC), were also affected.
The drug was heralded by President Biden as one of the silver bullets to fight the pandemic at his 2022 State of the Union address.
Paxlovid was central to his ‘test to treat’ Covid program launched earlier this year that offered it to Americans who tested positive for the virus at select pharmacies.
It is prescribed as three pills taken twice a day for five days
The study, which is available in pre-print and still pending peer review, gathered data from 170 patients.
Each of the patients were offered Paxlovid after testing positive for the virus. Among them, 127 accepted to offer while 43 chose not to use the drug.
Patients were also given 12 at-home COVID-19 tests and instructed to test themselves every other day.
They reported test results and daily symptoms to researchers.
In the weeks following completion of the course, 18 people in the Paxlovid group, or 14 per cent, once again tested positive for the virus.
Another 22, or 19 per cent of the study group, reported that their Covid symptoms had returned but did not record a positive test.
In the control group, only four testes positive again – nine per cent – while three had symptoms return despite negative swabs – or seven per cent.
In total, 33 per cent of Paxlovid users and 16 per cent of non-users experienced either a symptom bounce back or positive test after recovering from virus.
Dr Michael Charness, chief of staff at the Veterans Affairs Boston Healthcare System, told CNN: ‘There is an indication that symptomatic rebound is more frequent in Paxlovid-treated participants than in untreated controls, but larger numbers are needed to draw confident conclusions.’
This study is another setback for Paxlovid, which was billed as a pandemic ‘game-changer’ when it first hit the market in late 2021.
Clinical trials showed it reduced the likelihood of hospitalization or death caused by the virus 90 per cent.
Authors: Reported By: IANSNew YorkPublished on: November 24, 2022 INdia
For the first time since the beginning of the pandemic in early 2020, a majority of Americans dying from Covid were at least partially vaccinated, according to the new analysis of federal and state data.
In a startling revelation, a Washington Post analysis has found that more vaccinated people are now dying of the Covid disease and 58 per cent of coronavirus deaths in August in the US “were people who were vaccinated or boosted”.
For the first time since the beginning of the pandemic in early 2020, a majority of Americans dying from Covid were at least partially vaccinated, according to the new analysis of federal and state data.
“In September 2021, vaccinated people made up just 23 per cent of coronavirus fatalities. In January and February this year, it was up to 42 per cent,” the report mentioned.
The death among vaccinated people is increasing due to the waning efficacy of Covid vaccines and “increasingly contagious strains of the virus being spread to elderly and immunocompromised people” among those who have taken at least one vaccine dose.
“We can no longer say this is a pandemic of the unvaccinated,” said Kaiser Family Foundation vice president Cynthia Cox, who conducted the analysis on behalf of the Washington Post.
Outgoing White House Chief Medical Adviser, Anthony Fauci has emphasised the safety and efficacy of the approved Covid vaccines in preventing severe illness and deaths, encouraging people to get vaccinated and boosted as soon as possible.
He said that coronavirus vaccine effectiveness wanes over time and the disease shouldn’t be compared to other vaccine-treatable illnesses because of new emerging variants.
“My message, and my final message, maybe the final message I give you from this podium, is that please, for your own safety, for that of your family, get your updated Covid-19 shot as soon as you’re eligible to protect yourself, your family and your community,” Fauci said.
“I urge you to visit vaccine.gov to find a location where you can easily get an updated vaccine, and please do it as soon as possible.”
Older people were always especially vulnerable and now make up a higher proportion of Covid fatalities than ever before in the pandemic, reports Scientific American.
Today in the US, about 335 people will die from Covid — a disease for which there are highly effective vaccines, treatments and precautions, it added.
“Covid deaths among people age 65 and older more than doubled between April and July this year, rising by 125 per cent,” according to the Kaiser Family Foundation.
The World Health Organization reported a nearly 90 per cent drop in recent Covid-19 deaths globally compared to nine months ago, but still urged vigilance against the pandemic as new variants continue to rise.
Overall, the WHO has reported 629 million cases and 6.5 million deaths linked to the pandemic.
Abstract The massive COVID-19 vaccination campaign is the first time that mRNA vaccines have been used on a global scale. The mRNA vaccines correspond exactly to the definition of gene therapy of the American and European regulatory agencies. The regulations require excretion studies of these drugs and their products (the translated proteins). These studies have not been done for mRNA vaccines (nor for adenovirus vaccines). There are numerous reports of symptoms and pathologies identical to the adverse effects of mRNA vaccines in unvaccinated persons in contact with freshly vaccinated persons. It is therefore important to review the state of knowledge on the possible excretion of vaccine nanoparticles as well as mRNA and its product, the spike protein. Vaccine mRNA-carrying lipid nanoparticles spread after injection throughout the body according to available animal studies and vaccine mRNA (naked or in nanoparticles or in natural exosomes) is found in the bloodstream as well as vaccine spike in free form or encapsulated in exosomes (shown in human studies). Lipid nanoparticles (or their natural equivalent, exosomes or extracellular vesicles (EVs)) have been shown to be able to be excreted through body fluids (sweat, sputum, breast milk) and to pass the transplacental barrier. These EVs are also able to penetrate by inhalation and through the skin (healthy or injured) as well as orally through breast milk (and why not during sexual intercourse through semen, as this has not been studied). It is urgent to enforce the legislation on gene therapy that applies to mRNA vaccines and to carry out studies on this subject while the generalization of mRNA vaccines is being considered
Cases of heart inflammation after COVID-19 vaccination were more common among Moderna recipients than those who received Pfizer’s shot, according to a new study.
Canadian researchers analyzed a database and identified 141 cases of myocarditis, a form of heart inflammation, within 21 days of a dose of the Pfizer or Moderna vaccine, both of which utilize messenger RNA (mRNA) technology.
That was compared with an expected number of just 20 cases.
Cases were much higher for young males, as previous studies have found, but were elevated even higher following receipt of a second dose of the Moderna vaccine compared with a second dose of the Pfizer shot.
The incidence, though, was higher after receipt of a third dose of the Pfizer vaccine.
“In this population-based cohort study, observed rates of hospital admissions or emergency department visits for myocarditis after mRNA vaccination for SARS-CoV-2 were higher than expected based on historical background rates, particularly after the second dose, among those who received the mRNA-1273 (Moderna) vaccine, among males and among younger patients (18–29 yr),” Dr. Zaeema Naveed and other researchers with the University of British Columbia and British Columbia Centre for Disease Control wrote.
Moderna and Pfizer did not respond to requests for comment.
Latest to Find Moderna Higher
Research dating back to mid-2021 shows that the incidence of heart inflammation is higher following a Moderna second dose for young males when compared to a Pfizer second dose.
Both vaccines are recommended as two-dose primary series.
Dr. Anish Koka, a cardiologist based in the United States, said on Twitter that the new study highlights the lack of action by the U.S. Centers for Disease Control and Prevention (CDC), which continues to recommend that young males receive either vaccine.
“The rates of Moderna are really much higher for dose 2 in young men,” Dr. Walid Gellad, a professor of medicine at the University of Pittsburgh, said. “I remain perplexed why US never acted on this information, which has been known for a year.”
Some other countries have suspended administration of Moderna’s vaccine—or both vaccines—for young people based on the vaccine side effects and the fact that healthy youth face little risk from COVID-19.
The CDC has also detected more cases of myocarditis (pdf) after receipt of a Moderna second dose in the highest-risk populations, using surveillance data.
U.S. authorities added myocarditis as a possible side effect for both vaccines in 2021, but have not changed their recommendations, which call for virtually all people to receive not only a primary series, but at least one booster shot.
U.S. authorities have said the benefits of the vaccines—primarily protection against severe illness—outweighs the risks.
The Canadian researchers said as much, though their only citation was to a non-peer-reviewed CDC paper from June 2021.
Other studies since then have concluded that the risks outweigh the benefits for one or more populations, particularly young males. The calculus has tilted because of the growing evidence of side effects like myocarditis and the worse performance of the vaccines against the Omicron virus variant and its subvariants, some experts say.
More on New Paper
The Canadian researchers analyzed information from a British Columbia surveillance platform that has data such as laboratory tests and hospital admissions. They examined data from Dec. 15, 2020, to March 10, 2022.
They found that 105 males and 36 females experienced myocarditis and went to a hospital or emergency room within 21 days of a shot.
Approximately 60 percent of the cases happened after a Pfizer jab, but the overall dataset included a higher level of Pfizer administration than Moderna administration.
Researchers calculated an overall rate of 1.37 cases of myocarditis per 100,000 mRNA vaccine doses, above the expected rate of 0.39 cases per 100,000 population. The expected rate was drawn from the incidence of myocarditis in the general population from before the pandemic.
The rates were far higher after a second dose and among young males.
For males aged 18 to 29 after a second dose, the rate was 23 per 100,000 after a Moderna shot and 5.8 per 100,000 after a Pfizer shot.
For males aged 30 to 39 after a second dose, the rate was 7 per 100,000 after a Moderna shot and 1.3 per 100,000 after a Pfizer shot.
We report two cases of optic nerve pathology after the administration of the Pfizer-BioNTech and AstraZeneca-Oxford COVID-19 vaccines, respectively, and describe the implications for management of post-vaccination central nervous system (CNS) inflammation.
A 69-year-old woman presented with bilateral optic nerve head oedema, 16 days after the second dose of the Pfizer-BioNTech vaccine. She was diagnosed with post-vaccination CNS inflammatory syndrome and was treated for five days with intravenous methylprednisolone at a dose of 1 gram per day. Her optic disc swelling improved, and her vision stabilised. A 32-year-old woman presented six days after her first dose of the AstraZeneca-Oxford vaccine with two days of sudden onset of progressive blurring of vision in her left eye. Posterior segment examination revealed left optic disc swelling, and an MRI of the brain, orbit, and cervical spine was significant for left optic nerve enhancement. The patient was diagnosed with a unilateral post-vaccination optic neuritis. She was treated with a three-day course of intravenous methylprednisolone followed by oral prednisone. Her optic disc swelling and visual field improved, and she recovered 6/6 vision.
Clinicians and patients should be aware of the potential for post-vaccination CNS inflammatory syndromes associated with COVID-19 vaccine administration. Neuroimaging and cerebrospinal fluid analysis may aid in the diagnosis of the cause of vision loss. Further studies are needed to evaluate the spectrum and frequency of optic nerve involvement associated with COVID-19 vaccination.
Authors: The Journal of Heart and Lung Transplant November 18. 2022
Purpose: Anti-severe acute respiratory syndrome corona virus 2 (SARS- CoV-2) vaccination is recommended by AST, ISHLT, and CDC in all transplant recipients. Lung transplant recipients (LTR) are at a higher risk of developing severe symptoms due to higher immunosuppression (IS) an baseline compromised graft function. Limited antibody response to messenger RNA (mRNA) vaccines has been reported in LTR, with the majority mounting a response after the 2nd dose. In this series, 3 patients developed new and significant respiratory compromise after their 2nd vaccine dose consistent with antibody mediated rejection (AMR). To our knowledge, this is the first published case series of vaccine induced rejection in LTR.
Methods: Retrospective chart review of our cohort showed 46% fully vaccinated and an additional 2.5% partially vaccinated patients. Three fully vaccinated patients with approved mRNA vaccines (2 Moderna, 1 Pfizer-BioNTech) were identified after developing severe respiratory compromise post 2nd vaccine dose. Evaluation revealed AMR as the underlying etiology. Results: All patients were female, ages 50-70 years old, between 6 months and 2 years post-transplant. No previous rejection episodes. All were on standard IS as per institution protocols. Two were hospitalized with hypoxic respiratory failure within 2 weeks of their 2nd vaccine dose. The 3rdwas seen at clinic for milder similar symptoms, later progressing and requiring supplemental oxygen (O2) and hospitalization. Imaging showed new lung infiltrates, infectious work up was negative. Biopsies did not show any cellular rejection. All developed new DSAs and received treatment for AMR with plasmapheresis, IVIg, and Rituximab. Two recovered their lung function and are off supplemental O2, the 3rd did not and is re-listed for transplant. Conclusion: While LTR have a diminished response to SARS-CoV-2 vac-cines making them more vulnerable to the disease, their immune system’s response may not always be clear. We report three cases of patients developing severe AMR from new DSAs that appear to be triggered by theCOVID-19 vaccine. This vaccine responses should be collected in a data-base where each case can be investigated to help better understand the mechanism behind them and hopefully identifying LTR at risk. This can then be used to modify vaccination strategies and aid in preventing adverse outcomes in this vulnerable group of patients.(1332)First Report of Daratumumab in Clinical Lung Transplantation P. Jaksch,1 G. Murak€ozy,2 G. Fischer,3 H. Konrad,4 and A.Benazzo.4 1Department of Thoracic Surgery, Medical University Vienna, Vienna, Austria; 2Medical University Vienna, Wien, Austria; 3Departmentof Blood Group Serology and Transfusion Medicine, Medical University Vienna, Vienna, Austria; and the 4Department of Thoracic Surgery, Medical University Vienna, Vienna, Austria. Purpose: Antibody mediated rejection (AMR) after lung transplantation is difficult to treat and often results in death or graft loss. Therapies targeting antibodies or B cells are in many cases inadequate for decreasing donor specific antibodies (DSA), particularly when they are directed against MHC class II. Daratumumab (DAR) is a humanized anti-CD38 monoclonal antibody, which induces plasma cell death through multiple mechanisms including complement- dependent cytotoxicity, antibody-dependent phago-cytosis, and apoptosis. Based on these properties it may have the potentialto reduce the amount of DSAs and therefore improve outcome after AMR. Methods: Retrospective analysis of all patients who received daratumumab as an add on rescue therapy for AMR or for desensitization pre/post- transplant at our center.
Results: Six patients received daratumumab due to the following reasons:5 patients with de novo DSAs and AMR, 1 patient with pre-transplant DSAs and post-transplant immunoadsorption without clinical AMR. Daratumumab was safely administered with just mild infusion reactions and no severe adverse event. Of not, none of the patients developed and infectious complication. Five of the 6 patients showed a significant decrease of their DSAs with a reduction of MFI values after 6-8 weeks to<50% of the baseline . Despite this early success, four patients developed CLAD (,two of which required transplantation. The remaining two patients stabilized with their lung function and did not develop CLAD. Conclusion: Treatment for AMR remains challenging, especially in the presence of class II HLA antibodies. Daratumumab might be a promising addition to the AMR treatment panel, prospective clinical studies are needed. (1333)The Normalized Acute Rejection Score in the First Year Post Transplant and Its Association with CLADN. Belousova,1 R. Gabarin,2 A. Vasileva,1 L. Levy,3 R. Ghany,1 E.Huszti,1 A. Roux,4 C. Chow,1 and T. Martinu.1 1University Health Network, Toronto, ON, Canada; 2University of Toronto, Toronto, ON, Canada; 3Tel-Aviv University, Tel-Aviv, Israel; and the 4Foch Hospital, Suresnes, France. Purpose: The Acute Rejection Score (A-score) is a measure of the burdenof acute cellular rejection (ACR) over time in lung transplant (Ltx) recipients which is often incorporated into multivariate analyses assessing LTx outcomes. We aim to assess the correlation between A-score at 6 and 12months post Ltx and chronic lung allograft dysfunction (CLAD).Methods: We performed a retrospective cohort analysis on adult 1st double LTx recipients from January 2003 to March 2018, with minimum 6 months of follow-up and 1 or more evaluable transbronchial biopsies (TBBX) in the 1st year post Ltx. A-score was calculated at 6 months (approximated as 10 days) and 1 year (400 days) post-LTx as the sum of all ACR histologic A-grades, divided by the number of TBBX up to that time point. AX grade biopsies were excluded from the calculation. CLAD was determined according to 2019 ISHLT guidelines. Kaplan-Meier curves were compared using the Log-Rank test. Results: Of 828 1st double lung transplants, 31 were excluded with lessthan 6 months of follow-up and 23 had no evaluable biopsies in the first year post Ltx (final n=774). Mean follow-up was 2102 days to graft failure death or retransplant) or last available pulmonary function test. 345patients (45%) developed CLAD. Mean A-score was 0.37 and 0.32 at 210 and 400 days, respectively; median non-zero A-score was 0.5 and 0.4.Kaplan-Meier curves comparing A-score 0 with scores above and below the median were not significantly different (p=0.08 and 0.78 for 210 and400 days) (Figure A,B). Log-rank comparison of only non-zero groups was not significant (p=0.78 and 0.93 at 210 and 400 days).Conclusion: A-score at 210 and 400 days post Ltx, when setting a cutoff atthe median, is not significantly associated with CLAD. Further study withCox proportional hazard modelling of the A-score as a continuous and time-dependent variable will be conducted to assess the impact on the risk of CLAD.(1334)Unilateral vs Bilateral Lower Lobe surveillance Transbronchial Biopsies in Patients with Bilateral LungTransplantPatientsR.Dandeboyina,1 K. Ausloos,2 T. Grazia,2 K. Vandervest,2 and C.Naik.2 1University of Texas Dallas, Dallas, TX; and the 2Baylor University Medical Center, Dallas, TX. Abstracts S533
Vaccination is a major tool for mitigating the coronavirus disease 2019 (COVID-19) pandemic, and mRNA vaccines are central to the ongoing vaccination campaign that is undoubtedly saving thousands of lives. However, adverse effects (AEs) following vaccination have been noted which may relate to a proinflammatory action of the lipid nanoparticles used or the delivered mRNA (i.e., the vaccine formulation), as well as to the unique nature, expression pattern, binding profile, and proinflammatory effects of the produced antigens – spike (S) protein and/or its subunits/peptide fragments – in human tissues or organs. Current knowledge on this topic originates mostly from cell-based assays or from model organisms; further research on the cellular/molecular basis of the mRNA vaccine-induced AEs will therefore promise safety, maintain trust, and direct health policies.
Fighting the COVID-19 pandemic with SARS-CoV-2 S protein-encoding mRNA vaccines
COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Box 1 ) and has resulted in millions of deaths worldwide. Nevertheless, for the majority of SARS-CoV-2-infected individuals, COVID-19 will remain asymptomatic or only mildly symptomatic [1,2]. Although SARS-CoV-2 may also circulate in the gastrointestinal tract , being a respiratory virus, the virus itself or its related antigens will not, in most cases, impact tissues and organs other than the respiratory system (RS) (Box 1) [4., 5., 6.]. In patients with severe disease, infection of airway and lung tissues may cause pneumonia and excessive inflammation which can lead to acute respiratory distress syndrome (ARDS) (see Glossary) (Box 1) [7., 8., 9., 10.]. ARDS may then lead to organ damage beyond the RS because of micro-/macro-thromboembolism, hyperinflammation, aberrant complement activation, or extended viremia [7., 8., 9., 10., 11., 12., 13.]. This may be due to the broad expression of its receptor angiotensin-converting enzyme 2 (ACE2) in several cell types and tissues [14., 15., 16.] which results in an expanding tropism of SARS-CoV-2 for various critical organs (heart, pancreas, kidneys, etc.). If systemic collapse and death are avoided, the postulated direct virus ‘attack’ – or indirect effects due to cytokine storm [10,13] or imbalance of the renin–angiotensin system (RAS)  – causing multiorgan damage, possibly foster systemic defects which cause a chronic condition (referred to as long COVID-19) which is independently associated with the severity of the initial illness .
SARS-CoV-2 infection of human cells
SARS-CoV-2 infection of human cells proceeds via its binding to the cell surface protein ACE2 through the RBD of its protruding S glycoprotein  which remains in a metastable prefusion state through the association of subunits 1 (S1) and 2 (S2) via noncovalent interactions [18,19]; the infection process is also facilitated by host proteases [127,128]. In most of SARS-CoV-2-infected carriers the virus is contained in the upper RS, resulting in either no symptoms or mild symptoms [1,2]. A minority will require hospitalization; this is due to severe symptoms which develop due to extensive inflammation, a process often referred to as a ‘cytokine storm’, causing ARDS which may be accompanied by viremia and can lead to systemic multiorgan collapse [7., 8., 9., 10.]. The risk for severe COVID-19 increases significantly with age or pre-existing comorbidities [1,2,129], and younger individuals have a substantially lower risk – even compared to influenza infection  – for developing severe COVID-19 [130,131]. It has been postulated that higher pediatric innate interferon responses restrict viral replication and disease progression . In a recent trial, in which young people were intentionally exposed to a low dose of SARS-CoV-2, nearly half of the participants did not become infected, some were asymptomatic, and those who developed COVID-19 reported mild to moderate symptoms, including sore throats, runny noses, sneezing, and loss of sense of smell and taste; fever was less common, and no one developed a persistent cough .
SARS-CoV-2 infection in healthy individuals triggers innate as well as adaptive immune system responses, that is, CD4+ and CD8+ T cells and antibodies, including neutralizing antibodies (NAbs) produced by terminally differentiated B cells, which altogether suppress the extent of infection [132,134,135]. As SARS-CoV-2 initially infects the upper RS, defensive immune responses start to develop at respiratory mucosal surfaces, and this is followed by systemic immunity [136,137]. These immune responses are age- and gender-dependent and may either mount poorly in a background of genetic causes and pre-existing morbidities, or become very intense and essentially uncontrolled in severe disease leading to ARDS and systemic failure [11., 12., 13.].
Following an unprecedented effort of biomedical research and mobilization of resources, two mRNA vaccines – namely BNT162b2 (ComirnatyTM) from Pfizer-BioNTech and the mRNA-1273 of Moderna (encoded antigen: SARS-CoV-2 S protein of the Wuhan-Hu-1 strain) [18., 19., 20.] – were the first to receive FDA emergency use authorization. In mRNA vaccines, which are characterized by relatively rapid prototyping and manufacturing on a large scale, the S protein-encoding mRNA is delivered via lipid nanoparticles (LNPs) to human cells that produce the mature viral protein or related antigens (Figure 1 , Key figure), which can exhibit a rather wide tissue/organ distribution (discussed later) [20., 21., 22.]. In addition to the plausible proinflammatory role of LNPs (evidenced also from reported immediate allergic reactions) [23,24] and of packaged mRNA – which has nonetheless been engineered by a replacement of uridine with pseudouridine [20,25,26] so as not to trigger innate immunity through pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs) receptors – we surmise that vaccination-mediated adverse effects (AEs) can be attributed to the unique characteristics of the S protein itself (antigen) either due to molecular mimicry with human proteins or as an ACE2 ligand.
Key figure. Antigen expression–localization following cell transfection with spike (S) protein mRNA-containing lipid nanoparticles (LNPs) used in anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines.
Following LNP internalization and mRNA release, the authentic viral signal peptide (as in the Pfizer–BioNTech and Moderna vaccines) drives antigen production in the lumen of the endoplasmic reticulum (ER) where it adopts its natural transmembrane localization via subunit 2 (S2) anchoring. After sorting in the trans Golgi network (TGN), S protein acquires its final position in the transfected human cell membrane, where S1 is exposed to the extracellular space (i.e., may face circulation). Although the extent of antigen expression per cell remains unknown, it is reasonable to assume that this process results in rather extended decoration of transfected cells with S protein. Furin-mediated proteolytic cleavage (as in SARS-CoV-2-infected cells) in the absence of a mutated S1/S2 furin cleavage site at the TGN may result in shedding of cleaved S1 and conversion of S2 into its postfusion structure (S2*). Antigen sorting and trafficking may also induce the release of S protein-containing exosomes. The events shown will occur in the apical and/or basolateral surfaces of polarized (e.g., epithelial) cells. The Pfizer–BioNTech and Moderna constructs do not contain a mutated S1/S2 furin cleavage site. Further research will clarify the impact of the S1/S2 subunits stabilizing D614G (or other) mutation or of a mutated furin cleavage site in antigen distribution, the immunogenicity of the vaccine, and induced adverse events (AEs). Also shown are dendritic cells (professional antigen-presenting cells, APCs) engulfing circulating antigens, and antibody-mediated binding of B cells to cell-anchored antigens.
As delivered mRNAs can theoretically trigger the production of distinct antigens that can distribute systemically , they are radically different from conventional platforms (i.e., inactivated whole-virus vaccines or even protein-subunit nanoparticle vaccines) (Box 2 ) where the produced antigen and its distribution are more predictable. As all COVID-19 vaccines rely on the S protein of the original Wuhan-Hu-1 strain [19,20], the differences across different vaccination platforms thus far reported (Box 2) may relate to the various vectors and formulations and/or the S protein constructs employed.
Other types of COVID-19 vaccine
In viral vector vaccines, the S protein coding information is delivered via a replication-deficient adenoviral vector system that contains an encoding dsDNA. In this case, transcripts from adenoviral vectors are generated in the cell nucleus. Here, a major reported AE is immune thromboembolism (including cerebral venous sinus thrombosis) in various organs, probably through excessive innate immune system and endothelial activation . Apart from the S protein itself, AEs can be also attributed to background expression of remaining adenoviral genes or to persisting adenovirus-vector DNA in a transcriptionally active form. Further concerns are the presence of other contaminant proteins, remnants of the vaccine production line, and to pre-existing antivector immunity ; this last issue does not apply to the recombinant ChAdOx1-S (Oxford–AstraZeneca) vaccine which employs a nonhuman adenovirus vector. More importantly, the infectious cycle of SARS-CoV-2 takes place exclusively in the cytoplasm, and thus there has been no evolutionary pressure against the presence of splice donor and acceptor sites in its genes. This is a major difference from mRNA vaccines that function in the cytoplasm, since various spliced transcripts from adenoviral vectors can be generated in the cell nucleus .
In protein subunit nanoparticle vaccines (e.g., NVX-CoV2373), the S protein is harvested in a cell culture system, purified, and delivered as a trimer via a nanoparticle assembly in an adjuvant. Although preliminary trials indicate that these vaccines can trigger robust immunity , reports on AEs are still scarce due to the limited amount of vaccination data.
Finally, in conventional vaccines, the whole virus is inactivated and inoculated using an appropriate adjuvant . A significant benefit is that whereas in the previously discussed technologies the S protein is the sole source of immunogenic epitopes, in this case a wide repertoire of epitopes in other viral proteins is presented. Possible disadvantages include lower immunogenicity, production issues, AEs due to used adjuvant(s) (e.g., aluminum hydroxide), as well as issues that relate to incomplete inactivation of the virus. Given that these vaccines have not reached mass production, reports on possible AEs do not exist.
Anti-SARS-CoV-2 mRNA vaccines and their reported adverse effects
Both the BNT162b2 and mRNA-1273 vaccines are administered intramuscularly and mobilize robust and likely durable innate, humoral, and cellular adaptive immune responses [27., 28., 29., 30.]. Existing data on the available mRNA vaccines are mostly limited to serological analyses. Nonetheless, beyond the assessment of immune responses, the understanding of the safety profile of these vaccines is critical to ensure safety, maintain trust, and inform policy. Reportedly, mRNA vaccines are in general well tolerated, with very low frequencies of associated severe postimmunization AEs. Although rare, AEs include serious clinical manifestations such as acute myocardial infarction, Bell’s palsy, cerebral venous sinus thrombosis, Guillain–Barré syndrome, myocarditis/pericarditis (mostly in younger ages), pulmonary embolism, stroke, thrombosis with thrombocytopenia syndrome, lymphadenopathy, appendicitis, herpes zoster reactivation, neurological complications, and autoimmunity (e.g., autoimmune hepatitis and autoimmune peripheral neuropathies [31., 32., 33., 34.]) (see Clinician’s corner). Apart from AEs documented in clinical trials, most of the syndromes or isolated manifestations have been reported in multicenter or even nationwide retrospective observational studies and case series. Although correlation does not necessarily mean causation, active monitoring and awareness regarding reported postvaccination AEs are essential. Importantly, these associated AEs are significantly less frequent than analogous or additional serious AEs induced after severe COVID-19 [31,32,34]. Some vaccine-induced AEs (e.g., myocardial infarction, Guillain–Barré syndrome) were found to increase with age, while others (e.g., myocarditis, anaphylaxis, appendicitis) were more common in younger people [35,36]. Although myocarditis cases are rather rare, in a study of US military personnel the number was higher than expected among males after a second vaccine dose ; similarly, the rate of postvaccination cardiac AEs was higher in young boys following the second dose [38,39]. Finally, a recent study showed an increased risk of neurological complications in COVID-19 vaccine recipients (which was nevertheless lower than the risk in COVID-19 patients) . The molecular basis of these AEs remains largely unknown. We postulate that, since most (if not all) of them are also apparent in severe COVID-19 , they may be related to acute inflammation caused by both the virus and the vaccine, as well as in the common denominator between the virus and the vaccine, namely, the SARS-CoV-2 S protein (Box 1). The vaccine-encoded antigen (S protein) is stabilized in its prefusion form in the BNT162b2 and mRNA-1273 vaccines [19,20]; it is therefore plausible that, if entering the circulation and distributing systemically throughout the human body (Figure 2 ), it can contribute to these AEs in susceptible individuals.
Schematic of the vasculature components showing vaccination-produced S protein/subunits/peptide fragments in the circulation, as well as soluble or endothelial cell membrane-attached angiotensin-converting enzyme 2 (ACE2).
(A,B) Parallel to immune system activation, circulating S protein/subunits/peptide fragments (B) binding to ACE2 may occur not only to ACE2-expressing endothelial cells, but also in multiple cell types of the vasculature and surrounding tissues due to antigen diffusion (e.g., in fenestrated or discontinuous capillary beds) (A, red arrows). These series of molecular events are unlikely for any severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related antigen in the absence of severe coronavirus disease 2019 (COVID-19), where SARS-CoV-2 is contained in the respiratory system. In (C) the two counteracting pathways of the renin–angiotensin system (RAS), namely the ‘conventional’ arm, that involves ACE which generates angiotensin II (ANG II) from angiotensin I (ANG I), and the ACE2 arm which hydrolyzes ANG II to generate angiotensin (1–7) [ANG (1–7)] or ANG I to generate angiotensin (1–9) [ANG (1–9)] are depicted. ANG II binding and activation of the ANG II type 1 receptor (AT1R) promotes inflammation, fibrotic remodeling, and vasoconstriction, whereas the ANG (1–7) and ANG (1–9) peptides binding to MAS receptor (MASR) activate antifibrotic, anti-inflammatory pathways and vasodilation. Additional modules of the RAS (i.e., renin and angiotensinogen, AGT) are also shown. Abbreviation: AT1R, angiotensin II type 1 receptor.
Given the plethora of existing data on the available mRNA vaccines, a major ‘known’ is that in the short-term mRNA vaccines are well tolerated by the recipient, and that they can induce a robust immune response and therefore provide prolonged protection against severe COVID-19 (including emerging variants of concern); thus, vaccination remains a major tool for mitigating the COVID-19 pandemic and saving thousands of lives.
It is well established that the risk for severe COVID-19 increases with age or pre-existing comorbidities. Given the ‘unknowns’ discussed herein, boosting doses in healthy children and even adolescents should be delivered only if the benefit–risk profile is clearly established.
Multidisciplinary clinical and basic research aiming at understanding the cellular–molecular basis of the COVID-19 mRNA vaccine-induced AEs – along with active pharmacovigilance and long-term recording in the clinical setting of reported AEs in vaccinated recipients – are critical components for improving vaccines, guaranteeing safety, maintaining trust, and directing health policies.
The technology of the mRNA vaccines will continue to evolve as it opens up a whole new era of novel applications for large-scale development of new vaccines against various infectious and other diseases, including cancer.
There is also evidence that ionizable lipids within LNPs can trigger proinflammatory responses by activating Toll-like receptors (TLRs) . A recent report showed that LNPs used in preclinical nucleoside-modified mRNA vaccine studies are (independently of the delivery route) highly inflammatory in mice, as evidenced by excessive neutrophil infiltration, activation of diverse inflammatory pathways, and production of various inflammatory cytokines and chemokines . This finding could explain the LNPs’ potent adjuvant activity, supporting the induction of robust adaptive immune responses . Interestingly, inflammatory responses can be exacerbated on a background of pre-existing inflammatory conditions, as was recently shown in a mouse model after administration of mRNA–LNPs ; this effect was proven to be specific to the LNP, acting independently of the mRNA cargo.
Although chemical modifications in the RNA molecules used in vaccines (detailed earlier) are intended to decrease TLR sensing of external single-stranded RNAs (and thus proinflammatory signals), there is some evidence that modified uracil residues do not completely abrogate TLR detection of the mRNA; also, while efforts are made to reduce double-stranded (ds) RNA production, there may be small amounts of dsRNA that can occasionally get packaged within mRNA vaccines .
In this context, frequent booster immunizations may increase the frequency and/or the severity of the reported AEs.
Vaccine-encoded antigen distribution in the human body and possible interactions with human proteins
Following vaccination, a cell may present the produced S protein (or its subunits/peptide fragments) to mobilize immune responses or be abolished by the immune system (e.g., cytotoxic T cells) . Consequently, the debris produced, or even the direct secretion (including shedding) of the antigen by the transfected cells, may release large amounts of the S protein or its subunits/peptide fragments to the circulation (Figure 1) [19,20]. The anti-SARS-CoV-2 vaccine mRNA-containing LNPs are injected into the deltoid muscle and exert an effect in the muscle tissue itself, the lymphatic system, and the spleen, but can also localize in the liver and other tissues [21,22,43,44] from where the S protein or its subunits/peptide fragments may enter the circulation and distribute throughout the body. It is worth mentioning that liver localization of LNPs is not a universal property of carrier nanoparticles, as specific modifications in their chemistry can retain immunogenicity with minimal liver involvement [43,45]. In line with a plausible systemic distribution of the antigen, it was found that the S protein circulates in the plasma of the BNT162b2 or mRNA-1273 vaccine recipients as early as day 1 after the first vaccine injection . Reportedly, antigen clearance is correlated with the production of antigen-specific immunoglobulins or may remain in the circulation (e.g., in exosomes) for longer periods [47,48], providing one reasonable explanation (among others) for the robust and durable systemic immune responses found in vaccinated recipients [49,50]. Therefore, there is likely to be an extensive range of expected interactions between free-floating S protein/subunits/peptide fragments and ACE2 circulating in the blood (or lymph), or ACE2 expressed in cells from various tissues/organs (Figure 2) [14., 15., 16.]. This notion is further supported by the finding that in adenovirus-vectored vaccines (Box 2), the S protein produced upon vaccination has the native-like mimicry of SARS-CoV-2 S protein’s receptor binding functionality and prefusion structure .
Additional interactions with human proteins in the circulation, or even the presentation to the immune system of S protein antigenic epitopes  mimicking human proteins (molecular mimicry) may occur [53., 54., 55., 56.]. Reportedly, some of the near-germline SARS-CoV-2-NAbs against S receptor-binding domain (RBD) reacted with mammalian self-antigens , and SARS-CoV-2 S antagonizes innate antiviral immunity by targeting multiple pathways controlling interferon (IFN) production . Also, a sustained elevation in T cell responses to SARS-CoV-2 mRNA vaccines has been found (data not yet peer-reviewed) in patients who suffer from chronic neurologic symptoms after acute SARS-CoV-2 infection as compared with healthy COVID-19 convalescents . Given the reported (rare) neurological AEs following vaccination, it was suggested that further studies are needed to assess whether antibodies against the vaccine-produced antigens can cross-react with components of the peripheral nerves . Further concerns include the possible development of anti-idiotype antibodies against vaccination-induced antibodies as a means of downregulation; anti-idiotype antibodies – apart from binding to the protective neutralizing SARS-CoV-2 antibodies – can also mirror the S protein itself and bind ACE2, possibly triggering a wide array of AEs . Worth mentioning is a systems vaccinology approach (31 individuals) of the BNT162b2 vaccine (two doses) effects, where anticytokine antibodies were largely absent or were found at low levels (contrary to findings in acute COVID-19 [61,62]), while two individuals had anti-interleukin-21 (IL-21) autoantibodies, and two other individuals had anti-IL-1 antibodies . In this context, anti-idiotypic antibodies can be particularly enhanced after frequent boosting doses that trigger very high titers of immunoglobulins . Frequent boosting doses may also become a suboptimal approach as they can imprint serological responses toward the ancestral Wuhan-Hu-1 S protein, minimizing protection against novel viral S variants [65,66].
The potential interaction at a whole-organism level of the native-like S protein and/or subunits/peptide fragments with soluble or cell-membrane-attached ACE2 (Figure 2) can promote ACE2 internalization and degradation [67,68]. In support of this, soluble ACE2 induces receptor-mediated endocytosis of SARS-CoV-2 via interaction with proteins related to the RAS . Prolonged loss or reduced ACE2 activity may result in extensive destabilization of the RAS which may then trigger vasoconstriction, enhanced inflammation, and/or thrombosis due to unopposed ACE and angiotensin-2 (ANG II)-mediated effects (Figure 2) . Indeed, decreased ACE2 expression and/or activity contributes, among other things, to the development of ANG II-mediated hypertension in mice, indicating vasculature dysfunction . The baseline expression levels of ACE2 in endothelial cells, or its induced expression levels upon stimulation from other tissue-resident cells, along with the potential of endothelial cells to shed ACE2 to the circulation, or their sensitivity to SARS-CoV-2 infection is debatable [70., 71., 72., 73.]. Nonetheless, even relatively low ACE2 expression levels in endothelial cells (e.g., compared to levels in epithelial cells) [15,16,70,71], along with the high expression levels of ACE2 in other cell types of the vasculature (e.g., heart fibroblasts/pericytes) [15,74], indicate that the vasculature can be sensitive to free-floating S protein or its subunits/peptide fragments (Figure 2). These effect(s), especially in capillary beds, and the prolonged antigen presence in the circulation [46., 47., 48.], along with the systemic excessive immune response to the antigen, can then trigger sustained inflammation (discussed later) which can injure the endothelium, disrupting its antithrombogenic properties in multiple vascular beds
The SARS-CoV-2 S protein-induced effects in mammalian cells or model organisms
Reportedly, intravenous (i.v.) injection of the S1 subunit in mice results in its localization in endothelia of mice brain microvessels showing colocalization with ACE2, caspase-3, IL-6, tumor necrosis factor α (TNF-α), and C5b-9; it was thus suggested that endothelial damage is a central part of SARS-CoV-2 pathology which may be induced by the S protein alone . Also, the S1 subunit (or recombinant S1 RBD) impaired endothelial function via downregulation of ACE2  and induced degradation of junctional proteins that maintain endothelial barrier integrity in a mouse model of brain microvascular endothelial cells or cerebral arteries; this latter effect was more enhanced in endothelial cells from diabetic versus normal mice . Similarly, the S1 subunit decreased microvascular transendothelial resistance and barrier function in cultured human pulmonary cells . Further, S protein disrupted human cardiac pericytes function and triggered increased production of proapoptotic factors in pericytes, causing endothelial cells death . In support of this, administration of the S protein promoted dysfunction of human endothelial cells as evidenced by, for example, increased expression of the von Willebrand factor . Other reports indicate that S1 can directly induce coagulation by competitive binding to both soluble and cellular heparan sulfate/heparin (an anticoagulant) [81., 82., 83., 84.], while cell-free hemoglobin, as a hypoxia counterbalance, cannot attenuate disruption of endothelial barrier function, oxidative stress, or inflammatory responses in human pulmonary arterial endothelial cells exposed to S1 . Consistently, S protein binds fibrinogen (a blood coagulation factor), and S protein virions have been found to enhance fibrin-mediated microglia activation (data not yet peer-reviewed) and induce fibrinogen-dependent lung pathology in mice , while S1 binding to platelets’ ACE2 triggers their aggregation . Interestingly, both the ChAdOx1 (AstraZeneca) and BNT162b2 vaccines can elicit antiplatelet factor 4 (anti-PF4) antibody production even in recipients without clinical manifestation of thrombosis .
Intriguingly, the S protein increases human cell syncytium formation [88,89], triggering pyroptosis of syncytia formed by fusion of S and ACE2-expressing cells . Also, in cells or mouse experimental models, it was shown that S removes lipids from model membranes and interferes with the capacity of high-density lipoprotein to exchange lipids , inhibits DNA damage repair processes , and induces Snail-mediated epithelial–mesenchymal transition marker changes and lung metastasis in a breast cancer mouse model .
In support of the possibility that there is a wide range of S protein binders, Aβ1–42 (the 42 amino acid form of amyloid β in cerebrospinal fluid) was found to bind with high affinity to the S1 subunit and ACE2 . Aβ1–42 strengthened the binding of S1 to ACE2 and increased viral entry and production of IL-6 in a SARS-CoV-2 pseudovirus infection mouse model. Data from this surrogate mouse model with IV inoculation of Aβ1–42 showed that the clearance of Aβ1–42 in the blood was dampened in the presence of the extracellular domain of the S protein trimers . Given the wide ACE2 expression in human brain , a study of particular interest showed that IV-injected radioiodinated S1 (I-S1) readily crossed by adsorptive transcytosis the blood–brain barrier in male mice, was taken up by brain regions, and entered the parenchymal brain space. I-S1 was also taken up by the lung, spleen, kidney, and liver; intranasally administered I-S1 also entered the brain, although at lower levels than after i.v. administration . Similarly, S1 was found to disrupt the blood–brain barrier integrity at a 3D blood–brain barrier microfluidic model . In support of this, biodistribution studies of the mRNA–LNP platform by Moderna in Sprague Dawley rats revealed the presence of low levels of mRNA in the brain, indicating that the mRNA–LNPs can cross the blood–brain barrier .
Finally, it was recently reported that human T cells express ACE2 at levels sufficient to interact with the S protein , while it had been shown previously that SARS-CoV-2 uses CD4 to infect T helper lymphocytes, and that S promotes a proinflammatory activation profile on the most potent antigen-presenting cells (APCs) (i.e., human dendritic cells) . If these observations are confirmed, they may explain a SARS-CoV-2 vaccination-mediated AE, namely, reactivation of varicella zoster virus [100,101]
S protein-induced proinflammatory responses and unique gene expression signatures following vaccination
Reportedly, S protein (apart from the LNP–mRNA platform discussed earlier) mediates proinflammatory and/or injury (of different etiology) responses in various human cell types [102., 103., 104.], and ACE2-mediated infection of human bronchial epithelial cells with S protein pseudovirions induced inflammation and apoptosis . Also, S protein promoted an inflammatory cytokine IL-6/IL-6R-induced trans signaling response and alarmin secretion in human endothelial cells, along with increased oxidative stress, induction of inflammatory paracrine senescence, and higher levels of leucocyte adhesion . Other reports indicate that S protein triggers an inflammatory response signature in human corneal epithelial cells , increases oxidative stress and DNA ds breaks in human peripheral-blood mononuclear cells (PBMCs) postvaccination , and binds to lipopolysaccharide, boosting its proinflammatory activity [109,110]. Furthermore, S protein induces neuroinflammation and caspase-1 activation in BV-2 microglia cells  and blocks neuronal firing in sensory neurons . The S protein-induced systemic inflammation may proceed via TLR2-dependent activation of the nuclear factor κB (NF-κB) pathway , while structure-based computational models showed that S protein exhibits a high-affinity motif for binding T cell receptors (TCRs), and may form a ternary complex with histocompatibility complex class II molecules; indeed, analysis of the TCR repertoire in COVID-19 patients showed that those with severe hyperinflammatory disease exhibit TCR skewing consistent with superantigen (S protein) activation . In in vivo mouse models, S protein activated macrophages and contributed to induction of acute lung inflammation , while intratracheal instillation of the S1 subunit in transgenic mice overexpressing human ACE2 induced severe COVID-19-like acute lung injury and inflammation. These effects were milder in wild-type mice, indicating the phenotype dependence on human ACE2 expression . Consistently, the S1 subunit has been found to act as a PAMP that, via pattern recognition receptor engagement, induces viral infection-independent neuroinflammation in adult rats .
These observations correlate with the finding of a systemic inflammatory signature after the first BNT162b2 vaccination which was accompanied by TNF-α and IL-6 upregulation after the second dose ; these effects may also relate to a proinflammatory action of the mRNA–LNP platform (see earlier). In a thorough systems vaccinology study of the BNT162b2 mRNA vaccine effects, younger participants tended to have greater changes in monocyte and inflammatory modules 1 day after the second dose, whereas older individuals had increased expression of B and T cell modules. Moreover, single-cell transcriptomics analysis at the same time point revealed the emergence of a unique myeloid cell cluster out of 18 cell clusters identified in total. This cell cluster does not represent myeloid-derived suppressor cells, it expressed IFN-stimulated genes and was not found in COVID-19 infection; also, it was similar to an epigenetically reprogrammed monocyte population found in the blood of donors being vaccinated with two doses of an influenza vaccine . Whether epigenetic reprogramming underlies the enhanced IFN-induced gene response in C8 cells after secondary BNT162b2 vaccination remains to be clarified. Finally, a comparison between the BNT162b2 vaccine-induced gene expression signatures at day 7 post-prime (d7PP) and post-boost (d7PB) doses and that of other vaccine types (e.g., inactivated or live-attenuated vaccines) exhibited weak correlation both between d7PP and d7PB as well as with other vaccines . These findings suggest the evolution of novel genomic responses after the second dose and, more importantly, the unique biology of mRNA vaccines versus other more conventional platforms. Of particular interest is also the report of a cytokine release syndrome (CRS) – an extremely rare immune-related AE of immune checkpoint inhibitors – post-BTN162b2 vaccination in a patient with colorectal cancer on longstanding anti-programmed death 1 (PD-1) monotherapy; the anti-PD1 blockade-mediated CRS was evidenced by increased inflammatory markers, thrombocytopenia, elevated cytokine levels, and steroid responsiveness . These proinflammatory effects could be particularly pronounced in the elderly, since recent data revealed that senescent cells become hyperinflammatory in response to the S1 subunit, followed by increased expression of viral entry proteins and reduced antiviral gene expression in nonsenescent cells through a paracrine mechanism 
The need to investigate the molecular basis of vaccination-induced AEs
Anti-SARS-CoV-2 mRNA vaccines induce durable and robust systemic immunity, and thus their contribution in mitigating the COVID-19 pandemic and saving thousands of lives is beyond doubt. This technology has several advantages over conventional vaccines  and opens a whole new era for the development of novel vaccines against various infectious and other diseases, including cancer. Based on currently available molecular insights (mostly in cell-based assays and model organisms), we hypothesize that the rare AEs reported following vaccination with S protein-encoding mRNA vaccines may relate to the nature and binding profile of the systemically circulating antigen(s) (Figure 1, Figure 2), although the contribution of the LNPs and/or the delivered mRNA is likely also significant [24,26,41]. Therefore, the possibility of subclinical organ dysfunction in vaccinated recipients which could increase the risk, for example, of future (cardio)vascular or inflammatory diseases should be thoroughly investigated. Given that severe COVID-19 correlates with older age, hypertension, diabetes, and/or cardiovascular disease, which all share a variable degree of ACE2 signaling deregulation, additional ACE2 downregulation induced by vaccination may further amplify an unbalanced RAS. Regarding localization of LNPs in the liver and consequent antigen expression, it is worth mentioning that the liver is continuously exposed to a multitude of self and foreign antigens and can mount efficient immune responses against pathogens as it hosts convectional APCs (e.g., dendritic cells, B cells, and Kupfer cells). Additional liver cell types – such as liver sinusoidal endothelial cells, hepatic stellate cells, and hepatocytes – also have the capacity to act as APCs . It is plausible, though as yet unproven, that as the S protein is produced in liver cells, both conventional and unconventional APCs may prime adaptive but also innate immune responses in the liver’s immunological niche. Despite the liver’s major tolerogenic role , the sustained expression of S protein-related antigens (Figure 1) can potentially skew the immune response towards autoimmune-like tissue damage, as in the observed cases of autoimmune hepatitis following vaccination [123,124]. It therefore merits further investigation whether LNPs can transfect any other nonimmunological body tissues bearing cells that can act as unconventional APCs, thus inducing a sustained immune response but also a self-response, as in cases of chronic viral infections [125
Although the benefit–risk profile remains strongly in favor of COVID-19 vaccination for the elderly and patients with age-related or other underlying diseases, an understanding of the molecular–cellular basis of the anti-SARS-CoV-2 mRNA vaccine-induced AEs is critical for the ongoing and future vaccination and booster campaigns. In parallel, the prospective pharmacovigilance and long-term monitoring (clinical/biochemical) of vaccinated recipients versus matched controls should evolve in well-designed clinical trials. Moreover, the use of alternative chemistries for LNPs, and of S protein in its closed form (not prone to ACE2 binding) , along with the use of SARS-CoV-2 nucleocapsid protein or solely the S RDB, may be valuable alternatives for refined, next-generation mRNA vaccines. Finally, as we essentially do not know for how long and at what concentration the LNPs and the antigen(s) remain in human tissues or the circulation of poor vaccine responders, the elderly, or children (see Outstanding questions), and given the fact that cellular immunity likely persists despite reduced in vitro neutralizing titers , boosting doses should be delivered only where the benefit–risk profile is clearly established.
What are the localization pattern, transfection efficacy, and clearance rates of the mRNA vaccine LNPs in the human body?
Can we refine LNP chemistry towards retaining transfection efficacy and at the same time assuring on-demand tissue distribution?
Do the adverse inflammatory reactions noted postvaccination also relate – and if yes, to what extent – to LNPs and/or the mRNA used in mRNA vaccines?
What are the mechanistic details of antigen expression, processing, and cellular localization following cell transfection with the LNPs?
What would the impact be of excessive ‘decoration’ of nonprofessional antigen-presenting transfected human (e.g., liver) cells with transmembrane S protein?
Does the antigen or related subunits‐peptide fragments leak into the circulation, and if so, in which form, at what concentration, and for how long? Is there any association with the vaccine-mediated immune responses?
Is the probable binding of the antigen to ACE2 in the vasculature accountable for the cardiovascular, metabolic, or other (e.g., inflammation-related) reported AEs?
Does the antigen cross the blood–brain barrier?
Is there any noteworthy molecular mimicry (especially of the major antigenic sites) between the S protein and the human proteome?
What is the profile of mucosal immunity induced by the mRNA COVID-19 vaccines?
It is the case that vaccination-mediated immunity (two doses) against the used ancestral antigen (Wuhan-Hu-1 S protein) wanes over time, or do we simply partially lose protection due to evolutionary leaps of the S protein (e.g., at the Omicron variant)? In that case, do we really need boosting doses with the same antigen?
Does boosting, apart from raising antibody titers, also promote antibody diversification?
What would be the profile of immune responses and AEs following mRNA-guided expression of the S protein in its closed form (a form not prone to ACE2 binding)?
Alt-text: Outstanding questions
Overall, parallel to the ongoing research on the most challenging topics of SARS-CoV-2 biology, evolving dynamics and adaptation capacity to human species (i.e., transmission–infection rate and disease severity), the basic and clinical research (see Outstanding questions) aiming to understand the molecular–cellular basis of the rare AEs of the existing first-generation mRNA vaccines should be accelerated as an urgent and vital public health priority.
Acute respiratory distress syndrome (ARDS)
a life-threatening condition in which fluid builds up in the lungs, interfering with the gas exchange function and preventing oxygenation of the blood and organs.
Adverse effect (AE)
an undesired effect of a medication or clinical intervention with potentially harmful consequences.
Angiotensin-converting enzyme 2 (ACE2)
an enzyme involved in the homeostatic regulation of circulating angiotensin I and angiotensin II levels by converting them to angiotensin (1–9) and angiotensin (1–7) peptides respectively.
an idiopathic episode of facial muscle weakness or paralysis on one side of the face. This condition results from dysfunction of the seventh cranial nerve (the facial nerve).
Cerebral venous sinus thrombosis
a rare blood-clotting event that occurs in the venous sinuses of the brain and prevents blood from draining out of the brain. As a result, pressure builds up and can lead to swelling and hemorrhage.
a characteristic of COVID-19 (or other disease) where abnormally high levels of circulating cytokines are produced and contribute to disease severity.
a rare, autoimmune neurological disorder in which the body’s immune system erroneously attacks the peripheral nerves, causing muscle weakness and, if left untreated, paralysis.
a term that refers to a range of new, returning, or ongoing symptoms that persist beyond the initial phase of a SARS-CoV-2 infection.
the process in which an immune response against a foreign antigen is inadvertently also directed against a self-antigen that closely resembles the triggering foreign antigen.
Receptor-binding domain (RBD)
the part of a binding protein (e.g., in SARS-CoV-2 S protein) used to anchor the protein to its receptor.
Renin–angiotensin system (RAS)
a system that is critical in the physiological regulation of (among others) neural, gut, cardiovascular, blood pressure, and kidney functions, as well as fluid and salt balance. It involves the enzyme renin which catalyzes the production of angiotensin I.
any analysis performed with blood serum, usually focusing on measuring antibody levels.
a cell with multiple nuclei resulting from multiple fusions of uninuclear cells.
the detection of replication-competent viral particles in the bloodstream.
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According to the findings of German research, one in every 700 children under the age of five who received the Pfizer mRNA Covid vaccine was hospitalized with severe adverse events (SAE), and one in every 200 children had ‘symptoms that were currently ongoing and thus of unknown significance.’
The study, “Comparative Safety of the BNT162b2 Messenger RNA COVID-19 Vaccine vs Other Approved Vaccines in Children Younger Than 5 Years,” was published in JAMA on Tuesday, two days before the CDC’s Advisory Committee on Immunization Practices voted to recommend COVID-19 to be included in the 2023 childhood immunization schedule.
Participants in this retrospective cohort study were German parents or caregivers who had enrolled their children in a Covid-19 vaccination program at 21 outpatient care facilities. The survey used in the study was conducted in a secure online environment. From April 14th, 2022, till May 9th, 2022, a total of 19 000 email addresses were contacted using data from vaccine registration databases.
It concluded that the symptoms reported after Pfizer vaccination were “comparable overall” to those for other vaccines. Let’s see.
Any symptoms: 62% higher
Musculoskeletal (muscles and bones) symptoms: 155% higher
Dermatologic (skin) symptoms: 118% higher
Otolaryngologic (ears, nose and throat) symptoms: 537% higher
Cardiovascular (heart etc.): 36% higher
Gastrointestinal (stomach etc.): 54% higher
It calls these “modestly elevated.” (Note that not all are statistically significant and some confidence intervals are wide, see below.)
In 0.5% of the children (40 of 7,806) symptoms were “currently ongoing and thus of unknown significance”. This is in a study with a 2-4 month follow-up period. That means 0.5% of children had an adverse effect that lasted for weeks or months. In two cases (0.03%), symptoms were confirmed to have lasted longer than 90 days.
Ten children were hospitalised with reported serious adverse events (SAEs), compared to zero with the other vaccines. This reported as 0.1%, as it is out of 7,806. However, the study also states that no hospitalisations were reported for children administered the low dosage of 3 μg. Since it also tells us that 6,033 children received at least one dose of over 3 μg (or unknown dosage), the rate in the relevant cohort is closer to 0.2%, or around one in 500.
Four of the hospitalisations were for cardiovascular injury; one child was hospitalised after both doses for this reason. Four were pulmonary (lung) related. Symptoms of the hospitalised children lasted an average of 12.2 days and a maximum of 60 days. None reported a myocarditis diagnosis. Mercifully, no deaths were reported in this relatively small sample.
The mortality rate in under-20s has been shown to be 0.0003%. The figure for under-fives will be even lower. But even if we unrealistically assume this is the mortality rate for under-fives and the vaccines reduce it to zero, this still means that at least 500 children are hospitalised for every life the vaccines save. In reality the ratio will be much worse than this.
The Vaccines For Children (VFC) program is a federally funded program that provides vaccines at no cost to children who might not otherwise be vaccinated because of their inability to pay, according to the CDC.
The CDC buys vaccine at a discounted rate for distribution to registered VFC providers. Children who are eligible* for VFC vaccines are entitled to receive those vaccines recommended by the Advisory Committee on Immunization Practices (ACIP).
The advisory committee voted 15-0, without objection.
On Thursday, the CDC’s Advisory Committee on Immunization Practices voted to recommend COVID-19 to be included in the 2023 childhood immunization schedule in 15 unanimous votes.