This rebuttal was written a few days following the publication of Dr. Glatt’s article. It has been lightly edited for grammar and clarity.

Claim: Lockdowns significantly reduced covid transmission.

Source: Rabbi Dr. Aaron Glatt COVID-19 Update, June 11, [2020] 9:00 PM

Background: Major lull in covid heading into the summer of 2020.

The Facts

Lockdowns: The Biggest Scam of the Pandemic (at the time)

Lockdowns have been the biggest scam of the whole covid-19 pandemic. There is no data that shows any correlation between lockdowns, or lockdown severity, and superior covid outcomes at all (see above 2 charts). In fact, the correlation is precisely the opposite, as countries and states that didn’t lock down, or had less severe lockdown restrictions, tended to fare far better as far as covid went. In Italy, retrospective mathematical modeling calculated that the R-0 started decreasing before the deaths started to occur en masse (graphs on page 19 - link).

JP Morgan conducted an analysis on states and countries (link) and discovered that those that locked down had a higher rate of transmission than those states and countries that did not lock down; and they further discovered that there was a correlation between lifting lockdown restrictions and a steeper rate of declining new infections subsequently, which was even more pronounced when compared to the states and countries that did not ease lockdown restrictions.

Switzerland very famously eschewed locking down, and fared very well, in the middle of the pack of European countries; if you factor in that 70% of Sweden’s deaths were from NH’s and LTC facilities, which are unaffected by a potential lockdown, then their per capita death rate drops to Norway-level numbers. In fact, in people under 65 in Switzerland, the number of deaths from all-cause mortality is slightly less this year than the mean of the previous 5 years of all-cause mortality:

(This more than anything else demolishes entirely any notion that Sweden suffered excess deaths because of their failure to implement a lockdown.)

Japan didn’t lock down either, and despite having the world’s most populous and one of the most densely populated cities, (Tokyo population: 35,000,000+). Japan had a grand total of 916 deaths to date from a population of 126,476,461! That works out to 1 death for every 138,074 people. (More on this later.)

And Japan fared better than their Asian neighbors too, which means that the difference can’t be wholly attributed to demographic differences between European/American populations and Asian populations.

Here are some more examples of science against the lockdowns that was already published when Dr. Glatt wrote his article:

• UK’s coronavirus lockdown was ‘futile and hasn’t saved any lives’, claim leading experts

• Full lockdown policies in Western Europe countries have no evident impacts on the COVID-19 epidemic

• Let’s Visualize State-by-State Shutdown Effectiveness on COVID-19

In 2007, the CDC published Community Strategy for Pandemic Influenza Mitigation, which stated

“These results suggest that the effectiveness of pandemic mitigation strategies will erode rapidly as the cumulative illness rate prior to implementation climbs above 1 percent of the population in an affected area.  Thus, pre-pandemic, scenario-based contingency planning for the early, targeted use of NPIs likely provides the greatest potential for an effective public health response.”

By the time lockdowns were implemented, at least 3 months into covid-19’s invasion of American soil, the 1% benchmark had definitely been eclipsed (and just going by the initial positive test result % of 1-2% of the tests that were exclusively testing only for the upper bounds of symptomatic presentation, which as we now know is a slim minority of all cases, demonstrates this as well).

And just for kicks, here is a breakdown of US states by lockdown duration. As you can see, lockdowns did have a discernable impact on covid metrics - more lockdowns = MORE covid:

I am including a list of 50 more studies/articles on lockdowns at the end to illustrate how utterly delusional this contention was even back then.

Dr. Glatt’s junk science:

Turning to the two ‘studies’ cited by RD Glatt to support his factual assertion that lockdowns saved lives or slowed transmission.

The first one claimed that “shutdown orders prevented about 60 million novel coronavirus infections in the United States and 285 million in China”.

I attempted to figure out how they arrived at that number, but the vast quantity of data they assembled proved too insurmountable a task to bother with. Plus I couldn’t find the data, due to what I think is their tardiness in publishing it. I may be wrong about the last point and simply missed the link. In any event, I am absolutely confident that their data, which was purportedly drawn from tens if not more than 100 countries, is not capable of producing any sort of accurate result because it, and suffers from several deadly flaws:

1-    Their data is not homogenous at all, as one would expect when drawing from many countries who have widely divergent cultural and legal norms, as well as societal norms, that would frustrate all but the most arduous and rigorous analysis. That would, by definition, take a dedicated research team months at least to unwind and adjust the data into a form that can be used as a matrix upon which to measure them against each other. Otherwise, all you have is a patchwork of incongruous reporting characteristics and reliability, laws, norms, enforcement mechanisms, and compliance rate, among other relevant variables that foreclose the possibility of running a comparative analysis. (This is a fairly typical type of issue that tends to invalidate a data set.)

2-      It is a fait accompli that the scientists running the study engaged in some sort of data manipulation (ostensibly for the purpose laid out in the previous point), but considering the lightning speed that they ran their study, and then layering on top of that likely egregious bias, would inevitably produce a badly mangled data set that is largely divorced from the underlying data that was used to generate it.

It is axiomatically true that a Rube-Goldberg type analysis always loses when contradicted by the elementary, real-world, observed reality. And it seems to me unwise to cite a study whose foundational data is opaque to feasible review.

On to the second study cited, which comes courtesy of the now-infamous Imperial College. Simply pointing out the provenance of this one should be sufficient to invalidate it. The now-disgraced Prof. Neil Ferguson of the IC had issued another infamous model on covid, the now thoroughly trashed (debunked doesn’t begin to do it justice) IC model that predicted, among other things, that the US was staring at 2.2 million deaths, and the UK >500,000 deaths, if they eschewed lockdowns.

Mr. Ferguson is unequivocally a genuine quack and con-artist on account of his long and robust history of utterly delusional epidemiological predictions:

a.     [Imperial College epidemiologist Neil] Ferguson was behind the disputed research that sparked the mass culling of eleven million sheep and cattle during the 2001 outbreak of foot-and-mouth disease. (Sheep genocide!!) He also predicted that up to 150,000 people could die. There were fewer than 200 deaths.

b.    In 2002, Ferguson predicted that up to 50,000 people would likely die from exposure to BSE (mad cow disease) in beef. In the U.K., there were only 177 deaths from BSE.

c.     In 2005, Ferguson predicted that up to 150 million people could be killed from bird flu. (And then he said maybe 200,000,000!) In the end, only 282 people died worldwide from the disease between 2003 and 2009.

d.    In 2009, a government estimate, based on Ferguson’s advice, said a “reasonable worst-case scenario” was that the swine flu would lead to 65,000 British deaths. In the end, swine flu killed 457 people in the U.K.

Clearly, not one to be trusted. Anyway, let’s take a look at the internal machinations of this new IC study claiming that the lockdowns saved 3.2 million lives in Europe.

To begin, we must see how, exactly, they arrived at such a fantastical number (the current covid death toll worldwide stood at a comparatively paltry 430,000+ (and that’s a rather morbidly obese inflation too)).

The first thing that I noticed was that they are back to assuming the debunked 2+ million deaths in the US and 500k in the UK. Strike one (should be automatic strikeout).

Then there’s this gem:

“If mitigation including enhanced social distancing is pursued, for an R0of 3.0, we estimate a maximum reduction in infections in the range 30-38% (median 33%) and a range of reduction in mortality between 19%-55% (median 39%) representing 16 million lives saved for R0=3 (assuming the mortality patterns observed in China).”

In other words, they are assuming as an input that lockdowns (“enhanced social distancing”) are effective. This effectively ends this study as useful in determining whether, a priori, lockdowns work that well in the first place, let alone if they work at all. Their assumption of R0=3.0 is soundly contradicted by their own gov’t, which was discovered to have known for quite some time already that the R0 outside of hospitals and similarly situated settings was much lower, possibly well below even 1. Whoops.

Onward. Next up is this astounding claim:

“Globally, we estimate that a completely unmitigated COVID-19 epidemic would lead to 7.0 (range 6.4-7.2) billion infections for a basic reproduction number, R0, of 3.0 (range 2.4-3.3). Applying estimates of the age-specific IFR from China, this could result in 40 (range 35-42) million deaths.”

That would make covid the most infectious virus or disease in all of human history. If that doesn’t sound fishy, I don’t know what to tell you. This is kooky. This is also refuted by all of the mortality data. For example, according to the CDC mortality data extrapolated for 100% of the US population becoming infected, the total number of deaths would be a staggering. . . 594,865 (of which 442,949 would be aged 65+). That’s not anywhere near 2.2 million (that was also assuming only 70% infection). And the 600,000 number from the CDC is based on wildly over-inflated coding for covid deaths, that has led to what we can charitably describe as peculiarities such as a bunch of gunshot victims across the country dying of covid mere hours after being shot (וכאלה רבים על זה הדרך). It seems that the only limiting factor abutting their predictive prowess is the total global human population (and even then, who knows, maybe they’ll start factoring in the deaths of babies yet to be conceived, over the next billion years, that’s a lot of babies).

Next, we have their prediction range for Sweden, which famously did not lockdown, and haven’t quite made it to 5,000 deaths yet either:

Clearly, their methods are kind of faulty…….

Here’s an even worse example:

Japan had less than 1,000 deaths to date, and they did not have any sort of lockdown whatsoever.

Note that this is the kind of “Science” that Dr. Glatt thinks is authoritative.

Clearly, this Imperial College model is as delusional as its predecessor. This is authentic scientific quackery that has no place informing policy choices. Nor is it a good look for anyone who cites it. There is more to debunk in this abomination, but I think that this is already a full-blown refutation.

In the meantime, the list of European countries that are committing to not doing another lockdown in the event of a second wave continues to grow, and now includes Denmark, Norway, England, Iceland, Ireland, and a few others. (Unfortunately, their commitments would prove to be rather noncommittal the subsequent winter.)

In Norway – that pesky country that all the experts kept using as a (false) foil to “prove” how inept Sweden was – the director of the Norway Institute of Public Health said that she thought the lockdown was a mistake and that they shouldn’t have done it.

What the actual science said:

Below are 50 published papers/articles finding that lockdowns had little or no efficacy (despite unconscionable harms) along with a key quote or two from each: (I did not put this compilation together, and unfortunately I don’t remember who did either.)

1.onlinelibrary.wiley.com/doi/abs/10.111…

“there is no evidence that more restrictive nonpharmaceutical interventions (“lockdowns”) contributed substantially to bending the curve of new cases in England, France, Germany, Iran, Italy, the Netherlands, Spain, or the United States in early 2020” 

2.medrxiv.org/content/10.110…

“Inferences on effects of NPIs are non-robust and highly sensitive to model specification. Claimed benefits of lockdown appear grossly exaggerated.” 

3.thelancet.com/journals/eclin…

“government actions such as border closures, full lockdowns, and a high rate of COVID-19 testing were not associated with statistically significant reductions in the number of critical cases or overall mortality” 

4.advance.sagepub.com/articles/prepr…

“Official data from Germany’s RKI agency suggest strongly that the spread of the coronavirus in Germany receded autonomously, before any interventions become effective” 

5.arxiv.org/pdf/2005.02090…

“the decline in infections in England...began before full lockdown…[S]uch a scenario would be consistent with...Sweden, which began its decline in fatal infections shortly after the UK, but did so on the basis of measures well short of full lockdown” 

6.datascienceassn.org/sites/default/…

“the UK lockdown was both superfluous (it did not prevent an otherwise explosive behavior of the spread of the coronavirus) and ineffective (it did not slow down the death growth rate visibly).” 

7. The end of exponential growth: The decline in the spread of coronavirus. A similar pattern – rapid increase in infections to a peak in the sixth week, and decline from the eighth week – is common everywhere, regardless of response policies

“Given that the evidence reveals that the Corona disease declines even without a complete lockdown, it is recommendable to reverse the current policy and remove the lockdown” 

The end of exponential growth: The decline in the spread of coronavirusA similar pattern – rapid increase in infections to a peak in the sixth week, and decline from the eighth week – is common everywhere, regardless of response policieshttps://www.timesofisrael.com/the-end-of-exponential-growth-the-decline-in-the-spread-of-coronavirus/

8.medrxiv.org/content/10.110…

“stay at home orders, closure of all non-essential businesses and requiring the wearing of facemasks or coverings in public was not associated with any independent additional impact” 

9.medrxiv.org/content/10.110…

“these strategies might not have saved any life in western Europe. We also show that neighboring countries applying less restrictive social distancing measures … experience a very similar time evolution of the epidemic.” 

“since the full lockdown strategies are shown to have no impact on the epidemic’s slowdown, one should consider their potentially high inherent death toll as a net loss of human lives” 

10.medrxiv.org/content/10.110…

“the model does not support [the] estimate that lockdown reduced the case reproduction number R by 81% or that more than three million deaths were averted by non-pharmaceutical interventions.” 

11.nicholaslewis.org/did-lockdowns-…

“The case of Sweden, where the authors find the reduction in transmission to have been only moderately weaker than in other countries despite no lockdown having occurred, is prima facie evidence” 

12.bmj.com/content/371/bm…

“general social distancing was also projected to reduce the number of cases but increase the total number of deaths compared with social distancing of over 70 only”

“Strategies that minimise deaths involve the infected fraction primarily being in the low risk younger age groups—for example, focusing stricter social distancing measures on care homes where people are likely to die rather than schools where they are not.”
“results presented in the report suggested that the addition of interventions restricting younger people might actually increase the total number of deaths from covid-19” 

13.medrxiv.org/content/10.110…

“We show that [lockdown] is modestly superior in saving lives compared to [focused protection], but with tremendous costs to prevent one case of death. This might result in overwhelming economic effects that are expected to increase future death toll” 

14.ncbi.nlm.nih.gov/pmc/articles/P…

“For pathogens that inflict greater morbidity at older ages, interventions that reduce but do not eliminate exposure can paradoxically increase the number of cases of severe disease by shifting the burden of infection toward older individuals” 

15.papers.ssrn.com/sol3/papers.cf…

“Current policy can be misdirected and can therefore have long and even short-term negative effects on human welfare and thus result in not actually minimizing death rates (incorporating externalities), especially in the long run.” 

16.imgcdn.larepublica.co/cms/2020/05/21…

“For example, the data…shows a decrease in infection rates after countries eased...lockdowns with >99% statistical significance. Indeed...infection rates have declined after reopening even after allowing for an appropriate measurement lag. 

This means that the pandemic and COVID-19 likely have its own dynamics unrelated to often inconsistent lockdown measures that were being implemented.” 

17.jamanetwork.com/journals/jama/…

“restrictions imposed by the pandemic (eg, stay-at-home orders) could claim lives indirectly through delayed care for acute emergencies, exacerbations of chronic diseases, and psychological distress (eg, drug overdoses).”
“In 14 states, more than 50% of excess deaths were attributed to underlying causes other than COVID-19; these included California (55% of excess deaths) and Texas (64% of excess deaths)" 

18.medrxiv.org/content/10.110…

“We found that 180-day of mandatory isolations to healthy <60 (ie schools and workplaces closed) produces more final deaths if the vaccination date is later than (Madrid: Feb 23 2021; Catalonia: Dec 28 2020; Paris: Jan 14 2021; London: Jan 22 2021)” 

19.papers.ssrn.com/sol3/papers.cf…

“Comparing weekly mortality in 24 European countries, the findings in this paper suggest that more severe lockdown policies have not been associated with lower mortality. In other words, the lockdowns have not worked as intended” 

20.nber.org/papers/w27719

“Our findings … further raise doubt about the importance in NPI’s (lockdown policies in particular) in accounting for the evolution of COVID-19 transmission rates over time and across locations” 

21.bmj.com/content/370/bm…

“[the] President...has flatly denied the seriousness of the pandemic, refusing to impose a lockdown, close schools, or cancel mass events…Yet the country’s death rate is among the lowest in Europe-just over 700 in a population of 9.5 million” 

22.medrxiv.org/content/10.110…

“living with children 0-11 years was not associated with increased risks of recorded SARS-CoV-2 infection, COVID-19 related hospital or ICU admission but was associated with reduced risk of COVID-19 death (HR 0.75, 95%CI 0.62-0.92).” 

23.pandata.org/wp-content/upl…

“Consistent with observations that .. lockdown has not been observed to effect the rate...of the country reproduction rates significantly, our analysis suggests there is no basis for expecting lockdown stringency to be an explanatory variable” 

24.medrxiv.org/content/10.110….

“This study shows that the virus is already here, and we must find ways of living with it such that it caused no or minimal human and socioeconomic losses in ... Nigeria as a whole…. going back to the lockdown should never again be entertained” 

25.nejm.org/doi/full/10.10…

“recruits were under the constant supervision of Marine Corps instructors. Other settings in which young adults congregate are unlikely to reflect similar adherence to measures intended to reduce transmission." 

26.frontiersin.org/articles/10.33…

“The national criteria most associated with death rate are life expectancy and its slowdown, public health context (metabolic and non-communicable diseases (NCD) burden vs. infectious diseases prevalence), economy (growth national product, financial support), and environment (temperature, ultra-violet index). Stringency of the measures settled to fight pandemia, including lockdown, did not appear to be linked with death rate” 

27.tandfonline.com/doi/abs/10.108…

“Whether a county had a lockdown has no effect on Covid-19 deaths; a non-effect that persists over time. Cross-country studies also find lockdowns are superfluous and ineffective (Homberg 2020). This ineffectiveness may have several causes. " 

28.upmc-biosecurity.org/website/resour…

“There are no historical observations...that support.. confinement by quarantine of groups of possibly infected people for extended periods...The negative consequences...are so extreme…this mitigation..should be eliminated from serious consideration” 

29.medrxiv.org/content/10.110…

“we present data demonstrating that mortality due to covid-19... could have been largely predicted even before the pandemic hit Europe, simply by looking at longitudinal variability of all-cause mortality rates in the years preceding the...outbreak” 

30.medrxiv.org/content/10.110…

“Our analysis shows that while infection levels decreased, they did so before lockdown was effective, and infection numbers also decreased in neighbour municipalities without mandates” 

31. nature.com/articles/s4159…

"After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis...we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons” 

32. nature.com/articles/s4158…

"Sweden is worthy of particular attention, given...no lockdown took place.” “Notably, the estimated effectiveness of…[merely a]…public events ban in Sweden is comparable to that of lockdown in the 10 countries in which one was implemented” 

33. ssbhalla.org/wp-content/upl…

"For the first time in human history, lockdowns were used as a strategy to counter the virus. While conventional wisdom, to date, has been that lockdowns were successful…we find not one piece of evidence supporting this claim.” 

34. papers.ssrn.com/sol3/papers.cf…

"on average, mandated behavioral changes accounts for only 9% (median: 0%) of the total effect on the growth of the pandemic stemming from behavioral changes. The remaining 91% (median: 100%) of the effect was due to voluntary behavioral changes” 

35. bmjopen.bmj.com/content/bmjope…

"Potential determinants assessed were…the stringency index, as a measure of country-level response to COVID-19”
Results? Shotgun blast (zero correlation):

36. medrxiv.org/content/10.110…

"The decline of infections...can be attributed to relatively small interventions & voluntary behavioral changes. Additional effects of later interventions cannot be detected [&]...liberalizations of measures did not induce a re-increase of infections" 

37. heritage.org/public-health/…

"The evidence suggests that, among other important findings, broad lockdown orders that fail to focus primarily on the most vulnerable members of the population—particularly the elderly—have not produced superior outcomes to less restrictive policies” 

38. nature.com/articles/s4156…

"measures can substitute for a full lockdown in terms of effectiveness, while reducing adverse impacts on society, the economy, [humanity]” “Less disruptive & costly NPIs can be as effective as more intrusive, drastic, ones (eg, a national lockdown).” 

39. medrxiv.org/content/10.110…

"The peculiar aspect of the claim that lockdown accounts for 81% of the reduction in R is that Sweden did not implement any lockdown, but still see a similar decrease in R as the other countries” 

40. medrxiv.org/content/10.110…

"It is evident...that the growth of a COVID19 epidemic does not follow an exponential growth law even in the very first days, but instead its growth is slowing down exponentially with time...it is decelerating from the first day" 

41.medrxiv.org/content/10.110…

"Japan took the atypical step of not instituting a mandatory lockdown. During this time, businesses, restaurants, & transportation were kept open, & public life continued relatively unabated. Nevertheless, the second wave peaked and subsided on its own” 

42.medrxiv.org/content/10.110…

"We found no evidence that the shielding program [extreme lockdown] per se reduced COVID rates” “The effectiveness of shielding vulnerable individuals was limited by the inability to control transmission in hospital & from other adults in the household” 

43. c2cjournal.ca/2021/03/do-loc… 

"The stay-at-home orders...seem to have made no observable tangible impact on the daily cases & deaths. Further the most severe restrictions, such as prolonged lockdown...in California in Nov, did not prevent the subsequent spike in cases or fatalities"

44.thelancet.com/journals/lanpu…

"governments need to...apply available measures in a way that is much more targeted to different generational groups...; from March to June, 2020, 96% of additional deaths related to COVID-19 in Europe occurred in patients aged older than 70 years." 

45. jclinepi.com/article/S0895-…

"We demonstrate that effects of NPIs are non-robust and highly sensitive to model specification, assumptions and data employed to fit models."
"The model proposing major benefits from lockdown in European countries had the worse fit to the data" 

46. pnas.org/content/118/15…

"Shelter-in-place [SIP] orders had no detectable health benefits, [&] only modest effects on behavior"
"We reanalyze 2 prior studies purporting...that SIP orders caused large reductions in disease prevalence & show that those results are not reliable." 

47. bmj.com/content/372/bm… 

"of those who reported having experienced symptoms of covid-19 in the past seven days...only 20.2%...said they had not left home since developing symptoms"

"Non-adherence was associated with...lower socioeconomic grade...and working in a key sector" 

48. papers.ssrn.com/sol3/papers.cf…

"life loss due to lockdowns themselves has never been taken into consideration"
"pro-lockdown evidence is shockingly thin & based largely on comparing real-world outcomes against dire computer-generated forecasts derived from empirically untested models" 

49. medrxiv.org/content/10.110…

"All regions have peak incidence prior to the first lockdown with total incidence for England in decline well before lockdown"
"Furthermore all regions have R < 1 by either lockdown, with average R < 1 some days before either lockdown" 

50. papers.ssrn.com/sol3/papers.cf… 

"social isolation at a given date showed a strong positive correlation to COVID-19 deaths 39 days later"
"strong empirical evidence that.. adoption of restrictive measures increasing social isolation have worsened the pandemic… instead of mitigating it" 

Dr. Glatt would have us all believe that the debunked Imperial College model is the authoritative “science”, not the dozens and dozens of peer reviewed papers that analyzed the results and say otherwise.

In conclusion, the continuing utter failure of the current crop of so-called experts who the Rabbis have been relying upon to guide the community is something that needs to be addressed. The experts, and the medical community at large, have systematically failed to grasp the nakedly political interference that has been directing policy from the beginning. Additionally, it seems that they do not actually read data tables, much less investigate how the data put out and cited across the world is constructed and disseminated. It is unsettling to contemplate that our experts appear to be ill-informed, misguided, and  seemingly lacking credible expertise informing their judgement.

I left in the conclusion to emphasize that we knew with certainty well before even June 2020 that the entire establishment narrative regarding covid was illiterate nonsense.

This rebuttal was written a few days following the publication of Dr. Glatt’s article. It has been lightly edited for grammar and clarity.

Claim: Covid metrics as of June 11 2020 indicated a significant resurgence of covid spread.

Source: Rabbi Dr. Aaron Glatt COVID-19 Update, June 11, [2020] 9:00 PM

Background: Major lull in covid heading into the summer of 2020.

Dr. Glatt claimed that the number of infections is increasing in some areas of the country, as seen in twenty states’ “significant[ly] rising” number of infections; RD Glatt chose to highlight Texas and California as specific examples that illustrate this. The argument - though not articulated fully and relying upon the reader to work out on his own - essentially is that the rising number of covid infections indicates that covid transmission is rising, and is responsible for the increase in infections. This, in turn, indicates that covid is not in its death throes, or irreversibly dying out. If that is true, then it follows that a covid breakout can recur in a place that seems past it, like NYC, particularly if seeded by someone/s traveling from one of these new “hot spots”.

The Facts:

Exploring how the covid data are curated

All 50 states kept track of some basic covid statistics, including:

• the raw number of positive tests, negative tests, total tests

• the % of total tests that are positive

• the number of hospitalized patients who have covid

• the number of ICU patients who have covid

To understand what any particular data point represents in the real-world reality, it is necessary to begin with an introductory analysis.

The number of positive tests refers to the total number of covid tests conducted that returned a positive result. This is not the number of people who have a new, previously unreported covid infection, which is an entirely distinct group of people.

• Firstly, the standard for being considered “recovered” from a covid infection is that you test negative twice, consecutively. This means that one person can easily be responsible for 4 or 5, positive tests, as people (especially those who developed symptoms after the symptoms have subsided and wish to be tested immediately) get tested repeatedly until they produce the requisite two consecutive negative tests, in order to return to work, leave quarantine, or even to feel comfortable resuming a less restrictive social life (all things considered, obviously). This has become considerably more prevalent than a month or two months ago, when testing capacity wasn’t anywhere near as large, and tests were being triaged and thus not as readily available on a whim for everyone.

• Secondly, the PCR tests that are used also can pick up dead viral debris, sometimes even weeks after someone cleared the infection (I saw a case where a woman was testing positive 8 weeks after full symptom remission), thus some as yet unidentified % of the positive tests are in reality dead virus in people who already got rid of covid, and therefore cannot possibly be indicating of a current spike in active covid infections.

These factors skew the positive % higher, despite it being a mirage. And even after factoring a more carefully nuanced grasp of what the # of positive cases represent, there is another critical factor necessary to understand the context, insofar as determining the transmission rate:

What is the % of positive cases that exist at the time that are captured in the public infection surveillance (i.e. testing).

In other words, if we make a thought experiment, supposing that if two months ago (due to the much more limited testing capacity), only 10% of active cases were discovered, but now, 45% of active cases are discovered, then the number of active cases we see now versus the number of active cases discovered 2 months prior would have to be radically adjusted to get an idea of [what turns out to be] the plummeting number of active infections day by day. This is true even to compare now to a week ago – you have to compare the number of tests carried out to extrapolate a rough ratio of active cases from then to now.

For the most part, once taken into account, the increased testing usually by itself demonstrates – through a declining positive % of test results – a steady, downward trend of covid infection. This is emphatically true on a country-wide level.

Importing Mexico’s Covid into US Covid Data

There is a further confounding variable that, although rather scandalous that it is not reported straightforwardly, is critical, especially to the two states specifically cited by RD Glatt, among others:

There has been a massive surge – as in many thousands – of Mexicans who crossed the southern border to get better hospital treatment for acutely severe cases of covid, almost 100% of whom previously hospitalized in Mexico. This is in addition to the nearly 200,000 dual citizens or of other legal status in the US that work in Mexico and have crossed the border recently, from Mexico’s hottest hot zone.

Importing thousands of highly symptomatic – and therefore, highly contagious – individuals into the country leads the logical observer to anticipate that this phenomenon should manifest itself in the form of higher transmission, as these contagious individuals proceed to infect others. The importation of a more than 100k individuals from a covid hot spot likewise would lead one to conjecture that there will be a spike in the number of positive cases discovered, especially considering that these individuals are disproportionately likely to be tested because they came from a covid hot zone.

Hospital Stats

Another big data point being trumpeted as proof that covid infections are spiking is the hospitalization numbers.

Dr. Glatt is arguing that “more people in the hospitals with covid = rise in severe covid cases”. This argument collapses like a flimsily built sandcastle by mere cursory analysis.

“The # of hospitalized people with covid” refers to exactly that, without differentiating between those hospitalized because of covid, and those hospitalized with [typically asymptomatic] covid. Now that states have finally allowed elective procedures to resume, there is a surge of hospitalizations; this is a good thing, however!

Hospitals test every living thing that crosses the doorway. This means that every individual coming in for any surgery, procedure, or appointment of any sort is getting tested. Obviously, some % will test positive for covid asymptomatically. They are counted as covid hospitalizations, though (hmm… might juice the stats…).

Now, as of a few days ago, only 1.9% of ER visits in the US were because of “influenza-like symptoms” (which include covid symptoms, and let’s even concede that the vast majority of those are symptomatic covid). That is nowhere near remotely high enough volume to sustain the number of reported “covid hospitalizations”.

Similarly, there is mass confusion by many medical professionals between hospital bed usage, and hospital admissions.

The hospital usage stat refers to the cumulative number of hospital beds that are occupied on any given day, treating equally someone recovering from surgery 2 weeks ago and someone admitted today. That number has been steadily rising, and will probably rise until hospital capacity is a bit higher than average, as a backlog tens of thousands of procedures long, is finally addressed.

The # of hospital admissions of covid positive people, however, is dropping, even in some of the states that are supposedly the hospitalization hot spots. Yet it is usually the former stat that is cited by the experts.

Importing Mexico’s Hospitalized Covid Patients Too

It is also necessary to point out what I referred to earlier about the Mexican covid invasion, as particularly in regard to the covid hospitalization #, the Mexican invasion debunks it completely.

The NYT, of all newspapers, published a very lengthy, in-depth piece about this. (Staying true to form, though, they somehow neglected to mention the massive statistical impact that this represents.) They even discovered hospitals that were overrun and had to move covid patients farther north where there wasn’t a covid run on hospitals (before they started receiving “out of town” covid patients anyway).

This meant that there were thousands of Mexican severe covid cases being imported into the US healthcare system being counted as US severe covid cases (and even deaths).

That the official US data is incorporating another country’s cases, and worse, that these cases were themselves largely responsible for the bulk of the spike in hospital admissions because of covid is quite scandalous.

But it gets much worse. Very symptomatic covid patients - the sort that are progressing to hospital-worthy severity - are going to be far more infectious cases. Thus, they would almost definitely be the source of any genuine spike in covid infections too – particularly the hospitalized cases. So not only did the US data get inflated by foreign cases, the US population was exposed to an extra artificial surge of super-spreader patients too!

This is what Dr. Glatt would have us all believe is a signal of natural covid resurgence.

(This statistical chicanery follows the pattern established by the US death count, which included gunshot victims, suicides, alcohol poisoning, inoperable brain tumors, hospice residents, and drug overdoses, among other “clear alternate causes of death”, in addition to the many thousands of asymptomatic covid cases that were somehow killed by the asymptomatic covid infection.)

Experts Cannot Read Basic Hospital Data

Additionally, almost everyone I see is unable to distinguish between the date a case (or death) is entered into the published data, and the date on which it occurred or was discovered.

This is a far more confounding issue than one might think, because many states, especially now that the daily new reported cases have plummeted, have begun to sort out backlogged reports that were still waiting to be entered into the published data.

What they did not do, however, was to backfill the new cases to the date on which they were discovered on their dashboard displays. (Some do have a breakdown hidden in some random appendix type data document that is not perused by any except the most daring and patient data nerds).

This means that, depending on the state, an oftentimes significant % of their “new” positive cases are not exactly new, yet they are being counted as new cases! This creates the mirage of more cases than actually exist currently.

And this isn’t only due to bureaucratic incompetence. In Virginia, they managed to only enter the positive cases from the non-electronically reported lab conducted tests, while leaving out all of the more than 43,000 negative ones, and conveniently neglecting to inform anyone of this. This predictably completely skewed the positive test result %. The positivity # which also happened to be one of their critical indicia of covid reduction tied to their reopening phases.

There has been lots of data malfeasance in many states. A few states even managed to somehow combine the PCR tests with the antibody tests in the reported tests, which is an extremely illiterate thing to do. (Arizona, we’re looking at you in particular.) PCR tests (in theory) are measuring active infections. Antibody tests are measuring infections that have been resolved already, and are no longer active covid cases. Combining them denudes the positive test % and overall number of any coherent meaning, and is something that a competent 9th grade student would know not to do.

I have resisted the temptation (mostly because I was too lazy to do the hours of tedious work it would entail) to go state by state through their data detailing the various perfidies and entanglements of unrelated categories in their data sets. The broader ‘therefore’ from all of this, though, is that the data did not show any meaningful spike among US covid cases at that time, especially not one that would indicate that transmission has meaningfully risen anywhere in the country. The failure to realize this was a failure to read the elementary data, and a failure to pay attention to outside confounding factors that introduced rampant exaggeration to the critical categories that are used as proxies to determine covid transmission levels.

Unfortunately, I did not include in this section charts, and I don’t have time to spend a few hours reconstructing them from datasets at the moment. But I think (hope) that this still stands on its own.

At any rate, the arguments made definitely have aged well. Obviously, covid did come back in multiple waves with a vengeance. But that happened months later, and was certainly not indicated from any of the data contemporaneously available like Dr. Glatt was trying to argue. And the mitigation measures he was arguing in favor of definitely did nothing to hamper covid in any way.

What is quite clear though is that Dr. Glatt was functionally clueless and unable to properly read or understand the data and studies.

This rebuttal was written a few days following the publication of Dr. Glatt’s article. It has been lightly edited for grammar and clarity.

Claim: Asymptomatic transmission is a significant driver of covid transmission.

Source: Rabbi Dr. Aaron Glatt COVID-19 Update, June 11, [2020] 9:00 PM

Background: Major lull in covid heading into the summer of 2020.

The Facts

ASYMPTOMATIC TRANSMISSION:

To begin, the WHO stated at the beginning of the covid pandemic:

“Asymptomatic #2019nCoV infection may be rare, and transmission from an asymptomatic person is very rare with other coronaviruses, as we have seen with MERS. Thus, transmission from asymptomatic cases is likely not a major driver of transmission”.

This reflected a (formerly) basic axiom of respiratory viruses, which is that asymptomatic carriers are rarely contagious. This places the burden of proof upon those who wish to claim that covid is truly novel in this regard, to at least show that the observed transmission data, to some degree, can indicate this. The data, however, show no such thing.

“Stealth Community Spread” going way back to January

Firstly, even the CDC has (finally) acknowledged that there was “stealth” community spread going back to early January. (I believe that covid was romping about in December too at a minimum, based on travel numbers, which was always obvious to anyone who cared to think critically.)

This means that there were roughly 10,000 sports-stadium level “mass-gatherings”, and mostly indoors, that had no discernable impact at all whatsoever on covid transmission.

This would also be despite the at-least 35,000,000 cases (per the CDC’s estimations based off of the serology testing at the time), at least some portion of which had to have been running around in January and February, some of whom would have attended these events.

And all of the preceding points don’t even begin to account for the millions of lesser, but still supposedly highly contagious, gatherings (i.e. business meetings).

There is no way that asymptomatic people can be meaningfully contagious yet millions of them didn’t infect anyone, this despite also having thousands of sports games and the like.

Contact Tracing Studies

Secondly, contact tracing from around the world consistently finds that asymptomatic people are not infecting others:

• This study (link) found that “all the 455 contacts [of asymptomatic people] were excluded from SARS-CoV-2 infection and we conclude that the infectivity of some asymptomatic SARS-CoV-2 carriers might be weak”.

• This study (link) found that 20% of index cases infected 80% in Hong Kong, and 70% of infected people didn't infect anyone.

• This study (link) found that 80 percent of transmissions were caused by 8-9 percent of cases.

• The same pattern is true in Israel, where a pre-print study (link) found that “between 1-10% of infected individuals resulting in 80% of secondary infections”.

In other words, the vast majority of infected people don’t actually infect anyone (despite having potentially infectious social interactions!).

Another study found that:

“Infection prevention and control guidelines should take into account that patients with severe or critical COVID-19 may shed infectious virus for longer periods of time compared to what has been reported for in patients with mild COVID-19. Infectious virus shedding drops to undetectable levels below a viral RNA load threshold and once serum neutralizing antibodies are present.”

(The authors were typically restrained in their assessment; they only were able to detect virus shedding in 23/129 (17.8%) of the patients studied, all of whom were hospitalized, which would suggest that that there are yet unidentified factors that can prevent shedding even in someone severely symptomatic.)

And yet another study found that

“A study examining 468 confirmed COVID-19 cases in China indicated that only 59 (12.6%) of case reports resulted from presymptomatic transmission. Although this study was also based on secondary data sources, they obtained reliable information from confirmed cases in online reports from 18 provincial centers for disease control and prevention. Perhaps the most convincing study on presymptomatic transmission of COVID-19 was performed in Singapore. Direct contact tracing of 157 locally acquired cases indicated that just 10 (6.4%) of the cases occurred through presymptomatic transmission. Together these studies indicate COVID-19 transmission is 10- to 20-fold more efficient after symptom onset..” And this “Nevertheless, the various coronavirus studies described here indicate that if we focus on one parameter of transmission (pre-symptom vs. post-symptom onset exposure), we find that although presymptomatic transmission of COVID-19 is possible, it appears inefficient compared to transmission after symptom onset.”

It is important to point out that there is a huge difference between pre-symptomatic and asymptomatic. The studies that find transmission in non-symptomatic individuals are all in pre-symptomatic people, meaning that they develop symptoms eventually (usually within 1 or 2 days of infecting someone at most). Genuinely asymptomatic people, on the other hand, do not appear to be contagious at all, or at least to such a miniscule extent that it’s almost impossible to see any indication in case or transmission data.

That the WHO walked back their recent admission that asymptomatic spread is rare - under massive political pressure - does not indicate at all that their initial assessment was wrong, Dr. Glatt’s insinuations notwithstanding. Dr. Glatt does not understand politics, and therefore does not grasp the political nature of the WHO’s actions or statements. (I believe that their initial statement about asymptomatic transmission being rare was itself only put out for political reasons, namely to run cover for the massive Floyd protests/riots that would have otherwise been indefensible.)

Multiple mass gatherings did not lead to any spike in covid cases

Furthermore, there have been quite a few notable mass gatherings (before the recent rioting) that failed to generate any sort of spike at all, including the (in)famous Wisconsin primaries (a few hundred thousand people, no social distancing on those long lines to get into a polling location, etc.), the Lake Ozark parties, and a few others that I can’t remember off the top of my head. (Although the most obvious reason for these is the lack of outdoor transmission generally, there were enough instances of actual physical contact, in addition to the very indoor transportation involved in these events, that should have been enough to be infectious judging by some of the pictures.)

Debunking the junk science cited by Dr. Glatt

Let us now turn to the meta-study cited by RD Glatt from the Annals of Internal Medicine. To begin, they admit that:

“To be clear, the asymptomatic individual is infected with SARS-CoV-2 but will never develop symptoms of COVID-19. In contrast, the presymptomatic individual is similarly infected but eventually will develop symptoms. The simple solution to this conundrum is longitudinal testing—that is, repeated observations of the individual over time. Unfortunately, only 5 of our cohorts include longitudinal data. We must therefore acknowledge the possibility that some of the proportions of asymptomatic persons are lower than reported.”

That’s 5 out of 16. Not a very auspicious start – right off the bat we have them admitting that there may exist a significant degree of entanglement between pre-symptomatic and asymptomatic that is not disentangled in the results. And they admit further in that their data set has other issues that compromise the validity of their results.

The study’s authors further seems to miss all of the studies that I quoted above, and every similar study, also not particularly auspicious. (I have noticed that many researchers aren’t very imaginative when choosing the keywords to use in attempting to survey the existing literature on a subject, and miss lots of otherwise relevant results (that for some reason also seem to always include those most incompatible with what they are about to assert in their paper).)

Anyway, here is the part cited by RD Glatt:

“The early data that we have assembled on the prevalence of asymptomatic SARS-CoV-2 infection suggest that this is a significant factor in the rapid progression of the COVID-19 pandemic. Medical practice and public health measures should be modified to address this challenge.”

This argument is at best blatant speculation. What they are arguing is that “since asymptomatic cases make up such a large % of the covid infections, therefore it follows that they are responsible for an at least somewhat similar % of transmissions”.

There is nothing in their study however that supports the contention that the % of resultant covid infections is indicative somehow of the contagious-cases profile - there is no reason that a minority of the covid cases are responsible for the majority of secondary covid infections and that 40% of covid infections are asymptomatic.

And it certainly does not provide a mechanistic explanation as to why it should be that severely symptomatic individuals are equally contagious with asymptomatic individuals. Citing a solitary study that claimed to discover equal viral loads in asymptomatic and symptomatic (this part was not quoted above) - an astounding observation to say the least, and one that the study does not bother to offer an explanation for - to promote a revolutionary assertion is rank negligence for a peer-reviewed paper.

More importantly, this contention is refuted by the numerous contact tracing studies (that do not suffer from the plethora of self-identified analytical shortcomings within their data sets as does this study), which all show that a small minority – as in 9-20% - are responsible for >80% of infections - the opposite of what the authors of this study decided to just assume without evidence!!

This study is simply not authoritative whatsoever, nor even remotely significant, in attempting to determine the degree of asymptomatic contagiousness.

Dr. Glatt’s reliance on this “study” to provide an evidentiary basis for the claim that asymptomatic transmission is potentially a significant transmission vector is itself asymptomatic of the elementary scientific rigor that ought inform such weighty proclamations.

I can go on about this, but this should be sufficient to make the case. I will say that there were no competent studies that I was aware of then that purport to show that genuinely asymptomatic transmission (as opposed to pre-symptomatic) is a significant covid transmission vector.

Asymptomatic transmission has since become a significant driver in vaccinated people, because the vaccines can blunt or eliminate covid disease symptoms without reducing the viral load of covid infection, something that does not typically occur naturally.

Claim: “There is not even a plausible biologic theory to account for [the] possibility” of the vaccines affecting fertility.

Source: On the Covid Vaccinations, published 6/21/21

Background: The establishment was still trying to pretend that the widespread reports of menstrual problems were false.

The Facts:

To be very clear, Dr. Berman claimed that there wasn’t widespread documentation of a massive quantity - incorporating a wide array – of side effects. He further - astoundingly - claimed that there is neither a basis nor even a plausible biologic theory to sustain legitimate skepticism that the vaccines may cause fertility problems. While I don’t doubt that Dr. Berman sincerely believes his assertions, they do give a distinct impression of the typical unwarranted hubris endemic to scientists, which is an apt description for whomever is ultimately the provenance of these assertions. We shall demonstrate that there are multiple viable mechanistic options for causing a vast assortment of severe adverse effects, including infertility.

Novel Biological Components of the Covid Vaccines

At least two foreign, hitherto-undeployed biological entities are introduced to the human body by the covid vaccines (1 in the case of J&J) – the lipid nanoparticle and the spike protein. Common sense cautions that a novel foreign biological substance inserted into the human body may disrupt normal biological functions. This by itself already exceeds the low bar of ‘plausibility’. An analysis of both however will indicate that they are not only ‘plausible’ but very possibly (likely?) to cause widespread reproductive damage.

Lipid nanoparticles (LNP’s) have long been recognized as highly reactogenic and possessing a variety of toxicological characteristics across various configurations, which was a primary consideration when determining – that is to say, limiting - vaccine dosage. An early hurdle (after the breakthrough discovery of replacing uridine with N1-Methyl-Pseudouridine made the mRNA essentially “usable”) that stymied mRNA research was arranging a molecular structure for the LNP that was not [lethally] toxic. (Many animals ultimately gave their lives in pursuit of this achievement.)

Vaccine proponents claimed that the LNP’s would remain around the injection site, that the spike proteins wouldn’t escape the cells producing them, and that they lacked pathogenicity in any event. Unsurprisingly, none of these claims are true.

Pfizer’s pre-clinical bio-distribution study on the vaccinated animals (released by the Japanese government) found that high quantities of LNP’s accumulated in various organs and tissues, including the ovaries and uterus, and accumulation was vastly disproportionate in the ovaries compared to most other organs (the liver (not shown) and spleen were even more LPN-friendly):

(For the record, this result was derived using the lowest sensitivity test, which we shall return to later.)

A sizeable quantity of a highly reactogenic (picture something that ‘offends’ and ‘triggers’ the immune system) biological agent amassing in any organ system is obviously troubling. Besides for their potent reactogenicity, this suggests a discrete possibility that mRNA may have been intruded into organ tissue cells – which would then produce spike proteins per the vaccine’s design. These cells would subsequently present antigenic Spike proteins, precipitating an immune system attack upon some of the organ tissue directly.

Needless to say, a sizeable quantity of a highly reactogenic biological agent amassing in the ovaries doesn’t merely provide mechanistic plausibility for alleged fertility complications, it frankly predicts such injuries to occur. Also needless to say, the thought of, say, heart muscle cells or kidney cells producing spike proteins is a shuddering prospect whose mere utterance requires no supplementary explanation.

A second plausible mechanism for vaccine side effects, including fertility injuries, is the spike protein itself. A recent Oxford study found spike protein circulating in the plasma in 11/13 vaccinated individuals studied, including detached S1 spike protein subunits in some.

Although not a technical requirement for vaccine approval, bio-distribution studies are considered de regueur, and considering the novel architecture of the covid vaccines, the decision to forgo conducting one is highly suspect. (As spelled out by Dr. Robert Malone, proper bio-distribution study necessarily entails fully dissecting vaccinated animals to extract tissue samples from its entire anatomy, not the notoriously inadequate Luciferase bioluminescence assays they performed for the FDA.) This observation was articulated in the BMJ by one of their senior editors.

(Other extraordinarily unusual decisions made running the vaccine trials include excluding all demographics at substantial risk from covid and unblinding and de-controlling the studies by vaccinating the control arms(!!!), a move that forecloses the possibility (read: threat) of high-powered long-term safety data emerging. Ever.)

This demonstrates why we don’t rely on scientists claims of “implausibility” – they are too often encumbered by unimaginative thinking and inflated egoism – and this is clearly demonstrated through their ‘anchor’ modification, which ‘unexpectedly’ failed to prevent the mass exodus from their progenitor cells. Regardless, it is undeniably eminently plausible that spike proteins are circulating in significant quantities in many vaccinated people.

Native S-protein has likewise been firmly established to possess genuine pathogenic traits, which lacking toxicological assessment are presumed to be uninhibited by any of the novel modifications of the vaccine’s adaptation. The many toxic properties of spike proteins have been thoroughly documented (partial list of studies off the top of my head that clearly describe a spike protein toxicity in their title):

• The spike protein of SARS-CoV-2 induces endothelial inflammation through integrin α5β1 and NF-κB | bioRxiv

• The S1 protein of SARS-CoV-2 crosses the blood–brain barrier in mice | Nature Neuroscience

• SARS-CoV-2 Spike Protein Impairs Endothelial Function via Downregulation of ACE 2 | Circulation Research

• The SARS-CoV-2 spike protein alters blood brain barrier

• Exaggerated cytokine production in human peripheral blood mononuclear cells by recombinant SARS-CoV-2 spike glycoprotein S1 and its inhibition by dexamethasone | bioRxiv

• Be aware of SARS-CoV-2 spike protein: There is more than meets the eye - PubMed

• SARS-CoV-2 direct cardiac damage through spike-mediated cardiomyocyte fusion | Research Square

• SARS-CoV-2 Spike Protein Elicits Cell Signaling in Human Host Cells: Implications for Possible Consequences of COVID-19 Vaccines

In SARS-CoV-2 Spike Protein Impairs Endothelial Function via Downregulation of ACE 2, the authors found exactly what the title suggests, namely that merely binding to ACE2 receptors without executing any further pathogenic function disrupted numerous biological processes. (This was an ingeniously crafted experiment, where the researchers grafted S-proteins onto a pseudo-virion platform and infected cute Syrian hamsters with their Frankenstein pseudo-virus, whose sole potentially pathogenic attribute was the ability to bind with cellular surface proteins.)

The S1 protein of SARS-CoV-2 crosses the blood–brain barrier in mice study found that lab animals injected with spike protein into their bloodstream developed cardiovascular problems. More horrifyingly, they also discovered that S1 subunits can cross the blood-brain barrier (BBB), potential triggering all sorts of neurological adverse effects like the large assortment found in the various pharmacovigilance databases.

Critically, none of the vaccine makers actually demonstrated that the vaccine’s versions of the spike protein was cured of toxicity or pathogenicity (remember, the point of a vaccine is that you get injected with a dead or inert virus/part of the virus, not the virus’ active ingredient in a fully functional form). It is a small step, not a giant leap, to conjecture that nomadic spike proteins journeying to other organ systems, including reproductive organs, may bind with the ACE2 receptors ubiquitous throughout almost every organ system in the body, again including reproductive organs.

Furthermore, incredibly immunogenic spike proteins could conceivably induce a potent immune response wherever they accumulate, leading to inflammation or other detrimental effects in (previously) healthy tissue (or trigger another unknown and unanticipated biological process exerting a detrimental impact)… This, too, easily eclipses the low hurdle of plausibility, especially considering that the lipids -including some still possibly carrying mRNA - seem to drain into the ovaries. Uh-oh, anyone?

As the shocking nature of the revelations just divulged poses a formidable obstacle to most people who have until now only heard widespread claims of properly determined safety by the FDA and other regulatory bodies, it is necessary to provide the specific failures of the FDA in approving these vaccines.

It is instructive by way of introduction that the development timeline was severely truncated while under unprecedented moral, social, and political pressures. A layman readily intuits from this simple fact with common sense derived from all of human history a cautionary signal of hasty decision making inadequately accounting for numerous considerations, of which many are far from obvious, especially to a stressed and burdened mind. It is axiomatic to human nature that commensurate with increasing urgency amid ongoing devastation is an increasing tolerance of risk and corner-cutting, because a rational risk/benefit analysis properly concludes that “desperate times call for desperate measures”. A consistent outgrowth of such circumstances is the predilection to allow such a mindset to persist even in the subsequent absence of its precipitating conditions. This possibility is amplified where previous decisions in hindsight seem unjustified; and becomes probable where previous decisions created a systemic flaw still present which would require not only the embarrassing admission of error but potentially vitiating significant progress towards an unachieved objective, such as the development of a covid vaccine. To pretend otherwise is to be willfully and delusionally ignorant, which is of course the (ill-) considered position of the medical community. We shall provide a few examples of specific decisions that are manifestly defective.

For this, I will simply paraphrase Robert Malone, the first person to synthesize mRNA that successfully infiltrated a cell. The following comes from an interview with Daniel Horowitz (the first one):

The FDA involved only their vaccinologists, leaving out the gene therapy experts. The predictable result was that since traditional vaccines don’t undergo assessment to characterize the duration, distribution, or levels of expression of any component, nor do they undergo reproductive toxicology or genotoxicity, the covid vaccines were spared such testing despite being gene therapies where such enquiries are germane to their safety profile. Thus, the FDA never required that vaccine developers rigorously characterize level, duration, or distribution of manufacturing of spike proteins, or the distribution and toxicology of novel lipid compounds used to construct the LNP delivery vehicle. The meager animal bio-distribution study that was conducted by Pfizer used the least-sensitive testing mechanism. To assess bio-distribution, they coded for Luciferase in the mRNA, a bioluminescent protein responsible for the glow of a firefly’s tail. To analyze the biodistribution of luminescent proteins, they used photon multiplier tubes on whole animals, the least sensitive way of testing, since biological tissues deflect or refract photons, preferentially favoring extremely high concentration areas that are close to the surface. Unsurprisingly, the injection site accounted for the vast majority of the detected luminescence. Proper protocol – which would have been insisted upon had gene therapy experts been consulted – calls for dissecting the animals, extracting samples from the entire anatomy, which are then put in tubes with a buffer, followed by lysing (popping open) the cells and obtaining a sample which is tested for luminescence. This would indicate accurately how much of the mRNA-encoded protein was synthesized in each anatomical sample. That the FDA et al accepted the lowest sensitivity test, reveals their cluelessness in assigning inadequate standards for assessing these vaccines.

Furthermore, the spike protein isn’t the only potential vaccine target antigen, and may not even be the best one. Spike protein itself has many mechanisms that could be targeted besides for the RBD “which could’ve been left in a native structure” [ie, “closed conformation”, in other words, they chose to lock the S1 in an ‘open conformation’, which would keep the RBD exposed, but also enhances ACE2 binding affinity]. Covid contains other proteins that elicit cytotoxic T-Cell lymphocytes [those are the assassin T-Cells that destroy infected cells]. Hasty decision making for vaccine development led to ill-considered and harried decisions to target the easiest candidate without considering other implications.

Pfizer explicitly acknowledged in their clinical protocols that reproductive toxicity wasn’t studied in their animal trials.

As far as the LNP’s are concerned, new compounds contain inherently plausible risk that can be retired only through proper trials designed for that specific purpose, something manifestly absent from the trial protocols.  

The FDA and vaccine manufacturer’s obviously cut corners in the development and trials of these vaccines.

Other mechanistic theories for vaccine induced injuries -including fertility - is explored in great depth in Worse Than the Disease? Reviewing Some Possible Unintended Consequences of the mRNA Vaccines Against COVID-19 (whose original link seems to have gone offline… gee, I wonder why…), whose abstract includes:

We then review both components of and the intended biological response to these vaccines, including production of the spike protein itself, and their potential relationship to a wide range of both acute and long-term induced pathologies, such as blood disorders, neurodegenerative diseases and autoimmune diseases. Among these potential induced pathologies, we discuss the relevance of prion-protein-related amino acid sequences within the spike protein.

Remember, it’s on vaccine manufacturers to prove that their product is safe, even from highly speculative dangers.

I think a final point is in order concerning Dr. Berman’s claim that there is “no plausible biologic theory” that these vaccines can affect fertility. Frankly, such a claim does not possess legs to stand upon even unchallenged by contra-indicating evidence. Were scientist’s inability to conceive of a plausible mechanism for speculative harms a viable standard to adjudicate safety concerns, the FDA could be largely retired, what with little need for the robust testing regiment all novel therapies and biological agents are subjected to in the face of staunch expert claims of lack of plausibility for unexpected adverse effects to occur. Such proclamations are critically examined precisely because we (properly) accord due deference to humanity’s eternal burden of not knowing what we don’t know. Regrettably, experts seldom recognize the limits of their expertise, and vis-à-vis covid seem unaware that any exist altogether. Dr. Berman is no exception.

For more on fertility, see Is There A Plausible Basis for Fertility Concerns

Claim: The risk/benefit analysis of the covid vaccines clearly favors vaccinating children.

Source: On the Covid Vaccinations, published 6/21/21

Background: Most of the data on vaccine injuries in kids, especially myocarditis, was not available at the time.

The Facts:

• Kids without severe covid risk factors are not at risk for serious covid-caused problems.

• Kids are at risk from the vaccines far more than from covid itself.

• Since kids don’t spread covid in meaningful numbers, vaccinating kids is unlikely to have a significant effect in reducing transmission.

• Vaccines might have a negative impact on kids naturally amazing immunity to covid.

Despite the absurd apologetic contretemps of the CDC, children younger than 18 face considerably greater threat of illness and severe adverse incidents from the vaccines than from covid itself. Notably, almost every alleged pediatric covid death befell children with severe comorbidities, and that’s without controlling for overcounted pediatric deaths due to hyper-aggressive COD coding policies implemented throughout the US and the world. The CDC themselves recently acknowledged (at the time) this was occurring in a MMWR report, mmwr/volumes/7 (the report represents the absolute minimum % of covid deaths misattributed to covid, not the ceiling):

(It is worth emphasizing that the CDC discovered 39 ‘self-inflicted deaths’ and 13 homicides (for the record, a significant undercount) somehow attributed to covid, plainly exhibiting the remarkable extent of political entanglement with clinical judgement, whereby political considerations superseded elementary judgement so much so that coroners engaged in blatant fraud whose deceit was self-evident to even the common layperson.)

Additional evidence demonstrating the negligible covid mortality borne by the pediatric demographic comes from a study published in the Lancet, a portion of it reproduced below:

Since early reports from China stated that severe COVID-19 disease was rare in children, we have analyzed child COVID-19 mortality in seven countries. To put the deaths into a context that would help the understanding of parents, clinicians, and policy makers, we previously made comparisons of COVID-19 deaths with modelled mortality from all causes and other causes. Our first publication in April, 2020, was followed by a trend analysis up to August, 2020. We also update a data table online. Here, we update this analysis to February, 2021, in light of increases in adult mortality through the 2020–21 winter, and concerns about variant B.1.1.7, first identified in the UK in December, 2020 (probably circulating since September).

Table of Age-specific data for seven countries showing estimated all-cause deaths compared with COVID-19 deaths:

The sources of these data are provided in the appendix (p 2).

* Includes all deaths from approximately March 1, 2020, to Feb 1, 2021.

† Includes all COVID-19 deaths reported from the start of the pandemic up to Feb 3, 2021 (USA), Jan 29, 2021 (UK), Jan 20, 2021 (Italy), Feb 9, 2021 (Germany), Feb 10, 2021 (Spain), Feb 11, 2021 (France), or Feb 3, 2021 (South Korea).

(I used the table from the PDF version, which is why you won’t find the source †/*.)

This sort of politicized bureaucratic malfeasance has similarly contaminated all of the other covid metrics. The hospitalization data for pediatrics (and for everyone else to a vaguely lesser extent) is riddled with the same rank incompetence/malevolence.

The Public Health Agency of Canada recently published a report that found “only 36.6% of pediatric patients hospitalized with COVID-19 were admitted due to an acute respiratory infection”.

This is in line with recent studies in the US that similarly found an estimated 45% of pediatric hospitalizations to be incidental positives in patients admitted for wholly unrelated reasons (although I suppose that admittance for a gunshot whose lethal pathology is attributable to covid upon death can be reasonably categorized as a hospitalization because of covid).

Long Covid

One of the most enduring and pernicious deceptions promulgated regarding pediatrics is that there is that children, including healthy children, are susceptible at a statistically significant rate to the poorly defined, dreaded ‘long covid’. This is, firstly, yet another form a long line of unwarranted and reckless assumptions by the medical community devoid of any evidentiary basis that can exclude other causes or places the rate of children presenting any of over 200 symptoms (!!!) significantly higher for documented covid infections than for those without covid infection. Secondly, research attempting to assess these characteristics has found that there is not a documentable phenomenon of children suffering from long covid. Vaccinating children to prevent long covid? More caution is needed in interpreting current epidemiological data, published in the BMJ, declared:

The ONS figures6 need context. Our understanding is that they represent the proportion of children who—within five weeks of a positive covid-19 test—have one of the following symptoms: fatigue, cough, headache, loss of taste or smell, myalgia, sore throat, fever, shortness of breath, nausea or vomiting, diarrhea, or abdominal pain. For some of these symptoms the ONS reported prevalence is as follows: fatigue 3.5%; cough 4%; headache 5.3%; loss of taste or smell 2%; myalgia 1.7%. These seem in line with or lower than the population prevalence—for example, cough reported in an unselected cohort of 7670 children aged 1-18,7 or headache and fatigue in children from a representative Finnish cohort.8 Given current high levels of family and community stress we would expect the prevalence of some of these symptoms to be higher.

The prevalence estimates being discussed need a comparator group, and to be interpreted with much more caution, especially given the lack of a dedicated case definition for children.9 Specifically, we need to know how many survey participants (by age) without covid-19 had similar symptoms. In the meantime—especially while there are no safety data in children—we endorse the Royal College of Paediatrics and Child Health’s sensible position on covid-19 vaccination.10

Long-term symptoms after SARS-CoV-2 infection in school children: population-based cohort with 6-months follow-up, found that:

“[s]eropositive children, all with a history of pauci-symptomatic SARS-CoV-2 infection, did not report long COVID more frequently than seronegative children. This study suggests a very low prevalence of long COVID in a randomly selected population-based cohort of children followed over 6 months after serological testing.”

And to cap it off, even the CDC hasn’t officially declared there to be a causal link (or any link at all) between MIS-C (one of the primary alleged ‘long covid’ outcomes) and covid. From the CDC website:

On the other hand, the Canadian study Environmental epidemiology of Kawasaki disease: Linking disease etiology, pathogenesis and global distribution found about Kawasaki (functionally renamed MIS/-C last year) that:

“Patients with KD were generally less exposed to environmental allergens in many aspects of their daily lives (Table 3). They were more likely to primarily drink filtered or bottle water, were exposed less to household pets, were more likely to live in a dwelling constructed in the last 10 years and were less likely to live in an area with dense tree coverage, near a park, body of water or a farm. There was some statistically significant associations between the different exposures reported in Table 3.”

In other words, Kawasaki/MIS-C is more likely to occur in the absence of exposure to natural allergens and pathogens… a likely byproduct of lockdowns. Whoops.

Furthermore, there has not yet been any attempt as far as I’m aware of to quantify the epidemiological impacts of the covid policies on the individual health of populations, such as prolonged wearing of facemasks, lockdowns, social isolation, diet, exercise, and various markers of health, among other factors. Simply claiming that any adverse conditions found in anyone without a clear or observable cause is attributable to covid is rank negligence unbefitting a college student, much less amply credentialed medical practitioners and health experts.

The flu/influenza, by comparison, imperils children with considerably graver morbidity and mortality risks compared to covid, including ‘long Flu’, from the routine seasonal flu/influenzas:

On the flip side, the outsized quantity and severity of pediatric adverse health events post-vaccination is a phenomenon widely documented, from both pediatric vaccine trial data and from subsequent real-world deployment. The following table shows the frequency of systemic side effects to children in the Pfizer pediatric vaccine trial, and it is not a pretty sight:

Remember, per the actual trial, this prevented a mere 30 MILD COVID CASES.

Then there are the Serious Adverse Events (SAE’s). Even per Pfizer's own trial data (p. 27), from just one month of observation, there is an approximately 1-in-330 risk for a serious adverse event (SEA) from the vaccines (5/1,127 12- to 15-year-olds in the vaccine group, vs 1/1,127 of the placebo group).

Israeli data on the Pfizer vaccine for adults published in the New England Journal of Medicine (p.22) found that the "needed to treat" # per life saved is 27,778 (19 fewer deaths/vaccine group vs placebo group, out of 526,877 people = 1/0.00036 = 27,778).

This indicates that the ‘need to treat #’ as applied to children is stratospherically higher, because as a significant majority of the population already possesses immunity of whatever provenance (infection, cross-reactive, and/or vaccine), the epidemiological impact of each additional vaccinated individual is greatly reduced. This trait is especially pronounced in children, whose proven disposition as non-vectors severely diminishes their baseline individual forward-transmitting capability.

Furthermore, the NTT# to clinically benefit healthy children is comically colossal simply because there is literally almost no documented covid deaths in the healthy pediatric demographic. Again, this is using Pfizer’s own data (which I would strongly contest and is possibly a topic for a future essay).

Further recent research such as  Shared B cell memory to coronaviruses and other pathogens varies in human age groups and tissues indicates that children possess uniquely potent cross-reactive immunity from common-cold CoV’s, further cautioning against potentially adversely interfering with their amazing innate immunity with vaccines lacking a comprehensive immunological profile (as indicated by the myriad ongoing vaccine trials attempting to flesh this out).

A wholesome look at the broad mosaic of data regarding pediatric immunity plainly stresses that prior mediated immunities of any provenance is palpably and vigorously manifest in the statistically negligible disease burden carried by the pediatric demographic. This combined with the definite risks of vaccine SAE’s already visible, the wholly uncharted vistas of these vaccine’s long-term impacts, and recent research signaling potential hobbling of prior covid immunity by the vaccines, emphatically discourages vaccination of any children. (Vaccinating children uniquely susceptible to covid is another matter that requires further elucidation, although there are plenty of treatments that work on these kids that are safer than the vaccines.)

This is all in addition to the macro- level arguments against mass vaccination spelled out in Dr. Berman on Vaccinating to Herd Immunity.

So no, there is no basis for vaccinating children whatsoever, and the risk/benefit is basically entirely risk and no clinical benefit.

Claim: “For those with natural immunity, whether or not to get vaccinated is for another discussion.”

Source: On the Covid Vaccinations, published 6/21/21

Background: Widespread establishment opinion was that natural immunity is unreliable and inadequate. (To his credit, Dr. Berman did acknowledge that natural immunity had been effective to that point.)

The Facts:

• Immunity acquired from covid infection has been demonstrated to be strong, durable, long-lasting, and effective for all variants.

• Every comparison between infection immunity and vaccine immunity at the time showed infection immunity to be superior.

• There was not then, nor has there ever been, any basis to doubt infection immunity.

• There was already evidence then that vaccine immunity might be weak against already circulating variants.

• There was already evidence that the vaccines, especially the 2nd dose, can weaken prior immunity from infection.

Although Dr. Berman did not affirmatively endorse previously infected individuals getting vaccinated, his espousal of vagueness is itself markedly inconsistent with the available clinical evidence.

Studies such as:

• Covid-19 immunity likely lasts for years | MIT Technology Review

• Lasting immunity found after recovery from COVID-19 | National Institutes of Health (NIH)   

• Protracted yet coordinated differentiation of long-lived SARS-CoV-2-specific CD8+ T cells during COVID-19 convalescence

• SARS-CoV-2-specific T cell immunity in cases of COVID-19 and SARS; and uninfected controls

• SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans;

• Exposure to SARS-CoV-2 generates T-cell memory in the absence of a detectable viral infection 

• Longitudinal analysis shows durable and broad immune memory after SARS-CoV-2 infection with persisting antibody responses and memory B and T cells

• A 1 to 1000 SARS-CoV-2 reinfection proportion in members of a large healthcare provider in Israel: a preliminary report | medRxiv

have all found that natural immunity induced by infection running the gamut of severity including asymptomatic exposure is extremely robust, durable, and/or sharing bio-markers with the immunity induced by the 1918 Spanish Influenza (Neutralizing antibodies derived from the B cells of 1918 influenza pandemic survivors found remained active after 100+ years) and SARS-CoV-1 (SARS-CoV-2-specific T cell immunity in cases of COVID-19 and SARS, and uninfected controls found still currently present in infected individuals nearly 20 years later).

Assessment of protection against reinfection with SARS-CoV-2 among 4 million PCR-tested individuals in Denmark in 2020: a population-level observational study found a 0.65% reinfection rate compared to 3.27% for uninfected individuals, a reduction >80%. SARS-CoV-2 antibody-positivity protects against reinfection for at least seven months with 95% efficacy describes its findings in the title.

This finding is bolstered by the similarly substantially documented cross-reactivity of other immunities to SARS-CoV-2, such as:

• Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals - which reported “we detected SARS-CoV-2-reactive CD4+ T cells in 40%–60% of unexposed individuals, suggesting cross-reactive T cell recognition between circulating ‘‘common cold’’ coronaviruses and SARS-CoV-2”;

and

• Presence of SARS-CoV-2-reactive T cells in COVID-19 patients and healthy donors, who reported “[w]e demonstrate the presence of S-reactive CD4+ T cells in 83% of COVID-19 patients, as well as in 34% of SARS-CoV-2 seronegative healthy donors (HD) […]”.

(Cross-reactive immunity is a long-established phenomenon going back decades easily discovered by means of a simple Google search, and also documented for, off the top of my head, H1N1, Seasonal H1N1 Influenza Virus Infection Induces Cross-Protective Pandemic H1N1 Virus Immunity through a CD8-Independent, B Cell-Dependent Mechanism).

Furthermore, practically every study that compared natural immunity and vaccination immunity found natural immunity to be superior (the slim minority that found otherwise all did so on the basis of surrogate bio-markers never proven to correlate with immunological outcomes, and refuted by ubiquitous real-world observation):

• Pfizer’s massive study surveying almost the entire Israeli population, Protection of previous SARS-CoV-2 infection is similar to that of BNT162b2 vaccine protection: A three-month nationwide experience from Israel, found that natural immunity was slightly more effective than their vaccine’s immunity.

• A recent Cleveland Clinic study likewise found that while there were a few breakthrough cases of covid amongst their vaccinated employees, the natural immunity among unvaccinated employees was a perfect 100% impervious(!), leading the study’s authors to conclude “Individuals who have had SARS-CoV-2 infection are unlikely to benefit from COVID-19 vaccination” (which, translated from the typically restrained parlance of academia, means that they couldn’t fathom any plausible justification to vaccinate PI’s).

• Single cell profiling of T and B cell repertoires following SARS-CoV-2 mRNA vaccine found that “[n]atural infection induced expansion of larger CD8 T cell clones occupied distinct clusters, likely due to the recognition of a broader set of viral epitopes presented by the virus not seen in the mRNA vaccine”.

• Recent data from Israel where vaccinated people were almost 7x as likely to be infected as recovered individuals likewise suggests the superiority of infection induced immunity to its vaccine counterpart.

Natural immunity has further been demonstrated superior to vaccine induced immunity regarding variants. At that time, there had not been any legitimate study that found natural immunity’s neutralization capacity suspect for any variant, versus a few that specifically found the protection of natural immunity against variants to be robust, such as:

• SARS-CoV-2 specific memory B-cells from individuals with diverse disease severities recognize SARS-CoV-2 variants of concern

• Identification of SARS-CoV-2 Nucleocapsid and Spike T-Cell Epitopes for Assessing T-Cell Immunity

• CD8+ T cell responses in COVID-19 convalescent individuals target conserved epitopes from multiple prominent SARS-CoV-2 circulating variants

• SARS-CoV-2–specific CD8+ T cell responses in convalescent COVID-19 individuals

The NIH itself even issued a statement confirming the obvious: T cells recognize recent SARS-CoV-2 variants.

In sum, the worldwide incapability to document genuine reinfection phenomena in significant numbers (how many nursing homes have you heard about experiencing multiple covid outbreaks months apart?) despite the emergence of numerous ‘variants of concern’ foreclosed rational skepticism regarding natural immunity’s potency repelling covid variants.

Neutralization capacity of vaccine immunity, on the other hand, presents obstruse ambiguity in light of recent studies such as Multiple SARS-CoV-2 variants escape neutralization by vaccine-induced humoral immunity and Live virus neutralisation testing in convalescent patients and subjects vaccinated against 19A, 20B, 20I/501Y.V1 and 20H/501Y.V2 isolates of SARS-CoV-2, which cast serious doubts on the viability of vaccine immunity to hold up against emerging variants (something that has since been proven to be a well-founded concern.

(There were other studies, but I was unsuccessful in quickly locating them when I first wrote this, which was around the time that Dr. Berman’s article was published.)

The superiority of natural immunity is anyway a practically tautological proposition, simply because whereas the induced immunity of the vaccines employing a gene therapy mechanism is limited to epitopes of the spike protein in its prefusion state, natural immunity develops antigen-specific immune cells to most if not all epitopes of the covid virion from all stages of viral activity, which studies such as T cell Epitopes: adaptive immune response against COVID-19 have found to number well over a thousand.

Furthermore, studies such as Immunity after SARS-CoV-2 infections, published in Nature Immunology, found that the non-Spike protein epitopes accounted for a majority of SARS-CoV-2 antigen-specific T-Cells in previously infected subjects, and suggested enhancing the vaccines by broadening the targeted epitopes beyond specific S-protein ectodomains.

Unbiased Screens Show CD8+ T Cells of COVID-19 Patients Recognize Shared Epitopes in SARS-CoV-2 that Largely Reside outside the Spike Protein summed up their findings thusly:

In total, we identified 3–8 epitopes for each of the 6 most prevalent human leukocyte antigen (HLA) types. These epitopes were broadly shared across patients and located in regions of the virus that are not subject to mutational variation. Notably, only 3 of the 29 shared epitopes were located in the spike protein, whereas most epitopes were located in ORF1ab or the nucleocapsid protein. We also found that CD8+ T cells generally do not cross-react with epitopes in the four seasonal coronaviruses that cause the common cold. Overall, these findings can inform development of next-generation vaccines that better recapitulate natural CD8+ T cell immunity to SARS-CoV-2.

This echoes simple timeless wisdom embodied in modest aphorisms such as “don’t put all your eggs in one basket” that ought inform clinical judgement that immunological memory which catalogues all epitopes of a pathogen surely is preferable to immunity restricted to a limited number of epitopes, all native to one protein. (The indignant howls of vaccine proponents that the spike protein’s uniquely potent immunogenicity more than adequately compensates for the vaccine’s narrow targeting are merely that, and not particularly accurate besides.) Covid infection furthermore engages the entirety of the immune system naturally, which is presumptively catalyzing the peak robust, healthy and balanced priming of the immune system, which cannot be said for a vaccine whose mechanism is a marked deviation from the manner in which natural infection engages the immune system.

Regarding mass-vaccination in general, Impact of Influenza Vaccination on Seasonal Mortality in the US Elderly Population found rather startlingly that:

“We could not correlate increasing vaccination coverage after 1980 with declining mortality rates in any age group. Because fewer than 10% of all winter deaths were attributable to influenza in any season, we conclude that observational studies substantially overestimate vaccination benefit.”

This suggests at minimum that the scientific community has a distorted sense of the utility of mass vaccination campaigns.

A comprehensive UK report on ‘variants of concern’, SARS-CoV-2 variants of concern and variants under investigation in England, discovered that the death rate from the Delta variant among fully vaccinated was more than 6x the rate among the unvaccinated (26/4087 vs 34/35,521):

(Yes, this is far from clear cut, and there are numerous confounding factors in play. However, the medical community’s pattern of willful blissful ignorance of possibly embarrassing raw data with potential implications at odds with their favored positions and promulgations is demonstrative of rampant intellectual dishonesty by policy makers and their medical advisors.)

The primary consideration that militates unabashedly against vaccinating previously infected persons is that there exists evidence that vaccines can “exert a detrimental effect” upon the already existing immunity from prior covid infection. As reported by Differential effects of the second SARS-CoV-2 mRNA vaccine dose on T cell immunity in naïve and COVID-19 recovered individuals,

“the second vaccination dose appears to exert a detrimental effect in the overall magnitude of the spike-specific humoral response in COVID-19 recovered individuals”, and “the second BNT162b2 vaccine dose results in a reduction of cellular immunity in COVID-19 recovered individuals, which suggests that a second dose, according to the current standard regimen of vaccination, may be not necessary in individuals previously infected with SARS-CoV-2.”

(Random observation – the authors assertion that “that a second dose, according to the current standard regimen of vaccination, may be not necessary in individuals previously infected with SARS-CoV-2” is bizarre – the rational conclusion from their finding isn’t that the 2nd dose ‘may not be necessary’, rather it is ‘the 2nd dose for already infected persons must be precluded for that entire population’ (at least until further research can assess the immunological impact of the 2nd dose).)

Moreover, studies such as Self-reported real-world safety and reactogenicity of COVID-19 vaccines:  An international vaccine-recipient survey and Previous COVID-19 infection but not Long-COVID is associated with increased adverse events following BNT162b2/Pfizer vaccination found that the risks of adverse effects is potentially greater by orders of magnitude for non-naïve individuals. Besides, there are no studies or reports derived from real-world observation suggesting that vaccination enhances natural immunity in any setting. (The CDC attempted to simply lie about this and was caught by Congressman Thomas Massie.)

In conclusion, employing Dr. Berman’s own articulated standard requiring provision of demonstrable basis undergirding any speculation, one cannot conceivably doubt the immunity engendered by natural infection, whereas the immunity profile of the currently available covid vaccines still contains significant unresolved ambiguity. Taken  concomitantly with the already-identified potential reductive impact of vaccination on prior immunity and considerably heightened risks of adverse effects, this presents an unrebuttable case that vaccinating previously infected individuals lacks discernable clinical benefit, and is an immoral violation of foundational medical ethics resolved in Nuremberg and Helsinki.

Claim: Mass vaccinations of the entire eligible population (excluding those previously infected) is the surest and most optimal path to achieve herd immunity and end the covid pandemic.

Source: On the Covid Vaccinations, published 6/21/21

Background: Widespread establishment opinion was that the vaccines stopped transmission.

The Facts:

• Pushing mass vaccination as the only solution exaggerates the risk in the minds of an already frightened population, making it that much more difficult to break the inbred instinctive fear of covid, something necessary to the restoration of normal life.

• Mass vaccination can have unexpected results because of various factors, especially evolutionary pressure of a mass-distributed narrowly tailored and leaky vaccine, leading to new vaccine-resistant variants.

• Mass vaccination might reduce the quality of everyone’s immunity if it succeeds in driving covid out of circulation temporarily.

• Since kids don’t spread covid in meaningful numbers, vaccinating kids is mathematically unable to have a significant effect in reducing transmission.

• Covid treatment is a superior alternative to vaccination that carries none of the vaccine’s risks, both side effects and the epidemiological macro-level evolutionary pressure that can (and did) drive the virus to mutate around the existing immunity while becoming more potent.

Every documented pandemic involving a respiratory pathogen ultimately transformed from epidemic to endemic. All of the common-cold coronaviruses in circulation today initially began as pandemics, whose lethality collapsed following successive waves that established across the general population enduring immunological memory as they receded into perpetual endemicity. There was never any reason to presume that the SARS-CoV-2 coronavirus will prove novel in this regard. It is therefore prudent to assume that SARS-CoV-2 will ultimately remain a part of the seasonal coronavirus pantheon indefinitely.

Working within this paradigm, there were two conflicting epidemiological strategies regarding how best to mitigate the impact of SARS-CoV-2 moving forward. The choices were either to attempt to drive covid out of circulation to the greatest extent possible by maximal immunization of the population, or to focus disease burden mitigation efforts on those uniquely vulnerable to covid while allowing it to circulate freely in the same manner as do the other seasonal respiratory viruses. (I don’t see a “compromise” option as being epidemiologically distinct from the latter option; I framed it as targeted protection because that would be the most sensible, as there would be little utility from an epidemiological standpoint in vaccinating covid-‘naive’ persons without risk factors under this rubric.)

Since historical observation strongly counsels that SARS-CoV-2’s eventual endemicity is unlikely to be thwarted, it is imperative that public policy enable and assist societal readjustment from regarding covid as an exceptional danger towards acceptance of covid as an unremarkable routine virus (which despite the teeth-gnashing and litany of anecdotes from doctors has nonetheless per worldwide data been the case for the majority of demographics). Cognizance of the baseline susceptibility of the frail end elderly to common-cold coronaviruses is likewise essential moving forward, as a society with vivid recollections of covid devastation is liable to erroneously interpret any death caused by covid as indicative of enduring potent lethality.

(For the purpose of providing at least a basic response to anyone whose sensibilities are triggered or traumatized by what I just asserted, NYC represents a wildly aberrant manifestation of covid where numerous aggrandizing factors coincided to produce a disease profile markedly at odds with the rest of the world, including the subsequent winter recurrence of covid in NYC. The initial covid wave that overtook NYC is a subject that desperately requires its own article. It is my emphatic contention that little practical or functional epidemiological knowledge can be extrapolated from that particular covid manifestation (excluding obvious, clearly reproducible micro-level factors, such as the failure of aggressive ventilation). It is irresponsible in any event to extrapolate dispositive fact from the most aberrant instance of something faced by insurmountable contra-indicating evidence. The failure of many doctors and clinicians in the frum world to recognize the profound influence of their traumatization from experiencing (and heroically soldiering through) an apocalyptic nightmare signals disqualifying incorrigible bias afflicting their clinical judgement.)

Returning from our digression, this adjustment is true both psychologically and physiologically.

Psychologically, people must be emotionally detoxed from the overwhelming fear of covid, which presents a formidable barrier not only to the resumption of normal life, but to the necessary wholesale restoration of mental and emotional health (currently itself a catastrophic epidemic), especially for children who have been horrifically impacted in ways we are only beginning to appreciate. A persistent and ubiquitous vaccination push, especially for those who bear no meaningful statistical risk from covid, sends a strong and unmistakable signal to the public that covid not only remains a lurking peril, but that it is likely to retain its perilous character indefinitely. A vaccination campaign furthermore inevitably results in evocative fearmongering (aptly illustrated by contemporaneous rhetoric), as medical professionals seeking to convince recalcitrant people ultimately cannot afford to refrain from portraying real or imagined risks of covid in stark, vivid terms employed specifically for their capacity to convey a sense of horror and terror necessary to overcome stiff emotional resistance, especially regarding children where there is an incomparably greater wariness for potential harms by their parents.  

Physiologically, the tendency for immunity to wane when not confronted by its complementary pathogen (as circulating antigen-specific immune cells die out and are not replaced, among other reasons) suggests that the apex strength of a population’s collective immunity to an endemic virus occurs when there remains at least a minority within the population capable of transmitting the virus onwards thus keeping it at minimum in low-ebb circulation, similar to the famous Laffer Curve visualizing the optimal tax rate for the government to maximize its tax receipts. Sufficiently widespread vaccination that temporarily drives a virus out of circulation may result in a population with unexpected novel epidemiological characteristics. As immunity wanes without sporadic re-exposure to a virus, an entire population deprived of such periodic interactions may result in greatly enhanced pathogenicity and lethality of the virus when it inevitably returns to face a somewhat debilitated immunity, unleashing the very monster thought defeated.

This possibility is compounded by the preferential evolutionary pressure inevitably resulting from indiscriminate mass vaccination prejudicially favoring vaccine-resistant variants (due to) carrying a spike protein mutation, seriously jeopardizing not only immunity potency but also threatening severe ADE pathologies greatly enhancing the pathogenicity of subsequently emerging variants. Each additional booster further complicates a cost/benefit analysis that must recalibrate to account for increased occurrence of side effects, which continuing the trend of the sizable rate increase from the 1st to 2nd doses would present an exponential model of increasing risks for a largely undefined epidemiological impact.

Furthermore, a virological ‘arms race’ with a pathogen likely possessing artificially enhanced adaptability traits increases the probability for the ascendance of an immunologically invisible SARS-CoV-2 strain, similarly far more pathogenic and lethal than any currently circulating variant. Although the probability of these hypothesized possibilities is difficult to gauge, the very fact of this abstrusity cautions restraint until further research can reasonably prognosticate differing future epidemiological progressions of covid with elevated granularity.

In any event, the lack of a comparable epidemiological precedent buttressing the conjectured virtues of indiscriminate mass-vaccination makes difficult to justify that those who don’t bear covid disease burden should assume definite risks in the hopes of maybe warding off speculative future injury to others.

Dr. Berman asserted that the ideal path forward is to pursue the extirpation of covid to the extent physically possible for now, however fleeting the victory may prove to be, which in his mind justifies vaccinating the population to the greatest extent possible, including demographics who bear insignificant statistical risk of covid morbidity and mortality. Undoubtedly this is inextricably bound to his assumption of numerous faulty premises, including his inaccurate perception of the vaccine’s risk profile.

This sort of assessment ultimately is perfectly consonant with the myopic, one-dimensional thinking emblematic of the historically unparalleled public-policy abominations that have not only wrought hitherto incomprehensible harm and societal upheaval far disproportional to any potential benefits of such barbaric cruelty but also worsened the targeted virological harms that the policies sought to alleviate. At a minimum, a course of action whose benefits are at best heavily speculative concurrently burdened by serious doubts of possibly encouraging the very harm it attempts to prevent is one ill-suited for practical implementation (and I would add certainly unscrupulous to be employed as a justification to require others to assume personal risk of harm, however slight).

Additionally, the epidemiological impact of vaccinating children is likely insignificant, because there is ample data from around the world demonstrating that children aren’t significant transmission vectors in the first place, in addition to a marked and profound dearth of actual observed cases of asymptomatic transmission (one of the most pernicious lies disseminated during this pandemic). Further bolstering this argument is the fact that – [even!!] per the CDC - almost 40% of children by this point in time had already been infected with covid, which (besides for proving the impotence of the barbaric mitigation efforts such as masking and social distancing,) indicates that there is already some degree of herd immunity among children in any event; married to their innate disposition as non-vectors even without a significant portion bearing immunity unambiguously, this refutes the utility of vaccinating even the remaining naïve children.

A further absolutely critical point is that Dr. Berman seems to be operating under the profound misconception that there exist no viable alternatives to vaccination effective in warding off covid infection or treating covid disease. This is emphatically untrue. The medical community’s standards regarding covid treatments is pockmarked with rank hypocrisies, inconsistently applied vacillating standards, and undeniable corruption. As Dr. Berman didn’t explicitly raise this issue, and considering the already mind-numbing length of this essay, I will forgo a proper analysis of prophylactic and treatment options. Vitamin D, HCQ protocols, and especially Ivermectin are all incredibly potent treatments/prophylaxis that are supported by incredibly robust data, which present not just viable but superior alternatives to the dangerous vaccines currently in use.

Regardless, incomplete transient geographically-constricted extinction is a strategy both epidemiologically unsound and ethically dubious.

Claim: “There were [approximately] 10,000 “breakthrough” covid cases amongst the first 100,000,000 vaccinated individuals, which equaled a 90% reduction from the [contemporaneous] case rate amongst unvaccinated individuals.”

Source: On the Covid Vaccinations, published 6/21/21

Background: This claim was made before the sheer numbers of vaccinated covid cases made maintaining this claim untenable for the establishment.

The Facts:

• The 10,000 number comes from the CDC who admitted it is a ‘substantial undercount’.

• The standards for a positive test were changed specifically for vaccinated people.

• The CDC stopped counting breakthrough cases after they started piling up much faster than they expected, probably because they were afraid it would undermine the claims of “95%” efficacy.

Dr. Berman claimed that there had been a mere 10,000 covid cases among the vaccinated, 90% less than cases in unvaccinated people over the same time frame. The significance of this point is to highlight the wide chasm between one’s susceptibility to covid unvaccinated versus vaccinated. As we shall document, this claim cannot withstand even mild scrutiny.

Firstly, the CDC (in)famously changed the standards by which covid cases would be identified and curated from the (however comically absurd) prior standards by which covid cases were documented, limiting positive case reports to those confirmed by a PCR test ≤ 28 CT count, down from as high as 45 CT’s (!!!!) employed by various laboratories throughout the pandemic, which would eliminate going forward anywhere from 40-90% (depending on the time, place and laboratory) of positive lab results counted throughout the pandemic. Furthermore, the CDC itself admitted in its Breakthrough Infections Report pertaining to the specific data point cited by Dr. Berman that

“[sic] the number of reported COVID-19 vaccine breakthrough cases is likely a substantial undercount of all SARS-CoV-2 infections among fully vaccinated persons. The national surveillance system relies on passive and voluntary reporting, and data might not be complete or representative. Many persons with vaccine breakthrough infections, especially those who are asymptomatic or who experience mild illness, might not seek testing.”

When even under the new restrictive standard breakthrough cases quickly began accumulating at an unanticipated clip (remember that the CDC was aware the official # was only capturing a slight fraction of the actual cases), threatening to undermine the trial-‘confirmed’ claims of “95%/96%/97%/98%/99% efficacy” (depending on the media appearance or guidance document), the CDC simply eliminated positive case tracking altogether, deciding to only track covid cases that resulted in hospitalization amongst vaccinated individuals (officially) beginning May 1st. In other words, not only is this number utterly devoid of meaning regarding conveying a measure of covid case incidence amongst vaccinated individuals compared to the case incidence amongst the previously unvaccinated population throughout the pandemic, it is equally irrelevant concerning even a basic comparison of contemporaneous case rates between the vaccinated and unvaccinated populations, as they are subject to wildly differing standards in both positivity threshold and minimum characteristics for reportability.

The bluntest rebuttal of this misguided contention is that it is akin to the Soviet Geiger-counters employed at Chernobyl. These Geiger-counters having a maximum reading of 3.6 rads/hour, showed exactly that upon reactor #4 melting down, which was dutifully reported as fact, despite the actual radiation leakage of reactor #4 exceeding 20,000 rads/hour. Similarly, the CDC simply ceased tabulating breakthrough cases after they hit 10,000. To quote one of my favorite lines from the bench, “to state the proposition bluntly is to refute it decisively”.

It is understandable that practicing doctors do not have the time to adequately peruse numerous studies and extensive guidance documents from various bureaucratic-minded agencies. Just as true, however, is that Dr. Berman quoted as definitive a CDC statistic that the CDC admitted publicly in supplementary documentation is “a substantial undercount”. This profound inaccuracy underscores the wide discrepancy between the statements and knowledge base of even the most noble-minded doctors and reality (and also highlights the devious politically-minded chicanery employed by the CDC in releasing a topline number they knew to be false, understanding that a gullible and compliant medical community/media would acquiescently present it as established fact without inquiring about its provenance or precision).