To SEIR, With Love
The classic and universally-relied-upon epidemiological transmission model is the interesting differential equation based set of inputs and simulations for Susceptible, Exposed, Infected, and Recovered (SEIR) populations. This common model might be used to address any misunderstanding about anomalous genetic vaccine reactions for any sub-population. I recommend this article for current information on the SEIR model.
On the other hand, the recent editorial by Fareed Zakaria , which is titled “As with 9/11, COVID theater puts risks over rewards” appears to argue that the recent cluster of reactions to one of the new Covid-19 vaccines was not significant:
“..the virus would still be thousands of times more dangerous than the vaccine.”
Covid-19 and the vaccines are, if anything, all antigens. If the reactions to both antigen types were modeled with SEIR – compatible variables, then the risks from Covid-19 could be routinely quantified against the risks of the vaccines. Success will depend on the expert definitions of Infected and Exposed for both. For example, a SEIR model for the vaccine reaction could be simplified to a SIR model. That’s because there would be no need to further define how many were Exposed (all in the groups were).
In contrast, the Covid-19 case would have to be unequivocal about what “Infected” means. It typically means that one must have been ill with the disease. And Exposed might now include the entire population of the Earth.
Until the CDC and their colleagues stop moving Covid illness numbers around, the study of Fareed’s Risks vs Rewards (where we take the risks and through greater readership, he receives the rewards) might be best discarded in favor of more SEIR inspired estimations. I’ll try one.
First, one wonders how many Covid-19 “Infected” have there been? Again, that depends on the definition. Over the first year of the Covid crisis, John’s Hopkins Covid dashboard had detached itself from reality by counting the healthy as Infected (because then, they never posted hospitalizations). In any case the Infected must be ill if the SEIR is to apply. Again the SEIR expects any Infected to be sick. So I am more focused on the numbers of hospitalizations, where being ill with a respiratory disease is an important index.
Here’s a resource excerpt to start:
Sadly, the information is incomplete for pre June 2020 for the CDC’s host country. And no totals are apparently provided at this Euro inclusive page. This may be a challenge to reconcile with a snap from the Johns Hopkins Dashboard, which finally began to post Covid Hospitalizations long after I requested that. In this annotated excerpt below, one can confirm that by late April of this year, basically, right now, only about 10,000 (US) are attributed to be Covid-19 US hospitalizations. The above chart claims 4 times that number. If we are comparing the same things, then they both cannot be right. For example, if Johns Hopkins is right about the total number of Hospital beds in the US, then the resource above appears to assign more hospitalized patients over January 2021 than there were actual beds.
As a quick detour, I note this chart is relatively new from Johns Hopkins. Take a close look at the orange band. For the most part, about 10,000 US hospital beds were taken up with patients who were assigned the Covid-19 label, even in 2020. That’s rather ordinary when compared to some flu years and seasons is it not? I think it’s also interesting to examine the top gray band. Where is that period we all routinely heard about when available ICU beds were consumed by Covid-19 patients?
Again the the Johns Hopkins hospitalization chart and the one above it both don’t show hospitalizations until after June 2020 began. Yet we all were given to understand that the classic Infected population (those requiring hospitalizations) would have emerged starting in January of that year. And none of us were informed (even to this day) that the CDC’s and the collective health care establishment’s Right Size Roadmap (RSR) had been quietly rescinded. The RSR was their quality control and best practices guidance. It details how the testing for new and existing viral strains within SICK populations is to be conducted. It expects that healthy populations are not to be tested. It expects that among the sick, more than one viral antigen is to be tested for. None of that has happened, as this and other posts describe.
At least in my state, tabulations of Covid hospitalizations were not published until after I somewhat formally relayed my concern around May 2020 to their leaky information pipeline. Whether they finally listened to me or to some other nobody, It’s clear that in line with both of the charts above, months went by before public health officials would first disclose the population size for this critical component of the SEIRS complement, namely again, the Infected. Perhaps other states then followed suit on hospitalization disclosures. You’d have to ask your own government officials or dig around in their public Covid accounting systems.
I do seriously wonder what these charts above would now look like if I hadn’t asked about hospitalization numbers back in May/June 2020. When I did ask, it was interesting. In any case, these hospitalization numbers as well should be considered with great skepticism, if only because health professionals when pressed will acknowledge that Covid-19 hospitalization numbers are not mutually exclusive from influenza hospitalizations. I know because on several occasions I did the pressing. So to be clear, this meant to me that many of the hospitalized may have been ill with a different virus than the one that Covid-19 is attributed to.
Perhaps this example below of a “double accounting system” is the most significant indicator that Covid-19 Infected numbers have been blended with basic influenza numbers and so the real values are much lower than any would likely believe. That’s because the CDC accessed about half a million basic Influenza-Like-Illness (ILI) numbers which includes visits to a clinician as well as hospitalizations, that were originally designated as influenza, and moved them into the Covid-19 category, but only at one of the sibling sites that present influenza data and only for a certain time frame. It is strange that none question this.
Nor do any appear curious why online records show that Covid-19 is attributed to have reached the US months before Johns Hopkins U and literally all other authorities claim it arrived.
For what it is worth this last chart seems suggestive that Covid-19 was simply one of many influenza-like strains that was diverted from the RSR pipeline to bloom into the images above as well as our new realities. Scientists were able to divert by simply redefining what a flu means.
Good thing I’ve kept excellent CDC site download records and have maintained those in “safe spaces”, because I don’t believe you can go to the CDC web site and transparently find this data anymore. In any case, now that you may begin to understand that the data cannot be unconditionally trusted, these items help to set the stage for my crude estimate of vaccine risk.
I’m going to apply the frequentist assumption, that the numbers of cases can be converted to a probability of a future occurrence, i.e. a risk. I’m going to simply eyeball (grossly estimate) some numbers because of the above documentation that CDC’s numbers cannot be validated.
I will work from the current example of vaccine “infected” given that 17 or so women (mostly) between 20 and 40 years of age, roughly, took a new vaccine and became seriously ill. 8 million doses of that vaccine had been administered. This is the event that Fareed Zakaria was referring to in his essay. I’d guestimate that a quarter of those doses were administered to this subpopulation of woman of that age span. Finally, given the above chart and link which documents the health communities’ CDC-blessed practice of doubling “infected”, I’m going to follow that lead and double the vaccine infected as a rational but crude estimate to 34. Therefore, 34 infected, divided by 2 million exposed, and mulitplied by 100 suggests that .002 percent of the vaccine Exposed became Infected (sick) for this subpopulation.
Now that seems in the same ballpark as the risk from Covid-19 itself for the full population, depending as usual on what we could infer. And again that is challenging given that CDC has changed the actual numbers significantly without a transparent disclosure throughout media. I’ll have to make assumptions for this very crude and possibly biased exercise, but it will be somewhat more data based than Fareed’s assessment: I’m going to assume that only 100,000 people have been hospitalized with the real Covid-19 illness. If you must disagree, please note the context above. Also for what it is worth, and this is amazing, but as this post notes, only healthy people actually seem to get genetically tested for Covid-19. Rational viral surveillance has been turned on its head in other words.
This merits some additional background. One can be hospitalized with a respiratory ailment and regardless of what even the clinician might tell anyone, including the patient, the hospitalization could still end up being tallied as a purely COVID case. Because the Right Size Roadmap (RSR) has been canceled, there are no longer any checks and balances, such as testing for other antigens or phyla/strains that would prevent a bean counter from their goal of amplifying Covid-19 numbers into the public record.
So again, until the RSR is restored and strengthened, one might benefit to remain skeptical of everything regarding this illness. And as a skeptic, I’m proceeding with this example exercise, using 100,000 as the presumed actual number of Covid-19 infecteds, out of a rough total global population of 7 billion humans. Then 100,000 divided by 7 billion and multiplied by 100 yields a crude frequentist probability of .001 percent.
Yes, these numbers are hard to swallow, much like my last post. And I won’t even support these numbers. I’m only making a point that assertions of risk need to be based on reproducible data. Said data must also be MUTUALLY EXCLUSIVE from INFLUENZA. But if the numbers I’ve estimated were close, then it would signify (again in a very crude way) that the risk from taking the vaccine is twice the risk from not taking it
By the way others have noted some additional risks. There is the novelty of the vaccine antigen which is now only a manufactured spike glycoprotein. Take a look at the link to better imagine how these proteins are little whirring and fluttering machines. Do they die completely when and in the manner that other vaccines are supposed to? Only time will tell, but the risk component associated with novelty used to be accounted for by a long testing period. Now along with the RSR, the normally mandated testing period requirement of a few years has been terminated. And there are other concerns.
Finally you may begin to cognify that because the RSR has been quietly rescinded right under your nose, there is now no means to confirm the effectiveness of any vaccine against any respiratory virus. Accordingly, if you were administered a Covid-19 vaccine and yet contracted a flu-like illness anyway, you cannot ask for a refund from Pfizer, Moderna, J&J, or others. They can always say you caught some other flu-like illness. No testing of any worth will ever be conducted to confirm one way or another. But if you don’t develop a flu-like illness, you can be certain that each of those companies, along with the broad health care community will give all credit to their new artificial glycoprotein machine vaccines.
Because of all of these serious problems, the public requires a resuscitated, expanded, and strengthened CDC Right Size Roadmap (RSR) viral surveillance program. I’m not sure what you are waiting for before you ask your own local lawmakers to ensure that taxpayers are only funding RSR-compliant Covid-19 (and all of its variants) surveillance. Perhaps it would add value to ask them to return vaccine introductions to standard protocols including the full testing timeline. Feel free to cc me here as you reach out to your lawmakers and health professionals. Maybe I’ll have one or more examples which can then be turned into a new post (I’ll remove the names to protect the innocent).
Then, along with the quantitatively rational SEIR and SIR models, FZ’s estimates of statistical significance might receive more beneficial examination and discussion. I would love this.
 Zakaria, F. 2021, published as an opinion piece in the Albuquerque Journal April 27, 2021 on p. A10 or see https://www.washingtonpost.com/opinions/global-opinions/our-policymakers-are-still-obsessing-over-risks-but-forgetting-about-rewards/2021/04/22/9ae72c7a-a39b-11eb-a774-7b47ceb36ee8_story.html
BONUS template, If you are feeling concerned and want to do something or anything, consider pressing for a return of our/your health care system to the principals and practices of the RSR. Just copy and paste into any form or email you can for reaching out to Lawmakers and/or Health Care Officials:
Dear ________ I am opposed to funding any viral surveillance that doesn’t follow the Right Size Roadmap which is currently defined at Influenza Virologic Surveillance Right Size Roadmap (aphl.org). The current surveillance for COVID-19 does not follow that guidance. Please restore our viral surveillance to these best practices without any further delay.
You are also welcome to cite this page but they already know all about the RSR.
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