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Tag: breakthrough infections

Vaccines, Breakthroughs, and Omicron: A Summary

Graziani, Carlo J. 0 Comments

What Was This Thread About?

I began writing this series of posts as a way of systematizing a number of ideas—some of them mere discomforts—that I’ve been accumulating as I’ve learned to to do technical/statistical/data-driven work in COVID-19 epidemiology over the course of the past year or so. I don’t exactly know what to do with these ideas: not being a trained life scientist, I don’t have a great track record at writing life science papers, and in any event I’m not certain that the things that I’ve discussed in the past few posts could be fit comfortably in a “real” scientific paper. There isn’t a lot of data in what I’ve discussed, for one thing (which makes it a weird thing for a “data scientist” to be trafficking in!). On the other hand, I do think that I have seen a few things that have value, and uncovered some unexamined assumptions that really need to be held up to sunlight. So the best I can think to do for now is blog them, and hopefully get the parts of the ideas that turn out not to be wrong into scientific discussions.

I’ve pretty much emptied the sack at this point, so I’m not planning to keep on writing more of these, unless I notice anything else. What I’d like to do today is draw up a coherent summary of where we stand with respect to breakthroughs, vaccines, Omicron, and the state of epidemic surveillance, drawing on the last four posts.

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How To Detect a Real Vaccine-Escape Variant In Real Time, And Why We May Need To

Graziani, Carlo J. 0 Comments

Genome Dominance And Co-Infection

In my previous post, on Omicron’s actual status in the typology of breakthrough infections, I alluded near the end to a fact that strikes me as requiring much more of an explanation than is usually given. The fact in question is that in the SARS-CoV-2 epidemic, every time a new, rapidly-reproducing variant has burst on the scene, within a few months it has driven all its rival variants clean out of the community-spread genome.

Take a look at The Covariants.org Per-Country page, and let your mouse scroll over the United Kingdom chart—the UK has been consistently sequencing more specimens more assiduously, completely, and regularly and since far earlier than any other nation, as you can see from the number of sequences (the “num seq” pop-up figure), so it’s the best case study. You can see that until 12 October 2020 there was a variant winningly named “EU1” cruising to dominance over its competitors. But on 14 September something new had happened: 3 specimens had turned up with a new variant, named “Alpha”. By 8 March, Alpha has secured 98% of the circulating genome (34648/35670 specimens) and appeared on its way to crushing EU1 (173/35670 specimens), but again, something new had just happened: 6 specimens of a new variant, “Delta” had just shown up. You already know how this story goes: Delta swept the board. By Mid-August, Alpha sightings were as common as Elvis sightings (21/75887), and EU1 sightings were like unicorn sightings (2/75887). In the 1 November data—just prior to Omicron’s appearance—out of 96120 specimens only 9 were not Delta or some cousin of Delta. At that level, to explain the non-Delta signal, we’re really looking at accidents rather than spread: things that interfere with good mixing, such as small, isolated communities perhaps, or travel from distant areas. Natural alternatives to Delta had clearly been driven out of the larger circulating SARS-CoV-2 genome by the time Omicron showed up.

Why?

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Is Omicron A “Vaccine-Escape” Variant?

Graziani, Carlo J. 1 Comment

How Can We Know What Kind Of Breakthrough Infections Omicron Produces?

At the end of my previous post on breakthrough infections I suggested that there are in fact very good reasons to believe that the Omicron variant is not creating “dangerous” (to the patient) breakthrough infections, that is, it is not creating “Type 1 breakthrough infections”, in the typology that I set out in that post. The ability to create Type 1 breakthrough infections would make a variant very dangerous, because an infection by such a variant would evade vaccine-primed human immunity, and the patient’s immune system would have to start from scratch on the time-consuming process of learning to identify the virus, and to create the armament of antibodies and immune cells to fight it without the assistance of a vaccine. The “Type 3” breakthroughs (whether of the “false breakthrough” or “semi-breakthrough” sub-types) are much less dangerous. Neither sub-type actually evades vaccine-primed immune response: they merely appear to, because the rapid reproduction of the virus in the body leads to measurable, often infective levels of viral load despite neutralizing antibodies’ efforts to restrain that growth; but the infection’s early doom is already sealed, because the cellular part of the adaptive immune system—specifically the killer T-cells—are on their way, and will wipe it out in short order. So while the infected individual may be infective (Type 3b), he or she is not usually at risk of severe disease.

This is an orderly proceeding for an immune response to an infection, incidentally. Neutralizing antibodies are only the first layer of the adaptive immune system, and despite their prominence in media discussion of vaccines and therapies they are not responsible for either preventing severe disease or clearing an infection. Antibodies merely slow down the rate of growth of the infection, buying time for the real heavy hitters—the T-Cells—to be mobilized to fight off the infection. That’s the key difference between Type 1 and Type 3: if a variant can create Type 1 breakthroughs, the T-Cells can’t fight it, whereas if it can only create Type 3 breakthroughs, the T-Cells will kick the crap out of it.

So it actually matters what kind of breakthrough infections Omicron is producing, and that’s the reason I’m trying to create some badly-needed clarity in the discussion surrounding the wretchedly ill-chosen term “breakthrough”. What, then, is the evidence that Omicron is not a Vaccine Escape Variant of Concern?

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Omicron and a Typology of “Breakthrough Infections”

Graziani, Carlo J. 0 Comments

What Do You Mean, “Breakthough”?

It’s amazing to me how much damage a badly-chosen scientific term can do, in a high-consequence scientific field such as COVID-19 epidemiology. The term “Breakthrough infection”, which quickly filtered from journal literature on vaccine effectiveness to public media, turns out to be so poorly defined that it even confuses scientific discussions, and when it enters public discourse it engenders mostly misinformation and panic. It is downright daft terminology, which is unfortunately as ineradicable now as the virus itself. In this post, I’d like to at least try to fix it a little, so the damn term can do some useful work for a change.

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Observing Epidemic Surveillance

Graziani, Carlo J. 0 Comments

What’s This About?

I haven’t posted a lot about vaccine efficacy lately, largely because the frenzy of vaccine development and clinical trial results basically slowed way down around June 2021, and there hasn’t been that much to write about since on the subject. I’ve been thinking about what to do with this site ever since. Some people do seem to find it useful—there have been nearly 25,000 visitors from all over the world since I started writing about vaccine efficacy, and I hope that those people found information that was valuable to them. To the extent that they did, it makes me somewhat proud, since I am a statistics person rather than a clinically-trained person, so having an impact in the COVID-19 pandemic, however small, feels like an achievement. On the other hand, that same lack of clinical training means that I have to watch myself so as to write things that are justified by data, and keep from making wild, poorly-informed statements that do more harm than good. I feel that so far I’ve stayed on the right side of that line.

I may risk that balance in the next phase of this blog’s development.

I plan to write a few observations of my own on the state of epidemic surveillance, epidemic modeling, and epidemic data, specifically with respect to the COVID-19 epidemic. I am doing this in part because I’ve been more deeply involved in data-driven epidemiological work, especially with respect to vaccine effectiveness (different from efficacy, because it characterized real risk reduction in real populations, rather than “pure” clinical properties of vaccines), culminating in a paper demonstrating the possibilities of large-scale data analytics for epidemic surveillance and vaccine assessment. In the process, I have become somewhat frustrated with the state of data curation and availability, but also with some of the model-premises underlying discussions of subjects such as vaccines, “breakthrough” infections, variants and their potential for vaccine escape, and so on. In my opinion there is a great deal of intellectual confusion about these terms and concepts, and this confusion is feeding needless media and policy panic (and occasionally distracting from necessary panic). I feel I need a place to write down everything that I feel is (usually) subtly or (occasionally) grossly wrong about the public and scientific discussions of these issues. And I happen to have a more-or-less epidemiological blog. So I might as well do it here.

The cost of this change of direction is that I doubt that I can maintain the careful stance of defensible scientific statements that I tried to keep this blog to so far. Quasi-editorials on epidemiology by a statistically well-informed but barely-clinically-literate observer of the field should by no means be taken as authoritative refutations of anything, or in fact as anything more than spurs for further discussion by people working in the field, with whom I would be delighted to engage, and be told in exhausting detail all the reasons why what I’m writing is wrong-headed. I do listen, and try to learn. But I will also argue. I feel that I will have accomplished something useful if I at least bring to light a few unexamined or under-examined assumptions, and occasion a fruitful discussion of those assumptions, even if in the end I am the only one who feels educated by the process.

Nonetheless, I have a strong suspicion that I’ve seen some real issues—defects in how clinical data is created and curated and made available, defects of modeling, catastrophic terminological confusion—that need to be brought into the light. I’ll be discussing these in a series of posts.

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