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Tag: Epidemic Surveillance

Vaccines, Breakthroughs, and Omicron: A Summary

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.

Viral Load: The Missing Surveillance Window

Why Do We Need A “False Breakthrough” Type, Anyway?

In the first post of this series, I introduced a typology of breakthrough infections that helped me organize the rest of this discussion. If you review that typology now (go ahead, I’ll wait) there is something about it that ought to strike you as a bit weird. Why on Earth should there even exist a Type 3a, “False Breakthrough Infection” category? How could it have come about that we are arguing about whether or not an infection is, in fact, an infection?

There is actually something unusual about the clinical status of a COVID-19 diagnosis: rather than being assessed on the basis of a constellation of symptoms, as is the case for most diseases, a case is declared a case on the base of a laboratory test, almost always a “Reverse Transcriptase Polymerase Chain Reaction” (RT-PCR) assay. And an RT-PCR test is a very sensitive test, capable of detecting very low levels of virus particle concentration (AKA “viral load”). It’s actually very impressive that a laboratory technology such as PCR, which would ordinarily require PhD-trained scientists to understand and operate, was rolled out so quickly and widely, and is now operated by hundreds of thousands of hourly-salary technicians, millions of times per day, with extremely low error rates. This is one of the scientific responses to the pandemic that went very right.

With every benefit there is a cost, however. In this case, the ability to detect extremely low levels of viral load collides with the characteristic response of a correctly-functioning, vaccine-primed, SARS-CoV-2-aware human immune system. As we have already discussed, when a human organism in possession of such an immune system is attacked by the virus, the virus does not explode on contact or bounce off some kind of impenetrable armor. Instead, the virus gains entry and begins to infect cells and reproduce itself. Early on in the process, however, the immune system becomes aware of the infection and duly moves to shut it down. What level of peak viral load can be attained before the response gains the upper hand is a matter of competing rates—rate of viral growth versus rate of various immune system infection-clearing processes. With the original SARS-CoV-2 strain, and some of the early prominent variants, it was very clearly the case that a lot of “breakthrough” infections that people were panicking over, and which were ostensibly lowering estimates of vaccine protective effectiveness against infection, were actually cases of perfectly normal vaccine-primed immune systems doing their jobs without a fuss, and of RT-PCR test ringing up “Positive” based on the very low viral loads characteristic of a failed infection. Hence, Type 3a, “False Breakthrough”.

But wait. Something’s not right. Why is this super-sensitive assay being fooled?

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

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 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.