Kink educator, community organizer, and activist.
Tornus
Karma: 176
Strong work: thank you!
I believe there’s a small mistake: in the first table (after “In equilibrium, we see the following amounts of sars-cov-2 relative to no filtration:”), I believe the second column should be labeled “presence”, not “reduction”.
Lots of cool data here—thank you!
(Edited to remove a comment based on misremembering the Most Penetrating Particle Size)
I’m overdue for making another pass through the latest data, so my opinions on this are weakly held. But briefly: my current thinking is that many people (including Zvi and me) have made the mistake of conflating a number of different phenomena into the single category of “long covid”. I believe Zvi is correct that if a large number of people were suffering long-term debilitating impact, we’d know it.
I suspect that after I plow through the data again, I’ll update significantly in the direction of believing that:
“Long covid” is a debilitating phenomenon that affects a very small number of people for a long time.
“Post-acute covid” is significantly impactful and impacts a non-trivial number of people moderately for weeks or maybe a few months.
Anecdata: I don’t know anyone who’s been profoundly impacted by covid for a very long time. I know multiple people who’ve suffered significant impairment for weeks / months.
The impact of long covid is (small incidence #) x (large impact #), and the impact of post-acute covid is (medium incidence #) x (medium impact #). I think for most people, the total expected impact of getting covid will be somewhere between a day and a few weeks of useful live lost, with large error bars and much of the impact being in low-likelihood events.
Eliezer, back in 2009:
Yet there is, I think, more absent than present in this “art of rationality”—defeating akrasia and coordinating groups are two of the deficits I feel most keenly.
This is not a small project, and I’m too new here to have a clear sense of how it might happen. But this feels important.
To more directly address your initial question: to my mind, Zvi’s analysis isn’t obviously wrong, but it’s pretty far to the optimistic end of what I see as the reasonable range.
My best model suggests that for me (55 but very healthy), 1,000 µCoV of risk has an expected life cost of about 15 minutes.
Based on that, my approach to risk is very situational. Is eating in a restaurant worth 75 minutes of lying in bed with flu wishing I was dead (based on today’s numbers)? No, it isn’t. Is going to a friend’s wedding worth that? Yes, it probably is.
I’d love to see a more structured approach to the kinds of questions you’re raising here. LW does a good job of creating a space for smart people to share their thoughts about individual topics, but isn’t so good at building toward a coherent synthesis of all those pieces.
The original microCOVID white paper did a good job of summarizing a lot of relevant evidence back in the day, but (like the rest of the site) has been only sporadically updated.
Put me down as tentatively interested in being part of some larger project, if one comes together.
Also: may I humbly request that if this ever takes off, it be named LessSick?
That all makes complete sense.
And yes, the specifics of the population make a huge difference. Honestly, I think that accounts for the breadth of my estimate range more than uncertainty about abstract test performance does.
I think it’s important to emphasize that antigen+ people are much more contagious than antigen-. It’s hard to quantify that, but based on typical differences in Ct value, it’s probably a very substantial difference (factor of 10+?).
You’re absolutely right that the reference class is the key issue (if there’s one thing I’ve learned from hanging out with epidemiologists, it’s that they’re always grumpy about people using the wrong denominator).
In a perfect world, where everyone with any symptoms whatsoever stayed home and was scrupulous about following what the CDC exit guidance ought to be, antigen tests would be significantly less useful. But in the real world, people absolutely go out when they have mild symptoms. That’s advocated for in the comments right below this, which are from people who are presumably much more conscientious than average.
IMHO, the biggest value of antigen tests is in catching people who are mildly symptomatic but think it’s just allergies / they had a negative test last week so it can’t be covid / they’re probably over the worst of it. Within my (not enormous) extended social circle, I’m aware of two very recent cases when antigen tests flagged as infectious people who would otherwise have been out and about despite having mild symptoms.
Let me give you two answers for the price of one:
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FDA and others have been very clear about this: you should use the tests as directed.
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I (a decades-long amateur epidemiologist who’s done a deep dive on antigen test research), my partner (a medical epidemiologist who works full-time on Covid), and several other epidemiologists I’m aware of, all use throat + nasal swabs.
I wouldn’t worry at all about false positives: they really haven’t been an issue with antigen tests. If I got a positive from a throat + nasal swab, I’d follow it up with a nasal-only swab or a PCR, just to be sure.
There is non-zero risk that you’d get false negatives, by some unknown mechanism. That seems unlikely given that some countries like the UK use throat swabs, but it’s possible. It’s my well-informed but not data-supported belief that the benefit of swabbing your throat probably exceeds the downside.
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Why do I respect Michael Mina? Weak but deep answer: because in my experience he’s been consistently smart, and insightful about Covid and especially about testing (and he’s a professional epidemiologist / immunologist). Strong but shallow answer: because my partner, who is a medical epidemiologist working full-time on Covid, thinks highly of him.
If you’re not already familiar with her, you might also be interested in Katelyn Jetelina (Your Local Epidemiologist). IMHO, she produces by far the best deep research summaries for laypeople. Here’s a recent piece of hers on antigen tests.
In the interest of staying focused on truth-finding, here’s my understanding of the crux of our disagreement—does this look right to you?
I believe that using antigen tests before social gatherings substantially reduces the amount of transmission at those gatherings. It’s very hard to put a number on this—if I had to guess, I’d say a 70% reduction, but probably somewhere between 25% and 90%. If I’m understanding you correctly, you’d pick a very low number: less than 10%?
Let me try to explain my thinking, which I believe reflects the current medical / scientific consensus (though I think most scientists would balk at the rationalist proclivity for picking best-guess numbers).
There’s a massive body of evidence that antigen tests can detect all strains of Covid, including Omicron. Antigen tests are much less sensitive than PCR tests, meaning that they will consistently return false negatives when viral levels are low, but they have excellent sensitivity when viral levels are high.
The standard interpretation of that data is that antigen tests are an unreliable way to tell if you have Covid early on in an infection, but they are quite good at detecting Covid when viral levels are high (and therefore when you’re infectious).
The Soni et. al. chart you included is an example of this in action. Antigen tests gave nearly universal false negatives during the first two days that PCR tests were positive. Viral levels (and therefore infectiousness) tend to be low during the first couple of days, especially among vaccinated people (which most of the Soni subjects were). So what we’re seeing there is that antigen tests would consistently have missed people early in their infections, when they were minimally infectious.
From day 3 onward, however, antigen tests were extremely accurate. This corresponds to them consistently detecting people during their period of maximum infectiousness.
So there’s a huge amount of evidence that antigen tests are highly sensitive during periods of peak viral load / infectiousness. That’s easy to measure, and I think it’s pretty definitively established at this point. The question we’re really asking, however, is how that affects infectiousness. Unfortunately, there’s no really clear way to answer that.
We believe most transmission happens during periods of high viral load, and we know antigen tests are very accurate during that time. But we don’t know exactly how viral levels impact transmission, and figuring that out would require complex, expensive studies that would likely not be approved for ethical reasons.
I’m wondering if you can explain a bit more about your thinking here.
From my perspective, there’s a strong prior that antigen tests work well for Covid screening:
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There are numerous peer-reviewed studies to that effect. Here are two recent ones, but there are many others Soni et. al., Jüni et. al..
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Multiple experts in the field continue to assert that antigen tests work very well for Covid screening. Michael Mina is extremely knowledgeable on this topic and particularly vocal about it.
It’s important to note here that PCR / antigen discordance early in an infection is not evidence that antigen tests aren’t working. Because antigen tests are less sensitive than PCR, they are good at detecting people who are infectious, but not people who are infected but not infectious. Mina has an excellent explanation of why symptoms often start several days before people become infectious.
This post presents new data, which is interesting (especially from a perspective of real-world failure scenarios), but this is weak data: it isn’t peer reviewed, there’s no study protocol, and the results are inconsistent.
It seems to me the correct interpretation is to update slightly in the direction of antigen tests working less well than previously believed, but to continue to believe they are useful and effective (but far from perfect). Am I missing something?
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This is a really good start, and I look forward to the inevitable improvements in the quality of discourse. But to fully leverage the potential of this exciting new system, I think you should create a futures market so we can bet on (or against) specific individuals writing good posts in future.
Also: from now on, I vow to ignore any and all ideas that aren’t supported by next-level puns.
When I had an acute bout of insomnia, one of the things I found most helpful was listening to sleep-focused bedtime stories. The key part wasn’t the sleepy imagery, but rather just having something boring and inconsequential that my mind could latch onto, to replace the busy inner dialog.
I particularly like the stories from the Headspace app—they’re slightly randomized each night, which prevents you from using the story progress as a timer (“Oh no, we’re up to the skunk already and I’m still not asleep!”)
Related: I also found it extremely helpful to get rid of my bedside clock and to use a smart watch for sleep tracking rather than keeping track of my sleep manually. Worrying about sleep makes your sleep worse, and keeping track of how you’re doing tends to feed the sleep anxiety.
I’m excited about this sequence, and look forward to the rest of it.
Having just read this introduction, it almost feels to me like the tail is wagging the dog. I completely agree that relinquishing options is a critically important part of civility. But my instinct is that the relinquishment is in service of a greater (and defining) goal, not the goal itself. So, something like “civility is prioritizing cooperation over autonomy, which in many cases requires relinquishing physically possible options”. But I assume it will all become clear as the sequence proceeds.
Thank you for this.
I want to start by echoing your gratitude to the microCOVID team: they’ve done amazing work and the tool they’ve produced has been incredibly valuable to me and to many others. And I agree with your assessment that microCOVID is much less useful than it has been in the past. I’ll add to your points:
Their calculation of prevalence strikes me as far too clever. I understand what they’re trying to do, and it makes sense in theory. But Covid surveillance is something I know a lot about, and I believe they’re over-driving the data. Test positivity seems like a really valuable signal, until you understand what goes into it—once you do, it seems much less useful. In my own modeling, I use (current case rate) * (under-reporting constant), with a manually determined adjustment for trend. But I’d rather use the current level than the fancy extrapolated level used by microCOVID.
They’re using significantly inaccurate constants in some places that matter. For example: their household Secondary Attack Rate (SAR) for a fully boosted person is 15%, but a better value would be 25% paper 1, paper 2.
I don’t know the answer here. We need a tool like microCOVID, and I understand how hard it is to maintain a volunteer-based tool.
Thank you for the reminder to explain and not scold—I shall strive to do so.
I’d caution you against spending too much time diving down infinite crank rabbit holes: true believers will always find some new detail or theory for you to rebut. At some point, if someone is committed to denying the clear scientific consensus, there’s no point trying to get through to them.
At a high level, we have a pretty deep understanding of how covid vaccines work and how they perform over time, and there’s absolutely nothing in there to suggest that, unlike every other vaccine ever, covid vaccines display the bizarre transition from positive protection to negative protection that you’re asking about.
Vaccine effectiveness declines over time because (in large part) antibody levels wane over time. That’s very well understood and in no way unique to covid vaccines.
Protection has shifted from protection against infection to infection against severe outcomes because of antigenic drift: the vaccines are most closely targeted to the ancestral strain. That match is most important for antibody protection: since antibodies are critical to protection against infection, the vaccines produce significantly less protection against infection as the virus drifts further from the ancestral type. T cell immunity is less affected by antigenic drift, so their protection against severe disease isn’t as attenuated.
ADE is a real thing, and it was a concern early on. In particular, there was a feline coronavirus vaccine some years ago that triggered ADE, so there was concern that covid might have similar issues. But we’ve seen no sign of that.
Original antigenic sin is also a real thing, but wouldn’t produce the effect you’re asking about.
Two pieces of advice:
Get your next vaccine. It’s incredibly safe and incredibly effective.
Spend less time engaging with lunatics on the internet. Too much time listening to cranks is bad for your epistemic health.
To a greater or lesser extent, I think that’s true for many of us here. Which is a good thing in some ways, but can make it challenging to fully understand and engage with people who are more hive-oriented.
The key thing is that it’s low-commitment / low-guilt. I was inspired to start it by a friend who started a book club during the pandemic, fell catastrophically behind on the reading, and ultimately ended up ghosting her own book club.
I’ve noticed that book clubs tend to become machines for making people feel guilty / overloaded, so I tried hard to avoid that. We do a book every 2 − 3 months, and the default expectation is that people won’t attend unless that specific book is interesting to them.
Shortly before the discussion, I send out a summary of the book (which was my motivation for writing this), so that people can attend and participate without needing to finish (or even start) the book.
It’s still a fairly new endeavor, but it seems to be working so far.
This stuff is super hard.
I’d recommend (with reservations) Consent Academy, who do a lot of training on incident response, accountability processes, etc. They’re good folks who have figured out a lot of really useful things about doing this kind of work.
Their classes can sometimes get pretty rambling and theoretical, but I’ve learned a lot from them.