It's interesting to look back on the things that I got wrong over the course of the COVID pandemic, and to understand why.

I think I got a fair bit right as well—perhaps most notably in being the first to point out the problems in the IHME model...
...and in arguing early-on about the futility of a natural herd immunity strategy.

But let's look at what I got wrong, roughly in order, and why. In almost every case my mistake was in anchoring too strongly on influenza.
1. Early on I was skeptical that R0 was >3 instead of <2.

This was anchoring on flu directly, and also in looking at the epidemic curve. You can't tell from the epidemic curve whether you have higher R0 and longer generation interval, or lower R0 and shorter generation interval.
COVID had a longer pre-infectious latent period and a much longer infectious period than I expected, again thinking about influenza. That means fewer generations than expected after time t, and thus higher R0 than one might expect.
2. Even after we knew that airborne transmission was very important, I remained concerned about fomite transmission through July 2020. (I still don't have solid evidence to rule it out, but to date it seems minimal at most.) Again, anchoring on flu.
3. I considered it unlikely—though not impossible—that we'd see increases in transmissibility of the scale of B.1.1.7 or B.1.617.2 within the first few years of circulation. Here I was both anchoring on flu and comparing to the greater genetic variation available to flu.
4. Through fall 2020 I thought that a very best case scenario for vaccine effectiveness was 85%. Again, anchoring on flu to some degree, even though I knew that the antigenic variation we see in flu would not be there for COVID.
In retrospect, where I've been wrong about things for COVID, it's been not so much because of poor inferences from available data, but rather because my priors were not flat enough.
Why? Probably because I spent the 2000s thinking about how prepare for a flu pandemic and clearly this—along with knowledge of the epidemiology of other human respiratory RNA viruses—influenced my priors around COVID too strongly.

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More from @CT_Bergstrom

31 May
Online exam proctoring software is bullshit.

Instructors, don't use it.

Students, if you're been forced to use Proctorio or other academic spyware, consider contacting the dean of students or dean of undergraduate education at your college.…
The article above is so horrifying that it's hard to know what to pick out to highlight.

By no means the worst part of the article, but if your clients don't know how to use the bullshit software you pushed on them and cause active harm, that's on you, not on them.
Early in the pandemic I wrote a long thread about the harms these kinds of programs cause.

It's a bit tl;dr, but I'd encourage instructors and students to explore the problems with academic spyware in more detail:

Read 4 tweets
24 May
This is your weekly reminder that "up to" means something different than "at least".

BMPCs were sampled 7-8 months after infection and remained present at that time. This sets a lower bound, not an upper bound, on persistence.
Here's a simple cheat sheet.
Indeed this very paper sampled a subset of the patients again at 11 months, and found the BMPC levels stable in almost all of them, providing direct evidence of persistence beyond 7 months.
Read 4 tweets
22 May
No, this paper doesn't show that COVID antibodies are lost within a year.

The title could certainly be better, because it does provide a misleading impression if you don't read any further.… Image
If you look at the text, though, you'll see that the "up to" is not meant to set 12 months as an upper bound to persistence; it's the period of observation. Image
Most importantly, let's the look at the data they present. Antibody titers are substantial after 12 months, and there is some suggestion that they may be stabilizing. Image
Read 5 tweets
20 May
In light of the recent paper claiming to provide "initial evidence for bullshit ability as an honest signal of intelligence", I think it's useful to talk a bit about what a signal is, as compared to cue.
Let's start with Grice's distinction between natural and non-natural meaning, in his 1957 paper entitled simply "Meaning".


"Storm clouds mean rain."


"The symbol ♂ means male".

(Here I loosely follow my paper with @KevinZollman et al…)
"Storm clouds mean rain" involves what Grice calls natural meaning.

This is a naturally arising correlation that an observer can use to learn about unobserved features or predict future happenings. No intent is involved; storms clouds don't form to tell us about impending rain.
Read 23 tweets
19 May
Changing denominators are a bear.

This is a comparison of reservation occupancy at the places open and taking reservations currently, to their pre-pandemic occupancies.

It doesn’t tell anything like the whole story. The places that went out of business are not counted.
This matters because (1) with fewer open restaurants, we expect an increased demand on the remaining venues, and (2) there is a selection effect here in that being included in the sample is correlated with having done well during the pandemic (instead of going out of business).
Maybe more importantly, the same article looks at overall traffic including walk-ins, as opposed to just reservations.

This has not recovered even given the caveats above.
Read 4 tweets
19 May
There is a new paper out which claims that the ability to bullshit is an honest signal of intelligence.…

I have thoughts.
Imagine a colleague came to you with a purported explanation for fighting ability among territorial vertebrates.

“Over the eons,” he claims, “the ability to kick ass has been selected because it is an honest signal of the ability to kick ass.”
I hope it would be transparent to you that an honest signalling story is unnecessary here. The ability to kick is ass is selected because one can then kick the asses of those whose asses need kicking, and no signaling is needed.
Read 12 tweets

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