Update: We decided not to finish this post, since the points we wished to convey have now mostly been covered well elsewhere. But Kyle may still write up his notes about the epidemiological parameters at some point.
I'm currently working with Kyle Scott and Anna Salamon on an estimate of deaths due to hospital overflow (lack of access to oxygen, mechanical ventilation, ICU beds), which we'll hopefully post in the next few days. The post will review evidence about basic epidemiological parameters.
This study suggests some airplane seats expose passengers to significantly more infection risk than others. I'm confused by the writing, but my understanding is that window seats are best.
I would also guess, though I can't tell if the paper is suggesting this, that you're at less risk if you don't use the bathroom, don't have row-mates, and sit where people are least likely to pass you to go to the bathroom. If true, one could potentially reduce risk significantly by buying e.g. three seats next to each other halfway between two bathrooms, limiting water intake before the flight and sitting near the window.
I think the bodies probably do need to be in the same room for CFAR workshops to work, unfortunately.
I'm curious about your first and second hypothesis regarding obesity?
Ben just to check, before I respond—would a fair summary of your position here be, "CFAR should write more in public, e.g. on LessWrong, so that A) it can have better feedback loops, and B) more people can benefit from its ideas?"
To be clear, others at CFAR have spent time looking into these things, I think; Anna might be able to chime in with details. I just meant that I haven't personally.
Thanks for spelling this out. My guess is that there are some semi-deep cruxes here, and that they would take more time to resolve than I have available to allocate at the moment. If Eli someday writes that post about the Nisbett and Wilson paper, that might be a good time to dive in further.
(Unsure, but I'm suspicious that the distinction between these two things might not be clear).
I wouldn't describe any posts I've seen as conveying the idea sufficiently well for my taste, but would describe some—like this NY Times piece—as adequately conveying the most decision-relevant points.
When I started writing, there was almost no discussion online (aside from Wei Dai's comment here, and the posts it links to) about what factors might prove limiting for the provision of hospital care, or about the degree to which those limits might be exceeded. By the time I called off the project, the US President and ~every major newspaper were talking about it. I think this is great—I much prefer a world where this knowledge is widespread. But given how fast COVID-related discourse was evolving, I think I erred in trying to make loads of points in a single huge post, rather than publishing it in pieces as they became ready.
There is one potentially decision-relevant point that I hoped to make, that I still haven't seen discussed elsewhere: there may be two relevant hospital overflow thresholds. The ICU bed threshold and the ventilator threshold are fairly low; given our current expected supply in a crisis, we'll exceed them if more than about 70k people require them at once. But I think (not confident in this yet) that our capacity for distributing oxygen is something like 10x higher. And if that threshold gets exceeded, the infection fatality rate may rise by something like 10%. So on this model, while it would obviously be ideal to nuke the curve such that it ends up below both thresholds, it's imperative to at least flatten the curve beneath the oxygen threshold. Which is easier, since it's higher.
I'm not sure this model is accurate, and I haven't yet decided whether to try figuring it out/writing it up. I feel a bit hesitant, after having wasted 10 days or so underestimating the efficiency of the coronavirus modeling market, but it does seem useful to propagate if true. If someone else is interested in looking into it, I would happily talk them through what I've learned.