Update: We decided not to finish this post, since the points we wished to convey have now mostly been covered well elsewhere; Kyle may still write up his notes about the epidemiological parameters at some point.
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 push the curve 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 write it up. I feel hesitant, after having wasted 10 days underestimating the efficiency of the covid-modeling market, but it seems useful to propagate if true. If someone else is interested in looking into it, I’d be happy to discuss.
Update: We decided not to finish this post, since the points we wished to convey have now mostly been covered well elsewhere; Kyle may still write up his notes about the epidemiological parameters at some point.
Alas. Could you briefly link to the other places that have conveyed the ideas sufficiently well for your tastes?
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 push the curve 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 write it up. I feel hesitant, after having wasted 10 days underestimating the efficiency of the covid-modeling market, but it seems useful to propagate if true. If someone else is interested in looking into it, I’d be happy to discuss.