First of all, I basically think that conditional upon uncontrolled spread, bad policies are the default in the US, so it’s not convincing to me that you can list a specific set of policies that a geopolitical region did wrong and assume that your region will do better conditional upon uncontrolled spread.

Are you suggesting there wasn’t a better policy option than sending COVID-19 positive individuals into nursing homes?

Secondly, I did read your section on nursing homes. I just don’t find it convincing. If you exclude people in nursing homes, the IFR will go down.

The point of that section was to walk through the difference between a backwards looking “divide the number of people who died by the number of people who were infected” and “estimating a given population’s average probability of dying when infected”. The former will be influenced by the infection dynamics of the specific outbreak, the latter will not, and the latter is more useful for assessing risk.

I think it’s better to think of coronavirus as not disproportionately killing older people except inasmuch as everything disproportionately kills older people

I don’t think talking about “older people” as if they are a homogenous group is super useful given New York, etc.’s nursing home policies. Only 6.5% of the elderly live in long-term care settings, but these people are clearly considerably less healthy than elderly who are not in nursing homes (see the Geneva seroprevalence study as one example). So, again, if nursing homes were disproportionately infected relative to the rest of the population, the naive IFR will overestimate the fatality risk for the overall population. The fact that at least 12% of New Jersey’s entire nursing home population has died of COVID-19 makes me think the corresponding percent in NYC could be very, very high.

I’m confused why the map is relevant.

The map is showing how ZIP codes with high nursing home densities had high death rates.