I noticed the prudishness, but "rudeness" to me parses as people actually telling you what's on their mind, rather than the passive-aggressive fake niceness that seems to dominate in the Bay Area. I'll personally take the rudeness :).
On the other hand, the second-best place selects for people who don't care strongly about optimizing for legible signals, which is probably a plus. (An instance of this: In undergrad the dorm that, in my opinion, had the best culture was the run-down dorm that was far from campus.)
Many of the factors affecting number of deaths are beyond a place's control, such as how early on the pandemic spread to that place, and how densely populated the city is. I don't have a strong opinion about MA but measuring by deaths per capita isn't a good way of judging the response.
That's not really what a p-value means though, right? The actual replication rate should depend on the prior and the power of the studies.
What are some of the recommendations that seem most off base to you?
My prediction: infections will either go down or only slowly rise in most places, with the exception of one or two metropolitan areas. If I had to pick one it would be LA, not sure what the second one will be. The places where people are currently talking about spikes won't have much correlation with the places that look bad two weeks from now (i.e. people are mostly chasing noise).
I'm not highly confident in this, but it's been a pretty reliable prediction for the past month at least...
Here is a study that a colleague recommends: https://www.medrxiv.org/content/10.1101/2020.05.03.20089854v3. Tweet version: https://mobile.twitter.com/gidmk/status/1270171589170966529?s=21
Their point estimate is 0.64% but with likely heterogeneity across settings.
I don't think bubble size is the right thing to measure; instead you should measure the amount of contract you have with people, weighted by time, distance, indoor/outdoor, mask-wearing, and how likely the other person is to be infected (I.e. how careful they are).
An important part of my mental model is that infection risk is roughly linear in contact time.
As a background assumption, I'm focused on the societal costs of getting infected, rather than the personal costs, since in most places the latter seem negligible unless you have pre-existing health conditions. I think this is also the right lens through which to evaluate Alameda's policy, although I'll discuss the personal calculation at the end.
From a social perspective, I think it's quite clear that the average person is far from being effectively isolated, since R is around 0.9 and you can only get to around half of that via only household infection. So a 12 person bubble isn't really a bubble... It's 12 people who each bring in non trivial risk from the outside world. On the other hand they're also not that likely to infect each other.
From a personal perspective, I think the real thing to care about is whether the other people are about as careful as you. By symmetry there's no reason to think that another house that practices a similar level of precaution is more likely to get an outside infection than your house is. But by the same logic there's nothing special about a 12 person bubble: you should be trying to interact with people with the same or better risk profile as you (from a personal perspective; from a societal perspective you should interact with riskier people, at least if you're low risk, because bubbles full of risky people are the worst possible configuration and you want to help break those up).
I think the biggest issue with the bubble rule is that the math doesn't work out. The secondary attack rate between house members is ~30% and probably much lower between other contacts. At that low of a rate, these games with the graph structure buy very little and may be harmful because they increase the fraction of contact occurring between similar people (which is bad because the social cost of a pair of people interacting is roughly the product of their infection risks).