To be clear, I think the 71% result needs more investigation and (on priors) is probably lower. Yes, there is reason to expect overshoot. It seems the amount of overshoot would vary based on (a) NPIs being taken at the time (e.g. are some people never leaving the house) and (b) proportion of people who have cross-immunity or innate reduced susceptibility. (In principle, you could imagine 80% of people in a town live as normal and 20% won't leave the house till the pandemic is over.) Again, I think if we did a lot of studies, we'd get a sense of both the minimum herd immunity threshold and the variability in overshoot.
I wasn't saying I'm a fan of the Imperial Model and I agree with most of these points. I think there are epi modelers who aware of the limitations of models.
This naive model is not a straw man! Such obvious nonsense models are the most common models quoted by the press, the most common models quoted by so-called ‘scientific experts’ and the most common models used to determine policy.
I think you underestimate the sophistication of the top epidemic modelers: Neil Ferguson, Adam Kucharski, Marc Lipsitch, and others. I tend to agree we need urgent empirical work on herd immunity thresholds (see my other comment) but the top epi people are aware of the considerations you raise. Communicating with the public is very challenging under the current circumstances and so it's reasonable these people would choose words carefully.
Your statement is also empirically false. One of the most influential models is the "Imperial Model", which certainly impacted UK policy and probably US and European policy too. Other countries did versions of the model. The lead researcher on the model literally became a household name in the UK. The Imperial Model is an agent-based model (not an SIR model). It has a very detailed representation of how exposure/contact differ among different age groups (work vs. school) and in regions with different population densities. It doesn't assume the only intervention is immunity, and follow up work has tested many different interventions. (AFAIK, it does assume equal susceptibility. But as it's an agent-based model you could experiment with heterogeneity in susceptibility. And I think evidence for variable susceptibility for reasons other than age remains fairly weak: https://twitter.com/OwainEvans_UK/status/1268873649202909185)
IMO what's needed here is detailed empirical analysis. There are many places round the world that have had spread that was only weakly controlled. If you get the % seropositive for a bunch of places, you could (to some extent) extrapolate to Europe/US/East Asia, where there's currently more control. Here's where I'd look:
How long is the event? Is there perfect compliance with groups staying apart? Is everyone wearing masks? Are people singing/shouting?
My snapshot. I put 2% more mass on the next 2 years and 7% more mass on 2023-2032. My reasoning:
1. 50% is a low bar.
2. They just need to understand and endorse AI Safety concerns. They don't need to act on them.
3. There will be lots of public discussion about AI Safety in the next 12 years.
4. Younger researchers seem more likely to have AI Safety concerns. AI is a young field. (OTOH, it's possible that lots of the top cited/paid researchers in 10 years time are people active today).
Probably the only engineering fields that are doing really well are computer science and maybe, at this point, petroleum engineering. And most other areas of engineering have been bad career decisions the last 40 years … Nuclear engineering, aerospace engineering [were catastrophic fields to go into]
Where's his evidence on this? This data suggests average salaries for engineers outside software engineering were not much different from software engineering. I'd guess there's more exciting new companies in computing than in aerospace, but it doesn't mean it was a "catastrophic career move". US companies also sell a lot of products abroad and there's been huge growth in use of aircraft, cars, and other engineered products worldwide (due to catch up growth).
Why did all the rocket scientists go to work on Wall Street in the ‘90s to create new financial products?
Because the Cold War ended. There's no big mystery. If you weren't "allowed" to make rockets, how to explain SpaceX (started in 2002)? Not to say regulation doesn't limit innovation, but I'd want to see actual data on this and not just bluster.
You are understanding correctly. Here are some things to keep in mind:
There is a small number of studies that distinguish spouse from other relationships. See Figure S5 of this paper. I don't think there's enough data to draw a strong empirical conclusion. Most of our data for estimating SAR is from China/Korea/Taiwan and I'd guess these are mostly nuclear families or extended family (not many group house / flatmates).
I've talked extensively over many posts about why I think herd immunity is a bigger deal than people think
I understood the argument as "there'll be herd immunity faster in specific locations (e.g. subway riders or people under 20 in some neighborhood)". The logic makes sense but I'd guess the effect is small, due to population mixing / small-world network effects. Young people are probably getting infected more but they are still far from HI everywhere and they are probably well mixed. I haven't seen any positive empirical evidence for your view over my take (big first wave --> people take precautions more seriously and have slower reopening + 20-30% drop in R due to fewer susceptible).
There's Google/Apple style mobility (which actually records amount of time spent in work/home/retail/public transit) and questionnaires that ask for "number of contacts per day". People have used both to model cases/deaths and they are both pretty useful. Some papers (China) and UK. The point is that we know you can predict spread using these proxies for contact. So you can actually see if the amount of predicted contact is lower in NYC, London, Madrid and Lombardy vs. places that didn't have a big first wave (e.g. LA, Miami, Phoenix). And the predicted contact was lower in the former places. (But I haven't done a careful study).
2. Sweden did badly, but it's important to notice that it did far less badly than a naive model would expect it to do. Why did things end up getting contained when they did? Why wasn't it much worse?
Public transit use was down 55% in Sweden at peak and is still at -7%. Norway was down 65%. Swedes stopped going to the cinema and other high-risk venues were way down. Without a formal lockdown, there was a huge change of behavior in Sweden. I'd guess Swedes were aware that all the countries around them had tighter restrictions and much lower death tolls. So they acted to reduce risk. (People in the UK also reduced risk more than was required by government.) So I don't see any mystery in Sweden. The real mysteries: Vietnam, Thailand, Cambodia, Laos and Indonesia. And I'm surprised how well the SF Bay has done.
4. It's shocking because those people are having very intimate contact over extended periods of time
Agree it goes against the naive model. But if you take seriously that 20% of people do 80% of infecting (or maybe a bit less than that), then it's likely that a decent proportion are essentially not infectious. Also note that many household members are younger children, who are harder to infect.