1. I don’t think mobility data correlates well with risk taken—it’s easy to screw up and limit the wrong things (get stuck together indoors), or to limit the right things without changing mobility much (move outdoors). It’s indicative of trying to do anything at all, at least. I’ve talked extensively over many posts about why I think herd immunity is a bigger deal than people think, especially in On R0.
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? Pointing out that Norway did better doesn’t change the need to answer that. This is not me saying Sweden is a model, it’s a control group and we need to understand its data.
4. It’s shocking because those people are having very intimate contact over extended periods of time in indoor situations, often sleeping together, touching, sharing food and cooking, over periods of days of infectiousness, etc etc. It’s certainly not the naive or public perception of such risks if precautions aren’t taken. And it then requires an explanation for why we can’t contain this thing with relatively light countermeasures.
On the children, the metastudy doesn’t seem like a methodology I’d think would produce good results on such matters. I’m open to evidence that difference is that small but it definitely seems like the vector is mostly harmless...
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. Somepapers (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.
1. I don’t think mobility data correlates well with risk taken—it’s easy to screw up and limit the wrong things (get stuck together indoors), or to limit the right things without changing mobility much (move outdoors). It’s indicative of trying to do anything at all, at least. I’ve talked extensively over many posts about why I think herd immunity is a bigger deal than people think, especially in On R0.
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? Pointing out that Norway did better doesn’t change the need to answer that. This is not me saying Sweden is a model, it’s a control group and we need to understand its data.
4. It’s shocking because those people are having very intimate contact over extended periods of time in indoor situations, often sleeping together, touching, sharing food and cooking, over periods of days of infectiousness, etc etc. It’s certainly not the naive or public perception of such risks if precautions aren’t taken. And it then requires an explanation for why we can’t contain this thing with relatively light countermeasures.
On the children, the metastudy doesn’t seem like a methodology I’d think would produce good results on such matters. I’m open to evidence that difference is that small but it definitely seems like the vector is mostly harmless...
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).
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.
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.