I live in Southern England and so have a fair bit of personal investment in all this, but I’ll try to be objective. My first reaction, upon reading the LSHTM paper that you referred to, is ‘we can no longer win, but we can lose less’ - i.e. we are all headed for herd immunity one way or another by mid-year, but we can still do a lot to protect people. That would have been my headline—it’s over for suppression and elimination, but ‘it’s over’ isn’t quite right. Your initial reaction was different:
Are We F***ed? Is it Over?
Yeah, probably. Sure looks like it.
The twin central points last were that we were probably facing a much more infectious strain (70%), and that if we are fucked in this way, then it is effectively already over in the sense that our prevention efforts would be in vain.
The baseline scenario remains, in my mind, that the variant takes over some time in early spring, the control system kicks in as a function of hospitalizations and deaths so with several weeks lag, and likely it runs out of power before it stabilizes things at all, and we blow past herd immunity relatively quickly combining that with our vaccination efforts.
You give multiple reasons to expect this, all of which make complete sense—Lockdown fatigue, the inefficiency of prevention, lags in control systems, control systems can’t compensate etc. I could give similar reasons to expect the alternative—mainly that the MNM predicts the extreme strength of control systems and that it looks like many places in Europe/Australia did take Rt down to 0.6 or even below!
But luckily, none of that is necessary.
This preprint model via the LessWrong thread has a confidence interval for increased infectiousness of 50%-74%.
I would encourage everyone to look at the scenarios in this paper since they neatly explain exactly what we’re facing and mean we don’t have to rely on guestimate models and inference about behaviour changes. This model is likely highly robust—it successfully predicted the course of the UK’s previous lockdown, with whatever compliance we had then. They simply updated it by putting in the increased infectiousness of the new variant. Since that last lockdown was very recent, compliance isn’t going to be wildly different, weather was cold during the previous lockdown, schools were open etc. The estimate for the increase in R given in this paper seems to be the same as that given by other groups e.g. Imperial College.
So what does the paper imply? Essentially a Level 4 lockdown (median estimate) flattens out case growth but with schools closed a L4 lockdown causes cases to decline a bit (page 10). 10x-ing the vaccination rate from 200,000 to 2 million reduces the overall numbers of deaths by more than half (page 11). And they only model a one-month lockdown, but that still makes a significant difference to overall deaths (page 11). We managed 500k vaccinations the first week, and it dropped a bit the second week, but with first-doses first and the Oxford/AZ vaccine it should increase again and land somewhere between those two scenarios. Who knows where? For the US, the fundamental situation may look like the first model—no lockdowns at all, so have a look.
(Also of note is that the peak demand on the medical system even in the bad scenarios with a level 4 lockdown and schools open is less than 1.5x what was seen during the first peak. That’s certainly enough to boost the IFR and could be described as ‘healthcare system collapse’, since it means surge capacity being used, healthcare workers being wildly overstretched, but to my mind ‘collapse’ refers to demand that exceeds supply by many multiples such that most people can’t get any proper care at all—as was talked about in late feb/early march.)
The nature of our situation now is such that every day of delay and every extra vaccinated person makes us incrementally better off.
This is a simpler situation than before—before we had the option of suppression, which is all-or-nothing—either you get R under 1 or you don’t. The race condition that we’re in now, where short lockdowns that temporarily hold off the virus buy us useful time, and speeding up vaccination increases herd immunity and decreases deaths and slackens the burden on the medical system, is a straightforward fight by comparison. You just do whatever you can to beat it back and vaccinate as fast as you can.
Now, I don’t think you really disagree with me here, except about some minor factual details (I reckon your pre-existing intuitions about what ‘Level 4 lockdown’ would be capable of doing are different to mine), and you mention the extreme urgency of speeding up vaccine rollout often,
We also have a vaccination crisis. WIth the new strain coming, getting as many people vaccinated as fast as possible becomes that much more important.
...
With the more reasonable version of this being “we really really really should do everything to speed up our vaccinations, everyone, and to focus them on those most likely to die of Covid-19.” That’s certainly part of the correct answer, and likely the most important one for us as a group.
But if I were writing this, my loud headline message would not have been ‘It’s over’, because none of this is over, many decisions still matter. It’s only ‘over’ for the possibility of long term suppression.
*****
There’s also the much broader point—the ‘what, precisely, is wrong with us’ question. This is very interesting and complex and deserves a long discussion of its own. I might write one at some point. I’m just giving some initial thoughts here, partly a very delayed response to your reply to me 2 weeks ago (https://www.lesswrong.com/posts/Rvzdi8RS9Bda5aLt2/covid-12-17-the-first-dose?commentId=QvYbhxS2DL4GDB6hF). I think we have a hard-to-place disagreement about some of the ultimate causes of our coronavirus failures.
We got a shout-out in Shtetl-Optimized, as he offers his “crackpot theory” that if we were a functional civilization we might have acted like one and vaccinated everyone a while ago
...
I think almost everyone on earth could have, and should have, already been vaccinated by now. I think a faster, “WWII-style” approach would’ve saved millions of lives, prevented economic destruction, and carried negligible risks compared to its benefits. I think this will be clear to future generations, who’ll write PhD theses exploring how it was possible that we invented multiple effective covid vaccines in mere days or weeks
He’s totally right on the facts, of course. The question is what to blame. I think our disagreement here, as revealed in our last discussion, is interesting. The first order answer is institutional sclerosis, inability to properly do expected value reasoning and respond rapidly to new evidence. We all agree on that and all see the problem. You said to me,
And I agree that if government is determined to prevent useful private action (e.g. “We have 2020 values”)...
Implying, as you’ve said elsewhere, that the malaise has a deeper source. When I said “2020 values” I referred to our overall greater valuation of human life, while you took it to refer to our tendency to interfere with private action—something you clearly think is deeply connected to the values we (individuals and governments) hold today.
I see a long term shift towards a greater valuation of life that has been mostly positive, and some other cause producing a terrible outcome from coronavirus in western countries, and you see a value shift towards higher S levels that has caused the bad outcomes from coronavirus and other bad things.
Unlike Robin Hanson, though, you aren’t recommending we attempt to tell people to go off and have different values—you’re simply noting that you think our tendency to make larger sacrifices is a mistake.
″...even when the trade-offs are similar, which ties into my view that simulacra and maze levels are higher, with a larger role played by fear ofmotive ambiguity.”
This is probably the crux. I don’t think we tend to go to higher simulacra levels now, compared to decades ago. I think it’s always been quite prevalent, and has been roughly constant through history. While signalling explanations definitely tell us a lot about particular failings, they can’t explain the reason things are worse now in certain ways, compared to before. The difference isn’t because of the perennial problem of pervasive signalling. It has more to do with economic stagnation and not enough state capacity. These flaws mean useful action gets replaced by useless action, and allow more room for wasteful signalling.
As one point in favour of this model, I think it’s worth noting that the historical comparisons aren’t ever to us actually succeeding at dealing with pandemics in the past, but to things like “WWII-style” efforts—i.e. thinking that if we could just do x as well as we once did y then things would have been a lot better.
This implies that if you made an institution analogous to e.g. the weapons researchers of WW2 and the governments that funded them, or NASA in the 1960s, without copy-pasting 1940s/1960s society wholesale, the outcome would have been better. To me that suggests it’s institution design that’s the culprit, not this more ethereal value drift or increase in overall simulacra levels. There are other independent reasons to think the value shift has been mostly good, ones I talked about in my last post.
As a corollary, I also think that your mistaken predictions in the past—that we’d give up on suppression or that the control system would fizzle out, are related to this. If you think we operate at higher S levels than in the past, you’d be more inclined to think we’ll sooner or later sleepwalk into a disaster. If you think there is a strong, consistent, S1 drag away from disaster, as I argued way back here, you’d expect strong control system effects that seem surprisingly immune to ‘fatigue’.
I always see [rates of compliance, lockdown fatigue, which kinds of restrictions are actually followed, etc.] discussed in very qualitative, intuitive terms. We talk of cases, tests, fatality rates, and reproduction numbers quantitatively. We look at tables and charts of these numbers, we compare projections of them.
But when the conversation turns to lockdown compliance, the numbers vanish, the claims range over broad and poorly specified groups (instead of percentages and confidence intervals we get phrases like “most people,” or merely “people”), and everything is (as far as I can tell) based on gut feeling.
Even a simple toy model could help, by separating intuitions about the mechanism from those about outcomes. If someone argues that a number will be 1000x or 0.001x the value the toy model would predict, that suggests either
(a) the number is wrong or
(b) the toy model missed some important factor with a huge influence over the conclusions one draws
Either (a) or (b) would be interesting to learn.
----
One basic question I don’t feel I have the answer to: do we know anything about how powerful the control system is?
As long as this trend holds, it’s like we’re watching the temperature of my room when I’ve got the thermostat set to 70F. Sure enough, the temperature stays close to 70F.
This tells you nothing about the maximum power of my heating system. In colder temperatures, it’d need to work harder, and at some low enough temperature T, it wouldn’t be able to sustain 70F inside. But we can’t tell what that cutoff T is until we reach it. “The indoor temperature right now oscillates around 70F” doesn’t tell you anything about T.
Doesn’t this argument work just as well for the “control system”? A toy model could answer that question.
Many of the same thoughts were in my mind when I linked when I linked that study on the previous post.
----
IMO, it would help clarify arguments about the “control system” a lot to write down the ideas in some quantitative form.
...
This tells you nothing about the maximum power of my heating system. In colder temperatures, it’d need to work harder, and at some low enough temperature T, it wouldn’t be able to sustain 70F inside. But we can’t tell what that cutoff T is until we reach it. “The indoor temperature right now oscillates around 70F” doesn’t tell you anything about T.
I agree, and in fact the main point I was getting at with my initial comment is that in the two areas I talked about—namely the control system and the overall explanation for failure, there’s an unfortunate tendency to toss out quantitative arguments or even detailed models of the world and instead resort to intuitions and qualitative arguments—and then it has a tendency to turn into a referendum on your personal opinions about human nature and the human condition, which isn’t that useful for predicting anything. You can see this in how the predictions panned out—as was pointed out by some anonymous commenter, control system ‘running out of power’ arguments generally haven’t been that predictively accurate when it comes to these questions.
The rule-of-thumb that I’ve used—the Morituri Nolumus Mori effect—has fared somewhat better than the ‘control system will run out of steam sooner or later’ rule-of-thumb, both when I wrote that post and since. The MNM tends to predict last-minute myopic decisions that mostly avoid the worst outcomes, while the ‘out of steam’ explanation led people to predict that social distancing would mostly be over by now. But neither is a proper quantitative model.
As to the second question—overall explanation, there is some data to work off of, but not much. We know that preexisting measures of state capacity don’t predict covid response effectiveness, which along with other evidence suggests the ‘institutional schlerosis’ hypothesis I referred to in my original post. Once again, I think that a clear mechanism - ‘institutional sclerosis as part of the great stagnation’ - is a much better starting point for unravelling all this than the ‘simulacra levels are higher now’ perspective that I see a lot around here. That claim is too abstract to easily falsify or derive genuine in-advance predictions.
I live in Southern England and so have a fair bit of personal investment in all this, but I’ll try to be objective. My first reaction, upon reading the LSHTM paper that you referred to, is ‘we can no longer win, but we can lose less’ - i.e. we are all headed for herd immunity one way or another by mid-year, but we can still do a lot to protect people. That would have been my headline—it’s over for suppression and elimination, but ‘it’s over’ isn’t quite right. Your initial reaction was different:
You give multiple reasons to expect this, all of which make complete sense—Lockdown fatigue, the inefficiency of prevention, lags in control systems, control systems can’t compensate etc. I could give similar reasons to expect the alternative—mainly that the MNM predicts the extreme strength of control systems and that it looks like many places in Europe/Australia did take Rt down to 0.6 or even below!
But luckily, none of that is necessary.
I would encourage everyone to look at the scenarios in this paper since they neatly explain exactly what we’re facing and mean we don’t have to rely on guestimate models and inference about behaviour changes. This model is likely highly robust—it successfully predicted the course of the UK’s previous lockdown, with whatever compliance we had then. They simply updated it by putting in the increased infectiousness of the new variant. Since that last lockdown was very recent, compliance isn’t going to be wildly different, weather was cold during the previous lockdown, schools were open etc. The estimate for the increase in R given in this paper seems to be the same as that given by other groups e.g. Imperial College.
So what does the paper imply? Essentially a Level 4 lockdown (median estimate) flattens out case growth but with schools closed a L4 lockdown causes cases to decline a bit (page 10). 10x-ing the vaccination rate from 200,000 to 2 million reduces the overall numbers of deaths by more than half (page 11). And they only model a one-month lockdown, but that still makes a significant difference to overall deaths (page 11). We managed 500k vaccinations the first week, and it dropped a bit the second week, but with first-doses first and the Oxford/AZ vaccine it should increase again and land somewhere between those two scenarios. Who knows where? For the US, the fundamental situation may look like the first model—no lockdowns at all, so have a look.
(Also of note is that the peak demand on the medical system even in the bad scenarios with a level 4 lockdown and schools open is less than 1.5x what was seen during the first peak. That’s certainly enough to boost the IFR and could be described as ‘healthcare system collapse’, since it means surge capacity being used, healthcare workers being wildly overstretched, but to my mind ‘collapse’ refers to demand that exceeds supply by many multiples such that most people can’t get any proper care at all—as was talked about in late feb/early march.)
(Edit: the level of accuracy of the LSHTM model should become clear in a week or two)
The nature of our situation now is such that every day of delay and every extra vaccinated person makes us incrementally better off.
This is a simpler situation than before—before we had the option of suppression, which is all-or-nothing—either you get R under 1 or you don’t. The race condition that we’re in now, where short lockdowns that temporarily hold off the virus buy us useful time, and speeding up vaccination increases herd immunity and decreases deaths and slackens the burden on the medical system, is a straightforward fight by comparison. You just do whatever you can to beat it back and vaccinate as fast as you can.
Now, I don’t think you really disagree with me here, except about some minor factual details (I reckon your pre-existing intuitions about what ‘Level 4 lockdown’ would be capable of doing are different to mine), and you mention the extreme urgency of speeding up vaccine rollout often,
But if I were writing this, my loud headline message would not have been ‘It’s over’, because none of this is over, many decisions still matter. It’s only ‘over’ for the possibility of long term suppression.
*****
There’s also the much broader point—the ‘what, precisely, is wrong with us’ question. This is very interesting and complex and deserves a long discussion of its own. I might write one at some point. I’m just giving some initial thoughts here, partly a very delayed response to your reply to me 2 weeks ago (https://www.lesswrong.com/posts/Rvzdi8RS9Bda5aLt2/covid-12-17-the-first-dose?commentId=QvYbhxS2DL4GDB6hF). I think we have a hard-to-place disagreement about some of the ultimate causes of our coronavirus failures.
He’s totally right on the facts, of course. The question is what to blame. I think our disagreement here, as revealed in our last discussion, is interesting. The first order answer is institutional sclerosis, inability to properly do expected value reasoning and respond rapidly to new evidence. We all agree on that and all see the problem. You said to me,
Implying, as you’ve said elsewhere, that the malaise has a deeper source. When I said “2020 values” I referred to our overall greater valuation of human life, while you took it to refer to our tendency to interfere with private action—something you clearly think is deeply connected to the values we (individuals and governments) hold today.
I see a long term shift towards a greater valuation of life that has been mostly positive, and some other cause producing a terrible outcome from coronavirus in western countries, and you see a value shift towards higher S levels that has caused the bad outcomes from coronavirus and other bad things.
This is probably the crux. I don’t think we tend to go to higher simulacra levels now, compared to decades ago. I think it’s always been quite prevalent, and has been roughly constant through history. While signalling explanations definitely tell us a lot about particular failings, they can’t explain the reason things are worse now in certain ways, compared to before. The difference isn’t because of the perennial problem of pervasive signalling. It has more to do with economic stagnation and not enough state capacity. These flaws mean useful action gets replaced by useless action, and allow more room for wasteful signalling.
As one point in favour of this model, I think it’s worth noting that the historical comparisons aren’t ever to us actually succeeding at dealing with pandemics in the past, but to things like “WWII-style” efforts—i.e. thinking that if we could just do x as well as we once did y then things would have been a lot better.
This implies that if you made an institution analogous to e.g. the weapons researchers of WW2 and the governments that funded them, or NASA in the 1960s, without copy-pasting 1940s/1960s society wholesale, the outcome would have been better. To me that suggests it’s institution design that’s the culprit, not this more ethereal value drift or increase in overall simulacra levels. There are other independent reasons to think the value shift has been mostly good, ones I talked about in my last post.
As a corollary, I also think that your mistaken predictions in the past—that we’d give up on suppression or that the control system would fizzle out, are related to this. If you think we operate at higher S levels than in the past, you’d be more inclined to think we’ll sooner or later sleepwalk into a disaster. If you think there is a strong, consistent, S1 drag away from disaster, as I argued way back here, you’d expect strong control system effects that seem surprisingly immune to ‘fatigue’.
Many of the same thoughts were in my mind when I linked when I linked that study on the previous post.
----
IMO, it would help clarify arguments about the “control system” a lot to write down the ideas in some quantitative form.
As I wrote here:
Even a simple toy model could help, by separating intuitions about the mechanism from those about outcomes. If someone argues that a number will be 1000x or 0.001x the value the toy model would predict, that suggests either
(a) the number is wrong or
(b) the toy model missed some important factor with a huge influence over the conclusions one draws
Either (a) or (b) would be interesting to learn.
----
One basic question I don’t feel I have the answer to: do we know anything about how powerful the control system is?
Roughly, “the control system” is an explanation for the fact that R stays very close to 1 in many areas. It oscillates up and down, but it never gets anywhere near as low as 0, or anywhere near as high as the uncontrolled value of ~4.5.
As long as this trend holds, it’s like we’re watching the temperature of my room when I’ve got the thermostat set to 70F. Sure enough, the temperature stays close to 70F.
This tells you nothing about the maximum power of my heating system. In colder temperatures, it’d need to work harder, and at some low enough temperature T, it wouldn’t be able to sustain 70F inside. But we can’t tell what that cutoff T is until we reach it. “The indoor temperature right now oscillates around 70F” doesn’t tell you anything about T.
Doesn’t this argument work just as well for the “control system”? A toy model could answer that question.
I agree, and in fact the main point I was getting at with my initial comment is that in the two areas I talked about—namely the control system and the overall explanation for failure, there’s an unfortunate tendency to toss out quantitative arguments or even detailed models of the world and instead resort to intuitions and qualitative arguments—and then it has a tendency to turn into a referendum on your personal opinions about human nature and the human condition, which isn’t that useful for predicting anything. You can see this in how the predictions panned out—as was pointed out by some anonymous commenter, control system ‘running out of power’ arguments generally haven’t been that predictively accurate when it comes to these questions.
The rule-of-thumb that I’ve used—the Morituri Nolumus Mori effect—has fared somewhat better than the ‘control system will run out of steam sooner or later’ rule-of-thumb, both when I wrote that post and since. The MNM tends to predict last-minute myopic decisions that mostly avoid the worst outcomes, while the ‘out of steam’ explanation led people to predict that social distancing would mostly be over by now. But neither is a proper quantitative model.
In terms of actually giving some quantitative rigour to this question—it’s not easy. I made an effort in my old post, by saying how far a society can stray from a control system equilibrium is indicated by how low they managed to get Rt—but the ‘gold standard’ is to just work off model projections trained on already existing data like I tried to do.
As to the second question—overall explanation, there is some data to work off of, but not much. We know that preexisting measures of state capacity don’t predict covid response effectiveness, which along with other evidence suggests the ‘institutional schlerosis’ hypothesis I referred to in my original post. Once again, I think that a clear mechanism - ‘institutional sclerosis as part of the great stagnation’ - is a much better starting point for unravelling all this than the ‘simulacra levels are higher now’ perspective that I see a lot around here. That claim is too abstract to easily falsify or derive genuine in-advance predictions.