I don’t understand the hypothetical.
If every country in the world had closed their borders well enough to stop all movement before it left China, yes, spread would have been prevented. But that’s unfeasible even if there was political will, since border closures are never complete, and there was already spread outside of China by mid-January.
Once there is spread somewhere, you can’t reopen borders. And even if you keep them closed, no border closure is 100% effective—unless you have magical borders, spread will inevitably end up in your country. And at that point, countries are either ready to suppress domestic spread without closures, or they aren’t, and end up closing later instead of earlier.
In general, I think that earlier closures would potentially have delayed spread enough to save lives due to getting vaccines and testing further along than they were.
I’m also claiming that now, with a fully in place and adequate test-and-trace program, including screening for passengers and isolation for positives, border closures have low marginal benefit. Without such a test and trace program, travel modifies the spread dynamics by little enough that it won’t matter for places that don’t have spread essentially controlled. The key case where it would matter is if the border closures delayed spread by long enough to put in place such systems, in which case they would have been very valuable. And yes, border closures in place have allowed this in some places, but certainly not the US or UK.
So, conditional on the policy failures, I think border closures were effectively only a way to signal, and if they distracted from putting in place testing and other systems by even a small amount, they were net negative.
See the back-and-forth with John Wentsworth in the comments earlier—https://www.lesswrong.com/posts/B7sHnk8P8EXmpfyCZ/a-personal-interim-covid-19-postmortem?commentId=ntGR3rpnSW6yKRoAP
The claim that Harvard is just a status symbol is that the entire variance in success from attending Harvard is explained by the two factors of 1) the characteristics of individual people entering the program, and 2) the prestige from being able to claim they graduated.
This seems implausible—so to extend this, I’d say all of the variance can be explained by those two plus a third factor, 3) the value of networking with Harvard students, faculty and staff.
In either case, the central point is that benefit from the services provided by Harvard are unrelated to the education they claim to provide.
Again, it didn’t actually stop spread—it slowed it slightly. Borders haven’t been actually closed. Flights have continued, you just need connections to get a visa. But people have been able to return home—and dual citizens have been able to travel both ways—the entire time.
Assuming away the political problem of making it stick, it seems clear that without universal border closures by countries, it would have made only a minor difference in spread—most cases that came to Europe, the US, and elsewhere didn’t come from China.
If some set of countries were willing to completely shut down all borders, those countries might have avoided infections—might, but I’m skeptical. Even now, the countries that shut down international travel still have a fair amount of international travel, from diplomatic travel to repatriation of citizens to shipping and trucking. So it could plausibly have delayed spread by a month. In places that mounted a really effective response, a month might have made the difference between slow control and faster control. In most places, I think it would have shifted spread a couple weeks later.
The better link is the final version - https://www.mdpi.com/2504-2289/3/2/21/htm
The link in the original post broke because it included the trailing period by accident.
I agree that it could be a death spiral, and think the caution is in general warranted. My personal situation was one where I had fairly little personal interaction with members of the community—though this is likely less true not—but that was why I decided that explicitly considering the consensus opinions was reasonable.
“The more interesting thing is when you make a bet where a negative outcome should force a large update.”
I think that’s what odds are for. If you’re convinced (incorrectly) that something is very unlikely, you should be willing to give large odds. You can’t really say “I thought this was 40% likely, and I happened to get it wrong” if you gave 5:1 odds initially.
(And on the other side, the person who took the bet should absolutely say they are making a small update towards the other model, because it’s far weaker evidence for them.)
“In the absence of an oracle, I would end up writing up praise for, and updating towards, your more wrong model, which is obviously not what we want.”
Perhaps I’m missing something, but I think that’s exactly what we want. It leads to eventual consistency / improved estimates of odds, which is all we can look for without oracles or in the presence of noise.
First, strength of priors will limit the size of the bettor’s updates. Let’s say we both used beta distributions, and had weak beliefs. Your prior was Beta(4,6), and mine was Beta(6,4). These get updated to B(5,6) and B(7,4). That sounds fine—you weren’t very sure initially, and you still won’t over-correct much. If the priors are stronger, say, B(12,18) and B(18,12), the updates are smaller as well, as they should be given our clearer world models and less willingness to abandon them due to weak evidence.
Second, we can look at the outside observer’s ability to update. If the expectation is 40% vs. 60%, unless there are very strong priors, I would assume neither side is interested in making huge bets, or giving large odds—that is, if this bet would happen at all, given transaction costs, etc. This should implicitly limit the size of the update other people make from such bets.
There is a lot here to reply to, and I’m only going to address a few points.
First, on forecasting, I think there is a lot to discuss, and I think Johnwentsworth’s comment and my reply are all that I have to say about this for now.
Second, on Government response, I’m also unsure how much we disagree. I definitely think that I have a number of useful insights about institutions, but this is an area where expertise seems to be non-predictive. That means I’m less sure how valuable it is—but I discussed this in more depth here, on Ribbonfarm. That said, I’ll make comments anyways.
I agree that many countries were underprepared, but they also historically relied on American leadership for many of these types of events. America was the acknowledged world leader in biodefense and preparation, has spent more time and money on the problem than elsewhere, and has much more money and expertise than most places—so the failure is much more noteworthy than it otherwise would be.
I also think the EU “failures” should be counted as partial successes, since they mostly have case counts declining, and are well prepared to avoid the worst of a possible second wave. That’s a solid half credit in an absolute sense, since they seems poised to have gotten it under control before it ended up everywhere, though they didn’t catch it enough to prevent spread at first, which would have been the goal. The US (and to a lesser extent, the UK,) didn’t manage to control things enough to even get past the first wave, and they are poised to fail to herd immunity in most places—a shocking level of failure, especially given how well other countries have managed this.
For counterfactual predictions, on B, if the US did as well as Germany, Japan, France, and other G-7 nations, they would have kept deaths under 20,000, or at least around there. I’d give at least 50% to keeping it below 20k so far. (I’m unsure how bad the Republican Governors would have made this, or what the rest of the world looks like under Clinton. Would the Chinese have cooperated earlier? Counterfactual predictions this far back are basically about writing an alternative timeline—there are WAY too many potential issues to really consider well.) But the epidemic seems under control in the EU, contra the US. So that seems like the relevant counterfactual. (Aside: It seems non-coincidental, though a surprisingly strong effect, that right-wing populist leaders are especially bad at controlling infectious diseases—BoJo, Trump, and Putin all got this very, very wrong. I think the default reaction of trying to control the narrative over dealing with problems is a particularly dangerous approach with infectious diseases.) And for the A counterfactual, it’s similar, but with 20+% probability mass on “this was stopped enough before it left China that there was no pandemic.”
Yes—it took me until mid or late March to be fully on board. See my comment here to a post arguing for pushing handwashing instead of suggesting masks, which I changed my mind about in mid to late March.
Agreed—but **for protecting the wearer alone**, I’d say that 10% more handwashing by most people would easily beat 50% more mask wearing.
I think Tyler’s way too impressed by himself and his discipline than he should be. There’s a saying about economists making fortune tellers look good that seems appropriate here. And he probably shouldn’t be posting insulting things about epidemiologists in the same breath as saying most economists are just as bad—which he followed up with saying he wants to be rude by asking questions he could have spent half an hour googling—he hadn’t even done basic research. I also think that people on lesswrong give too little credit to public health officials for being properly cautious about overreacting, especially given that even for COVID-19, many people are saying that we went too far, and the economic harms were not worth the damage.
Also see this thread: https://twitter.com/davidmanheim/status/1235274008142270466
Next, should academics and public servants in epidemiology simply be paid more? No, and no. If anything, there is not enough disincentive to enter academia, since there are so many more good applicants than spots, across disciplines. Something else needs to be fixed there first. (Everything, actually.) And government isn’t set up well to pay people more in ways that gets better candidates—doubling salaries wouldn’t be enough to get anyone more competent to run for the Senate, much less be a senior government appointee, unless they already wanted to do that and didn’t actually care about the money. (There are other ways we underpay and sabotage government that money could fix, but that’s a different discussion.) And I’m surprised that an economist doesn’t know enough about these structures to see why higher pay isn’t a useful lever.
I mean now—it’s clear that masks are not particularly effective at preventing people from getting COVID, and are somewhat but not very effective at preventing people who have COVID from infecting others. That’s enough to be incredibly important at a population level, which is obviously a key thing to do, but it’s not anything similar to what proponents had been claiming.
Yeah, Wordpress isn’t the best platform for this.
I could imagine clear ways of doing this by having, say, python scripts to ingest the data, running, say, hourly on cloud servers, and then producing RSS for that could then be used in Wordpress—but I’m guessing there are people on here who would have far better ideas for how to engineer this.
Thank you—and I strongly endorse this answer. And now that you point this out, I realize that it should have been clear. I have speculated in the past that a large part of the value of Superforecasting is that there are people actually motivated to investigate and do the expensive updating I have also said that I’m unsure how worthwhile it is to pay for the time of the types of people who can superforecast. This seems like a clear case where it is worthwhile, if only it worked.
Given that, I think there’s a strong case that we need large rewards for early correct updates away from consensus, especially for very rare events. (In a case like COVID, the value of faster information is in the tens or hundreds of billions of dollars. A tiny fraction of that would be more than enough.) But the typical time-weighted forecast scores don’t account for heterogeneous update costs or give sufficient reward to figuring it out a day sooner than the average—though metaculus’s score and the scoring Ozzie Gooen has looked at are trying to do this better. This seems very worth more consideration.
Given OP’s question, an obvious, if perhaps annoying / difficult idea to implement, is to have an expandable [+] next to each post which allows seeing it on that site, and a nested expandable button to see comments.
I’m unsure how happy or unhappy people who are indexed would be with this for non EA Forum / LW blogs.
You said that “In epidemiology it is a basic fact in the 101 textbook that slowing long distance transmission (using quarantines / travel restrictions) is very important.” The parentheses make the statement incorrect. Obviously there are discussions of this, but I just checked my copy of “Modern Infectious Disease Epidemiology: Concepts, Methods, Mathematical Models, and Public Health.” It discusses travel and the contribution to spread, but mostly focuses on the way IHR limits the imposition of travel bans, and why such bans are considered problematic. It does mention quarantines and travel restrictions, but they aren’t the key tools that are recommended.
Also, you said “I would be interested in some justification of the claim that face masks are not very useful.” That isn’t what I said. I said that “mask wearing by itself is only marginally effective.” See this FHI paper, which estimated, albeit with very low confidence, that mask policies were almost entirely ineffective—far more pessimistic than my claim. That is because that paper is likely to be understating the impact, as they admit. It seems clear that maks wearing reduces spread somewhat, but note that this is because of reducing spread from infectious individuals, especially pre-symptomatic and asymptomatic people, not protecting mask wearers. The early skepticism was in part based on the assumption, which in March seemed to have been shared by both promoters and skeptics, that the benefits were that masks were individually protective, rather than that they helped population-level spread reduction. It turns out that (contra the FHI paper,) there seems to be some impact helping spread reduction. Even so, it’s not enough to bring R<1 without other interventions, either closures, or an effective test and trace program, as our forthcoming paper argues. (I will also note that one key thing that is changing from that pre-print version is because reviewers pointed out that we were likely too optimistic in our estimate of mask effectiveness, and the literature supports much smaller impacts.)
EDIT: I notice I am confused about why people downvote comments that make substantive points without replying. If the tone or substance is problematic, I certainly think downvotes are acceptable, but I think the norm is supposed to be that you also tell people what you think they did wrong.
You’re right—but the basic literature on principle agent dynamics corrects this simple model to properly account for non-binary effort and luck, and I think that is the better model for looking at luck and effort.