All the modeling efforts talked about in the write-up are doomed because they don’t understand the role of the control system
Is that a forward-looking prediction? What consequences was UIUC doomed to? The article ends its coverage of UIUC in early September, declaring it a failure. But, in fact, it achieved its goal of keeping infections below 5k. You could credit that to “the control system,” the panic caused by the early spike that got all the news coverage, but it’s still a success.
If it’s so cheap and easy to make vaccines, why aren’t commercial ones made this way? In particular, the Novavax vaccine sounds similar, so why wasn’t that the first vaccine to market?
Added: Specifically, the ultimate purpose of a vaccine is to get protein into the body. Traditional vaccines grow the virus using its own reproductive apparatus. Fancy new vaccines, like the adenovirus and mRNA vaccines inject instructions and induce the subject to manufacture proteins. But if it’s so easy to just print proteins, why don’t we do that? That’s what Novavax does, unlike the ~7 vaccines that beat it to market.
Added: one difference is that all the vaccines that made it to market, including, I think, Novavax, used the whole spike protein, whereas this proposal uses short peptides. Identifying the right short snippets takes time, while using the whole protein is simpler and more likely to work. The cost of peptides is probably super-linear in length. Still, I remain confused about Novavax.
By default, we should expect viruses to become less deadly over time rather than more severe, but more severe is always a risk. We also believe the new strain carries generally higher viral loads, which could plausibly be a cause of higher severity.
That is the consensus among biologists, or maybe even evolutionary biologists, but the consensus among people who study the evolution of parasites is exactly the opposite. Theory predicts and observation agrees that parasites evolve to become more virulent over time, especially ones that have just jumped from one host to another. See Paul Ewald, for example.
I suspect that Gates had a long-standing specific plan for manufacturing old-fashioned vaccines, but was unable to pivot to funding new vaccines. It’s a lot harder to spend money to speed up deployment of new technologies, especially at arm’s length.
AZ claims this week that the EU negotiations being delayed for a couple months delayed their factories. Why couldn’t they just start earlier? This is a clear claim that money would have mattered. But maybe there is a lot more to this than physical construction. The EU is currently threatening to confiscate AZ vaccine, so maybe AZ didn’t see any point in building factories in countries that hadn’t pre-paid.
Why is the Gates foundation a charity, as opposed to just a non-profit? If he wants to take Buffett’s money and give him a tax benefit, then it has to be charity, but for spending his own money, he doesn’t need this status.
Given the high dimension of the search space, I think (b) is negligible and the linear model (a) of your first comment is better. In low dimension the boundary of the unit sphere is small and you can have a lot of copies on the inside, having to pass through the sphere to reach new terrain. Whereas, in high dimensions, the population will quickly thin out and all be unique, so what matters is the total volume of space explored, not how long it takes to get anywhere.
Dynamically, that predicts that the advantage would rise over time, as a substantial proportion of the population got infected by the original strain. I think we’ve been monitoring the UK variant enough to see that this is not a large portion of its advantage.
Even statically, I think it’s difficult to make the numbers work out. The idea that the population has heterogeneous risk makes a lot of sense, but it doesn’t seem born out by the basic prediction that HIT is much lower than predicted by initial R. I suppose the control system might make it hard to observe how close we are getting to HIT, but I’m skeptical. And you need zero cross-immunity. But then wouldn’t the observed reinfections skyrocket and be obvious? Maybe if the reinfections are asymptomatic, but just as contagious. I guess that this very specific scenario predicts that the new variant has passed through the riskiest portion of the London population and no longer has an advantage there.
My (highly speculative!) hypothesis is that the emergence of these variant viruses arises in cases of chronic infection during which the immune system places great pressure on the virus to escape immunity and the virus does so by getting really good at getting into cells. 11⁄19
That’s plausible, but doesn’t explain why the chronic infections hadn’t done this earlier, and the English strain doesn’t escape immunity in this way (and we don’t know about the others) so I notice it doesn’t feel like it explains things.
That isn’t plausible to me. Not only does it not explain everything, I don’t think it explains anything. There are two traits, immune escape and infectiousness, which is pretty much the same as fitness. It makes sense that chronic infection causes immune escape, but we aren’t looking at immune escape. There might be tons of strains with immune escape out there, but we won’t notice until we achieve substantial herd immunity, at which point they will have increased fitness. If that happens, and we need to explain immune escape in future strains, then this is a possibility. But I see no reason to believe it about these strains. We are filtering by increased fitness, so we need to explain increased fitness. Immune escape is probably a side effect of increased fitness, because it’s a potential side effect of any change.
Fitness is fitness. There is no need for a weird environment to explain selection for fitness, because it’s the main story. In fact, this is exactly backwards. If there is any trade-off between reproduction between hosts and reproduction in hosts, then selection by chronic infection will favor the latter at the expense of the former and probably move away from optimal fitness. Whereas it appears, as Bedford claims, that these mutations are a free lunch. This is quite plausible for a young parasite that hasn’t finished adapting to its new host. But then what does a weird selection pressure explain? Every infection should be an opportunity to develop this. What matters is the number of roughly the number of virus-host-days. A virus trapped in one individual for 100 days is under roughly the same selection pressure as a virus passing through 20 individuals under the same time. I’ve seen a lot of people call that “fast evolution” because 20x as much selection happens in the host, but it’s not any faster in time.
Bedford seems to allude to this view, but he also uses similar points to make a different argument, which is that the UK variant appeared out of nowhere with a lot of mutations, without the intermediate forms being observed. Arising from a chronic infection would explain this. But we also know that all the mutations have arisen before, without seeming to do much on their own. So we know that it only took off when it reached the magic combination, which explains why we didn’t see the intermediate forms. I don’t think chronic infection adds much to this, although I could imagine a scenario in which the individual changes happen to be selected for in chronic infection at the slight expense of general fitness. So it needs the different landscape of a chronic infection to cross the valley and reach the final form. But this seems like excess detail to me.
I don’t believe that every infected cell is killed.
This is about the change that is shared between UK and SA, not about the change in the SA variant that is uniquely worrisome.
That doesn’t distinguish the new vaccines from traditional attenuated vaccines, which also inject nucleic acid into the cell, although inactivated vaccines don’t. Or, for that matter, getting infected. Every year I get a couple of colds, where way more cells get way more nucleic acid than in vaccine.
10% chance of survival is “unlikely” but it’s lives, not warm fuzzies. It may not have been cost-effective, but it was buying something real. (And it may well have been cost-effective if it was marginal use of facilities that already existed.)
10% is the number I have heard for the specific category I have heard triaged against in LA, namely patients whose heart the EMT can’t restart. Instead they can perform CPR to manually pump the heart on the trip to the hospital.
this is 3,809 deaths per day and the average had not at the time been over 2,700 for any 7-day period so far, that that projection was made on December 31, so that seems like a rather bold prediction
If cases are in unprecedented territory, you should expect deaths to go into unprecedented territory. Forecasting deaths 3 weeks in the future should be easy, since it’s forecasting the progression of the disease among people already infected. You just take the number infected of cases and multiply by the CFR. At 220k cases/day and a CFR of 1.75%, that’s 3850 deaths/day, so that sounds totally reasonable.
(This is all from eyeballing 7-day rolling averages. I got the CFR from comparing the plateau of cases in September to the plateau of deaths in October. I haven’t checked if it has held up or what the lag should be. The 220k cases/day is the 7-day average peak at Christmas. So this requires assuming that the dip after Christmas was an illusion, which has been vindicated by the week since the prediction was made.)
Added: over at Zvi’s blog, Alexander Gordon-Brown claims that CDC does not predict 80k deaths, but that its point estimate is only 64k. Also, that the prediction is dated 12⁄21.
If you make any of these hypotheses precise enough to calculate, then I don’t think that they are likely enough to be worth calculating. The point was just to give suggest how big the space of unknown unknowns is. I think you need an outside view to estimate it. You might hope to get that from the virologists, but they are dismissing it as a “founder effect” which is even more specific, rather than accepting the ignorance of an outside view.
I think I got them all from Francois Balloux, though I’m not sure what he was saying and I may have interpolated a lot of detail. I got 2a and maybe 1 from here. 2b is from here, a response to the first thread. Added: actually, I think I got 2a from the “Does it matter” video, which was generally hostile to reason and knowledge epidemiology, but did suggest something like this at the end.
How likely is it that the spread of this new strain was caused by a few superspreaders, and that most of the above is blown out of proportion?
The probability is basically zero. But the question is whether the obvious hypothesis is false, not whether a particular alternate hypothesis is true. There are many alternate hypotheses more likely than that, and most important the final bucket of “other,” hypotheses that I have not thought of. Here are a few suggestions I have heard: (1) It is biologically different and spreads easily in children, but if they closed the schools, it would be negligibly different. (2) Measurement error. 2a: All sars2 produces RNA after infectiousness is over, effectively false positives and maybe this produces lots of RNA for much longer, effectively higher false positives and more lagging signal. 2b: It is mainly detected by applying the usual 3 tests for sars2 and failing a particular one. So it is inherently a noisier test, although it is not at all clear how this could produce the observed pattern.
If the center goes to a unique limit and the tails go to a unique limit, then the two unique limits must be the same. CLT applies to anything with finite variance. So if something has gaussian tails, it just is the gaussian. But there are infinite variance examples with fat tails that have limits. What would it mean to ask if the center is gaussian? Where does the center end and the tails begin? So I don’t think it makes sense to ask if the center is gaussian. But there are coarser statements you can consider. Is the limit continuous? How fast does a discrete distribution converge to a continuous one? Then you can look at the first and second derivative of the pdf. This is like an infinitesimal version of variance.
The central limit theorem is a beautiful theorem that captures something relating multiple phenomena. It is valuable to study that relationship, even if you should ultimately disentangle them. In contrast, skew is an arbitrary formula that mixes things together as a kludge with rough edges. It is adequate for dealing with one tail, but a serious mistake for two. It is easy to give examples of a distribution with zero skew that isn’t gaussian: any symmetric distribution.
The central limit theorem is a piece of pure math that mixes together two things that should be pretty separate. One is what the distribution looks like in the center and the other is what the distribution looks like at the tails. Both aspects of the distribution converge to the Gaussian, but if you want to measure how fast they converge, you should probably choose one or the other and define your metric (and your graphs) based on that target.
Skewness is a red herring. You should care about both tails separately and they don’t cancel out.
There’s an inherent difficulty you don’t list. You might file it under “political agendas,” but the big problem isn’t the external constraint of conscious agendas, but of people fooling themselves.
Press releases about defined endpoints of phase 3 trials are the ones that move the stock price the most, next to mergers. Probably across all companies, not just pharma. The SEC would come calling if they were lies.
Recently I’ve run across several people offhandedly offering disclaimers that they’ve never personally checked that the earth is round. I’ve never thought to check either, but a moment’s reflection reveals that I’ve traveled far enough, both north-south and east-west that the curvature is obvious.
Time zones are a measure of the curvature of the earth. When I travel from New York to California, I know that noon has changed, trusting only my wristwatch. In fact, it was pretty clear to my circadian rhythm. Or just make a phone call to someone you trust on the other coast. Most of the time I don’t discuss sunlight on such calls, but it has come up.
I’ve traveled shorter distances north-south. I’ve been to Glasgow, which is 15 degrees north of NYC. If I went in the spring, there’s probably little to notice, but I went near the solstice, when it was obvious that the nights were much shorter. Similarly, if you go south 15 degrees to Miami, I’m told that the winter and summer day lengths are obviously moderated.
Accurate clocks and instant communication give us a big advantage over the ancients, but the north-south method is largely unchanged.