Astral Codex Ten

deer

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deer

Article

deer is a recurring concept in the Astral Codex Ten archive, appearing 2 times across 2 issues between March 28, 2024 and July 12, 2024. The archive places it in contexts such as ""COVID spread to deer and mink""; “Chronic wasting disease is a prion disease of deer”. It most often appears alongside Eric, 1980s, 1989.

Metadata

  • Category: Concepts
  • Mention count: 2
  • Issue count: 2
  • First seen: March 28, 2024
  • Last seen: July 12, 2024

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Practically-A-Book Review: Rootclaim $100,000 Lab Leak Debate
March 28, 2024 · Original source
Lineage A (left) was used by the Minoan Cretans, but has never been deciphered. Lineage B (right) was used by the Mycaeneans for lists of palace goods. This matches Saar’s story above. The lab leaked to somewhere else in Wuhan, not the wet market. The virus spread undetected in the population for a while. During this time, it mutated to Lineage B. Then one of the people with Lineage B went to the wet market and started a superspreader event. The authorities sampled the patients, found Lineage B, then started looking elsewhere. Later they detected some of the earlier Lineage A cases. The market is unlikely to be the origin of the pandemic, because the original Lineage A strain wasn’t found there. Peter: Although Lineage A is evolutionarily older, Lineage B started spreading in humans first. We know this because Lineage B is more common. Throughout the early pandemic, until the D614G variant drove all other strains extinct, a consistent 2/3 of the cases were B, compared to 1/3 A. Both strains spread at the same rate, so the best explanation is that B started earlier than A. Since COVID doubles every 3-4 days, probably Lineage B started 3-4 days earlier than Lineage A, which explains why it’s always been twice as many cases. But also, Lineage B also has more internal genetic diversity than Lineage A. In general, older viruses have more genetic diversity (the “molecular clock”). This is further evidence that B started spreading first. Pekar 2022 and Pipes 2021 do analyses with known parameters for spread rate and diversity, and find 90%+ odds that Lineage B was the first one in humans. Why did the older strain start spreading later? Probably the virus crossed from bats into raccoon-dogs on some raccoon-dog farm out in the country. It spread in the raccoon-dogs for a while, racking up mutations, including the (less mutated) Lineage A strain and the (slightly more mutated) Lineage B strain. Then several raccoon-dogs were taken to Wuhan for sale, including one with Lineage A and another with Lineage B. The one with Lineage B passed its virus to humans earlier. Then 3-4 days later, the Lineage A one passed its virus to humans. Lineage A was first found in a Wuhan neighborhood right next to the wet market (closer to the wet market than 97% of Wuhan’s population). Again, it would be a bizarre coincidence if a lab leak pandemic was first detected at a wet market. But it would be an even more bizarre coincidence if a lab leak pandemic separated into two strains, and both were first detected at a wet market! Although no known wet market cases were Lineage A, a positive Lineage A environmental sample was found at the wet market, and everyone agrees most cases went undetected. So maybe the Lineage B raccoon-dog spread its virus to a vendor, and that sub-strain mostly stayed in the market. But the Lineage A raccoon-dog spread its virus to a customer, who went back to his house nearby, and that strain spread in the neighborhoods next to the market. This is the only story that explains the evolutionary precedence of A, the greater spread and older molecular clock of B, and the fact that both strains were first found very close to the wet market. Yuri/Saar: Lineage B could be more common and diverse because it got the advantage of a super-spreader event in the wet market. There are a few scattered cases of intermediates between A and B, and a few other scattered cases of lineages that seem even more ancestral (ie closer to the bat virus) than either. This doesn’t make sense in a double spillover hypothesis. But it does make sense if the lineages separated in human transmission somewhere between the lab and the first super-spreader event at the wet market. Peter: Again, the wet market wasn’t a super-spreader event. COVID spread in the wet market at exactly its normal spread rate, doubling about once every 3.5 days. Stop calling the wet market a super-spreader event. The scattered cases of “intermediates” are sequencing errors. They were all found by the same computer software, which “autofills” unsequenced bases in a genome to the most plausible guess. Because Lineage B was already in the software, depending on which part of a Lineage A virus you sequenced, you might get one half or the other autofilled as Lineage B, which looked like an “intermediate”. We know this because all the supposed “intermediates” were partial cases sequenced by this particular software. We can confirm this by noting that there are too many intermediates! That is, where Lineage A is (T/C) and Lineage B is (C/T), the software found both (T/T) “intermediates” and (C/C) “intermediates”. But obviously there can only be one real intermediate form, and we have to dismiss one or the other. But in fact we can dismiss both, because they were both caused by the same software bug. The scattered “progenitor” cases - those closer to the ancestral bat virus than either A or B - are reversions, ie cases where a new mutation in the virus happened to hit an already-mutated base and shift it back towards the ancestral virus. We know this because all of these “progenitors” were scattered cases found months after the pandemic started, often in entirely different countries from Wuhan. If these were real progenitor viruses, they would have either fizzled out or exploded into a substantial portion of all cases, not be found one time in one guy in Malaysia. Given the number of mutations the virus developed over the course of the pandemic, it’s inevitable that some of them would be mutations that bring it closer to the original bat virus, and in fact we find the number of “progenitors” found very nicely matches the number of progenitor-appearing viruses we would expect by chance. And in many cases, we know the “progenitors” are newer than the original lineages, because they also have some of the later mutations that Lineage A or B picked up along the way, alongside their apparent ancestral-bat-virus-like mutations. Session 2: Viral Genetics Yuri: Two years before COVID, scientists at the Wuhan Institute of Virology, together with colleagues at the University of North Carolina, sent in a grant proposal for the DEFUSE program. This program, intended to locate and better understand potential future pandemic viruses, involved going into bat caves and collecting new coronaviruses. Once they had them, they would do gain-of-function: specifically, they would add a furin cleavage site to make them more infectious and see what happened. (quick interlude: COVID’s spike protein has two sections: one binds to human cells through the ACE2 receptor, the other helps fuse with the cell after binding. In order to avoid the immune system, it hides both of these into one spike. But when it reaches a cell, it needs to separate them again. It takes advantage of a human respiratory enzyme, furin, to do the separation - this also ensures that it only infects its primary target, human respiratory cells. The part of COVID that lets it get separated by furin is called the “furin cleavage site”. COVID’s bat-virus ancestors were gastrointestinal viruses; the addition of a furin cleavage site was what made them respiratory viruses.) We’ve found two close relatives of COVID: bat viruses called RATG-13 and BANAL-52. In particular, COVID looks more or less like BANAL-52 plus a furin cleavage site. There are 1500 sarbecoviruses, members of the family of viruses that includes SARS and SARS2/COVID. None of them except COVID have furin cleavage sites. BANAL-52, COVID’s closest ancestor, doesn’t even have anything resembling one that could mutate into a functional furin cleavage site like COVID’s. Instead, COVID - which mostly just resembles BANAL-52 with a few scattered single-point mutations - has twelve completely new nucleotides in a row - a fully formed furin cleavage site that came out of nowhere. There is nowhere else in the genome that COVID differs from BANAL-52 in such a profound way. It’s just BANAL-52 plus a little bit of random mutation plus a fully-formed furin cleavage site that came out of nowhere. Further, the furin cleavage site is weird. It uses the protein arginine twice. But instead of the nucleotides coding for arginine in the usual viral way, both times it uses the codons CGG - the way that higher animals code for arginine. This works fine - it’s just not how viruses do it. So the obvious conclusion is that WIV, which said in 2018 that it was going to find viruses and add furin cleavage sites to them, found a close relative of BANAL-52 and added a furin cleavage site. Since they were humans, and most familiar with the human way of encoding arginine, they added it as CGG both times. COVID seemed surprisingly optimized for infecting humans. Of fifty animals it was tested in, including the usual coronavirus intermediate hosts (pangolins, raccoon-dogs, etc), it was best at infecting human cells. Further, a virus that enters a new species will usually show a burst of mutations as it “figures out” the best way to adapt to that species’ unique biology. But COVID has had a pretty constant mutation rate in humans, from the beginning of the pandemic to the end. That suggests it was already adapted to humans. This could be because the lab screened for viruses with existing adaptations, because they passed it through humanized mice in the lab, or because it adapted in the hundreds of undetected cases that happened between the lab and detection in the wet market. Usually, research with potentially dangerous coronaviruses is done in BSL-3 or 4, ie high to very-high security. But WIV was irresponsibly doing it in BSL-2, ie medium security. The researchers weren’t even required to wear masks. In general, about 1/500 labs will leak any given pathogen they’re working on (?!). But because WIV was researching such an infectious virus in such an irresponsible way, the odds of a leak were much higher. The most likely explanation for all these facts is that WIV went ahead and did the gain-of-function research they said they were going to do (the particular DEFUSE grant proposal we know about got rejected, but it proves that Wuhan wanted to do this, and they could easily have gotten funding somewhere else, or done it out of their regular budget). They found a close relative of BANAL-52 and added a furin cleavage site as a simple twelve-nucleotide insertion, using the human method of encoding arginine that their genetic engineers were familiar with. Then it leaked, spread for a while in the general Wuhan population, and eventually made it to the wet market where it got detected. Peter: As mentioned earlier, the DEFUSE grant was rejected. Further, the grant said that the Wuhan Institute of Virology was responsible for finding the viruses, and the University of North Carolina would do all the gain-of-function research. This was a reasonable division of labor, since UNC was actually good at gain-of-function research, and WIV mostly wasn’t. They had done a few very simple gain-of-function projects before, but weren’t really set up for this particular proposal and were happy to leave it for their American colleagues. Even if WIV did try to create COVID, they couldn’t have. As Yuri said, COVID looks like BANAL-52 plus a furin cleavage site. But WIV didn’t have BANAL-52. It wasn’t discovered until after the COVID pandemic started, when scientists scoured the area for potential COVID relatives. WIV had a more distant COVID relative, RATG-13. But you can’t create COVID from RATG-13; they’re too different. You would need BANAL-52, or some as-yet-undiscovered extremely close relative. WIV had neither. Are we sure they had neither? Yes. Remember, WIV’s whole job was looking for new coronaviruses. They published lists of which ones they had found pretty regularly. They published their last list in mid-2019, just a few months before the pandemic. Although lab leak proponents claimed these lists showed weird discrepancies, this was just their inability to keep names consistent, and all the lists showed basically the same viruses (plus a few extra on the later ones, as they kept discovering more). The lists didn’t include BANAL-52 or any other suitable COVID relatives - only RATG-13, which isn’t close enough to work. Could they have been keeping their discovery of BANAL-52 secret? No. Pre-pandemic, there was nothing interesting about it; our understanding of virology wasn’t good enough to point this out as a potential pandemic candidate. WIV did its gain-of-function research openly and proudly (before the pandemic, gain-of-function wasn’t as unpopular as it is now) so it’s not like they wanted to keep it secret because they might gain-of-function it later. Their lists very clearly showed they had no virus they could create COVID from, and they had no reason to hide it if they did. COVID’s furin cleavage site is admittedly unusual. But it’s unusual in a way that looks natural rather than man-made. Labs don’t usually add furin cleavage sites through nucleotide insertions (they usually mutate what’s already there). On the other hand, viruses get weird insertions of 12+ nucleotides in nature. For example, HKU1 is another emergent Chinese coronavirus that caused a small outbreak of pneumonia in 2004. It had a 15 nucleotide insertion right next to its furin cleavage site. Later strains of COVID got further 12 - 15 nucleotide insertions. Plenty of flus have 12 to 15 nucleotide insertions compared to other earlier flu strains. Sometimes insertions happen because of a mistake in viral replication. Other times the virus gets confused between its own RNA and its host’s, and splices a bit of the host RNA into the virus. This would neatly explain why the insertion used the unusual coding CGG for arginine, which is common in animals but rare in viruses. On the other hand, it’s not that rare in viruses - COVID uses CGG for arginine about 3% of the time. And human engineers don’t necessarily use it any more than that - Peter was able to find one example of humans adding arginine to a virus, and 0 out of the 5 arginines added were CGG. COVID’s furin cleavage site is a mess. When humans are inserting furin cleavage sites into viruses for gain-of-function, the standard practice is RRKR, a very nice and simple furin cleavage site which works well. COVID uses PRRAR, a bizarre furin cleavage site which no human has ever used before, and which virologists expected to work poorly. They later found that an adjacent part of COVID’s genome twisted the protein in an unusual way that allowed PRRAR to be a viable furin cleavage site, but this discovery took a lot of computer power, and was only made after COVID became important. The Wuhan virologists supposedly doing gain-of-function research on COVID shouldn’t have known this would work. Why didn’t they just use the standard RRKR site, which would have worked better? Everyone thinks it works better! Even the virus eventually decided it worked better - sometime during the course of the pandemic, it mutated away from its weird PRRAR furin cleavage site towards a more normal form. Further, COVID’s furin cleavage site was inserted via what seems to be a frameshift mutation - it wasn’t a clean insertion of the amino acids that formed the site, it was an insertion of a sequence which changed the context of the surrounding nucleotides into the amino acids that formed the site. This is a pointless too-clever-by-half “flourish” that there would be no reason for a human engineer to do. But it’s exactly the kind of weird thing that happens in the random chance of evolution. COVID is hard to culture. If you culture it in most standard media or animals, it will quickly develop characteristic mutations. But the original Wuhan strains didn’t have these mutations. The only ways to culture it without mutations are in human airway cells, or (apparently) in live raccoon-dogs. Getting human airway cells requires a donor (ie someone who donates their body to science), and Wuhan had never done this before (it was one of the technologies only used at the superior North Carolina site). As for raccoon-dogs, it sure does seems suspicious that the virus is already suited to them. The claim that COVID is uniquely adapted to humans is false. The paper that claimed that defined how well COVID was adapted to different animals by those animals’ difference (on the relevant cell receptors) from humans. So in its methodology, humans came out #1 by default. If you don’t do that, COVID is better-adapted to many other animals. It’s not necessarily true that viruses see a burst of mutations when they enter a new host. COVID spread to deer and mink, and in neither case was there a burst of mutations. COVID has a pretty simple job of infecting respiratory cells and is already very good at it, regardless of species. In Yuri’s model, Wuhan Institute of Virology picked up a discarded grant and decided to do the gain-of-function half allotted to a different university, despite their relative inexperience. They skipped over all the SARS-like viruses they were supposed to work on, and all the standard gain-of-function model backbones, in favor of BANAL-52, a virus which would not be discovered for another two years, but which they somehow had samples of, which they had for some reason decided to keep secret despite its total lack of interestingness. Then they would have had to eschew all usual gain-of-function practices in favor of inserting a weird furin cleavage site that shouldn’t have worked according to the theory they had at the time, via a frameshift mutation. Then they would have had to culture it, a technique beyond their limited capabilities. Then it would have had to leak, and magically show up again in front of the raccoon-dog stall at a wet market. Yuri: WIV wouldn’t have needed to keep BANAL-52 “secret” in some kind of sinister way. Plenty of researchers have backlogs of work they haven’t published yet. Probably they a found BANAL relative in one of their normal sampling trips, did some preliminary studies on it, and planned to publish it later once they cleaned up their data. Everyone works like this. The part of DEFUSE saying that they would only work on viruses that were 95% similar to SARS is unclear and might mean something else. It looks more like they say they’ll start with those viruses, but also do some work on novel viruses. BANAL-52 could have been one of the novel viruses. The furin cleavage site is weird, but the researchers might have done that on purpose, to make the virus easier to keep track of, or to test different furin cleavage sites. Depending on the exact BANAL-52 relative they used, it might not even be a frameshift; there’s a particular way to spell serine that would make the insertion more natural. The claims that COVID can’t be cultured in normal media are based on speculative original research by Peter and might not hold up. Peter: WIV did most of its virus-gathering in a trip to a Yunnan cave between 2010 and 2015. All those viruses have long since been processed and added to the database. There’s no sign that they made more trips to Yunnan caves, and no reason for them to keep that secret. So the idea that they might just have some new viruses they didn’t publish doesn’t hold up. But suppose they did make more trips. Given the amount of time between the DEFUSE proposal and COVID, if they kept to their normal virus-collection rate, they would have gotten about thirty new viruses. What’s the chance that one of those was BANAL-52? There are thousands of bat viruses, and BANAL-52 is so rare that it wasn’t found until well after the pandemic started and people were looking for it very hard. So the chance that one of their 30 would be BANAL-52 is low. Also, they said in DEFUSE that they planned to go back to the same Yunnan cave. But BANAL-52 was found far away from that cave, so unless it ranged over a wide area, they probably couldn’t have found it even if they got very lucky. Session 3: Closing Arguments This third debate was supposed to be about “inference”, ie how much Bayesian evidence was provided by each of the facts given so far, and how to fit them into the Rootclaim probabilistic model. I’m going to relegate my summary of the more probabilistic half to the next section of this post, and just include the closing arguments here. Saar: Peter’s case hinges on the idea that it’s very improbable that a lab leak pandemic would first show up at a wet market. But this isn’t necessarily improbable. The Huanan Seafood Market had several factors that made it a likely location for a superspreader event. It was busy, with over 10,000 visitors a day. Many of the people there (eg the 1,000 vendors) came back daily, letting them reinfect each other. It had poor ventilation, especially in the high-positivity area near the raccoon-dog stall. It had cold wet surfaces on which the virus could survive for long periods. It was indoors, which prevented UV light from killing the virus. Given a small amount of sporadic COVID going around Wuhan, it’s not surprising for the first place it started spreading en masse to be a wet market. In fact, we have several examples of this. When China was COVID Zero, there would occasionally be small outbreaks that the authorities would have to contain. Most of these were at wet markets. For example, the big COVID outbreak in Beijing started at Xinfadi Market, their local seafood market. This couldn’t be an animal spillover, because there were no raccoon-dogs or other weird wildlife there. So it must be that wet markets are natural places for superspreader events. There are several other examples, which make up about half of the total outbreaks in Zero COVID era China, plus others in Singapore and Thailand. Since COVID clusters concentrate in wet markets even when there is no animal spillover, we should accept this as a property of the virus, and not attribute any significance to the fact that this happened in Wuhan too. Peter: About 1/10,000 citizens of Wuhan was a wet market vendor. So there’s a 1/10,000 chance that the first known COVID case should be a wet market vendor by chance alone. Weibo lists the most popular places for people to check in to their network on their phones, and the wet market was the 1600th most popular place in Wuhan, meaning that if you weight locations by busy-ness, there’s a less than 1/1600 chance that the first cases would be in the wet market. Yes, the wet market is indoors, has mediocre ventilation, has repeat visitors, etc. So do thousands of other places in Wuhan, like schools, hospitals, workplaces, places of worship. The wet market isn’t special in any way. And again, it wasn’t a superspreader event! COVID spread at the same rate in the wet market as it does everywhere else: doubling once per 3.5 days. It doesn’t matter what kinds of arguments you can come up with for why the wet market should have been the perfect superspreader event location, we can look at it and see that it wasn’t. It’s an environment that spreads COVID at exactly the normal rate. Zero COVID era Chinese outbreaks were concentrated in wet markets because they received infected animal products. We know why there was an outbreak in the Xinfadi Market in Beijing: it was because the seafood stall got frozen fish from some non-Zero-COVID country, the fish had COVID particles on it, and the vendor got infected and spread it to everyone else. Something like this is true for the other Chinese wet market based outbreaks we know about it. So this makes the opposite point you think it does: wet markets start outbreaks because there are infected goods being sold there. Then the virus spreads through the wet market at a completely normal rate. Saar: The Weibo list of 1600 places bigger than the wet market is likely inaccurate, because it's based on check-in data and people don't check in to seafood markets. Most of those 1600 places aren't amenable to superspread. The 70 markets supposedly bigger than Huanan are irrelevant, because they're supermarkets, open air markets, etc. Huanan is the largest seafood market in central China, and a more likely place for the first cluster of cases to be noticed. Markets weren't a common spillover location in SARS1, so the zoonosis hypothesis hasn't "called" this event in a way that should give them a high Bayes factor. And there’s still plenty of evidence for isolated (though not super-spreading) pre-market cases. A British expatriate in Wuhan, Connor Reed, says he got sick in November, three weeks before the first wet market case. Later the hospital tested his samples and said it was COVID. Another paper reports 90 cases before the first wet market one. Peter: Connor Reed was lying. The case wasn’t reported in any peer-reviewed paper. It was reported in the tabloid The Daily Mail, months after it supposedly happened. He also told the Mail that his cat died of coronavirus too, which is rare-to-impossible. Also, to get a positive hospital test, he would have had to go to the hospital, but he was 25 years old and almost no 25-year-olds go to the hospital for coronavirus. His only evidence that it was COVID was that two months later, the hospital supposedly “notified” him that it was. The hospital never informed anyone else of this extremely surprising fact which would be the biggest scientific story of the year if true. So probably he was lying. Incidentally, he died of a drug overdose shortly after giving the Mail that story; while not all drug addicts are liars, given all the other implausibilities in his story, this certainly doesn’t make him seem more credible. And in any case, he claimed he got his case at a market “like in the media” The other 90 cases are also fake. A lab leak guy found a paper that mentioned 90 more cases than other papers, and made up a conspiracy theory where the author was trying to secretly communicate that there had been 90 secret cases before any of the confirmed cases, even though there was nothing about this in the text of the paper. But actually that paper just counted cases differently than other papers, and they were referring to normal cases after the pandemic officially started. Again, I’ll come back to the discussion about inference later, but for now, here’s a table of both sides’ reasoning. This exact presentation comparing both analyses is mine3, but you can see Saar’s version here, and Peter’s starting at 45:33 of this video. Slightly made up; the two sides didn’t express their probabilities in the same way and I had to make editorial decisions to match them. Note that these aren't entirely comparable because Peter is being laxer about out-of-model probability than Saar. Although Saar's final odds here are 533-to-1, this just the central estimate. Rootclaim’s real final probability is 94% lab leak. You can see their analysis here. And The Winner Is . . . … … … … … Peter and the zoonosis hypothesis. This was a decisive victory. There were two judges, who each gave separate verdicts (or were allowed to declare a draw). Both judges decided in favor of Peter. You can see the judges’ own summary of their reasoning here (Will, Eric) Manifold agreed with the judges. There was a prediction market on who would win. It started out 70-30 in favor of lab leak. As the videos came out, zoonosis started doing better and better. I don’t want to take the exact final numbers too seriously, since I think some of the later price increases involved hints from the participants’ behavior. But it’s clear which way viewers thought the wind was blowing4. Around the same time, the Good Judgment Project - Philip Tetlock’s group studying superforecasters - put out a report on the lab leak hypothesis. After studying it in depth, his forecasters ended up 75-25 in favor of zoonosis. The Rootclaim debate was one of ten sources they said they found especially interesting. And also around the same time, and unrelated to any of this, the Global Catastrophic Risks Institute surveyed experts (“168 virologists, infectious disease epidemiologists, and other scientists from 47 countries”) and found the same thing (though see here for some potential problems with the survey): For what it’s worth, I was close to 50-50 before the debate, and now I’m 90-10 in favor of zoonosis. III. The Math And The Aftermath The third debate session was about “inference”, how to put evidence together. I put this part off until after disclosing the winner, because I wanted to talk about some of these issues at more length. The Math: Judges Both judges included a probabilistic analysis in their written decision. Here’s the same table as above, expanded to add the judges: I shoehorned the judges’ factors into the categories I already had; some of them were actually subtly different from Peter’s, Saar’s, and each other’s. The “priors” category is especially a mess here. We’ll go over these later, but I get the impression that they both thought of probabilistic analyses as an afterthought. For example, Judge Eric wrote 30,000 words about which considerations moved him, and only then includes the analysis, saying: I am not convinced that this Bayesian calculation is even an appropriate way to estimate the relative posterior probability of Z and LL; it just seemed fair that after criticizing Rootclaim’s calculations at length I should make an attempt at it myself. Judge Will’s decision ran to 10,000 words. He said he independently tried both reasoning it out intuitively, and running the Bayesian analysis, and was relieved when these two methods returned the same result. He said: I am skeptical that the Bayesian decision making/evaluation methods are any more "objective" than [intuitive reasoning]. I think they maximize legibility, not objectivity, and tend to hide the intuitive/heuristic portion in the data inclusion step and values, where it’s harder to see . . . I am not skilled in the Bayesian method, and I am sure I made significant mistakes. More time and practice would improve and refine my estimates. At the fundamental rules of the universe level, Bayesian analysis must be the best way to evaluate evidence. However, I am unsure that it’s a good strategy for a human given our cognitive limitations, and doubly unsure it’s truly being used (in the dispassionate sense) where the outcome is social desirability/fame/Twitter likes. I’m focusing on this because Saar’s opinion is that the debate went wrong (for his side) because he didn’t realize the judges were going to use Bayesian math, they did the math wrong (because Saar hadn’t done enough work explaining how to do it right), and so they got the wrong answer. I want to discuss the math errors he thinks the judges made, but this discussion would be incomplete without mentioning that the judges themselves say the numbers were only a supplement for their intuitive reasoning. That having been said, let’s look deeper into some of Saar’s concerns. The Math: Extreme Odds Saar complained that Peter’s odds were too extreme. For example, Peter said there was only a 1/10,000 chance that a lab leak pandemic would first show up at a wet market. Peter’s argument went something like: obviously a zoonotic pandemic would start at a site selling weird animals. But a lab leak pandemic - if it didn’t start at the lab - could show up anywhere. 1/10,000 Wuhan citizens work at the wet market. So if a lab leak was going to show up somewhere random, the wet market was a 1/10,000 chance. Saar had specific arguments against this, but he also had a more general argument: you should rarely see odds like 1/10,000 outside of well-understood domains. In his blog post, he gave this example: A prosecutor shows the court a statistical analysis of which DNA markers matched the defendant and their prevalence, arriving at a 1E-9 probability they would all match a random person, implying a Bayes factor near 1E9 for guilty. But if we try to estimate p(DNA|~guilty) by truly assuming innocence, it is immediately evident how ridiculous it is to claim only 1 out of a billion innocent suspects will have a DNA match to the crime scene. There are obviously far better explanations like a lab mistake, framing, an object of the suspect being brought by someone to the scene, etc. So the real p(wet market|lab leak) isn’t the 1/10,000 chance a pandemic arising in a random place hits the wet market, but the (higher?) probability that there’s something wrong with Peter’s argument. Then Saar tried to show specific things that might be wrong with Peter’s argument. I didn’t find his specific examples convincing. But maybe the question shouldn’t be whether I agreed with him. It should be whether I’m so confident he’s wrong that I would give it 10,000-to-1 odds. This makes total sense, it’s absolutely true, and I want to be really, really careful with it. If you take this kind of reasoning too far, you can convince yourself that the sun won’t rise tomorrow morning. All you have to do is propose 100 different reasons the sunrise might not happen. For example: The sun might go nova.
Inline links: Pekar 2022, Pipes 2021, says, 3, here, this video, https://substackcdn.com/image/fetch/$s_!8aU2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F815fe32d-d7ea-401b-b3a2-d8cd25b52ee8_490x780.png, https://substackcdn.com/image/fetch/$s_!0Tm_!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd0492f69-7b7e-4611-9d76-64ef8d7f59d5_511x511.png, Will, Eric, agreed, 4, put out a report on the lab leak hypothesis, https://substackcdn.com/image/fetch/$s_!g7k2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F37f1b493-b556-41ec-925e-03f9d8bc26cb_1456x849.webp, surveyed experts, see here, https://substackcdn.com/image/fetch/$s_!Zejl!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe9c88e87-b6ca-4c6d-840e-24da726f50b7_975x365.png, https://substackcdn.com/image/fetch/$s_!T5rV!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4983e2cd-4151-42de-9685-08037ef7a8e8_635x788.png
Your Book Review: The Family That Couldn’t Sleep
July 12, 2024 · Original source
Yeah. The conclusion DTM drew – and this was a common conclusion at the time – was that homozygosity somehow made you more vulnerable to CJD, and M/M homozygosity made you vulnerable to BSE-borne CJD in particular. We cannot criticise the author for not predicting the future, but we live in the future, and can say how this worked out. Turns out, nope, M/V heterozygotes totally get vCJD. After a British man in his 30s died of CJD in 2016, he was found to have vCJD and an M/V genotype. He was tested for vCJD only because he was exceptionally young for someone with a sporadic prion disease – meaning people developing it later in life would be missed6. Did you know up to 1 in 2000 people in the UK have latent vCJD? There is one line in The Family That Couldn’t Sleep that stopped me dead in my tracks when I read it: What happens to the Italian family in the end depends less on their own actions than on the world’s interest in prion diseases, which they cannot control. If lots of people are afraid of getting variant CJD, the family benefits. If fear of prion disease goes the way of the fear of swine flu or Ebola, then they will be orphaned again. THIS BOOK IS FROM 2006! Three years before the swine flu pandemic! Eight years before the Ebola pandemic! “If you’re looking for a sign, this is it.” --------------------------------------------------------- The last section of The Family That Couldn’t Sleep addresses BSE fears in America and a nascent internet subculture DTM calls “Creutzfeldt Jakobins” – people who track American CJD cases, trying to spot vCJD patterns. When reading his description of the Creutzfeldt Jakobins, my mind constantly, uncontrollably turned to covid. Here it was – an online community of people deeply skeptical about a disease’s official story, tracking every contradiction, every implausibility, every statistic that failed to apply to the individual. Self-described “redneck hippies” and “soccer mom Republicans” teaming up to find the truth hidden behind an impossible world. You know what they’re doing now. I’ve always combined a deep interest in medicine with a healthy distrust for it. People who are constitutionally inquisitive, anti-authoritarian, and suspicious about official narratives tend to end up skeptical of at least some mainstream claims in the field. This is not to say I think you should take bleach enemas or something, just that I understand the impulse behind concluding the US government was covering up a local vCJD wave. Traditionally, sporadic prion diseases are said to have a prevalence of one in a million. (Hold on to that for a second.) The last section of the book is a chronology of Americans finding bizarrely more than one in a million of their friends dying of sporadic CJD, often at inexplicably young ages, sometimes in geographical clusters. This is understandably suspicious. Then DTM goes on to reassure us by saying none of these cases were confirmed to have an M/M genotype, which OH GOD OH FUCK A number of high-profile people in the prion world, including Gajdusek, are clarified as not believing sporadic prion diseases exist. You get the impression DTM doesn’t, either. Now, how common are prion diseases? Eric Vallabh Minikel has an answer for you! Eric and his wife Sonia are prion researchers from a rather unique background – after Sonia was diagnosed as having a single-gene mutation with ~100% penetrance for prion disease, they left their previous jobs to dedicate their lives to curing it. It turns out, when you run the numbers, you get not one in a million but 1 in 5000 people dying of prion diseases. This is best described as “nightmarishly high”. I’m normed on genetic disorders. A genetic disorder that affects one in five thousand people is pretty common! I have known, in person, completely unselected, just from “random people I’ve met in my life in a non-medical context”, someone with a ~1/250k syndrome and someone with a ~1/50k-100k syndrome. I don’t think anyone in my extended family knows someone who died of a prion disease. I feel like it would’ve come up if they did! Prion diseases have distinctive phenotypes. Not distinctive enough, apparently, to avoid a lot of CJD being misdiagnosed as Alzheimer’s – but diagnosis is consistently insane. Something DTM reiterates throughout The Family That Couldn’t Sleep is just what prion dementia looks like. The characteristic dementia in prion diseases spares something – “self” or “recognition” or “reflection” – that is not spared by Alzheimer’s, or by most common dementias. Shouldn’t this be, uh, noticeable?7 They kill rapidly, often over the course of months, and often onset in midlife. ALS shares this pattern and is way, way more common than prion diseases; you hear about ALS far more in the “disorder people actually have” sense. What am I missing here? Anyway: 1 in 2000 prevalence of latent vCJD in the UK + extreme lack of clarity over whether scrapie is human-transmissible + blood donations spread vCJD + sporadic CJD prevalence keeps going up = ??? (Yes, I am annoyed that most countries have lifted their ban on UK blood donors, thank you for asking!) --------------------------------------------------------- But back to the book. The “American chapter” is one-third about the country’s response to vCJD, one-third about the Creutzfeldt Jakobins, and one-third about chronic wasting disease. The last part is the most interesting. Chronic wasting disease is a prion disease of deer. Like scrapie, it “probably, we hope” isn’t human-transmissible (eat venison at your own risk). Under natural circumstances, deer shouldn’t get prion diseases: A prion plague should not be possible among ruminants in the wild. Deer are not cannibals, as the cows that spread BSE were forced to be; and, because deer and elk are not domesticated, they do not have enough contact with one another to spread a prion infection the way sheep are thought to spread scrapie. But deer do not live as they used to live, humans having once again brought their ambitions to bear on the natural course of things. The Family That Couldn’t Sleep is a book of medical anthropology. Anthropology of the Veneto, anthropology of Papua New Guinea, anthropology of 1990s Britain. Here, it is an anthropology of America. Americans, having won the world, still fight to win their own backyard. The North American continent is geographically diverse, cutting through rain-snow-shine, mountains jutting over plains, cities sprawling into wilderness, habitations criss-cross dotted with surprisingly few empty zones. Go somewhere like Denver, the Mile High City, three million people fighting against nature. Few other countries have anything like this; geographically vast polities usually have uninhabitable blocks. Australians are twenty-five million people clustered against the shore. It still surprises me, after all this time, how every US state has a meaningful city8. Midcentury Denver, growing and sprawling out across its mountains, started to run into their natural inhabitants – deer. Starvation is one way nature adjusts the deer population to the available food supply. People did not usually see this process, but in the 1950s and 1960s Colorado became more densely settled, reducing forested areas and forcing deer to look longer and harder for food. At the same time, the state enacted conservation laws, limiting when and where hunters could shoot. Soon emaciated deer began wandering onto the lawns and through suburban streets looking for a meal. People began to feed them, only to find that they died anyway. They would drop dead by haystacks, along highways, and in flower beds. In the late 1960s, a young biologist named Gene Schoonveld tried to figure out why the deer starved even when they were fed.9 He deprived some deer of food for a while, “[h]e cut windows in their stomachs to see what went on inside, and then he began to feed them”. While this was going on, he had a control group of healthy, well-fed deer as backups in case anything went wrong. It did...but not to the experimental group. The pen in which the deer were kept also housed sheep, which, it turned out, were scrapie carriers. The deer somehow acquired scrapie – there’s a huge unanswered question here, which DTM doesn’t address. How did they get scrapie? They didn’t eat the sheep, presumably. Did it somehow transmit from casual contact? This is not supposed to happen. And yet: the deer in the sheep pen started dying of a mysterious scrapie-like disease, one never reported before, that would go on to infect thousands. These deer were released into the wild. Ten years later, the first reports of chronic wasting disease came out. The disease spread across deer and elk in the western half of the country. By the turn of the millennium, cases were exploding – and lost all geographical restriction. DTM can report up to 2005, at which point it was floating around Upstate New York. This kind of spread doesn’t track natural deer migration. That’s irrelevant, because nothing about CWD’s spread is natural. We shift gears into an anthropology of the American hunter. The hunter wants to shoot the most impressive buck, to bag himself one with as many “points” as possible – one whose antlers branch out most. A “ten-point buck” has five branches on each horn: Original by Ric McArthur Nature doesn’t make enough bucks with perfectly symmetrical ten-point horns. To fill the demand, the market had to step in. Thus was born the deer farm industry, which raises captive deer in better genetic and nutritional conditions than Nature permits, then ships them across the country so hunters who couldn’t get legit ten-point bucks get the taxidermy piece for their wall. These are controversial amongst hunters and illegal in numerous states – but the industry is big enough to spread CWD. (The kind of hunter who needs a deer shipped to his house is the kind of hunter who will fumble killing it.) Another problem is supplemental feeding – leaving out protein-enriched food for deer to eat. This produces “trophy class animals at an earlier age”, but again, what’s in that protein? (“It is much like feeding your cows 41 percent protein cottonseed cake during the winter to raise the protein level in the cow’s diet to a level that will maintain acceptable production”, says that article from 1991.)10 The book segues into a vignette. CWD was new in Wisconsin in the early 2000s, and the state’s Department of Natural Resources was optimistic it could eradicate it. In a state with a love of hunting, you could, in theory, recruit people to kill every single deer in a 400-square-mile radius: In many states, the state would have had to call out the National Guard for such an onslaught, but hunting is a passion in Wisconsin. Hunters shoot 450,000 deer every year, more than in any other state. “I’m looking for ardent hunters to help us, unless fear or their wives keep them away,” one DNR official told a Milwaukee magazine. The state extended the normal hunting season and waived the usual limit of one buck per hunter, and the hunters came out in force. The whole affair was gruesome – one official called it “hunting for slob hunters”. If you’re trying to eradicate a prion disease, you can’t very well let people take the carcasses home to eat. Bodies piled up in control stations, decomposition mingling with bleach. The 2002 hunt established a base rate of 2% for chronic wasting disease in Wisconsin deer, with the most affected areas getting up to 10%. Further hunts in 2003, 2004, and 2005 spread to wider and wider areas – and didn’t move the needle one bit. This is to say that CWD is quite a bit more common in the American deer population than BSE ever was in British cattle. Since publication, it’s popped up in Norway and South Korea. Notably, Norway doesn’t allow for the import of cervids, raising numerous questions about how it got there. There are no unambiguous cases of CWD transmission to humans, and in vivo/in vitro primate studies have mixed results. There sure are some unusually young hunters with sporadic CJD, though. But don’t worry, most of them aren’t M/M homozygotes! There is an absolute ton going on in this book. I’ve had to skim over whole sections. Parts that couldn’t be easily slotted into a narrative review include: When Gajdusek was invited to a party at Prusiner’s house, he was horrified to find his rival had purchased hundreds of New Guinean statues – all with the genitals removed.
Inline links: Yeah., nope, M/V heterozygotes totally get vCJD, 6, up to 1 in 2000 people in the UK have latent vCJD, bleach enemas, has an answer for you, 7, blood donations spread vCJD, going, up, 8, 9, not supposed to happen, https://substackcdn.com/image/fetch/$s_!0J8B!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2a64b287-a7c6-4d82-bbe1-da949fc93118_1024x683.png, Ric McArthur, controversial, amongst, hunters, illegal in numerous states, trophy class animals at an earlier age, 10, Norway, South Korea, mixed results, sure are
Original by Ric McArthur Nature doesn’t make enough bucks with perfectly symmetrical ten-point horns. To fill the demand, the market had to step in. Thus was born the deer farm industry, which raises captive deer in better genetic and nutritional conditions than Nature permits, then ships them across the country so hunters who couldn’t get legit ten-point bucks get the taxidermy piece for their wall. These are controversial amongst hunters and illegal in numerous states – but the industry is big enough to spread CWD. (The kind of hunter who needs a deer shipped to his house is the kind of hunter who will fumble killing it.) Another problem is supplemental feeding – leaving out protein-enriched food for deer to eat. This produces “trophy class animals at an earlier age”, but again, what’s in that protein? (“It is much like feeding your cows 41 percent protein cottonseed cake during the winter to raise the protein level in the cow’s diet to a level that will maintain acceptable production”, says that article from 1991.)10 The book segues into a vignette. CWD was new in Wisconsin in the early 2000s, and the state’s Department of Natural Resources was optimistic it could eradicate it. In a state with a love of hunting, you could, in theory, recruit people to kill every single deer in a 400-square-mile radius: In many states, the state would have had to call out the National Guard for such an onslaught, but hunting is a passion in Wisconsin. Hunters shoot 450,000 deer every year, more than in any other state. “I’m looking for ardent hunters to help us, unless fear or their wives keep them away,” one DNR official told a Milwaukee magazine. The state extended the normal hunting season and waived the usual limit of one buck per hunter, and the hunters came out in force. The whole affair was gruesome – one official called it “hunting for slob hunters”. If you’re trying to eradicate a prion disease, you can’t very well let people take the carcasses home to eat. Bodies piled up in control stations, decomposition mingling with bleach. The 2002 hunt established a base rate of 2% for chronic wasting disease in Wisconsin deer, with the most affected areas getting up to 10%. Further hunts in 2003, 2004, and 2005 spread to wider and wider areas – and didn’t move the needle one bit. This is to say that CWD is quite a bit more common in the American deer population than BSE ever was in British cattle. Since publication, it’s popped up in Norway and South Korea. Notably, Norway doesn’t allow for the import of cervids, raising numerous questions about how it got there. There are no unambiguous cases of CWD transmission to humans, and in vivo/in vitro primate studies have mixed results. There sure are some unusually young hunters with sporadic CJD, though. But don’t worry, most of them aren’t M/M homozygotes! There is an absolute ton going on in this book. I’ve had to skim over whole sections. Parts that couldn’t be easily slotted into a narrative review include: When Gajdusek was invited to a party at Prusiner’s house, he was horrified to find his rival had purchased hundreds of New Guinean statues – all with the genitals removed.
Inline links: Ric McArthur, controversial, amongst, hunters, illegal in numerous states, trophy class animals at an earlier age, 10, Norway, South Korea, mixed results, sure are
DTM refers to this as Quality Deer Management, but I think he’s wrong? QDM seems to be a particular attitude towards hunting that avoids shooting young bucks to optimize antler development, while shooting more doe than a pure “kill all the cool-looking ones” strategy will to avoid overpopulation. You can do QDM with or without supplemental feeding. I might be wrong – I know very little about deer hunting
Inline links: a particular attitude towards hunting