WHO is a recurring organization in the Astral Codex Ten archive, appearing 16 times across 16 issues between January 29, 2021 and June 18, 2025. The archive places it in contexts such as "some doctors and epidemiologists at the FDA and WHO"; "all the experts, the CDC, the WHO, the New York Times, et cetera"; "The WHO and CDC get billions of dollars in funding and neither of them has been able to communicate their perspective anywhere near as effectively". It most often appears alongside COVID, CDC, Scott.
- Article page
- WHO
- Mention count
- 16
- Issue count
- 16
- First seen
- January 29, 2021
- Last seen
- June 18, 2025
Outbound links
- http://esr.ibiblio.org/?p=8558
- http://wimflyc.blogspot.com/2021/01/the-henry-adams-curve-closer-look.html
- https://aiimpacts.org/historic-trends-in-ship-size/
- https://archive.is/Ie0MZ
- https://arstechnica.com/tech-policy/2023/06/lawyers-have-real-bad-day-in-court-after-citing-fake-cases-made-up-by-chatgpt/
- https://askwhocastsai.substack.com/
- https://asteriskmag.com/issues/1/why-isn-t-the-whole-world-rich
- https://astralcodexten.substack.com/p/open-thread-216/comment/5643253?s=w
- https://benthams.substack.com/p/the-anthropic-argument-for-theism
- https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark
- https://deeshaa.org/2021/10/12/economists-as-camp-following-whores/
- https://default.blog/p/lesbians-who-only-date-men
Issue trail
- Contra Weyl On Technocracy
- WebMD, And The Tragedy Of Legible Expertise
- Ivermectin: Much More Than You Wanted To Know
- Highlights From The Comments On Ivermectin
- Links For December
- Movie Review: Don't Look Up
- Predictions For 2022
- In Partial, Grudging Defense Of The Hearing Voices Movement
- Your Book Review: Viral
- Prediction Market FAQ
- Henrietta Lacks Seems Like A Nice Person, But Not A Scientific Hero
- The Psychopolitics Of Trauma
- Highlights From The Comments On The Lab Leak Debate
- Your Book Review: Nine Lives
- 1DaySooner's Trump II Health Policy Proposals
- ACX Grants 1-3 Year Updates
Related pages
Source context
4. Expert opinion vs. popular opinion. Again, obviously a spectrum. There are subquestions here where nobody disagrees - shouldn't there be some doctors and epidemiologists at the FDA and WHO? But I think it's fair to say that there's a distinction between a country where doctors and epidemiologists can unilaterally ban cigarettes vs. one where that decision is made at a more democratic level.
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...treatments. Putting aside the question of accuracy and grading only on presentation and scale, this is the most impressive act of science communication I have ever seen. The WHO and CDC get billions of dollars in funding and neither of them has been able to communicate their perspective anywhere near as effectively. Even an atheist can appreciate a cathedral, and even an ivermectin skeptic should be able to appreciate this website. What stands out most in this image (their studies o...
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Last June, the World Health Organization published a 300-page directive on the human rights of mental-health clients — and despite the mammoth bureaucracy from which it emerged, it is a revolutionary manifesto on the subject of severe psychiatric disorders. It challenges biological psychiatry’s authority, its expertise and insight about the psyche. And it calls for an end to all involuntary or coercive treatment and to the dominance of the pharmaceutical approach that is foremost in mental health care across conditions, including psychosis, bipolar disorder, depression and a host of other diagnoses. Psychiatry’s problematic drugs, the W.H.O. maintains, must no longer be an unquestioned mainstay.
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when you’re not sure which of many competing experts to trust, you should trust a prediction market instead of any of them Going through these claims one by one: 3.1: Why expect all prediction markets to agree with each other? Either all prediction markets agree with each other, or you can get rich quick: Suppose prediction markets disagreed. For example, suppose the RNC ran an Official Republican Prediction Market that said there was only a 10% chance Democrats would win the next election, and a 90% chance Republicans would. And suppose the DNC ran an Official Democrat Prediction Market that made the opposite prediction: 90% chance Democrats, 10% chance Republicans. Then you could buy a share of “Democrats will win” from the Republican market for 10 cents, plus a share of “Republicans will win” from the Democrat market for 10 cents, and be guaranteed to make $1 when one party or the other wins. You have turned 20 cents into a guaranteed $1. Repeat until you are rich or the mispricing has been corrected. This is just what financial experts call “arbitrage”. You may notice that in finance, people always give specific prices for things like shares of stock, barrels of oil, or Bitcoins. People say things like “Google stock is up to $300”, but never “Google stock is up to $300 on the NYSE, but down to $200 on NASDAQ”. If that was true, people would buy it on NASDAQ, sell it on NYSE, make $100 in free money, and get rich quick. In ideal situations, arbitrage forces everybody everywhere to agree on the same price for a financial instrument. Prediction markets turn claims about truth into financial instruments in a way which forces everybody everywhere to agree on how likely the claim is to be true. 3.2: Why expect prediction markets to be hard for special interests to manipulate? Either a prediction market is not currently mispriced because of a manipulation attempt, or you can get rich quick. Argument: Suppose a prediction market was currently mispriced because of a manipulation attempt. For example, suppose there is a prediction market for whether the sun will rise tomorrow. The true probability is obviously 100%, corresponding to a cost of $1.00. But suppose some special interest who wanted to trick people into believing the sun would not rise successfully spent money to bid the market down to only 10%. This means that you can buy, for $0.10, a share which pays $1 if the sun rises tomorrow. In other words, you can dectuple your money for free. Repeat until you are rich or the mispricing has been corrected. This may sound complicated in theory, but it plays out straightforwardly in real life. As a test, I tried to manipulate the market on whether Austin Chen, founder of Manifold Markets, would be charged with a felony. There’s no reason to think he should be, so the price started at 5%. I spent $200 in Manifold’s play money bidding it up to 95%. Within an hour, other investors noticed the mispricing and corrected it back down to 5% again. 3.3: Why expect prediction markets to be free from bias? Either a prediction market is not currently mispriced because of bias, or you can get rich quick. The argument: Suppose all smart people, including you, know that there is an 80% chance that the Democrats’ economic plan will create new jobs. But suppose that Republicans, because of their partisan biases, refuse to believe it, and say there is only a 40% chance. And suppose the Republicans set up their own prediction market where they bid the price of a share down to $0.40. You can, of course, go on this prediction market, buy shares for $0.40, and double your money in expectation. Repeat until you are rich or the mispricing has been corrected. I already described how something like this happens on PredictIt (a non-ideal prediction market that you can only make a few hundred dollars in expectation by correcting), and that I do in fact make a few hundred dollars every election season. 3.4: Why should I believe a prediction market’s consensus over my own opinion? This is the same argument as “the prediction market will always be at least as accurate as the top expert” only with you in the place of the top expert. Either prediction markets are at least as smart as you are, or you can get rich quick. The argument here is the same as “at least as smart as the smartest expert” argument in 2, except replacing “the smartest expert” with “you”. But just to lay it out explicitly: Suppose you were smarter than some prediction market. Then if you disagreed with the market, usually you would be right and it would be wrong. So look for cases where you disagree with the market, buy those shares, and you will make money in expectation. Repeat until you are rich or the mispricing has been corrected. I like this because it’s a good empirical test, and one that many people have tried. If you think you’re smarter than the prediction markets, bet on them and see what happens! I think most people will find that (over the long run) they lose money, and eventually this will cure them of their delusion that they can beat the markets. A few people might find that (over the long run) they do win money, just as a few people (eg Warren Buffett) can consistently win money on the stock market. Hopefully those people will quit their day jobs and become full-time prediction market traders. They’ll become multimillionaires, and their hard work will ensure that prediction markets stay more accurate than the rest of us. 3.5: Why should I believe that a prediction market makes good decisions about which of many competing experts to trust? Suppose you accept that a prediction market will always be at least as accurate as some well-known expert (eg Nate Silver). But what if you’re not sure who the real experts are? Or what if there are many experts, all saying different things, and nobody knows who to trust? In this case, a prediction market will always be at least as good as any other source (including you) at telling good experts from bad, or at figuring out which of many good experts is the best. By this point you should be able to predict the argument, but for completeness’ sake: Suppose you were better than the prediction market at determining which of many competing experts to trust, or how to aggregate the pronouncements of many experts into a single authoritative opinion. Then if you disagreed with the market, usually you would be right and it would be wrong. So look for cases where you disagree with the market, buy those shares, and you will make money in expectation. Repeat until you are rich or the mispricing has been corrected. To ground this in a real example, suppose there is some new virus which might or might not spread to the United States. A Harvard professor of epidemiology says there’s a 70% chance it will spread, a Yale professor of epidemiology says there’s an 90% chance it will spread, and a guy in a tinfoil hat on Infowars says there’s a 0% chance it will spread because it’s all a fake government plot. If I knew nothing else about this situation, I would probably think there’s about an 80% chance the virus will spread. I trust the Harvard and Yale professors equally much, and the tinfoil hat guy not at all. Suppose I saw a prediction market that was only at 10%, because most people trusted the tinfoil hat guy. I would want to buy YES shares until the price got up to 80%, because in expectation I would octuple my money. Suppose I saw a prediction market that was only at 70%. Now I wouldn’t be sure whether the prediction market was dumber than me (believed tinfoil hat guy) or smarter than me (they know a lot about epidemiology - or about the credibility of specific experts - and have decided to trust the Harvard professor over the Yale professor). Maybe I could improve on this. If I knew things about epidemiology, I could read over both professors’ arguments and try to figure out if one was better than the other. If I knew things about academia, I could pick over both professors’ resumes and see whether the Harvard professor seemed more distinguished or had more respect in her own field than the Yale professor. In the end, I might decide the prediction market was right to price it at 70% (in which case I wouldn’t do anything), or that actually both experts seemed equally expert (in which case I might bid it up to 80%), or that actually the Yale epidemiologist was better (in which case I might bid it up to 90%). 3.5.1: Isn’t it weird to give non-experts (like prediction market investors) the final judgment in which of two experts is right? Yes, but I don’t think this is avoidable. If there were no such thing as prediction markets, and the Harvard epidemiologist said 70%, and the Yale epidemiologist said 90%, and the tinfoil hat guy said 0%, and for some reason it mattered a lot to you which of these was true - then you would still have to make that decision. If there’s some extremely authoritative source who can make the decision for you - let’s say the World Health Organization says “after reviewing all experts’ arguments, we believe that the final probability is 75%” - then great! Either: The WHO is clearly the most trustworthy source - in which case we go back to the Nate Silver situation where the prediction market should be just as accurate as it is.
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My understanding of the situation: the first officially-confirmed case of COVID started December 11, 2019. Later in the pandemic, in 2021, the World Health Organization wanted to figure out if that was really the first, or whether there had been earlier ones. They scoured Chinese hospital records for illnesses that might be COVID during the two months before the official discovery (ie early October to early December) In particular, they asked Wuhan hospitals for records of any cases of fever, flu, respiratory illness, and pneumonia. The hospital gave them 76,253 cases, because China is big and flu is common. This was slightly more cases than usual, but there was a normal flu spreading too, so the researchers didn’t find this very compelling. Then they narrowed these cases down to those that were “clinically compatible” with COVID, and ended up with 92. Then they went over those 92 more carefully, including “review by the external multidisciplinary clinical team” and blood draws from the former patients. They were able to track down 67 of the 92. The clinical team decided none of those 92 cases really resembled COVID, and the blood draws were all negative. They published this as the results of their study: The retrospective search for cases compatible with COVID-19 illness identified 76 253 episodes with one of four indicator conditions. A rise in one of these conditions, [acute respiratory illness] (as well as [flu-like illness] and fever), was seen in this group of individuals in the over-60-year age group in early December. The clinical assessment of the 76,253 individuals revealed 92 cases clinically compatible with COVID-19. It is possible that the application of stringent clinical criteria, resulting in the identification of only 92 clinically compatible cases, may have decreased the possibility of identifying a group or groups of cases with milder illness. All the 92 cases were rejected as cases of SARS-CoV-2 infection on further clinical review. None of these cases (where blood could be obtained) was positive on SARS-CoV-2 serological testing carried out more than 12 months later. The use of retrospective serological testing so long after the illness cannot be relied on to exclude the possibility of SARS-CoV-2 infection at the time of the presenting illness, given the possible drop in SARS-CoV-2-specific antibody over time and the associated reduced sensitivity of commercial assays. The possibility that earlier transmission of SARS-CoV-2 infection was occurring in this community cannot be excluded on the basis of this evidence. In other words “we looked for early COVID, we didn’t find any, but we can’t promise we didn’t miss anything”. On Twitter, Giles Demaneuf makes an interesting point. The researchers took the samples in 2021, when China was in Zero COVID. When the Wuhan outbreak was finally contained in early 2020, 4.4% of Wuhanites had contracted COVID. So isn’t it surprising that 0/67 of the former patients who the researchers tested were had antibodies to COVID? The chance that 67 randomly-selected people in a population with 4.4% prevalence rate are all negative is only about 5%. Is this evidence of foul play? No. See the conclusions section of the report, which said: “The use of retrospective serological testing so long after the illness cannot be relied on to exclude the possibility of SARS-CoV-2 infection at the time of the presenting illness, given the possible drop in SARS-CoV-2-specific antibody over time and the associated reduced sensitivity of commercial assays”. You have a lot of COVID antibodies just after getting COVID. By a year or so afterwards, you might not have enough to detect. So it’s not surprising the WHO study didn’t detect any. Why did they even try looking for antibodies? There seem to be two reasons not to: first, they should have known antibodies would decay after a year. Second, even if some of them did have antibodies, how would we know they weren’t just infected in spring 2020 like everyone else? They don’t say. My guess: antibody decay is very variable. Some people’s antibodies might last more than a year. So if they found that way more than 4.4% of people had antibodies, that would be surprising and suggest that most of them had had COVID in autumn 2019. But instead they found that nobody had antibodies, which is consistent with one or two of them getting sick when everyone else got sick, and having their antibodies decay at the normal rate. But also, I think the antibodies were just intended to supplement the clinical review, and not be a very important part of their determination. I think this study is moderately strong evidence that there wasn’t much COVID going around before December 2019. Doctors looked for cases, they winnowed them down into the cases that looked most like COVID, but when they examined those cases closely, they didn’t look enough like COVID to be interesting. I don’t think the antibody tests add or subtract much from this assessment. I would be fine if someone else said they don’t think the WHO report provides much evidence either way. The main thing I want to insist on is that there’s no conspiracy to hide 92 previously-undiscovered cases. They searched really hard for potential cases, they subjected the most plausible candidates for further review, and then they decided those ones were not, in fact, COVID. (You can read all of this here. It’s not a very good description and I’d be interested if someone has a more thorough writeup of the research.) This was just one of many efforts that researchers made to try to identify pre-December-2020 COVID cases. For example, 30,000 people donated blood in autumn 2019, and the hospitals still had most of it. So they tested the blood samples for COVID antibodies and didn’t find any. I don’t think antibodies decay in stored blood samples (I might be wrong). There are 12 million people in Wuhan, so if even a few hundred people had COVID during that time, one of them should have turned up. None of them did. Finally, during COVID’s officially-recognized existence, its numbers doubled about once every 3.5 days. Again, if COVID existed a month earlier than previously believed, then it would be 256x more common than expected. This would be hard to miss! Nobody found evidence from excess mortality that COVID was 256x more common than expected. I’m using the version of the doubling time argument because it’s simple enough for me to understand, and I don’t have to worry about anyone trying to hide something in their complex model. It’s not exactly true, but it’s true enough to rule out COVID starting much before November 2019. If you want the fancy official version, it’s in Pekar 2021 and looks like this: This alone isn’t fatal to lab leak. It’s perfectly possible for the lab to leak (let’s say) November 5th, the virus spreads a bit, and then a month later someone goes to the wet market, coughs on a vendor, and starts the officially recognized pandemic. But if that were true, you’d expect (let’s say) 30 cases by early December. Let’s say the wet market vendor was exactly Case # 30. She infected the other wet market vendors, starting a pandemic with an obvious center at the wet market and lots of infected wet market vendors and patrons. What about Case # 29? If they were (let’s say) a barista, how come they didn’t infect people at their coffee shop? How come there wasn’t a second obvious cluster radiating out from a coffee shop, lots of coffee-shop-linked cases, etc? How come there weren’t 30 equally-sized clusters? In order to avoid this, you either need to claim that the wet market was a perfect superspreader location, or that the pattern with lots of cases in the wet market and few-to-none anywhere else was a result of ascertainment bias. Saar made both those arguments during the debate, but I thought Peter rebutted them effectively. 1.4: COVID in Brazilian wastewater Nicholas Halden (blog) writes: What should we make of this study, which found the presence of covid in Brazilian wastewater in late 2019? Consider the doubling times. The study says that scientists working in late 2020 found COVID in samples of Brazilian wastewater from November 27, 2019. This was long before the first detected case of transmission in Brazil on March 13, 2020. Between November 27, 2019 and March 13, 2020 is about 16 weeks, so 32 COVID doubling times. 32 doubling times with no lockdown is enough time for COVID to infect every single person in Brazil. If COVID had infected everyone in Brazil before the first recognized case, we would have noticed. (again, COVID doubling time isn’t exactly invariably 3.5 days, but here we’re talking about numbers big enough that the exact details don’t matter very much) So if COVID was in Brazil on November 27, it must have fizzled out instead of going pandemic. How likely is that? If one person had COVID, it’s not too unlikely - not all COVID cases transmit it forward. If (let’s say) twenty people had COVID, it’s very unlikely - at that point, the law of large numbers takes over; in a freak coincidence, every single patient would have to fail to infect anyone else. So almost certainly fewer than 20 people in Brazil had COVID in November 27. So which is more likely - that somehow 20 people had COVID long before the virus was officially detected, and on a totally different continent, yet somehow a scientist looking through wastewater found the water from exactly those people and managed to detect the virus? Or that there was a sampling error, which happens all the time in these kinds of things? Peter wrote a blog post on some of these issues. He found that there were positive tests from wastewater samples as early as March 2019, which doesn’t fit anyone’s timeline, including lab leakers’. And most of these positives (including the Brazilian sample) contained later strains of the virus with mutations it picked up late in 2020. So these were almost certainly false positives from contamination. 1.5: Biorealism’s 16 arguments Biorealism has a list of sixteen arguments, which he liked so much that he posted it three times in the ACX comments, twice on Less Wrong, twice on Manifold, and about a dozen times on Twitter under multiple account names. Some posts were slightly different from others, but a typical version is: Importantly, Miller incorrectly claimed the N501Y mutation would result from passage in hACE2 mice (mixed them up with BALB/c mice). The major papers Miller relied on have been seriously challenged since the debate. See Stoyan and Chiu (2024), Weissman (2024), Bloom (2023) and Lv et al (2024). Overall the circumstantial evidence makes lab v plausible: Peter admitted getting this wrong during the debate. I think this very minor point about mice mutations was approximately his only mistake in 15 hours of debating, and he admitted it as soon as he noticed. Biorealism somehow heard about this (obviously not through watching the debate, as we’ll see in a moment), then left about 20-30 comments starting with it, under various accounts, on various platforms, as if it somehow discredited Peter. This is making me somewhat less charitable to him and his 16 arguments than I would be otherwise. 1. Chinese researchers Botao & Lei Xiao observed lab origin was likely given the nearest known relatives to SARS-CoV-2 were far from Wuhan. Wuhan Institute of Virology (WIV) sampled SARS-related bat coronaviruses where the nearest relatives are found in Yunnan, Laos and Vietnam ~1500km away. They refuse to share their records. The ancestral viruses of SARS were found equally far from where SARS spilled over into humans, so we know it’s possible (and likely) for viruses to travel that far. 2. Patrick Berche, DG at Institut Pasteur in Lille 2014-18, notes you would expect secondary outbreaks if it arose via the live animal trade. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234839/ There are constant outbreaks of weird coronaviruses in animal handlers. See eg this paper, which estimates about 60,000 of these per year. None of these ever go anywhere, because the farmers are in rural areas that aren’t dense enough to sustain a high R0, and the epidemic fizzles out after a single digit number of cases. Any early outbreaks of COVID would have vanished into this long and mostly unnoticed list. 3. Molecular data: Only sarbecovirus with a furin cleavage site. Well adapted to human ACE2 cells. Low genetic diversity indicating a lack of prior circulation (Berche 2023). Restriction site SARS-CoV-2 BsaI/BsmBI restriction map falls neatly within the ideal range for a reverse genetics system and used previously at WIV and UNC. Ngram analysis of the codon usage per Professor Louis Nemzer https://twitter.com/BiophysicsFL/status/1667232580255490053?t=IJgitS5cw364ioclzVWxaA&s=19 The SARS2 backbone is very low in CG and CpG. While the 12-nt insert that gives it the FCS is extremely high in both. Almost as if it was some kind of chimera of a consensus sequence and a codon-optimized polybasic cleavage site? https://twitter.com/BiophysicsFL/status/1752800486837678377?t=EpIRgyybJVaPgeMP5xdstA&s=19 https://www.biorxiv.org/content/10.1101/2022.10.18.512756v1 https://link.springer.com/article/10.1007/s10311-021-01211-0?fbclid=IwAR1HMUMtLIAFOFppVasQDeoIAYrVhP8j4YoPO4wnaTOUiKLsllZl_oKryOw Most of this was discussed extensively in the second session of the debate, which I recommend. The CGG-CGG arginine codon usage is particularly unusual but used in synthetic biology. I asked a synthetic biologist about this. He said: » “Nope. I would literally never do this if I was designing a small insert (maybe I wouldn't notice if it happened by chance with ~1 in 25 odds in a naive codon optimization algorithm as part of a larger sequence). High GC% is bad. Tandem repeat is worse. Several other perfectly fine arginine codons. And I wouldn't engineer a viral genome using human codon usage. An engineer would not do it.” 4. DEFUSE full proposal: virus 20% different from SARS1, consensus seq assembled with 6 segments, without disrupting coding seq, BsmBI order, FCS. SARS2: 20% different than SARS1, 6 evenly spaced fragments w BsmBI and BsaI restriction sites, FCS. Jesse Bloom, Jack Nunberg, Robert Townley, Alexandre Hassanin have observed this workflow could have lead to SARS-CoV-2. Work often begins before funding sought or goes ahead anyway. Re: 4 - Also scattered across second section of debate, also not going to retread 5. Market cases were all lineage B. Lv et al (2024) indicates there was a single point of emergence and A came before B. So market cases not the primary cases. See also Bloom (2021), Kumar et al (2022). Peter Ben Embarek said there were likely already thousands of cases in Wuhan in December 2019.https://t.co/50kFV9zSb6 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/34398234/ https://academic.oup.com/bioinformatics/article/38/10/2719/6553661 There was a Lineage A sample in the market, lab leak proponents just try to ignore/dismiss/conspiracize it away. The first two known Lineage A cases were very close to the market. Lv (is this even a real name? It sounds like Roman numeral? But I guess that’s what you expect in a country ruled by someone named Xi) found some weird COVID variants in Shanghai that might or might not mean anything; you can see some discussion of the implications here, but I don’t think they’re strong evidence either way. If A was first, it means some really weird stuff coincidences have to happen to give us the spread rates and genetic clock data we get, but they’re not necessarily weirder in the zoonosis hypothesis than the lab leak one. The claim that there were “thousands of cases in Wuhan in December 2019” is very easy to disprove by doubling rate arguments like the one above, by the blood bank study mentioned above, by the WHO’s failed case search, and by many other lines of argument. 6. Evidence for lineage A in the market is based on a low quality sample according to Liu et. al. (2023). I really think lab leakers need to decide whether they think China is a sinister actor trying to cover up the truth, or whether they should trust every offhand comment by Chinese government officials as gospel. Dr. Liu doesn’t explain in what sense he thinks the Lineage A sample is “low-quality”, and the Western scientists who I asked about this said they didn’t understand this complaint and that the sample was fine. A Western team re-analyzing the same sample describes it as “conclusively contain[ing] Lineage A.” I think most lab leakers have switched from trying to deny the genetics to claiming that this was “contamination”, which also doesn’t make sense (the sample is genetically very early). Note that aside from this sample, the first two Lineage A cases discovered were both very close to the wet market. 7. Bloom (2023) shows market samples do not support market origin. There is also no evidence of transmission in the claimed susceptible animals elsewhere. https://academic.oup.com/ve/advance-article/doi/10.1093/ve/vead089/7504441 Discussed extensively in my article as well as the first section of the debate. 8. Lineage A and B only two mutations apart. François Ballox, Bloom and Virginie Courtier-Orgogozo note this is unlikely to reflect two separate animal spillovers as opposed to incomplete case ascertainment of human to human transmission (Bloom 2021). Discussed extensively in my article as well as the first section of the debate. 9. Sampling bias. George Gao, Chinese CDC head at the time, acknowledged to the BBC stating they may have focused too much on and around the market and missed cases on the other side of the city. David Bahry outlines the documented bias. Michael Weissman has shown this mathematically. https://journals.asm.org/doi/10.1128/mbio.00313-23 https://academic.oup.com/jrsssa/advance-article-abstract/doi/10.1093/jrsssa/qnae021/7632556 Re: Dr. Gao, see above comment about Chinese officials. See the section Ascertainment Bias below for why I disagree with this specific claim, which also addresses the Michael Weissman argument. 10. Spatial statistics experts show the Worobey claim the market was the early epicentre was flawed. https://academic.oup.com/jrsssa/advance-article-abstract/doi/10.1093/jrsssa/qnad139/7557954 Re: 10 - See Confirmation Of The Centrality Of The Huanan Market Among Early COVID-19 Cases, a response to the paper you cite: The centrality of Wuhan's Huanan market in maps of December 2019 COVID-19 case residential locations, established by Worobey et al. (2022a), has recently been challenged by Stoyan and Chiu (2024, SC2024). SC2024 proposed a statistical test based on the premise that the measure of central tendency (hereafter, "centre") of a sample of case locations must coincide with the exact point from which local transmission began. Here we show that this premise is erroneous. SC2024 put forward two alternative centres (centroid and mode) to the centre-point which was used by Worobey et al. for some analyses, and proposed a bootstrapping method, based on their premise, to test whether a particular location is consistent with it being the point source of transmission. We show that SC2024's concerns about the use of centre-points are inconsequential, and that use of centroids for these data is inadvisable. The mode is an appropriate, even optimal, choice as centre; however, contrary to SC2024's results, we demonstrate that with proper implementation of their methods, the mode falls at the entrance of a parking lot at the market itself, and the 95% confidence region around the mode includes the market. Thus, the market cannot be rejected as central even by SC2024's overly stringent statistical test. I think this response is pretty strong. In one analysis, they show that even though the other paper’s methodology is worse than theirs, if you apply it correctly (instead of inappropriately excluding various cases like the paper’s authors did), the center of all early cases in Hubei province lands on the wet market parking lot. In another analysis, they show that the other paper’s recommended tests wouldn’t have correctly pointed to the offending water pump in the famous John Snow cholera outbreak, but theirs would have. Still, I think it’s useful to supplement fancy statistics with normal common sense, so I recommend just looking at the map of early cases: …and deciding whether you think the assumptions behind a specific statistical test are likely to debunk the idea that cases are centered around the wet market. 11. Wuhan used as a control for a 2015 serological study on SARS-related bat coronaviruses due to its urban location. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6178078/ I don’t know why this point is supposed to matter. If you mean that Wuhan isn’t directly exposed to bats, nobody ever said it was. The zoonotic theory is that wildlife carted in from other areas of China started the pandemic in the wet market. 12. Superspreader events also seen at wet markets in Beijing and Singapore (Xinfadi and Jurong). This was discussed very extensively in the debates, both in section 1 and section 3. Wet markets weren’t “superspreader locations” - in fact, the disease spread no more quickly there than anywhere else. They were the first place in those cities that the pandemic started, due to contaminated animal products. If anything, this supports zoonosis. See also my discussion with Saar on this point below. 13. WIV refuse to share their records with NIH who terminated subaward in 2022. Wider suspension over biosafety concerns. https://www.bloomberg.com/news/articles/2023-07-18/us-suspends-wuhan-institute-funds-over-covid-stonewalling Although WIV has not been especially forthcoming, some of their databases were leaked in various ways and showed that they did not have any viruses capable of transforming into COVID. 14. PLA involvement at WIV and MERS research prior to SARS-COV-2. MERS features several similarities with SARS-CoV-2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7022351/ I can’t even tell what conspiracy theory you’re trying to propose with this one; if you spell it out I can try to explain why it might be false. 15. SARS1 leaked several times and SARS-COV-2 has leaked from a BSL-3 lab in Taiwan. Agreed that SARS leaked several times. It also spilled over from animals several times. During the debate, a lab leak rate of once per lab per 500 years was proposed (everyone agreed to steelman this by 10x for WIV numbers); I would be interested to know whether anything about the study of SARS challenges that number. 16. Unpublished infectious clone identified from Wuhan contradicting arguments such reverse genetics systems would be published. https://www.biorxiv.org/content/10.1101/2023.02.12.528210v1.full I asked some scientists about this paper and here’s what they told me. Wuhan University sequenced some rice. In the middle of the sequence, there’s an unexpected sequence from a common coronavirus, HKU4. The most likely explanation is that someone else in Wuhan was working on the coronavirus and there was cross-contamination. Plausibly this is Wuhan Institute of Virology, who is known to work with coronaviruses. This is cool detective work, but it’s not clear what it’s supposed to prove. I think some lab leakers are using it to prove that WIV can do reverse genetics, but they admitted this already in a published paper so that’s not too helpful. I think others are using it to prove WIV had “secret viruses” in their catalogue, but the rice virus wasn’t secret, it was HKU4, which is common and which WIV has already published papers about. 1.6: DrJayChou’s 7 Arguments Once again, I cannot stress enough how much better a take you might have on this debate if you watch it. “The first known case predates the market outbreak by a month” - this is not the consensus position. I cannot say for sure what Dr. Chou means by this, but I suspect he’s referring to one of the many claims to this effect that Peter effectively debunked during the debate (Connor Reed, Mr. Chen, the 92 cases, Brazil, etc).
Inline links: Giles Demaneuf, here, Pekar 2021, https://substackcdn.com/image/fetch/$s_!yRJv!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffcd7daff-43d3-416f-8896-173392bf3438_720x312.webp, blog, writes, this study, wrote a blog post on some of these issues, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234839/, this paper, https://twitter.com/BiophysicsFL/status/1667232580255490053?t=IJgitS5cw364ioclzVWxaA&s=19, https://twitter.com/BiophysicsFL/status/1752800486837678377?t=EpIRgyybJVaPgeMP5xdstA&s=19, https://www.biorxiv.org/content/10.1101/2022.10.18.512756v1, https://link.springer.com/article/10.1007/s10311-021-01211-0?fbclid=IwAR1HMUMtLIAFOFppVasQDeoIAYrVhP8j4YoPO4wnaTOUiKLsllZl_oKryOw, https://t.co/50kFV9zSb6, https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/34398234/, https://academic.oup.com/bioinformatics/article/38/10/2719/6553661, here, describes it as, https://academic.oup.com/ve/advance-article/doi/10.1093/ve/vead089/7504441, https://journals.asm.org/doi/10.1128/mbio.00313-23, https://academic.oup.com/jrsssa/advance-article-abstract/doi/10.1093/jrsssa/qnae021/7632556, https://academic.oup.com/jrsssa/advance-article-abstract/doi/10.1093/jrsssa/qnad139/7557954, Confirmation Of The Centrality Of The Huanan Market Among Early COVID-19 Cases, https://substackcdn.com/image/fetch/$s_!BNAm!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fffd4cddb-6e3e-41f5-8ef6-ec0b27bec600_626x426.webp, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6178078/, https://www.bloomberg.com/news/articles/2023-07-18/us-suspends-wuhan-institute-funds-over-covid-stonewalling, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7022351/, https://www.biorxiv.org/content/10.1101/2023.02.12.528210v1.full, a published paper, has already published papers about, https://substackcdn.com/image/fetch/$s_!yA9U!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F467dd304-190a-4437-8920-d498c433dffb_1600x960.jpeg
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