Astral Codex Ten

SARS-COV-2

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SARS-COV-2

Article

SARS-COV-2 is a recurring concept in the Astral Codex Ten archive, appearing 5 times across 5 issues between February 01, 2021 and May 13, 2024. The archive places it in contexts such as “a new variant of SARS-COV-2 due to a mutation”; “those who have already been vaccinated for SARS-CoV-2 (COVID-19)”; “SARS-CoV-2, the virus behind COVID-19”. It most often appears alongside US, China, COVID.

Metadata

  • Category: Concepts
  • Mention count: 5
  • Issue count: 5
  • First seen: February 01, 2021
  • Last seen: May 13, 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.

Metaculus Monday
February 01, 2021 · Original source
This week: the coronavirus.
Late last year, when coronavirus had already killed 285,000 Americans, Metaculus asked users to predict how many would be dead by the end of 2021. The guesses started at about 500,000. But as cases rose further through December and January, the guesses rose too, until now they're averaging almost 690,000 people.
Inline links: asked users to predict
When will some official like the Director of the CDC announce that a coronavirus vaccine is available to any adult who wants it (as opposed to just front-line workers, only seniors, etc)? The question as asked is a little odd, since it will probably be available in some areas before others, but this is apparently about some kind of nationwide announcement.
Inline links: When will some official like the Director of the CDC announce
Your Book Review: Viral
July 30, 2022 · Original source
The main case for the natural origins hypothesis is that it should be our default belief, in the absence of convincing evidence otherwise. The vast majority of disease outbreaks like this, including previous coronavirus outbreaks SARS (2003) and MERS (2012), came from nature, not from a lab. So, before examining the evidence, we should begin with a strong prior in favor of natural origins.
However, what’s still missing is any direct evidence for an animal source of SARS-CoV-2. Chan and Ridley note that during the SARS (2003) and MERS (2012) coronavirus outbreaks, the animal sources were discovered relatively quickly. But with COVID-19, now more than 2 years into the pandemic, an animal source still has not been identified, despite the fact that we now have access to better investigative tools, like faster and cheaper genome sequencing compared to the SARS and MERS investigations.
Photograph of the famous Latané and Darley experiment, cerca 1968. So, what could those participants have been thinking? Maybe something like: Hmm, why’s the room filling up with smoke? Is this a problem? *looks around the room* Well nobody else seems to care, so I guess not. Looking back at the early stages of the COVID-19 pandemic, I think maybe this is why so many of us didn’t think twice about the location of the initial outbreak. Hmm, is it kinda suspicious that this virus broke out near a major virology institute that works on bat coronaviruses? Should we maybe look into that? *looks around* Well nobody else seems to think so, so I guess not. I can’t speak for everyone else, but this was at least my mindset. I had vaguely heard something about how there was a virology research institute close to where the pandemic broke out, and that some conspiracy theorists were claiming it was the source of the virus. I looked around and noticed that nobody was really taking this idea seriously, so I figured I didn’t need to take it seriously either. Also, I was thinking something like: Eh, probably every major city has labs and research institutes doing this kind of research. And I’ll bet they purposely built the virology institute close to where these viruses occur in nature, to give them easy access for sampling. Well, it turns out both of these things are wrong. The type of research conducted at the Wuhan Institute of Virology (WIV) is pretty rare and specialized. It includes things like creation of chimeric coronaviruses [1, 2], infecting humanized mice with bat coronaviruses, and other types of gain of function research, which Chan and Ridley devote a chapter to. The WIV is one of only a few institutions in the world doing this type of research. It’s not the case, as I had assumed, that every major university has a couple labs doing similar work. So it does seem like a pretty remarkable coincidence that the outbreak happened in Wuhan. But maybe they purposely built the Wuhan Institute of Virology close to where these viruses are found in nature? Well, this also turns out to be wrong. The areas where viruses most similar to SARS-CoV-2 are found in nature are Yunnan province and Laos, which are more than a thousand kilometers away from Wuhan. The authors put this distance in perspective by noting that it’s more than the distance between Orlando and NYC. Image source: https://www.bloomberg.com/news/features/2020-12-30/china-is-making-it-harder-to-solve-the-mystery-of-how-covid-began If SARS-CoV-2 originated in an animal somewhere around the Yunnan / Laos area, how did it make it all the way to Wuhan without leaving a trail along the way? 4. The story of RaTG13 Although I enjoyed the book, I do have one pretty major criticism. The authors repeatedly make the claim that a virus called RaTG13, which was being studied at the WIV before the pandemic, is the closest known genetic match to SARS-CoV-2. But this claim is outdated and no longer correct. In September 2021 researchers identified a virus called BANAL-52 in Laos that’s a 96.8% match to SARS-CoV-2, closer than RaTG13’s 96.2% match. (Important note: a 96.8% match is still a long way off in genomic space, and does not imply that this is the same virus as SARS-CoV-2, or even necessarily a progenitor.) At first I thought maybe the authors didn’t mention BANAL-52 because it was discovered after the book was published, but this isn’t the case – Viral was published November 16, 2021, nearly two months after the discovery of BANAL-52 was published. Although I’m writing an overall-positive review here, I don’t want to go easy on the book where serious criticism is warranted. It’s completely unacceptable that BANAL-52 wasn’t mentioned. Even if it would have been inconvenient from a publishing standpoint, the authors should have rewritten the RaTG13 chapter, or at least included an addendum about the discovery of BANAL-52. With that being said, I think the story of RaTG13 is still interesting and important, so I’ll give a quick summary here. At the start of the pandemic in 2020, SARS-CoV-2 was quickly sequenced, and the full genome sequence was published by Dr. Shi Zhengli’s team at the WIV. In this paper, they also briefly mentioned that the genome was a 96.2% match with another bat coronavirus called RaTG13 – the closest known match at the time. Oddly, the mention of RaTG13 did not include any reference, footnote, or link to any previously published sequence. Although the WIV didn’t provide details on this mysterious RaTG13 virus, a group of internet volunteers, including both amateurs as well as professional scientists working in their free time, began to investigate. This loose collection of open-source researchers, called DRASTIC, uncovered a medical thesis describing an outbreak of a mysterious disease in 2012. Six men who had been working in a bat-infested mine in Mojiang County, China, fell ill and were admitted to a hospital with symptoms including dry coughs, shortness of breath, fevers, muscle aches, headaches, and fatigue. Three of the men eventually died of this mysterious illness. In the years following this incident, teams of researchers (including a team led by Dr. Shi Zhengli of the WIV) were sent to investigate the cause of this illness and collect samples from the Mojiang mine. This sampling led to the discovery of a novel SARS-like coronavirus in 2013, and a part of its genomic sequence was published under the name BtCoV/4991 in 2016. The DRASTIC researchers discovered that RaTG13 was genetically identical to the BtCoV/4991 sequence from the Mojiang mine – it was the same virus, and had just been renamed for some reason, without any public record of the change. They also discovered that at least eight other closely related coronaviruses were also sampled from this mine and brought to the WIV. Although unhelpful throughout the investigation, the WIV eventually verified these facts when pressed on them, and an addendum was added to the original paper confirming DRASTIC’s account of the origin of RaTG13. So what should we make of this? Well, as I mentioned before, RaTG13 is no longer the closest known genetic match to SARS-CoV-2, so maybe the whole story is less important as it pertains to the origin of the pandemic. But the discovery of BANAL-52 doesn’t really resolve things either [2]. Laos is very far away from Wuhan (actually even further than Yunnan), so we’re left with the same question as before – how did SARS-CoV-2 make it all the way to Wuhan from such a distant natural reservoir without leaving a trail along the way? 5. Lack of institutional transparency and competence A lot of the book is devoted to criticizing the Chinese government’s lack of transparency during the pandemic. Some brief examples: In the early days of the initial outbreak in Wuhan, hundreds of people were investigated and punished for the crime of “spreading rumors”. This included whistleblowing doctors who attempted to warn others [3] about the spread of the disease and its human-to-human transmission, which was being denied by the Chinese government at the time.
Inline links: 1, 2, infecting humanized mice with bat coronaviruses, https://substackcdn.com/image/fetch/$s_!6khv!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fac0a8f26-8f26-4712-b245-797d478ae385_1246x642.png, https://www.bloomberg.com/news/features/2020-12-30/china-is-making-it-harder-to-solve-the-mystery-of-how-covid-began, virus called BANAL-52, published by Dr. Shi Zhengli’s team at the WIV., addendum
Response To Alexandros Contra Me On Ivermectin
February 01, 2023 · Original source
This is an RCT from Israel. 47 patients got ivermectin and 42 placebo. Primary endpoint was viral load on day 6. I am having trouble finding out what happened with this; as far as I can tell it was a negative result and they buried it in favor of more interesting things. In a "multivariable logistic regression model, the adjusted odds ratio of negative SARS-CoV-2 RT-PCR negative test" favored ivermectin over placebo (p = 0.03 for day 6, p = 0.01 for day 8), but this seems like the kind of thing you do when your primary outcome is boring and you’re angry.
Highlights From The Comments On The Lab Leak Debate
April 09, 2024 · Original source
HKU1 might also fit these criteria. It’s a coronavirus discovered in 2004 that seems to have spilled over in China and spread globally (it’s fine; it just causes yet another subtype of common cold). The exact animal reservoir has never been identified, although Wikipedia says it “likely originated from rodents”.
In May 2003, Guan et al (2003) identified SARS-CoV-like virus in animals in a live-animal market in Shenzhen, Guangdong Province, China. Guan et al (2003) also tested for antibodies among workers in the market. They note that “8 out of 20 (40%) of the wild-animal traders and 3 of 15 (20%) of those who slaughter these animals had evidence of antibody, only 1 (5%) of 20 vegetable traders was seropositive.” This suggests that the majority of the infections of the 11 people with close contact with animals were zoonotic. Among 508 animal traders, 66 (13%) tested positive for IgG antibody to SARS associated coronavirus by ELISA, while the control groups including hospital workers, Guangdong CDC workers, and healthy adults at clinic had an antibody prevalence of 1–3%.
Secondly, a mystery of sars-cov-2 is how it acquired the furin cleavage site that makes it so transmissible. There are 850 known sars-like coronaviruses, and only one with a furin cleavage site. According to private messages exchanged by proponents of zoonosis, the furin cleavage site could not have been acquired in the market because the density of animals was too low (only 3-4 per cage). When avian influenza acquires a furin cleavage site that occurs on farms with thousands of chickens densely packed, i.e. not in the wild and not when there are a handful of animals in cages in a market. https://usrtk.org/covid-19-origins/visual-timeline-proximal-origin/
Inline links: https://usrtk.org/covid-19-origins/visual-timeline-proximal-origin/
24
May 13, 2024 · Original source
People changed their minds a little over time, but not in a very consistent way that mattered much in the end. What was the “client feedback”? The report says: Client feedback was provided to the Superforecasters on December 21. The client posed questions to the Superforecasters about their assessments up to that date and asked for their reactions to several studies and articles. In the days following the client engagement, the Superforecasters lowered their confidence in the natural zoonosis hypothesis from 73% to 67%, although zoonosis remained the most likely potential cause in their assessment. But following an active engagement with recent genomic studies and historical base rates of zoonotic spillovers, those numbers began to return to earlier levels. January also saw increased attention to the geopolitical context and transparency issues, particularly related to research activities in Wuhan Is this bad? I’m imagining a pro-lab-leak client saying “But what about [this list of pro-lab-leak arguments]?” and then the superforecasters read them and adjust. In one sense, it’s good that they got to see more arguments; on the other, it seems like a potential route by which clients could bias the results - probabilities never quite got back to where they were before the feedback, though they got pretty close. The last-minute spike for zoonosis might be the Rootclaim debate results, which were released on 2/18. So maybe the client feedback and the Rootclaim results both slightly affected the numbers, but mostly the superforecasters started out pro-zoonosis and stuck to their guns. Dan Schwarz and the FutureSearch team say that forecasting has a “rationale-shaped hole”. Despite the report making this sound like a pretty intense process, we don’t get much information about details: In their extensive discussions , Good Judgment’s Superforecasters assessed base rates and historical patterns, existing evidence and scientific analysis, geopolitical context and transparency concerns, trust in intelligence communities, and methodological constraints. 1. Base Rates and Historical Patterns: The Superforecasters frequently referenced base rates, i.e., the history of pandemics emerging from natural zoonosis versus the history of laboratory leaks, to anchor their probabilities. For the former, they discussed how the base rates are changing as the climate warms and as expanding human populations push farther into natural environments that previously saw little human presence. For the latter, they acknowledged that it has only been 12 years since the advent of CRISPR gene- editing tools, and the base rate of lab leaks in the short synthetic biology era is not yet well established. 2. New Evidence and Scientific Analysis: Throughout the period, the Superforecasters adapted their forecasts in light of new scientific evidence, including genomic analyses of SARS-CoV-2 and its relation to bat viruses, and the debate over potential laboratory manipulation. 3. Geopolitical Context and Transparency Concerns: The geopolitical implications of the virus’s origins, particularly in relation to China’s transparency and the involvement of international research institutions, played a significant role in the analysis. Concerns over data veracity, and over the political ramifications of determining that the pandemic’s origins were other than zoonosis, were extensively debated. 4. Trust in Intelligence: Commentary on trust in intelligence communities and discussions about the impact of geopolitical biases on the interpretation of evidence illustrated the complex interplay between science, politics, and human behavior in assessing the pandemic’s origins. 5. Methodological Critiques and the Evaluation of Evidence: The Superforecasters engaged in methodological critiques of the evidence base, including the scrutiny of laboratory practices and biocontainment levels [...] In the end, most Superforecasters were in rough agreement on issues like the base rates of zoonotic spillover. Where they most often disagreed was on the interpretation of actions by Chinese officials and whether their actions reflected how an authoritarian government would react in any crisis over which it did not have full control, or whether those actions were indicative of attempts to cover up a biomedical research-related accident that allowed the SARS-CoV-2 virus to enter circulation in China and, ultimately, the entire globe. Probably it would be too much to ask for to get a transcript of all their discussions - then they’d be nervous saying things that might make them look bad to an audience. What would be a good balance between getting more information and not imposing on their time? Forecasting is an unusually legible and easy-to-judge domain. One of the theories of change for forecasting was to use it to identify smart people with good reasoning, then turn them loose on less well-behaved problems. This is one of the first big attempts to do this at scale. How did it work? We can’t tell, because it’s inherently an illegible and hard-to-judge domain. Darn. I don’t know what I expected. Notes From A Local Optimum Austin’s concern - that forecasting has reached a local optimum - is widely shared. We have some good sites: Manifold, Metaculus, Polymarket, GJO, etc - all doing good work. We have good-ish probabilities for a few important questions. Every so often a news source cites them. Sometimes a decision-maker looks at them behind the scenes, maybe. Is this all there is? The FutureSearch team says the next step is to focus on “rationale”. We need to use forecasting not just to get a raw probability, but to explain what’s going on and why we think something. Then instead of just convincing policy-makers to trust forecasts, we can tell them why something is true, or inform their discussions even if they’re not willing to blindly trust a number. Is this a betrayal of the forecasting ethos? The original dream was that instead of a bunch of people giving arguments, we could just test who was right. Now we’re going back to the arguments? People have argued forever; what does forecasting add to that? Well, they add the knowledge that the arguments are from people who have been right a lot before and are incentivized to be right again. Still, it’s not a natural fit. Probably it’s relevant here that FutureSearch’s forecasting AI does a really good job of this by default, in a way humans can’t match. Nuno’s yearly forecasting roundup doesn’t have a single thesis, but the first part is a well-supported complaint that most forecasting sites aren’t good business. They either burn VC money, burn EA donations, or converge towards casinos to support themselves. He gives an honorable exception to Cultivate Labs, which sells prediction market software rather than the results themselves. Open Philanthropy (billionaire Dustin Moskovitz’s EA-aligned charitable foundation) has at least given forecasting a vote of confidence, recently choosing to promote it to one of their main donation areas. Still, they got a lot of pushback on the decision, for example SuperDuperForecasting here: This will be a total waste of time and money unless OpenPhil actually pushes the people it funds towards achieving real-world impact. The typical pattern in the past has been to launch yet another forecasting tournament to try to find better forecasts and forecasters. No one cares, we already know how to do this since at least 2012! The unsolved problem is translating the research into real-world impact. Does the Forecasting Research Institute have any actual commercial paying clients? What is Metaculus's revenue from actual clients rather than grants? Who are they working with and where is the evidence that they are helping high-stakes decision makers improve their thought processes? Incidentally, I note that forecasting is not actually successful even within EA at changing anything: superforecasters are generally far more relaxed about Xrisk than the median EA, but has this made any kind of difference to how EA spends its money? It seems very unlikely. And Marcus Abramovich here: I'm in the process of writing up my thoughts on forecasting in general and particularly EA's reverence for forecasting but I feel, similar to @Grayden that forecasting is a game that is nearly perfectly designed to distract EAs from useful things. It's a combination of winning, being right when others are wrong and seemingly useful, all wrapped into a fun game. I'd like to see tangible benefits to more broad funding of forecasting that seems to be done in t he millions and tens of millions of dollars. I would also be the type of person you would think would be a greater fan of forecasting. I'm the number one forecaster on Manifold and I've made tens of thousands of dollars on Polymarket. But I think we should start to think of forecasting as more of a game that EAs like to play, something like Magic the Gathering that is fun and has some relations to useful things but isn't really useful by itself. Eli Lifland has a long and hard-to-summarize comment here, response from Ozzie Gooen here, podcast between them on “Is Forecasting A Promising EA Cause Area?” here. I’m split on this. My previous hope was that the field would gradually grow, without any qualitative changes or discontinuities, until it became big enough that journalists and policy-makers were aware of it and took it seriously (compare eg the growth of the Internet as a scholarly resource). I think the strongest argument against this is Manifold’s relatively flat user numbers. Is there a new hope? I think if nothing else, forecasting might be useful as a testing ground: First, to create forecasting AIs (like FutureSearch) which can then get consulted on a variety of questions, eg by policy-makers. The biggest holdup has always been the need to gather 20 or 50 or however many hard-to-find superforecasters for whatever question you’re asking, and then trust their advice even though they’re fallible fleshbag humans. If you can use the 20 to 50 superforecasters to inspire an AI, and then test the AI and prove it’s good, people might be more interested. This is especially true if the AI can branch out beyond traditional forecasting questions. Once we have a few of these, we can start comparing the next generation of AIs to the previous generation, and skip the superforecasters.
Inline links: forecasting has a “rationale-shaped hole”, Nuno’s yearly forecasting roundup, Cultivate Labs, choosing to promote it to one of their main donation areas, here, superforecasters are generally far more relaxed about Xrisk, here, @Grayden, here, here, “Is Forecasting A Promising EA Cause Area?” here