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The Daily Beast article has some information about how other NYTimes employees are against de-anonymising Scott.
Yes, I’m meaning something along the lines of the actions suggested in the original comment but am doing a rubbish job at explaining this properly. Violence in particular was a poor choice of words and I have changed it to force in the grandparent comment.
All I was really wanting to say was that escalation isn’t the only solution and is usually a bad idea.
One example I’ve experienced is reading scientific papers. I have had the experience where I think “why haven't they presented this sub-result in this intuitive way?”. Sometimes this is just incompetence but at other times it leads me to find that the particular result in question goes against the hypothesis of the paper and that result is included only in the footnotes/supplemental material.
The claim I was against was that there’s no point trying to petition as force is the only solution which is covered in some depth in that piece. Currently there is a clash of norms but no force has been used. My feelings will change somewhat if they do publish.
The dispute here, then, is whether doxing is a concept like murder[1] (with intent built into the definition) or homicide (which is defined solely by the nature of the act and its consequences).
I feel like we're still talking past each other a bit here. I don't dispute that doxxing can mean any revealing of information about someone, it could be used even when no foreseeable damage is implied and someone just wanted to remain private. The strict definition is not the question.
The non-central fallacy is when a negative affect word is used to describe something where the word is technically true but the actual thing should not have that negative affect associated with it. Martin Luther King fits the definition of a criminal but the negative affect of the word criminal (the reasons why crimes are bad) shouldn't apply to him.
The problem I have using "dox" here is that some portion of the word's negative affect doesn't (or at least might not) apply in this case. An alternative phrasing would be "reveal Scott's true identity" or, to be snappier, "unmask Scott" which are more neutral. dontdoxscottalexander.com's title is Don't De-Anonymize Scott Alexander which I think is better than my ideas.
I think its hard to argue that a central example of doxxing doesn't involve intent to cause harm. The central example I think in most people's minds would be something like the hit list of abortion providers or anonymous. Wikipedia has a list of examples of doxxing - a rough count suggests ~13/15 involve providing information about someone ideologically opposed to the doxxer (confirming intent is more difficult). The non-centrality here isn't as extreme as it is in, say, "Martin Luther King was a criminal" but it is there.
On the relevance of the distinction, yes, I do think it is important. I would support different responses to the NYT depending on whether I thought they were acting out of a desire to endanger/silence Scott or were following a journalistic norm in a way I considered wrong.
Is dox the right word here? I guess this fits inside the definition but it feels kinda non-central to me. A typical example would include some intent to do harm. Considering a different principle more important feels importantly different.
Not that this is much consolation to Scott and I think the NYT is wrong to reveal Scott's identity (and have written in to say this), I just think doxxing is the wrong way to describe it.
You can generalise this for other required accuracies. If instead of 25% we use "a" then the optimal guess is of the current life which is correct of the time.
If we use an alternative optimisation criterion where we compare any two prediction methods and see, over the life of the bathing machine, which is closer to the correct answer most often then 200% (i.e. the halfway rule) is best.
So which rule of thumb you use depends on what you're looking to achieve - a guess which will be fairly good for as much of the lifetime as possible or a guess which is better for most of the lifetime, even if sometimes it's way off.
I was always a little underwhelmed by the argument that elections grant a government legitimacy - it feels like it assumes the conclusion.
A thought occurred to me that a stronger argument is that elections are a form of common knowledge building to avoid insurrections.
The key distinction with my previous way of thinking is that it isn't the choice of elections as such which is important but that everyone knows how people feel on average. Obviously a normal election achieves both the choice and the knowledge but you could have knowledge without choice.
For example if people don't vote on a new government but just indicate anonymously whether they would support an uprising (not necessarily violent) against the current government. Based on the result the government can choose to step down or not. This gives common knowledge without a choice.
I suspect this isn't an original thought and seems kinda obvious now that I think about it - just a way of looking at it that I hadn't considered before.
I have edited the original comment to more fully reflect my position.
I'm not confident in a 1% as an upper limit (especially in an overrun healthcare system) but I do think that comment gives good back-of-the-envelope estimates (as requested). Later on in that thread CBG also acknowledges it may be higher in than 1% in some places and conditions.
Detail in this case is useful as it shows multiple sources and back-of-the-envelope calculations. I'm not really assessing CBG (except trusting that he isn't picking and choosing his arguments), rather I'm assessing his back-of-the-envelope calculation and where likely errors can creep in - exactly what the great-grandparent mentioned was preferred.
If "Greg Cochran says 1.2%" is the counter-argument then I don't really know what to say except how likely is it that he's wrong this time and by what factor might he be off? What's his confidence interval? If someone can provide his working then at least that's something I can assess. It seems he is looking specifically at places with high infection rates and more stretched healthcare systems.
Anyhow, you repudiated this. When I pushed you on it, you came up with the number 1.4%.
The naive central estimate of a single back-of-the-envelope estimate where virus prevalence in Lombardy was estimated from one small town from a month previous isn't something I'd put much weight on. If pushed for an interquartile range based only on this calculation I would say 0.5<IFR<3.5. The point of that calculation wasn't to get an accurate answer but to show that 0.2% population fatality rate doesn't imply that the IFR is massive and 3,000,000 US coronavirus deaths this year is still highly unlikely.
The most detailed treatment I’ve seen on this is this from a couple of months ago.
EDIT: To clarify per discussion below, I do think there's a fair chance that given a lack of sufficient ventillators the IFR may be >1%.
I'm running at 100% thinking my past selves are assholes. That implies that my current self is probably an asshole by the standard of my future selves. Future selves know which dimensions you need to change in which directions to improve but that isn't straightforward to a current self.
With this in mind I both think my past selves were assholes but maintain some sympathy for them on the assumption that not being an asshole is incredibly difficult and I am still failing in ways that I don't even know about yet.
What are your estimates for how many nodes / causal relationships you would need to investigate to figure out one blueprint?
I was going to write an answer but this sums up my thought process perfectly.
Halloween as a counterexample? (Or possibly the exception which proves the rule?)
This is a math error...
Good point, thanks.
Lombardy had a population fatality rate of 0.2%
I don't think this really means anything without knowing the fraction infected. Robbio's antibody testing a month ago showed 13-14% infected so naively this gives 1.4% IFR. Possibly some sampling bias though. On the other hand this is a small town and presumably larger towns / cities would expect higher rates.
I'm willing to accept that IFR might push a bit over 1% but that doesn't overcome the need for a massive outbreak to happen across the whole US without significant action being taken to minimise the impact to get to 3M deaths.
This analysis suggests that even in Wuhan and NYC the IFR wasn't higher than 1%.
This paper put Lombardy IFR = 1.1 but with a large confidence interval (0.2 - 2.1). It predicts a higher IFR across the world than in Lombardy which is weird. That's the paper which has the highest IFR of any in the 13 included in the analysis above.
Ventilators aren't particularly effective, saving less than half of the people who go on them so even worst case ventilator shortage will less than double IFR. Not sure what other hospital equipment would become the choke point - possibly oxygen supply? Temporary general hospital beds are alot easier to get quickly than temporary ICU beds so I wouldn't anticipate this being unsolvable.
Not everyone will get infected (due to herd immunity) so 330M isn't the number to be looking at, although assuming a runaway infection we'd have R=3 so ~220M infected.
To get the 3 million deaths you would need to have the situation where almost everywhere in the US had a massive outbreak killing 1% of their population with their hospitals in meltdown and all of the government institutions doing nothing to stop it and most people on an individual level not taking precautions like masks etc.
Agreed, especially accounting for presumably significant overlap. I’m guessing whatever this claim is based on either has some sort of selection bias or is counting more people as a loved one than common usage
15 loved ones?
1. Bay Area lockdown (eg restaurants closed) will be extended beyond June 15:
2. …until Election Day:
3. Fewer than 100,000 US coronavirus deaths:
4. Fewer than 300,000 US coronavirus deaths:
5. Fewer than 3 million US coronavirus deaths:
6. US has highest official death toll of any country:
7. US has highest death toll as per expert guesses of real numbers:
8. NYC widely considered worst-hit US city:
9. China’s (official) case number goes from its current 82,000 to 100,000 by the end of the year:
10. A coronavirus vaccine has been approved for general use and given to at least 10,000 people somewhere in the First World:
11. Best scientific consensus ends up being that hydroxychloroquine was significantly effective:
12. I [Scott] personally will get coronavirus (as per my best guess if I had it; positive test not needed):
13. Someone I [Scott] am close to (housemate or close family member) will get coronavirus:
14. General consensus is that we (April 2020 US) were overreacting:
15. General consensus is that we (April 2020 US) were underreacting:
16. General consensus is that summer made coronavirus significantly less dangerous:
17. …and there is a catastrophic (50K+ US deaths, or more major lockdowns, after at least a month without these things) second wave in autumn:
…
19. At least half of states send every voter a mail-in ballot in 2020 presidential election:
20. PredictIt is uncertain (less than 95% sure) who won the presidential election for more than 24 hours after Election Day.
POLITICS:
21. Democrats nominate Biden, and he remains nominee on Election Day:
…
26. Trump is re-elected President:
27. Democrats keep the House:
28. Republicans keep the Senate:
29. Trump approval rating higher than 43% on June 1:
30. Biden polling higher than Trump on June 1:
…
33. Boris still UK PM:
34. No new state leaves EU:
35. UK, EU extend “transition” trade deal:
36. Kim Jong-Un alive and in power:
ECON AND TECH:
37. Dow is above 25,000:
38. …above 30,000:
39. Bitcoin is above $5,000:
40. …above $10,000:
…
42. Crew Dragon reaches orbit:
43. Starship reaches orbit:
Rearranging the above:
The doubling times in this post only really apply for the start of an outbreak. Once control measures are put in place the doubling time changes - see this post. To look at this properly you'd need a more complex model - SIR is a good starting point but proper models are much more complex.
If she ends up wanting to do more cooking there are plastic kitchen knives available. Our kids like to use them on the rare occasions that they want to help out. Probably good enough for a soft avacado but might struggle with onion!
Not sure if you're still looking for an answer on this but:
After the stay-at-home orders started (~22 March) we no longer expect to see exponential growth in actual infections so the delay between infections and cases identified causes there to be a varying ratio between them.
Add that to the fact that the testing rate was the main thing controlling how many cases were identified which messes everything up. In late March/early April the positive rate of tests in New York was ~50% which renders the numbers fairly meaningless.
The magnitude of the numbers here seem wrong to represent people being infected twice.
From April 9-17 there were 74 newly discovered positive tests in those who had previously recovered. Over the same period there were only 203 new cases discovered. If the 74 received a new infection then they are getting infected at 2000x the rate of the general population.
Obviously there are a fair few reasons why they might be getting reinfected at a higher rate but I can’t think of a way it would be that much more. The reoccurrence of an existing infection would make a lot more sense.
One advantage of calibration testing is that it doesn't require a market/opponent. I suspect that this is at least partly why Scott uses this method.
Some potentially useful numbers I've been working on estimating:
1. The number of days lag between registered cases and deaths
2. The adjusted CFR for each country taking this into account
The method is essentially to try different lags (dividing current deaths by cases from x days ago) and see which length of lag gives a constant CFR over time (normally CFR increases with time as the growth rate of cases slows earlier than that of deaths).
Here are the results for a few countries:
China: 9 day lag, CFR=4%
USA: 7 day lag, CFR=6.5%
Italy: 4 day lag, CFR=14.5%
Spain: 2 day lag, CFR=10.5%
Germany: 10 day lag, CFR=3.5%
France: 10 day lag, CFR=24%
Switzerland: 6 day lag, CFR=4%
UK: 4 day lag, CFR=18.5%
I'm not sure about these, especially UK but they do create nice constant values for CFR over a period of 2-4 weeks (UK only 10 days) which suggests a predictable pattern, despite variation in testing.
The France result is also not quite as consistent as the others and is surprisingly high so I don't quite trust it either. I could make a case that for an estimate of 7 day lag and 20% CFR.
The real rules have no exceptions
In Newton’s case the real rule (or at least the practical rule) is the meta-rule of when Newton is good enough and what to use when it isn’t. Without that knowledge you can’t form a meta-rule and you don’t know when to believe the model and when not to. You can maybe assess it probabilistically but I wouldn’t want to place much on the result.
Italy seems to me to have stalled in decreasing R at about R=0.9. China and South Korea both got down to R=0.5. I have a concern that the UK has stalled at about R=1.3 (25% confidence) but I suspect that a few days more data may disprove this.
The US appears to still be on a downwards trajectory (currently just above R=1) but where exactly it stops will make a huge difference to the final tally. If I were to be making a model then this is the main place where I would focus my attention to give reasonable confidence intervals.
The Bayes factor calculation which I did is the analytical result for which BIC is an approximation (see this sequence). Generally BIC is a large N approximation but in this case they actually do end up being fairly similar even with low N.
For example, Scott ending up ~60% right on the things that he thinks are 50% likely suggests that he's throwing away some of his signal
If we compare two hypotheses:
Perfect calibration at 50%
vs
Unknown actual calibration (uniform prior across [0,1])
Then the Bayes factor is 2:1 in favour of the former hypothesis (for 7/11 correct) so it seems that Scott isn't throwing away information. Looking across other years supports this - his total of 30 out of 65 is 5:1 evidence in favour of the former hypothesis.
This is great, I particularly like the grocery delivery idea.
As I understand it the R0 variance is a big reason (the main reason?) for the flu vaccine being given to children at least in the UK - they have the potential to have a very high R0. According to this study worrying about kids infections may not be helpful for COVID-19 but this seems like the right kind of thing to consider.
If the doubling time is 2.5 days and the serial interval is 5 days then R0 should be about 4, so each serial interval can let us double twice.
If I understand this correctly I think this would mean R0=3 as1in 4 people infected by the end of 5 days was already infected at the beginning.
Edit: I think I got this last bit wrong as only 3/4 of the people infected at the beginning are still infectious (according to the simplified model I’m imagining) so the original value of R0=4 is correct
I share your rough estimates of IFR in your other comment here although I was concerned about how high IFR might be with overwhelmed hospitals.
Sampling bias at its worst here would mean that IFR is 3 times more than those calculations (i.e. 1.5-2%). If this is the worst case in Lombardy where the hospitals are overwhelmed then it is something of a relief to me that higher rates are unlikely.
It isn't clear - that's a good point and would suggest that the upper bound might actually be higher than it appears at first glance. If we take 10% of infections being hospital based (which might not be accurate as that statistic is from South Korea and the above paper is in China outside Hubei) then 16% of the outside-the-home transmission might be hospital based.
I should say that only 284 of the 468 transmission events are included in either household and non-household. I don't know what the other 40% of cases were but I guess the researchers weren't able to identify the relationship from the public data that they were using. It does appear that this undefined 40% has a lower serial interval than either of the two defined groupings as the serial interval of all cases together is lower 3.96 [3.53, 4.39].
If initial viral load makes a difference one would expect to see shorter time from infection to diagnosis/hospitalisation in cases which are transmitted within households. There is suggestive evidence in this paper which includes data on the serial time for household (4.03 [3.12, 4.94]) and non-household (4.56 [3.85, 5.27]) secondary infections. The number in square brackets are the 95% CI.
This is fairly weak evidence that there is a difference and also gives some weak indication as to what the maximum effect of initial viral load might be.
The raw data from this paper, for example, might be used to give more information on this and also severity which is more what we're interested in - the Tianjin data appears to be fairly complete albeit with only 135 cases.
EDIT: added link to 2nd paper
I think even a few days has the potential to be extremely valuable if it can be pulled off. If worldwide reactions had happened a few days sooner then half of the cases could have been avoided. LW ringing an alarm bell a few days earlier might not have had an effect on policy but its important to note how big the potential gains are.
As you say in the OP, the next time any pandemic comes along the worldwide response is likely to be better. So my main question is how do we generalise this advice for other severe dangers.
To me one of the main issues if the speed at which things happen. Most things which happen gradually give enough time for people to react without disastrous consequences - COVID only gives a few days before your problem is doubled. This would be fairly high on my checklist specifically for a future pandemic - low doubling times - but for general alarm bell ringing speed of problem development should also be up there.
*insert obligatory FOOM comment here...*
Did you estimate how early using this would have caused an alarm to be raised for COVID-19?
I think the top 3 the harm questions were confirmed in this paper on 11th Feb but maybe there were other papers before this or we could have inferred from public data?
2,000 deaths was 18th Feb.
Escaping a lockdown attempt would probably be ~21st Feb in South Korea (the virus didn't really escape China lockdown - it had escaped before the lockdown)
Indirect transmissibility I'm not quite sure about a date?
Pre-symptomatic transmission again I'm not sure - from the papers in jimrandomh's post maybe early-mid Feb we had a good hint.
Yes, we definitely expect to see a lag between growth rates of cases and deaths, it is odd that even when this seems to be present it is only a couple of days to a week. I think this may be partly due to delays in diagnosis. 17.8 days is between onset of symptoms to death. However there is normally a lag between onset of symptoms and diagnosis (onset to hospitalisation I think is generally a bit less than a week) but even this still leaves a theoretical 10+ day lag.
That is all based on relative numbers within a country. Comparing CFR (case fatality rate) values between countries is notoriously unreliable due to testing capability. Looking at naive CFR I think the UK are about to overtake Italy as having the worst CFR in this set of 10 despite being earlier in their epidemic. This is either due to being worse at testing or better at diagnosing deaths as being COVID related (some countries aren't counting deaths which don't occur in hospital - source). CFR in the US is low compared to where other countries were at similar points in their epidemic so I guess it won't reach 10% but it is likely to reach 5%.
The ILO (international labour Organization, a UN agency) has a report on this.
Some key findings: Estimated increase in unemployment of 5-25 million - c.f. 22 million for 2008-9 crisis
These based on assumptions of 2-8% drop in global gdp
Value add from Chinese Industrial was down 13.5% in Jan/Feb
You might be interested in this post which explores similar territory.
South Korea, as always, are a treasure trove on information - they publish details every day which includes major outbreak clusters, some of which are hospitals. Of the non-cult related cases where they have managed to identify the source of the infection, hospital based infections account for 20%. If you include cases where they haven't identified the source then it's more like 10% which is probably a fairer reflection as hospital clusters probably mainly do get identified.
(They changed their reporting layout on March 25th and the new version doesn't quite contain as much information so I've based this on the 24th)
I think there's a decent amount of correlation with between lockdown dates and entering linear growth. Below are the lockdown dates and starts of the linear phase for some of the worst hit countries.
China 23rd Jan -> 5th Feb
S. Korea 20th Feb -> 1st March (This wasn't a mandated government lockdown but people did seem to stay inside in the worst hit areas)
March:
Italy 9th -> 21st
Spain 15th -> 26th
Germany 16th -> 27th
France 17th -> not yet linear (last 2 days have been high)
Switzerland 20th -> 21st
US 22nd (NY) -> not yet linear
UK 23rd -> approaching linear? Possibly already there
These are remarkably consistent at 10-14 days, apart from Switzerland (very fast) and France (looked like it had gone linear at about the normal time but has increased again).
This graph shows the same data but is annotated with containment steps taken by each country (it isn't averaged over 3 days so the exact numbers don't match up but the same pattern applies).
The obvious conclusion is that Japan just isn’t testing anyone. This turns out to be true – they were hoping that if they made themselves look virus-free, the world would still let them hold the Tokyo Olympics this summer.
I think this really needs to substantiated before claiming it is true (I realise this is a quote but still).
Personally I think people are looking at the wrong denominator for Japan - Japan’s tests / population is low but their tests / positive test is high (20:1 or so, S Korea is 30:1, Western nations are <10:1).
What’s your take on the South Korean data?
They were testing thoroughly (30 negative tests for every 1 positive) all the way through their outbreak so either they were useless at choosing who to test (seems unlikely as they got the outbreak under control pretty fast) or they were finding nearly everyone. Their CFR was 1.3%.
Comparing confirmed cases to deaths should identify that confounder if it’s there. Interestingly the US is one of the countries which showed up as possibly confounded at the beginning. More recently I suspect this is less of an issue.
My analysis suggests about a 40% decrease in R due to hygiene and social distancing. R0 is ~3 for COVID-19 so this bring R down to ~1.8 which means the virus is still growing fairly fast. For flu R0 is ~1.3 so after these measures R is ~0.8 and therefore is shrinking.
Yes, holding at a high number is tricky and not particularly desirable. If you get doubling time above 6 days then it’s likely that you’ll start decreasing cases.
I think that the most important thing if trying to hold at a low level whilst relaxing restrictions is ensuring that the doubling time is longer than the incubation time (which is the main lag in your control loop). That way if you have made an error the virus isn’t too far gone before you start to notice and contact tracing for containment remains viable.
A couple of additional points to leggi:
Elizabeth calculates roughly 25% of people are in essential roles. These people are less able to reduce numbers of contacts.
At least initially many people don’t take social distancing seriously so the effects are likely to ramp up over time.
In that case it makes sense that initially doubling times increase over 5 and over time they keep increasing.
In China the distance was enforced and Koreans took it seriously right away so it didn’t take long for their doubling times to increase.
You can't successfully sue Bob for giving you COVID unless you can prove it more likely than not that your COVID came from Bob. That's basically impossible.
In South Korea where the contact tracing is working this seems like it would be possible. Patient 31 has been more-or-less determined to have lead to 5k+ people getting infected.
If the numbers come down in the US such that the authorities are able to contain via contact tracing then this would become reasonable there too.