In the broader rationality/EA community there was also a Siderea post on Jan 30 and an 80K podcast on Feb 3 (along with a followup podcast on Feb 14).
These two, plus Matthew Barnett's late Jan EA Forum post (which you linked), are the three examples I recall which look most like early visible public alarms from the rationality/EA community.
Other writing was less visible (e.g., on Twitter, Facebook, or Metaculus), less alarm-like (discussions of some aspect of what was happening rather than a call to attention), or later (like the putanumonit Seeing the Smoke post on Feb 27).
I think this post is giving the stock market too much credit.
I'd date the start of the stock market fall as February 24 rather than February 20. The S&P close on Feb 20 & Feb 21 was roughly the same as it had been over the previous couple weeks, and higher than the close on Feb 7, 5, 4, or 3. The first notable dip happened on February 24th; that was the first day that set a low for the month of Feb 2020 (and Feb 25 was the first day that set a low for calendar year 2020).
Also, that was just the start of the crash. The stock market continued falling sharply and erratically for a couple more weeks, and didn't get within 10% of its current level until March 12th (2.5 weeks after it started its fall on Feb 24).
This is now my favorite way to read HPMOR. I love the Star Wars feel.
I think Scott linked to Pueyo's essay as an illustration of the ideas, not as the source from which the smart people got the ideas.
Which means that this post's attempt to track & evaluate the information flows is working off of an inaccurate map of how information has flowed.
Keep in mind that the trend in the number of confirmed cases only provides hints about the trend in new infections. The number of confirmed cases is highly dependent on the amount of testing, and increases in testing capacity will tend to lead to more confirmed cases. Also, there is a substantial delay between when a person is infected and when they test positive, typically somewhere in the range of 1-2 weeks (with the length of the delay also depending on the testing regime).
I think that's right. Although the data still can tell us something after we get into that ambiguous range where it's hard to distinguish increasing covid and decreasing flu.
One nice thing about this pattern is that it provides some evidence that the anti-covid interventions are reducing the spread of fever-inducing diseases. And the size of the drop in total fevers tells us something about how well they're working on the whole, even if it doesn't tell us the precise trend in covid cases.
Another thing that might be possible is to find other sources of data on the actual prevalence of flu, and use that to come up with a better "baseline" which reflects actual current conditions rather than an estimate of the trendline in the counterfactual world where there was no coronavirus pandemic.
A third thing is that 0 is a lower bound on the number of non-covid fevers, so the trend in total fevers is an upper bound on the number of covid cases.
This third thing already tells us something about Seattle (King County). Their peak in excess fevers happened March 9 at 1.76 scale points (observed minus expected), and the March 22 data show the total fevers at 2.77 scale points. As an upper bound, if those are all covid fevers, that is 1.6x as many new daily cases on March 22 compared to March 9. That's 13 days, and not even a full doubling in the number of daily new fevers. Which suggests that suppression there is either working or coming very close to working (even though the number of confirmed cases has kept curving upward, at least through March 21).
If you look at the time series for King County (Seattle area), it shows a spike peaking on March 9 with the upward trend beginning sometime around Feb 28 - Mar 2.
I think the pattern of a spike and then flattening & maybe decline (which has happened at different times in different regions) reflects a drop in the number of influenza cases, as people's anti-covid precautions also prevent flu transmission. So the baseline estimate of how many new fevers there would be if there wasn't a coronavirus pandemic doesn't actually represent the number of non-covid fevers, because there are fewer non-covid fevers than there would've been without this pandemic.
Elizabeth's comment also describes this.
Kinsa, a company that sells smart thermometers, has a dashboard that shows which regions of the US have an unusually high number of fevers. They have previously used these methods to track regional flu trends in the US. (FitBit has done something similar.)
I wrote a post here describing my attempt to turn their data into a rough estimate of the total number of coronavirus infections in the United States. Something similar could be done for smaller regions.
I agree that a lot could be done with those sorts of data.
One company that already is making some use of a similar dataset is Kinsa, who sells smart thermometers. They started a few years ago, tracking trends in the flu in the US based on the temperature readings of the people using their thermometers (along with location, age, and gender). Now they have a coronavirus tracking website up. It looks like the biggest useful thing that they've been able to do so far with their data is to quickly identify hotspots - parts of the country where there has been a spike in the number of people with a fever. That used to be a sign of a local flu outbreak, now it's a sign of a local coronavirus outbreak. From the NYTimes:
Just last Saturday, Kinsa’s data indicated an unusual rise in fevers in South Florida, even though it was not known to be a Covid-19 epicenter. Within days, testing showed that South Florida had indeed become an epicenter.
Companies like Fitbit could make a similar pivot, looking to see if they can find atypical trends in their data in the Seattle area Feb 28 - Mar 9, the Miami area Mar 2-19, etc. And they might be able to take the extra step of identifying new indicators that help identify individuals who may have coronavirus (unlike Kinsa, as high body temperature was already a known indicator).
There are potentially a bunch more useful things that could be done with all of these datasets, if more researchers had access to them. For example, it might be possible to get much more accurate estimates of the number of people who have been infected with coronavirus. I may make another post about this soon.
Has there been research from other similarish diseases breaking down the household secondary attack rate by relevant variables? It seems like there could be large differences between:
romantic partners who sleep in the same bed vs. housemates who sleep in different rooms
circumstances where the household has heightened concerns and is taking precautions vs. unsuspecting households
situations where people are removed from the household shortly after they're infected vs. households where people continue to live after infection
Group houses are mostly in the safer of the two possibilities for the first 2 of these 3.