Thank you for writing this post and tracking down everyone's stated beliefs and updates!
I fear MNM only operated in this case because the prosocial intervention of isolating yourself also happened to be a very selfishly effective intervention. In my view, what this community failed to predict is simply that other people would, with some delay, come to the same conclusions and act as this community did, i.e. going into some degree of isolation to protect themselves. It's a pretty embarrassing failure! I distinctly recall expecting that aggregate behavior won't change much until the local epidemic was visibly out of control, filling up hospitals and so on, whereas I of course was going to wisely ride all this out from my apartment.
This would explain why there was no MNM operating in most governments in Jan-Feb. It would also mean we can't rely on MNM helping out with future risks that have a different structure than pandemics.
Nice post! Agree on most conclusions except I put more weight on the herd immunity threshold being not much lower than the naive compartment models imply.
Serology data from the 1968 flu pandemic seem to rule out large effects of heterogeneity on the final attack rate. First wave seropositivity was ~35% (mostly 25-50%) with an R0 of ~1.5. R0 increased in the second wave to ~2.5, and seropositivity ended up mostly around 60-70%.
People claiming big heterogeneity impacts seem to have focused on models over empirics. Unfortunately the range of effects implied by different ways of modeling heterogeneity is very wide, with not much to choose between models but actual epidemic data.
Some very encouraging developments. There is a PCR protocol that can test 100,000 samples in a single machine run, making millions of samples per day feasible, ignoring sample collection capacity. On that front, FDA just approved (EUA, limited scope for now) a sample collection protocol relying on saliva samples rather than nasopharyngeal swabs (would mean enormous increase in sample collection capacity). The prospects for plan #1 look dramatically better.
On plan #3, I was hoping this would work as a backup or low-tech option for poor countries but it looks like most estimates tend to put asymptomatic + presymptomatic transmission at 50%+ of all transmission, which makes this pretty limited.
I was completely wrong, I don't think their data is subject to this worry. They now have a preprint up. From supplementary methods:
We define daily fever counts as the number of unique users per region that take multiple elevated temperature (37.7 C) readings over the past week, and then normalize these counts by the estimated number of unique users who have used the thermometer over the past year.
So lots of repeat readings shouldn't affect the gauge, and neither should more of their user base taking readings. Unless they are seeing a lot of new users, or lots of returning users that haven't used the thermometer in over a year, both of which seem somewhat unlikely, their metric should be fine.
Mass testing seems like a promising brute force strategy that can keep R < 1 after lockdown, without requiring contact tracing. I'm pretty early in thinking about this but wanted to share my thoughts to encourage parallel efforts. A few possibilities (not mutually exclusive):
1) RNA testing: If everyone is given a daily RNA test and positives are isolated, transmission will likely be very close to 0. The US is still a factor of 1000 away from doing this (for comparison, RNA testing has scaled by 400x in the last month). However it seems likely that even testing on average every 10 days could keep R < 1. Key questions: i) what frequency... (read more)
The Kinsa data is barely even weak evidence in favor of R0 < 1. The downward trend in fever readings are confounded, likely severely, by their thermometers having to be actively used vs. being a passive wearable. It seems plausible that more people will check their temperature when they are concerned about COVID-19, and since most people are healthy this will spuriously drive average fever readings down. Plausibly the timing of increased thermometer use will coincide somewhat with shelter-in-place orders since they correlate with severity & awareness of the local outbreak.
Their FAQ notes that they have seen 2-3x normal usage of their thermometers (this was as of March 29, they haven't updated this part of their FAQ since) and consider this "healthcare seeking behavior" a potential driver of their trends. This has not stopped them from promoting their data to government agencies and NYT, without mentioning this or any other limitations whatsoever (at least to the NYT).
Non manufacturing index just came out: 52.5, down 4.8 points. More affected than manufacturing but still in expansion. Confusing.
I would focus on the ISM non-manufacturing index over the manufacturing PMI since this recession is, in the short run, primarily a services recession. Non-manufacturing index will probably be hit harder and will be more indicative of Q1 GDP.
More generally, past indicators of GDP are probably going to lose some reliability. The sectoral breakdown and rapid timing & severity of the current shock are unique enough for many historical correlations to break down. Normally less important things like survey periods will also affect monthly time series more given the rapid timing.
Re: Q1 GDP, based on this monthly tracking, it would only take a 1.7% (non-annualized) decline in March vs February GDP to result in negative quarterly growth. Even if March is only partially affected, I'm moderately confident there will be a bigger hit. [ETA: this prediction is for revised numbers ~2 years from now, not necessarily for the first estimate of Q1 GDP. Revisions are often large, especially around recessions, and probably more so with this one.]
This looks sketchy to say the least (e.g all citations are self citations), but seems worth doing a very shallow dive into or pointing out if clearly flawed: claim that yogurt can prevent secondary bacterial pneumonia in COVID-19 patients. The argument seems to at least imply that secondary bacterial pneumonia leading to cytokine storm is a common pathway to fatal cases.
(H/t Rob Wiblin on Twitter)
A while back, I posted in the "What are you working on?" thread about a paper I was working on. A few people wanted to see it once I have a complete draft, and I'm of course independently interested in obtaining feedback before I move on with it.
The paper doesn't presuppose much philosophical jargon that isn't easily googleable, I think. Math-wise, you need to be somewhat comfortable with basic conditional probabilities. I'm interested in finding out about any math errors, other non sequiturs, and other flaws in my discussion. I'd also like to find out about general impressions, such as what I should have spilled more or less ink on. Some notation is... (read 292 more words →)
Wait, are you expecting positive total returns from stocks over the next few months? If so, this is very non-obvious from your post.