China is following a strategy of shutting down everything and getting R0 as low as possible. This works well in the short term, but they either have to keep everything shut down forever, or risk the whole thing starting over again.
UK is following a strategy of shutting down only the highest-risk people, and letting the infection burn itself out. It's a permanent solution, but it's going to be really awful for a while as the hospitals overload and many people die from lack of hospital care.
What about a strategy in between these two? Shut everything down, then gradually unshut down a little bit at a time. Your goal is to "surf" the border of the number of cases your medical system can handle at any given time (maybe this would mean an R0 of 1?) Any more cases, and you tighten quarantine; any fewer cases, and you relax it. If you're really organized, you can say things like "This is the month for people with last names A - F to go out and get the coronavirus". That way you never get extra mortality from the medical system being overloaded, but you do eventually get herd immunity and the ability to return to normalcy.
This would be sacrificing a certai...
If you first do lockdowns to get new cases to ~0 and then relax, optimistically you will get localized epidemics that you can contain with widespread testing, contact tracing, and distancing if needed. Cost of testing & tracing and having to do occasional local/regional lockdowns could end up being manageable until treatment/vaccine arrives.
My main reason for optimism is Korea's and China's success containing a large outbreak. We will be expecting the secondary epidemics and reacting quickly, so they will be small when detected, so should be much easier to contain than the first surprise outbreak.
We'll get data on this in the coming months as China loosens restrictions. There is option value in containing asap and first trying things other than deliberate infections.
I've spent some time thinking about endgames here. (Not that I feel like I've come to any conclusions. I wish I knew what e.g. the WHO thought the endgame was.) The biggest problem I see with this idea is the lag between input and output -- when you change your quarantine measures, you can't observe the result for at least the 5-7 days it takes the newly infected to get symptoms, and longer if you want to get a lot of confidence in your measurement, over the noise inherent in the system.
Control systems with high lag like this are incredibly difficult to work with. Especially in the presence of exponential growth like this system has -- if you accidentally let R get a bit too high, it will be a week or two before you notice, and in that time you will have seeded a ton of cases that you will have to track down and deal with.
I think the most hopeful endgame here, near-mid-term, is that we find a combination of antivirals with high effectiveness against COVID-19, which reduces the rate of severe pneumonia dramatically. At that point our hardest constraint, ventilators, will get relaxed. Beds are a lot easier to deal with a shortage of.
Mid-long-term, of course, we're all hoping for a va
...The first strategy leaves you with a huge population of people with no immunity to the virus, which means you have to keep holding the lid on it indefinitely or you're back to square one.
In the second strategy, everyone ends up either immune or dead, which doesn't mean the virus is gone -- it will remain endemic -- but there will be no giant flood of new cases when people resume their lives.
(Obviously it's not quite as simple as that if the virus doesn't generate durable immunity. Then you end up with something like the flu, where partial immunity keeps it vaguely tamped down with occasional flares.)
Update: the positions are now filled. See here for the official announcement.
Help wanted: pandemic.metaculus.com project lead
The high interest and proliferation of questions on the novel coronavirus calls for dedicated attention, which led to the formation of pandemic.metaculus.com. Managing it, though, is straining Metaculus's very limited staff and community moderator team. Contingent on acquisition of funding (which Metaculus is working to secure), Metaculus is looking to bring onboard someone to help manage this project. Components would include:
The above indicates a range of skills including pretty strong understanding of Metaculus, and data analysis capability. Science background would be great, and huge bonus for actual medical knowledge. This is probably a part-time role but full-ish time is also imaginable depending upon the person, the duration, and funding.
If you're interested, please send a note and CV to jobs@metaculus.com.
If this news article is accurate, masks will not be scarce for much longer. That article claims that China is now producing masks at 116M/day, a 12x increase compared to the start of February (5 weeks ago), and that they will export them. This is in addition to mask production in other countries.
I am not sure whether, when combined with production in other countries, this satisfies the entire world demand. But masks aren't complicated objects and aren't made of scarce materials, and this is pretty strong evidence that production can be scaled up even further, if necessary.
In a few weeks, a number of public figures may find themselves doing an awkward about-face from "masks don't work and no one should wear them" to "masks do work and they are mandatory".
(If you are able to buy masks for less than $1/each through ordinary channels, it means the shortage has abated, and you can buy them without worrying about depriving health care workers of those supplies, but you shouldn't stock up on more than you need in the short term until the price has been low for at least a few weeks.)
In a few weeks, a number of public figures may find themselves doing an awkward about-face from "masks don't work and no one should wear them" to "masks do work and they are mandatory".
I want to record and reward how this prediction seems to be correct: https://www.washingtonpost.com/health/2020/04/02/coronavirus-facemasks-policyreversal/
I heard a rumor of someone in the Bay area claiming to work in the intelligence community, to be terminally ill, and to have received an experimental COVID-19 vaccine. I think this rumor is false with respect to the specific person, but do note that "military officers with preexisting terminal illnesses who volunteer" is a group that may actually exist, and that at least one drug company claims to have shipped vaccines for a phase 1 trial on Feb 24.
This raises the question: if you're well resourced, desperate, competent, and in possession of expendable military volunteers, how long does development for a vaccine actually take?
Given a candidate vaccine, you need to do three things: find out if confers immunity (and how much immunity), find out if it causes side effects severe enough to not be worth it, and scale up production.
All three of these can be done in parallel. If you have expendable volunteers, you don't need to start with an animal model; you can just give them the vaccine, and see whether they suffer side effects. Testing efficacy can be done in parallel, with the same volunteers, and takes about three weeks--you give the vaccine, wait a week, expose to virus, then wait i
...Legally speak, yes they would. Practically speaking, however, the FDA has no enforcement power over secret programs in the intelligence community.
I think a lot of people are seriously overestimating the FDA's actual power, and that's causing pretty severe problems. Consider for example this tweet (and a long series like it) by the mayor of NYC, begging the FDA for approvals. While there is no legal precedent to refer to, it's extremely implausible that the FDA could ever get or enforce a judgment of the city of New York for actions taken during a state of emergency, when the FDA itself caused that emergency with culpable negligence.
(Warning: Long comment)
Two weeks ago Wei Dai released his financial statement on his bet that the coronavirus would negatively impact the stock market. Since then (at the time of writing) the S&P has dropped another 9%. This move has been considered by many to be definitive evidence against the efficient market hypothesis, given that the epistemic situation with respect to the coronavirus has apparently not changed much in weeks (at least to a first approximation).
One hypothesis for why the stock market reacted as it did seems to be that people are failing to take exponential growth of the virus into account, and thus make overly optimistic predictions. This parallels Ray Kurzweil's observations of how people view technological progress,
When people think of a future period, they intuitively assume that the current rate of progress will continue for future periods. However, careful consideration of the pace of technology shows that the rate of progress is not constant, but it is human nature to adapt to the changing pace, so the intuitive view is that the pace will continue at the current rate. [...] From the mathematician’s...
The idea that smart investors don't understand exponential curves is absurd on its face
I don't think this is necessarily absurd or false. Like, this is what Black Swan Farming was about.
I think people in finance are used to exponential curves with doubling times of 20 years, and this doesn't give them much of an edge when it comes to doubling times of 2 days. Like, even in semiconductor manufacturing, the progress of Moore's Law over someone's 40-year career corresponds to about a month of viral growth at that rate.
Startup finance people do work with stuff at roughly the same scale, and correspondingly freaked out much more.
The sheer insanity of such a prediction should give you an idea of how uncertain this whole thing still is.
I don't think this is crazy, once you consider healthcare system failure. What does the world look like if no one receives medical care for any condition besides a COVID infection for the next three months?
This blog post argues that the now popular idea of "flattening the curve", in the sense that most people get exposed but slowly enough to not overwhelm the health care system, is not feasible. The result is that we'll either achieve containment or at least widespread regional health care system collapse (and maybe Wei Dai's global health care collapse outcome). I haven't spent much time modeling this yet, but tentatively it looks like flattening the curve requires very precise fine-tuning of R0 to stay on a path very close to 1 for at least several months, which seems impossible to pull off.
It feels to me now that flattening the curve is just a nice graphic without anyone checking the math, but I am confused that many informed-seeming experts are promoting the idea. Anything I'm missing?
ETA: I made an epidemic + hospitalization model (Google Sheets), it sure looks like the usual flatten-the-curve chart is a comforting fiction. Peak hospital bed demand in the uncontrolled epidemic scenario is usually drawn at 2-3x hospital capacity. I'm getting 25x and the chart looks a lot less reassuring. My shakiest assumptions are hospitalization / intensive ca...
Disclaimer: I don't know if this is right, I'm reasoning entirely from first principles.
If there is dispersion in R0, then there would likely be some places where the virus survives even if you take draconian measures. If you later relax those draconian measures, it will begin spreading in the larger population again at the same rate as before.
In particular, if the number of cases is currently decreasing overall most places, then soon most of the cases will be in regions or communities where containment was less successful and so the number of cases will stop decreasing.
If it's infeasible to literally stamp it out everywhere (which I've heard), then you basically want to either delay long enough to have a vaccine or have people get sick at the largest rate that the health care system can handle.
That's an interesting question that seems like it ought to be able to be checked numerically.
I made an attempt using this simulator of the fairly-naive "SIR" model of disease transmission:
Note that this simulator appears to be someone's class project. However, its behavior seems to track more or less with what I'd expect. But I'd love for someone with more experience to reproduce this relatively simple model and check it.
You can read about the model at https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model .
I have limited confidence that I've understood it correctly, so take this for what it's worth. It looks to me the time step used in this simulator is one day. So the gamma parameter (rate of recovery per unit time) should be (Wikpedia says) 1/D where D is the duration of the disease. (For transmission modeling purposes, this should be the infectious duration, not the duration of symptoms.) I chose gamma=0.7, meaning D ~= 14 days, semi-arbitrarily, based on https://www.medrxiv.org/content/10.1101/2020.03.05.20030502v1 (
...Thanks for pointing me in this direction. I think the key worry highlighted in the post is that the health care system gets overwhelmed with even just a few percent of the population being infected. So even if we can bring peak infections down by a factor of 2-4 by slowing transmission, the health care system is still going to be creamed at the peak.
I've now built a discrete-time, Bay Area version of the SIR model (+ hospitalization) in this Google sheet. I assume 20% of infections need hospitalization, of which 20% need intensive care, and use raw bed-to-population ratios (non-COVID utilization vs stretching capacity should roughly cancel out). Hospital bed availability at peak infections is 4% (25x over capacity) in the uncontrolled beta=0.25 scenario and only improves to 10% (10x over capacity) in the "controlled" beta=0.14 scenario. Even if my hospitalization/ICU numbers are too high by a factor of 5 the "controlled" scenario still looks pretty terrible. Any feedback on the model assumptions would be super useful.
I saw the meme as mostly targeting people who were currently even more complacent "eh, there's nothing we can do, so fuck it", and getting them to instead go "okay, there's stuff that's actually worth doing."
That's a really interesting blog post, and it made me update (towards the idea that containment efforts in most countries will keep ramping up until containment actually succeeds). How did you come across it? I've been following Twitter, a couple of FB groups, and Reddit, and it didn't get linked by any of the posts I saw.
It feels to me now that flattening the curve is just a nice graphic without anyone checking the math, but I am confused that many informed-seeming experts are promoting the idea. Anything I’m missing?
I'm wondering this too.
I think it would be valuable to compile a list of estimates of basic epidemiological parameters of the coronavirus, such as incubation rate, doubling times, probability of symptomatic infections, delay from disease onset to death, probability of death among symptomatics, and so on. I find that my inability to model various scenarios accurately is often due to uncertainty about one or more of these parameters (uncertainty relative to what I suspect current expert knowledge to be, which is of course also uncertain to a considerable degree).
Did you end up finding one besides the MIDAS network, or develop your own? I'm assembling a parameter doc for inputs to a rough model that accounts for ventilator & hospital bed capacity, since it seems like we're lacking that.
Incidentally, also strong evidence against it being a lab-strain. It's a wild strain.
Closest related viruses: bats and Malayan pangolins
Polybasic Cleavage Sites (PCS): They seem to have something to do with increased rates of cell-cell fusion (increased rate of virus-induced XL multi-nucleated cells). Mutations generating PCS have been seen in Influenza strains to increase their pathogenicity, and they had similar effects in a few other viruses. So it's not exactly increasing virus-cell fusion, it's actually... increasing the rate at which infected cells glom into nearby cells. Fused cells are called syncytia.
O-linked glycans : Are theorized (with uncertainty) to help the virions masquerade as mucin, so hiding from the immune system. (Mutation unlikely to evolve in a lab on a petri dish)
(Just following the recommendation to move this out of shortform so it can be tagged later.)
More specifically:
Or in other words, it doesn't look planned. Its most recent mutations look much more like a "natural variation let it jump species" sort of situation.
This doesn't address situations like, for example, "dead bats with a wild-type virus being left near a bunch of ferrets or pangolins," or something to that effect.
(ETA: Or... accidental release like this is still possible.)
This thread was created on 3/8/2020, or approximately one million years ago in virus time. It’s getting pretty bloated now, and a lot of things that were high value at the time have been eclipsed by events, making karma not a very useful sorting tool. So I’m declaring this thread finished, and asking everyone to move over to the April Coronavirus Open Thread.
Interested in what happened in this thread? Here’s the timeless or not-yet-eclipsed highlights: