| Taleb's note was probably more relevant back when it was published. It's clear that current measures are measures devised against epidemics, and not "conventional risk-management approaches" for everyday business, which he seems to be criticizing. There's a lot to criticize about the note [1] but its conclusion was appropriate in that a response tailored to the particular epidemic based on conventional risk-assessment can't properly evaluate the harm of an epidemic until it is too late. But today, governments are responding in the typical fashion of responding to pandemics. Closing borders and quarantines are precisely the measure that dampens the more frequent so-called "superspreader" events, and social distancing is the exaggerated response that decimates the potential impact of these "superspreader" events. It's obvious and unfortunate that the authors didn't do their research on public health policy, since public health advisors and experts actually already knows the right measures to take; it's a matter of convincing the decision-makers that this is the right way to respond to a new pandemic rather than the risk-assessment models that were tailored to more frequent events. > Ioannidis' approach fails: "Preliminary investigations conducted by the Chinese authorities have found no clear evidence of human-to-human transmission..." [3] Authorities were looking for evidence before taking any action. I don't think this is the right conclusion. I think many flu strains occasionally transmit from animal to humans, but fail to spread from human to human, and imposing drastic measures upon this is too much of an overreaction, since they aren't usually much worse in public health impact than the seasonal flu. On the other hand, as soon as there is a spike of cases in a local area, that is enough evidence to be wary. That tweet just reads like a poor excuse by Chinese officials that doesn't make sense, something that the rest of the world have come to expect from them. You also have a misreading of what Ioannidis is trying to push for. He isn't advocating against taking action before good evidence comes out. Rather, he is highlighting that our current lack of good evidence about the epidemic is necessitating a greater reaction than may actually be necessary if we had better evidence. He is advocating for better evidence to be published with higher standards, so that if this pandemic is actually less dangerous than it actually is, decision-makers would continue to trust the public health experts to make decisions should a worse pandemic come about. [1] e.g. > Standard individual-scale policy approaches such as isolation, contact tracing and monitoring are rapidly (computationally) overwhelmed Computationally ??? |
This is kinda the core problem, though: We don't have access to the full evidence (yet) and things already look somewhere between very very bad and mildly catastrophic. (I'll reserve full catastrophe for Giant Asteroid.) If you don't plan for the 'catastrophic' case and it's on the table, you look pretty bad if the error tends in that direction. By the time you KNOW you're in the catastrophic case, it's too late to deal with it.
I've seen the Ioniaddis pieces showing up in a couple places, and he really comes off as a bit of a crank, more concerned about his Stanford-supported stock portfolio than considering the Actually Available evidence. I don't give a fsck about the initial wrong reports in China... Italy's got overflowing hospitals and a very exponential-looking death curve right now. Not enough testing means we're getting better numbers, leading to a sharp spike... but the numbers are still reflecting mostly the worst cases, coz that's who tests are available to. And the numbers of deaths and very bad cases are climbing very, very fast.
Here's a model you can tweak, with plots of available data and estimates of available hospital beds, etc... Just looking at the death curves (can't hide a body, amirite) the 'Fast/North' scenario looks like it fits well for the US. Moderate-to-no mitigation is then modeled at O(3MM) deaths, and strong mitigation drops to O(1MM). So, the error bars that we're playing with are measured in millions of lives.
https://neherlab.org/covid19/