Not that outrageous.
https://www.cnn.com/2020/05/11/us/california-inmates-coronav...
According to this Sep 2020 article on reason.com (1) they quote CDC numbers of a 99.98 survival rate (0.02% IFR) for 20-to-49-year-olds. Or a 99.997% survival rate (0.003 IFR) percent among people 19 or less.
1: https://www.google.com/amp/s/reason.com/2020/09/29/the-lates...
What happens when you decide it's good enough and release them, and the residual infection sweeps through that population like wildfire?
As opposed to 100% of the population? It sounds like an improvement to me. I'm suggesting relaxing restrictions for a part of the population, not increasing them.
This might not be a terrible idea, though, if compared to a several-year-extension of what we have now... because over time, the cumulative probably of exposure for the vulnerable will just keep rising and rising if we stay at something like the status quo.
But... that's where things like vaccine and treatment development come in. If a vaccine makes catching it much less likely in 6 months, or treatment improvements make it much less deadly even for the vulnerable in six months, then it's worth spending another 6 months in the current situation.
I'm 53. I have asthma and I really hate hospitals. Right now, I have been going for groceries about once a week. I've gone to some appointments, but I've cancelled others. I've been getting take-out some, and I even went on one shopping expedition for craft supplies. I'm relatively comfortable with that because, while I have to assume everyone else is potentially infected, I can also assume that most of them are taking steps to protect me---a mask is significantly more effective at preventing spread from someone infected than it is at preventing an infection of the wearer.
Close quarantine means no going to the store at all. Not going for walks. It specifically means no human contact outside the people you are quarantined with.
If you relax "restrictions" on a part of the population, more of that population becomes infected. If you do not increase the restrictions on the remainder of the population, the higher prevalence increases the transmission rate in that remainder. And thus deaths.
I'm sorry if my existence is inconveniencing you.
Here are the authors. They are well credentialed
Dr. Martin Kulldorff, professor of medicine at Harvard University, a biostatistician, and epidemiologist with expertise in detecting and monitoring of infectious disease outbreaks and vaccine safety evaluations.
Dr. Sunetra Gupta, professor at Oxford University, an epidemiologist with expertise in immunology, vaccine development, and mathematical modeling of infectious diseases.
Dr. Jay Bhattacharya, professor at Stanford University Medical School, a physician, epidemiologist, health economist, and public health policy expert focusing on infectious diseases and vulnerable populations.
One of the authors' twitter thread with the paper's summary: https://twitter.com/WesPegden/status/1288140129677332482
There are many problems with the GBD, but the simplest is that we don't know who the high-risk groups are. Yes, we know age and certain categories of pre-existing condition make for higher risk of death. But we also know that perfectly healthy young people end up with strokes, heart damage, and lung damage, and we're not really sure why. We don't know why some people end up with debilitating symptoms months after infection.
We don't even know if herd immunity is actually possible, or if we'd be committing ourselves to years of intermittent lockdown controls as local outbreaks come and go.
This paper is a similar (if slightly more mathematically detailed) approach, and is more recent: https://www.pnas.org/content/early/2020/09/21/2008087117. It comes to the opposite conclusion. What they find is that while it's technically possible to achieve herd immunity this way, it's logistically unfeasible. It needs monitoring, compliance, and reactiveness that we demonstrably can't (or won't) implement - if we could, we wouldn't be in this mess.
Besides which, neither this paper nor that supports any idea that these three are "leading experts". As far as I can see they're vocal and have a history of being proved wrong by events.
We absolutely do. We have such a wealth of data and the signal is very strong.
> That paper doesn't consider reinfection risk or non-fatal outcomes.
That's because reinfection is extremely rare and risk for non-fatal outcomes is typical of other influenza like illnesses. An interesting note is that many / most people have some sort of cross-protection through T-cell immunity (likely from other coronaviruses).
> We don't even know if herd immunity is actually possible
Yes we do. Pretty much every disease tails off. The only debate right now is where this threshold is at for various jurisdictions. It is likely as low as 20%. The 60% number quoted early in the pandemic was assuming homogenous population with equal susceptibility and perfect mixing.
> This paper is a similar (if slightly more mathematically detailed) approach, and is more recent: https://www.pnas.org/content/early/2020/09/21/2008087117. It comes to the opposite conclusion. What they find is that while it's technically possible to achieve herd immunity this way, it's logistically unfeasible.
All models are wrong but some are useful. If this model cannot explain real data from cities and countries (eg: stockholm, UK locales) then it is relatively useless.
We know who is likely to die. We do not know who is at risk of a life-long debilitating illness.
> That's because reinfection is extremely rare
We don't know this. What we know is that reinfection with a different strain is rarely detected, and that's a long way from the same thing.
> risk for non-fatal outcomes is typical of other influenza like illnesses
This is false.
> An interesting note is that many / most people have some sort of cross-protection through T-cell immunity (likely from other coronaviruses).
At best this is optimistic. We know some (less than half) have a T-cell response. We don't know yet if that response is beneficial, harmful, or has no effect at all. It would be premature to start any sort of public health intervention founded on this assumption.
> Yes we do. Pretty much every disease tails off.
This strongly depends on the reinfection rate. Which we don't know.
> The only debate right now is where this threshold is at for various jurisdictions. It is likely as low as 20%.
This is false. To get anywhere near 20% you need to know the effect of the T-cell response, or have some other mechanism for discounting a large portion of the population.
> All models are wrong but some are useful. If this model cannot explain real data from cities and countries (eg: stockholm, UK locales) then it is relatively useless.
Have you read either of them? Both models in this thread are predictive models of situations that haven't happened yet. Both use real data (from the US and the UK). Neither can describe reality, so do we throw them both out? That leaves the GBD lot with no epidemiological support at all, which would make my point rather concisely.
We simply don't have enough information to know whether the GBD proposal is safe or, even if it was, whether it could be implemented, and it's all the more suspicious because its three proponents have been making very similar arguments against general lockdown since at least April, when we knew dramatically less. They do not seem to have changed their stances based on new information, which moves the GBD out of science and into politics. Only they're leaning on their academic credentials to lend it airs of legitimacy it can't back up, which makes it complete, utter bullshit that nobody should pay any attention to. It's preying on desperation and optimism to deepen social division and reinforce political hysteria at the worst possible time. No credible health authority is paying any attention to it, nor should they. Please don't bring that sort of content to HN.