Wired? Seriously? I don’t even trust them for tech news.
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.
And I'm sure at some point one of them is going to publish an epidemiological model in support of their position, and not a press release. Until then, they're not even at Wired's level.
That paper doesn't consider reinfection risk or non-fatal outcomes.
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.
> but the simplest is that we don't know who the high-risk groups are
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.