A group of renown epidemiologists from Stanford, Oxford and Harvard demand: "Those who are not vulnerable should immediately be allowed to resume life as normal."
https://www.handelsblatt.com/politik/deutschland/coronakrise...
https://www.factcheck.org/2020/09/cdc-did-not-admit-only-6-o...
BTW: Who pays this self-proclaimed "fact checker"?
> He noted that the 6% figure includes cases where COVID-19 was listed as the only cause of death. “That does not mean that someone who has hypertension or diabetes who dies of Covid didn’t die of Covid-19. They did,” Fauci said on ABC’s “Good Morning America.”
Half the country has hypertension. Half the country has obesity. These fall in the 94% category, but chances are they weren't imminently dying of these things before getting COVID.
As for funding: https://www.factcheck.org/our-funding/
BTW: Seems like those self-proclaimed "fact checkers" are funded by the Annenberg Foundation which has close ties to big pharma...
Also he's inconsistent because within a few months Fauci went from “there’s no reason to be walking around with a mask” to double masking “makes common sense“...
This kind of çomment is unhelpful and uncharitable. It's an ad hominem attack.
That's an ad hominem attack.
https://www.wsj.com/articles/is-the-coronavirus-as-deadly-as...
> An epidemic seed on Jan. 1 implies that by March 9 about six million people in the U.S. would have been infected. As of March 23, according to the Centers for Disease Control and Prevention, there were 499 Covid-19 deaths in the U.S. If our surmise of six million cases is accurate, that’s a mortality rate of 0.01%, assuming a two week lag between infection and death. This is one-tenth of the flu mortality rate of 0.1%. Such a low death rate would be cause for optimism.
A 0.01% mortality rate as asserted would mean 34k deaths in the USA. We're well over 10x that, and we're hardly finished yet.
> This does not make Covid-19 a nonissue. The daily reports from Italy and across the U.S. show real struggles and overwhelmed health systems. But a 20,000- or 40,000-death epidemic is a far less severe problem than one that kills two million. Given the enormous consequences of decisions around Covid-19 response, getting clear data to guide decisions now is critical. We don’t know the true infection rate in the U.S. Antibody testing of representative samples to measure disease prevalence (including the recovered) is crucial. Nearly every day a new lab gets approval for antibody testing, so population testing using this technology is now feasible.
> If we’re right about the limited scale of the epidemic, then measures focused on older populations and hospitals are sensible. Elective procedures will need to be rescheduled. Hospital resources will need to be reallocated to care for critically ill patients. Triage will need to improve. And policy makers will need to focus on reducing risks for older adults and people with underlying medical conditions.
> A universal quarantine may not be worth the costs it imposes on the economy, community and individual mental and physical health. We should undertake immediate steps to evaluate the empirical basis of the current lockdowns.
They're very clearly making point estimates to contrast with the prevalent IFR estimates of the time (~2%), which we now know were entirely wrong.
0.01% (their estimate according to the post above) is far more "entirely wrong" than 2%. For instance, https://www.cdc.gov/library/covid19/112420_covidupdate.html mentions an estimate of 0.94% for Italy (in fact I have a hard time of viewing an early estimate 2% as significantly wrong when a later one with a lot more time and data is around 1%).
But IFR is so tied to age that it's unhelpful to compare it from country to country. One of the reasons Italy's observed fatality rate is higher than average is because of the higher average age of is population. The CDC's current best estimates are:
<20yo: .003%
<50yo: .02%
<70yo: .5%
>70yo: 5.4%
Even a small increase in the elderly population can dramatically influence the observed IFR for a given nation.
https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scena...
That's 0.1-1.1%, not 0.1-0.5%. And the initial context was the USA, whose age distribution is more like (though noticeably different) Italy's than it's like Kenya's.
I can't believe people are still confusing these. Dividing fatalities by known cases is completely wrong, and is not the definition of IFR.
0.01% is likely not a plausible IFR estimate for the entire US. 0.1% is consistent with your back-of-the-envelope calculation.
Whether you know it or not, your calculation has error bars on it. Those error bars do not exclude 0.1%.
We’ve seen what happens when the virus is allowed to run unchecked in populations. New variants start popping up that are resistant to the previous version. Further, this whole isolate those most likely to die doesn’t work. Due to things like... multigenerational households. Or people that don’t know they’re at risk etc.
There is no evidence that new variants have escaped natural immunity in any population. To date, all of the known variants are neutralized by existing antibodies and immune sera. This is a fact -- we've seen effects on titers in a few papers, but nothing has yet to escape immunity.
That said, immune escape is probably inevitable -- this virus mutates far too quickly and has too many zoonotic reservoirs to prevent that from happening. To the extent that this occurs, a mix of natural and vaccine-induced immunity is likely the fastest and most robust way to suppress the virus.
Gupta said 500k deaths worst case in uk was bollocks (bc according to her everyone already got it). They are already at 100k with aggressive lockdowns. Does that sound like crackpottery to you?
They certainly have been wrong -- wildly so -- in most of the world. He also projected 2.2M deaths in the United States, and that hospitals would be over-run in the UK even with interventions. The predictions from the original paper do not hold up well in retrospect:
https://www.imperial.ac.uk/media/imperial-college/medicine/m...
Gupta was saying that the Ferguson models were wrong. The models have...not been right. Objectively.
The Barrington folks were among those pushing for that premature reopening. It's their whole thing.
Jay Bhattacharya is a professor of Medicine at Stanford University, and a long-time researcher into public health policy: https://profiles.stanford.edu/jay-bhattacharya
Martin Kulldorff is a professor of medicine at Harvard University and Brigham and Women's Hospital, with a long history of research into epidemiological models: https://www.dfhcc.harvard.edu/insider/member-detail/member/m...
All of the authors are respected academics in the field of medicine, health policy and epidemiology.
That was true as of January 2020, it is no longer true now. I don't know about Gupta, but both Bhattacharya and Kulldorff (and Ioannidis) are being hated by most of their colleagues at the moment.
I would even conjecture - in 10 years, if you are a recent PhD/fellow from one of those labs, you will have a very tough time in the job market.
> She is one of the primary authors of the Great Barrington Declaration, a widely-discredited paper[15] which advocated a focused response to the COVID-19 pandemic based on levels of individual risk.[16][17] The World Health Organization, as well as other numerous academic and public-health bodies, have stated that the strategy proposed by the Declaration is dangerous, unethical, and lacks a sound scientific basis.
Cool!
It’s not deductively valid in the same way that modus tollens is, but it is a often a good heuristic: the authority wouldn’t be recognized as such if they weren’t often correct in their area of expertise.
Thus, it’s fine to find an appeal “rationally compelling” as long as you bear in mind that the authority is also not infallible.
In this particular case, you brought up Bhattacharya‘s credentials. If another plausible expert disagrees with him, we’re at worst back to where we started; the fact that there’s a consensus he’s wrong, rather than just two dueling authorities, pushes back even harder.