Even “reputable” sources like the Financial Times have been putting out utter rubbish: https://www.ft.com/content/5ff6469a-6dd8-11ea-89df-41bea0557...
My current theory is that we are seeing denial on a mass scale as a coping mechanism.
My current theory is that we are seeing denial on a mass scale as a coping mechanism.
Now we are in a stage where there's no good information source (except HN comments, though even there we must be selective).
https://www.wired.co.uk/article/coronavirus-infections-oxfor...
"He was behind disputed research that sparked the mass culling of farm animals during the 2001 epidemic of foot and mouth disease, a crisis which cost the country billions of pounds.
And separately he also predicted that up to 150,000 people could die from bovine spongiform encephalopathy (BSE, or ‘mad cow disease’) and its equivalent in sheep if it made the leap to humans. To date there have been fewer than 200 deaths from the human form of BSE and none resulting from sheep to human transmission."
https://www.telegraph.co.uk/news/2020/03/28/neil-ferguson-sc...
Your "example" of bad data is the only quoted data in that article and it doesn't even mean anything since the implicit assumption of the Gupta model is that we do not know how many asymptomatic cases of COVID exist (even WIRED concede this point). Testing in Italy is insufficient to tell us this. 1 in 1000 infections requiring hospitalisation could be a realistic number if a high percentage of infections are asymptomatic.
> we just won’t know the true proportion of people who have contracted the disease without showing any symptoms, but it is likely a much lower number than the Oxford study assumes.
Epidemiology is not done by "it is likely much lower than the study assumes" since that is pure guess work without the tests.
What the Oxford study offers is a strong argument that antibody testing is vitally important and nobody is doing it.
As I say this is a very long way from utter rubbish.
They also made the totally unsupported assumption that one of the lower end curves matched reality. (That's right. They actually made exactly the kind of assumptions you're accusing others of, rather than just argue for antibody testing).
The problem is that when the study was made, there was a lot more data available than just that 14 days of deaths from two countries. And a ton of it was totally incompatible with their modeling.
Here's some of the conflicting data points as of ten days ago: https://news.ycombinator.com/item?id=22698584
It's not about "testing in Italy":
"In Lombardy – despite the region being under lockdown since March 9 – more than one in every 1,000 of the entire population have already been hospitalised due to coronavirus."
An acquaintance's partner suspects having got it, because after two days (two days, not weeks) of very mild fever (~37.5C) he was hit by anosmia. An ex-coworker also mentioned "a horribly strong fever" with respiratory difficulties which lasted just a few days. Yes, anecdata, but shows that you can easily miss a large part of the infected cases if you only test those hospitalized.
To get such a count we would need 100% serological testing which has not been done. We know how many were infected at the time of testing but the whole point of the Oxford model is that we do not know how many have been infected, not shown symptoms, and recovered.
Once again, the core point of the Oxford model was to emphasise the need for serological testing.
We definitely know it’s higher than 1 in 1000.
Unless you all think that the number of people infected may be higher than the number of people.