Stats And Truth Re Covid-19
No, this won't be a stat heavy blog, but it will reproduce Greg Cochran's blog which strongly questions John Ioannidis' pooh-poohing the danger of Covid-19. I believe he makes a strong case for why public authorities need to take this very seriously.
John Ioannidis is sort of a rock star among medical statisticians--which means he's not an epidemiologist. Here is a link to his original article, which I haven't read: A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data . Ioannidis' thesis is largely based on the cruise ship experience. Those readers who regard this as a hoax pandemic, or maybe just an overreaction, will take comfort from it.
For my part, I finished Michael Osterholm's book last night, which concludes with chapters on his number one priority--preparing for a new flu pandemic, which he regards as a certainty (including a lucid explanation for why). It's very sobering and informative, as is his chapter on the prospects for a truly effective flu vaccine--vaccines in general are difficult, and in the case of rapidly mutating viruses, particularly so.
Greg Cochran takes out after Ioannidis in his blog today, which I reproduce. Some of what Cochran uses from history--an invaluable tool of epidemiological analysis--is also explained in Osterholm's book.
Posted on March 20, 2020 by gcochran9
He emphasizes the cruise ship (why ignore other whole countries?) and he distorts that example. Most never caught it: of those that did, all had excellent medical care. That’s no longer possible when many millions get infected – resources are limited.
He suggests a scenario in which 1% of the population gets infected and 0.3% of that 1% die. We are not seeing that low level of fatality in China, or Korea, or Italy. As for 1% getting infected, where the f*ck does that come from? When a new pathogen shows up, no-one is immune, and the only limiting factor (barring intelligent human action) is having enough contract it, recover, and develop immunity that the virus has trouble find vulnerable hosts. The limit, in a simple model, is when R0, multiplied by the fraction of never-infected people, is less than 1.0 . Since R0 [of Covid-19] is something like 3 (assuming no major efforts at social distancing etc), that would require infection of 2/3ds of the population of the United States – with a death rate well above the 1% we’re seeing in places like South Korea. More like 4%, or even higher.
[You can argue with Cochran's assumptions here--the R0=3, etc., the level of infection required for "herd" immunity, etc. The basic criticism is valid, IMO.]
Again, where in the name of God does this 1% come from? Is Wuflu supposed to quit because he’s caught his limit?
The Faroe Islands had an epidemic of measles in 1846, investigated by Peter Panum, a young Danish doctor. There had been no measles in the Faeroes for 65 years – so only some elderly people were immune. Out of some 8000 inhabitants, how many ended up contracting measles? Was it 1%, due to some kind of viral gentility? 2%? 10%?
No: 6000 out of 8000 faroese got the measles, the kind of result you expect from a simple model. 75%. Measles has a high R0 ( ~10), which would have predicted 90% – but people over 65 had already had it, and even the Faroese don’t practice perfect panmixia.
The Spanish flu had an R0 around 2.0 . There had been an antigenically related flu around 1890, so older adults were less vulnerable. The percentage infected ranged up to 50% – which, against, is approximately the limit predicted by the value of R0.
[The Spanish flu, which many believe originated in Kansas, did target the young and healthy, and pregnant women. It also came in several waves.]
When a pathogen is NOT novel, sure, you can have lower fractions of people vulnerable, because many people are already immune, and the fraction infected can be low. If it depends upon some regionally varying vector, like mosquitoes, sure, it doesn’t have to sweep across the whole country. If its R0 is only greater than 1.0 in some subpopulations, as was the case with HIV, it may spread only in those subpopulations.
But Wuflu IS novel, and does NOT depend upon a vector. The vulnerable subpopulation is those that breathe.
So, the surprise-free prediction is that it hits > 50% of the population –
Unless we stop it.