Re: [math-fun] Covid19 R_e considered misguided
I was obviously trying to be too cute; I'm tired of being told "you have to believe me because 400 of my closest friends have referenced my papers" aka "proof by # of Twitter followers" when my own lying eyes (or my own algebraic calculations) tell me something quite different. An epidemiologist who doesn't understand "fat tailed" distributions is a contradiction in terms, since almost no distribution in epidemiology has a thin tail. So it may be time for these people to get better educated, retire, or at least shut up. Their misunderstandings are wasting months of billions of people's lives and trillions of dollars. At the risk of mixing metaphors, I'd like to drive Dr. John Snow's wooden pump handle through the heart of the Kermack-Mckendrick 'R0' differential equation models. At 09:47 AM 5/22/2020, Brad Klee wrote:
H.B.: Perhaps those on this list will agree that proofs should end with 'QED', not 'PhD'.
Me: The current PhD system does not inspire too much confidence, but I'm not sure how much better it is to trade 'PhD' for 'QED', especially in messy circumstances. Anyways most people who write QED have a PhD, so what are you really saying? We need experts with training in formal mathematics?
The other issue is that QED is a Latin abbreviation, so it is very exclusive to elite westerners. Someone like Madhava would never think of ending an idea-in-verse with QED. Is this a reason for taking away credit from him? Elite westerners unequivocally answer yes. Is that answer fair?
Ha ha, tired of "proof by # of Twitter followers", me too! Glenn Greenwald published via The Intercept a nice essay about this "pathology" on May 18. As for Kermack-McKendrick, I am personally interested in using differential equations to create functions that fit data. So, naturally I would ask, how closely can SIR describe, for example, NYC cases: https://0x0.st/ipso.png This data set has exponential rise, exponential tail, and probably sharp enough peak, so it looks like a SIR fit might work. That could be a challenge for you. If you want to KILL SIR, why don't you just show that it doesn't do a good job of fitting this data set? I will look at your analysis if you come up with something by the numbers. Cheers, Brad On Fri, May 22, 2020 at 12:07 PM Henry Baker <hbaker1@pipeline.com> wrote:
I was obviously trying to be too cute; I'm tired of being told "you have to believe me because 400 of my closest friends have referenced my papers" aka "proof by # of Twitter followers" when my own lying eyes (or my own algebraic calculations) tell me something quite different. [...] At the risk of mixing metaphors, I'd like to drive Dr. John Snow's wooden pump handle through the heart of the Kermack-Mckendrick 'R0' differential equation models.
You can get fairly complicated behavior from these diff eq models (SIR, SEIR, etc.) by adding more “compartments”: age and demographic structure, for instance. You can also think about them on social networks and take community structure and degree distributions into account. I’ll be posting a paper soon about this and will send the link. - Cris
On May 22, 2020, at 12:01 PM, Brad Klee <bradklee@gmail.com> wrote:
Ha ha, tired of "proof by # of Twitter followers", me too! Glenn Greenwald published via The Intercept a nice essay about this "pathology" on May 18.
As for Kermack-McKendrick, I am personally interested in using differential equations to create functions that fit data. So, naturally I would ask, how closely can SIR describe, for example, NYC cases:
This data set has exponential rise, exponential tail, and probably sharp enough peak, so it looks like a SIR fit might work. That could be a challenge for you. If you want to KILL SIR, why don't you just show that it doesn't do a good job of fitting this data set?
I will look at your analysis if you come up with something by the numbers.
Cheers,
Brad
On Fri, May 22, 2020 at 12:07 PM Henry Baker <hbaker1@pipeline.com> wrote:
I was obviously trying to be too cute; I'm tired of being told "you have to believe me because 400 of my closest friends have referenced my papers" aka "proof by # of Twitter followers" when my own lying eyes (or my own algebraic calculations) tell me something quite different. [...] At the risk of mixing metaphors, I'd like to drive Dr. John Snow's wooden pump handle through the heart of the Kermack-Mckendrick 'R0' differential equation models.
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participants (3)
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Brad Klee -
Cris Moore -
Henry Baker