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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