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Re:Please poke holes in my Covid-19 math (SF Bay Area) by gbrown on Sunday March 15, 2020 @04:12PM (#22586316)
For sure, but the problem is that innumerable true situations might give rise to a given "observed" logistic curve - something that you can describe with two parameters arose from a mix of incomprehensibly complex transmission dynamics and dauntingly difficult (and often hidden) observation mechanisms. The result is that, even though after the fact we can often achieve a good fit with simple models, there's little reason to expect that simple models would have been generally predictive - the feedback loop even includes the models, since they might inform public health policy!

This isn't to say that modelling isn't useful, but that forecasting a novel infection while it's ongoing is really, really, really hard.

Edit: Also, I’d be interested to see a comparison of the fit between a typical logistic, four parameter logistic, Gompertz etc. Not all sigmoids are the same.

Re:Please poke holes in my Covid-19 math (SF Bay Area) by AstralStorm on Monday March 16, 2020 @12:45PM (#22594311)
Gompertz type 2 fits somewhat better.

Ultimately for such complex things like spread with barriers Tsalis statistics are good. That could mean q-Weibull distribution.