It has been a wild ride.
I began with exponential fits - which worked quite well
for Oklahoma until they didn't. Then I swung over to straight-line
fits until, as it seemed to me, the new day hits were all on the low
side. So what else but a logistic fit.
I used a logistic form that concealed even the time value of the peak
rate instead of the infinitely more sensible standard form used by bc
when he tried this fit for Andorra.(note 1)
When I shared these plots with others, I noted a certain rolling of the
eyes, concerning their simplicity - but I persisted, until I finally
realized my exponential fits were getting daily hits on the upside: not
from a data uptrend, but from the logistic curve downtrend in face of a
continued straight line fit.
So I retreated from the simple three factor fit, to the simpler two
factor straight line fit, whence I should not have too eagerly jumped.
My data source was Note 2.
Here are some comparisons of the [accumulated] US CASES time series, as
a straight line (which fits well) or a logistic, which is trying its best.
https://imgur.com/pcj4yR0 US CASES LINEAR https://imgur.com/YKCgfYP US CASES LOGISTIC
Though of less general interest, I offer the Oklahoma plots below, where
The straight line plots fit the facts for recent Cases and Deaths, and
the logistic is betrayed by the upside departures of the data.
https://imgur.com/bMVsB0v OK DEATH LOGISTIC https://imgur.com/sE0zP3G OK DEATH LINEAR