[FRIAM] COVID19 R(t) for each state over time.

[Steven A Smith]

It seems like estimating/tracking R(t) is key to re-opening (LIBERATE!)
strategies.


> This just in via Josh Thorpe.
>
> I've been wondering if anyone had a methodology for calculating the
> COVID19 R /Replication Number./
>
>     https://rt.live/
>
> apparently adapted from our own (LANL) Luis Bettencourt's work on H5N1
> using a Bayesian approach
>
>     https://github.com/k-sys/covid-19/blob/master/Realtime%20R0.ipynb
>
> This notebook is pretty big and pretty dense, I'm trying to skim
> through it and get a feel for it.
>
> It appears superficially that they are using nothing more than
> reported new cases smoothed by a Gaussian filter to remove
> reporting/test/delay artifacts.
>
> What I'm not clear on quite yet is how (if) this approach handles the
> intrinsic delay between exposure and onset of symptoms sufficient to
> yield a confirmed case?    If that (variable) delay is not factored in
> then the R(t) would seem to be a smeared reflection of R(t-n...t)
> where "n" is the maximum number of days between exposure and
> confirmation.   I'll keep looking.
>
> I wish the summary view had a time-slider to watch the states R(t)
> evolve... the buttons included for different previous times
> (yesterday, last week, 2 weeks, 3 weeks) give a hint of this.
>
> I'm surprised at how high some of the R values were even over 4.0 for
> some states at some time.
>
> I'm also surprised at how many states seem to have dropped to/below
> 1.0.   And also how many seem to have dipped below 1.0 and bounced
> back up.   This would seem to imply that many states hit a high level
> of social distancing/hygiene and then relaxed it (recently?).
>
> I also haven't sussed out why the different states have such different
> error envelopes...
>
> I look forward to others possibly digging into this and sharing their
> observations.
>
> - STeve
>
>
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