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COVID19 R(t) for each state over time.

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Steve Smith

COVID19 R(t) for each state over time.

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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Steve Smith

Re: COVID19 R(t) for each state over time.

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
.-. .- -. -.. --- -- -..-. -.. --- - ... -..-. .- -. -.. -..-. -.. .- ... .... . ...
FRIAM Applied Complexity Group listserv
Zoom Fridays 9:30a-12p Mtn GMT-6  bit.ly/virtualfriam
unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com
archives: http://friam.471366.n2.nabble.com/
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