Closed kaitejohnson closed 1 week ago
Thank you for your contribution, @kaitejohnson :rocket:! Your page is ready to preview here
Have you dug in to the original motivation for this? As this is likely to not be very informed by the length of data we are fitting to having a very weak prior means widening the prediction intervals essentially.
Was the motivation flawed or has the change in model over time changed the definition?
Have you dug in to the original motivation for this? As this is likely to not be very informed by the length of data we are fitting to having a very weak prior means widening the prediction intervals essentially.
Was the motivation flawed or has the change in model over time changed the definition?
I tried my very hardest to find where I had documented a bunch of posterior estimates of the infection feedback posterior from 2022-2023s data fits in cfa-forecast-renewal-ww
but I wasn't able to find them. In that instance, I think the mistake was mine that in order to see a meaningful (e.g. large) reduction in R(t) the magnitude of the term needed to be around 1e2 bc incidence was being estimated as peaking around 1e-3 -- however this is likely unrealistically low, and I probably was just trying too tight of priors that were already forcing incidence to be artificially low.
Basically I think my strategy at guessing these priors was flawed, and we had an open issue to revisit it but didn't get to it
Closing this in favor of #236
Originally was N(500,200) so lognormal(6, 0.4) which was a VERY strong infection feedback forcing term, especially at high incidence.
This PR proposes changing this prior to be centered around 1 but in the range of o to 400 (exp(6) ~ 400).
We plan to test this our in the
wweval
pipeline, look at performance, and then consider adding this to a version update.