An in-development R package and a Bayesian hierarchical model jointly fitting multiple "local" wastewater data streams and "global" case count data to produce nowcasts and forecasts of both observations
purple (RHS) is v1.0.0 (the currently, too strong infection feedback prior and two weak prior on eta_sd)
When we ran this on all forecast dates and location with a slightly more informative prior on eta_sd, the overall scores did look improved, though here the overall scores do not overall, if you look at the one by forecast date you see this is largely driven by 2024-01-08 as the pink (this proposed change) is an improvement most other times.
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This PR addresses #227. It modifies the stan model to take in a mean of the RW step size in R(t),
eta_sd
, and then it tests it using the subset of forecast dates and locations set up in https://github.com/CDCgov/wastewater-informed-covid-forecasting/blob/prod/_targets_subset_benchmarking.RBenchmarking results can be found here in the files labeled as
subsets
https://github.com/CDCgov/wastewater-informed-covid-forecasting/tree/205-test-out-less-informative-eta-sd/output/benchmarking. In the overall plots, the things to pay attention to are:eta_sd
)When we ran this on all forecast dates and location with a slightly more informative prior on
eta_sd
, the overall scores did look improved, though here the overall scores do not overall, if you look at the one by forecast date you see this is largely driven by 2024-01-08 as the pink (this proposed change) is an improvement most other times.