From @parksw3:

germany-age-stratified-nowcasting.Rmd

• "reporting delays are fixed across age groups and time" Do you mean that the delay "distribution" is fixed?

• I can't figure out where you're specifying the data when you're fitting the model here. Something missing? Need to be explained a bit more clearly.

fit <- enw_fit_opts(
  save_warmup = FALSE, output_loglik = TRUE, pp = TRUE,
  chains = 2, threads_per_chain = threads, 
  iter_sampling = 500, iter_warmup = 500,
  show_messages = FALSE, refresh = 0,
  adapt_delta = 0.98, max_treedepth = 15
)
nowcast <- epinowcast(pobs,
  fit = fit,
  model = multithread_model
)

• It looks like data = pobs argument is missing from the multithread_model?

• "To speed up model fitting we make use of posterior information from the previous model (with some inflation) for some parameters. Note that this is not a truly Bayesian approach and in some situations may be problematic." I think this is always problematic, right? You already have some sort of posterior from the data, and you're using the posterior to fit to the same data again (even though you're using the same model). So you're fitting to the same data twice right? I'm not saying you shouldn't do this but the limitation should be made clearer.

• Reference day of the week effect section. It looks like the original fit also captures the day of the week effect. So I'm a bit confused how the original model captures the day of the week effect. Also not sure what the differences are between two fits (besides the fact that they're using different models). It looks like the fits are qualiatatively similar? Maybe something wrong with the first figure? The code shouldn't even run because you haven't specified the data yet...?

• Posterior predictions figure is impossible to read... need to zoom in to show a concrete example?

• "As noted using the posterior predictions from the simple model fit above there appears to be a day of the week effect for reported observations" this is really hard to tell... also need to explain the differences between the day of the week effect for reporting day vs reference day more clearly. I didn't get you were talking about the reference vs reporting day until I got here. I also wonder if it's easier to explain the reporting day effect first and reference day after? I feel like you meant to do this because the reference day of the week model is more complicated:

expectation_module <- enw_expectation(
  ~ 0 + (1 | day_of_week) + (1 | day:.group), data = pobs
)

than the reporting day of the week model:

report_module_dow <- enw_report(~ (1 | day_of_week), data = pobs)

I think it makes sense to introduce the more complicated model later?

Original: #215 (comment)