Adjusted onset not being used for sampling
perone opened this issue · comments
Great notebooks. I was reviewing the notebook because I'm planning to use for some modelling and I found that block of code:
def create_and_run_model(name, state):
confirmed = state.positive.diff().dropna()
onset = confirmed_to_onset(confirmed, p_delay)
adjusted, cumulative_p_delay = adjust_onset_for_right_censorship(onset, p_delay)
return MCMCModel(name, onset, cumulative_p_delay).run()
The adjusted
series, which is the onset with the right censorship adjusted is being computed but it is not the one used on the MCMCModel
later, is there a reason for this or is it a typo ?
Correct. I'm so sorry for the lack of updates but we've been trying to productionize this code for the site. It's changed quite a bit but this version is still useful. The code is pretty messy though, I'm very sorry about that.
The model specifically takes onsets not expected actual cases... so the adjusted series is not useful (except for an interesting graph!)
Kevin
…On Tue, Apr 28, 2020 at 11:42 AM, Christian S. Perone < ***@***.*** > wrote:
Great notebooks. I was reviewing the notebook because I'm planning to use
for some modelling and I found that block of code:
def create_and_run_model(name, state):
confirmed =
state.positive.diff().dropna()
onset = confirmed_to_onset(confirmed,
p_delay)
adjusted, cumulative_p_delay =
adjust_onset_for_right_censorship(onset, p_delay)
return
MCMCModel(name, onset, cumulative_p_delay).run()
The adjusted series, which is the onset with the right censorship adjusted
is being computed but it is not the one used on the MCMCModel later, is
there a reason for this or is it a typo ?
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Thanks for the quick answer @k-sys !