Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Re-naming KITmetricslab-bivariate_branching, shortening model description. #1031

Merged
merged 4 commits into from
Oct 11, 2021
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Original file line number Diff line number Diff line change
@@ -1,21 +1,19 @@
team_name: KITmetricslab
model_name: bivariate_branching
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

You can actually keep this line as it was if you wish. The most important is to edit the line below: model_abbr. It is the one with the character limit

model_abbr: KITmetricslab-bivariate_branching
model_name: bivar_branching
model_abbr: KITmetricslab-bivar_branching
model_contributors: Johannes Bracher (Karlsruhe Institute of Technology) <[email protected]>
website_url: https://github.com/jbracher/branching_process_delta
license: cc-by-4.0
team_model_designation: primary
methods: Delta-variant and other cases are modelled as independent branching processes, with weekly growth\
\ rates following random walks. Vaccination progress is currently not included.\
\ Forecasts up to 4 wk are generated due to ensemble inclusion requirements, but 3 and 4 wk should be\
\ interpreted with great caution.
\ rates following random walks. Forecasts for 3 and 4 wk are likely unreliable.
team_funding: Helmholtz Innovation and Data Science Project "SIMCARD"
data_inputs: JHU (confirmed cases), RKI sequencing data (variants)
methods_long: "The total weekly incidence is modelled as the sum of two independent overdispersed branching\
\ processes (delta / non-delta cases; may be updated to other pairs of variants later), with the weekly
\ growth rates following multiplicative random walks. Sequencing data are included via an additional binomial\
\ observation process with the probabilities for the two variants proportional to their occurrence in the two
\ latent branching processes. Posterior samples are enerated using the JAGS software. Priors were chosen as\
\ latent branching processes. Posterior samples are generated using the JAGS software. Priors were chosen as\
\ 'uninformative' uniform distributions, but may be specified in a more informative fashion in the future.\
\ In order to be included in the ensemble forecasts are generated up to 4 wk into the future, but given the\
\ simple model structure, three and four-week-ahead forecasts should be interpreted with caution."