Each model is required to have metadata in yaml format, e.g. see this metadata file. This file describes each of the variables (keys) in the yaml document. Please order the variables in this order.
The name of your team that is less than 50 characters.
The name of your model that is less than 50 characters.
An abbreviated name for your model that is less than 30 alphanumeric characters. The model abbreviation must be in the format of [team_abbr]-[model_abbr]. where each of the [team_abbr] and [model_abbr] are text strings that do not include a hyphen or whitespace. The total length of the model abbreviation must be less than or equal to 31 alphanumeric characters. Note that this is a uniquely identifying field in our system, so please choose this name carefully, as it may not be changed once defined. An example of a valid model_abbr is UMass-MechBayes or UCLA-SuEIR.
A list of all individuals involved in the forecasting effort affiliations, and email address. At least one contributor needs to have a valid email address. All email addresses provided will be added to an email distribution list for model contributors.
The syntax of this field should be
name1 (affiliation1) <user@address>, name2 (affiliation2) <user2@address2>
(previously named model_output)
A url to a website that has additional data about your model. We encourage teams to submit the most user-friendly version of your model, e.g. a dashboard, or similar, that displays your model forecasts. If you have additionally a data repository where you store forecasts and other model code, please include that in your methods section below. If you only have a more technical site, e.g. github repo, please include that link here.
One of licenses.
We encourage teams to submit as a "cc-by-4.0" to allow the broadest possible uses including private vaccine production (which would be excluded by the "cc-by-nc-4.0" license). If the value is "LICENSE.txt", then a LICENSE.txt file must exist within the folder and provide a license.
Upon initial submission this field should be one of “primary”, “proposed” or “other”. For teams submitting only one model, this should be “primary”. For each team, only one model can be designated as “primary”.
Primary means the model will be scored in evaluations, eligible for inclusion in the ensemble, and visualized.
Proposed means the team would like the model to be considered as a "secondary" model rather than an "other" model. The Hub team will determine whether the model's methodology is distinct enough that the model should be included in the ensemble in which case the model will get the "secondary" designation. If the methodology is not distinct enough, e.g. it differs from the primary model by setting certain parameters to specific values, then the model will be designated as "other".
Secondary means the forecasts will be visualized and eligible for inclusion in the ensemble, but will not be scored in evaluations.
Other means the forecasts will not be visualized, included in the ensemble, nor scored in evaluations.
A brief description of your forecasting methodology that is less than 200 characters.
A boolean value (true or false) that indicates whether a model combines multiple hub models into an ensemble.
University or company names, if relevant.
Like an acknowledgement in a manuscript, you can acknowledge funding here.
(previously model_repo)
A github (or similar) repository url.
One or more twitter handles (without the @) separated by commas.
A description of the data sources used to inform the model and the truth data targetted by model forecasts. Common data sources are NYTimes, JHU CSSE, COVIDTracking, Google mobility, HHS hospitalization, etc.
An example description could be
hospitalization forecasts use and target HHS hospitalization data
A url (doi link preferred) to an extended description of your model, e.g. blog post, website, preprint, or peer-reviewed manuscript.
An extended description of the methods used in the model. If the model is modified, this field can be used to provide the date of the modification and a description of the change.