Each model is required to have metadata in yaml format, e.g. see this example metadata file.
Given the many possible approaches to modeling COVID-19 scenarios, we are collecting rich metadata to be able to understand heterogeneity among model projections. The information will only be used internally, unless otherwise indicated.
Please order the variables in this order.
The name of your team that is less than 50 characters, no spaces. Will be displayed online.
The name of your model that is less than 50 characters, no spaces. Will be displayed online.
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 are each less than 15 alphanumeric characters that do not include a hyphen or whitespace. Note that this is a uniquely identifying field in our system as it should be use in the filename and as name of the subfolder containing the submissions, metadata files, 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. The model abbreviation will be displayed online.
A version number or date in YYYY-MM-DD format, to designate the version of the model used for submitted model projections. Will be displayed online.
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 scenarios. If you have additionally a data repository where you store scenarios 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. Will be displayed online.
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).
Alternatively, add the name and URL of the license used, as in cc-by-4.0, https://creativecommons.org/licenses/by/4.0/
Or, add the value LICENSE.txt, if a LICENSE.txt file was added within the folder.
Will be displayed online.
A brief description of your forecasting methodology that is less than 200 characters. Will be displayed online.
A brief description of how Non-Pharmaceutical Interventions (NPI) were represented by the model, or "Not applicable".
A brief description of any additional assumptions made regarding compliance with NPI, beyond what was specified in the given scenarios; or "Not applicable".
A brief description of how contact tracing was represented by the model, or "Not applicable".
A brief description of what testing strategies were represented by the model, or "Not applicable".
A brief description of assumptions regarding vaccine efficacy against transmission, or "Not applicable".
If a delay was assumed in the build-up of vaccine efficacy, please describe the assumptions here; or "Not applicable".
A brief description of assumptions or representation of vaccine hesitancy, by priority target group such as healthcare workers, essential worksers, elderly, etc.; or "Not applicable".
Assumed length of vaccine-derived immunity, or "Not applicable".
Assumed length of the duration of natural immunity assumed, or "Not applicable".
Assumed fatality rate of detected COVID-19 cases, as a proportion between 0 and 1, or "Not applicable".
Assumed fatality rate of SARS-CoV-2 infections (detected or undetected), as a proportion between 0 and 1, or "Not applicable".
Assumed proportion of SARS-CoV-2 infections that remain asymptomatic, as a proportion between 0 and 1, or "Not applicable".
Age groups represented in the model, in years, given as a set of intervals between square brackets: as in [0-5, 6-10, 10-50, 50+], or "Not applicable".
Brief description of assumptions made or representation in the model of importations, or "Not applicable".
Brief description of the method used to compute confidence (or other uncertainty) interval, or "Not applicable".
Brief description of model calibration methods, or "Not applicable".
Brief description of how spatial structure is represented by the model, or "Not applicable".
Acknowledgement of funding source, by name of funding agency, grant title, and grant number.
A brief description of the data sources used to inform the model, using as much as possible standard terminology that includes a source name and the type of data, such as JHU CSSE case and death data, NYTimes death data, Google mobility data, etc.
A bibliographic citation to a paper, website, or other object that people can go to to find out more about the model, in the style used by PubMed, as Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, Whittaker C, Zhu H, Berah T, Eaton JW, Monod M; Imperial College COVID-19 Response Team, Ghani AC, Donnelly CA, Riley S, Vollmer MAC, Ferguson NM, Okell LC, Bhatt S. Nature. 2020 Aug;584(7820):257-261. doi: 10.1038/s41586-020-2405-7. Epub 2020 Jun 8. PMID: 32512579. Will be displayed
An extended description of the methods used in the model. If the model is modified, this field can be used to provide a description of the change. Use model_version to indicate the version or date of last update.