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README.md

Data submission instructions

This page is intended to provide teams with all the information they need to submit scenarios.

All scenarios should be submitted directly to the data-processed/ folder. Data in this directory should be added to the repository through a pull request.

Due to file size limitation, the file can be submitted in a in a .parquet or .gz.parquet.

All submissions will be automatically validated upon submission, for more information please consult the validation wiki page.

The submission file format has been updated starting round 4 (September 2024). For information on previous file format please refer to past version of this README file.

Example

See this file for an illustration of part of a (hypothetical) submission files with both required and optional target information.

Subdirectory

Each subdirectory within the data-processed/ directory has the format:

team-model

where

  • team is the teamname and
  • model is the name of your model.

Both team and model should be less than 15 characters, and not include hyphens nor spaces.

Within each subdirectory, there should be a metadata file, a license file (optional), and a set of scenarios.

Metadata

The metadata file name should have the following format

metadata-team-model.txt

where

  • team is the teamname and
  • model is the name of your model.

The metadata file must follow the documented description. An example hypothetical metadata file has been posted in the data-processed directory.

License (optional)

License information for data sharing and reuse is requested in the metadata, including a link to the license text. If you cannot link to the text of a standard license and have specific license text, include a license file named

LICENSE.txt

Date/Epiweek information

For week-ahead scenarios, we will use the specification of epidemiological weeks (EWs) defined by the US CDC which run Sunday through Saturday.

There are standard software packages to convert from dates to epidemic weeks and vice versa. E.g. MMWRweek for R and pymmwr and epiweeks for python.

Model Results

Each model results file within the subdirectory should have the following name

YYYY-MM-DD-team-model.parquet

where

  • YYYY is the 4 digit year,
  • MM is the 2 digit month,
  • DD is the 2 digit day,
  • team is the teamname, and
  • model is the name of your model.

"parquet" files format from Apache is "is an open source, column-oriented data file format designed for efficient data storage and retrieval". Please find more information on the parquet.apache.com website.

The "arrow" library can be used to read/write the files in Python and R. Other tools are also accessible, for example parquet-tools

For example, in R:

# To write "parquet" file format:
filename <- ”path/YYYY-MM-DD-team_model.parquet”
arrow::write_parquet(df, filename)
# with "gz compression"
filename <- ”path/YYYY-MM-DD-team_model.gz.parquet”
arrow::write_parquet(df, filename, compression = "gzip", compression_level = 9)

# To read "parquet" file format:
arrow::read_parquet(filename)

The date YYYY-MM-DD should correspond to the start date for scenarios projection ("first date of simulated transmission/outcomes" as noted in the scenario description on the main README, Submission Information).

The team and model in this file must match the team and model in the directory this file is in. Both team and model should be less than 15 characters, alpha-numeric and underscores only, with no spaces or hyphens.

If the size of the file is larger than 100MB, it should be submitted in a .gz.parquet format.

Model results file format

The file must have the following columns (in any order):

The output file should contains eight columns:

  • origin_date
  • scenario_id
  • target
  • horizon
  • location
  • age_group
  • output_type
  • output_type_id
  • value

No additional columns are allowed.

Each row in the file is a specific type for a scenario for a location on a particular date for a particular target.

origin_date

Values in the origin_date column must be a date in the format

YYYY-MM-DD

The origin_date is the start date for scenarios (first date of simulated transmission/outcomes). The "origin_date" and date in the filename should correspond.

scenario_id

The standard scenario id should be used as given in in the scenario description in the main Readme. Scenario id's include a captitalized letter and date as YYYY-MM-DD, e.g., A-2022-08-14.

target

The submission can contain multiple output type information:

  • 100 representative trajectories from the model simulations. We will call this format "sample" output type. For more information, please consult the sample section.
  • A set of quantiles value for all the tarquets (except peak timing). We will call this format "quantile" output type. For more information, please consult the quantile section.
  • A cumulative distribution function for the peak timing target. We will call this format "cdf" output type. For more information, please consult the cdf section.

The requested targets are (for "sample" type output):

  • weekly incident deaths (US level only)
  • weekly incident hospitalizations (US + State level)

Optional target (for "quantile" or "cdf" type output):

  • quantile:
    • weekly cumulative deaths (US level only)
    • weekly cumulative hospitalizations (US + State level)
    • weekly incident deaths (US level only)
    • weekly incident hospitalizations (US + State level)
    • peak size hospitalizations (US + State level)
  • cdf:
    • weekly peak timing hospitalizations (US + State level)

For all the targets, the age group "0-130" is required, all the incident and cumulative targets can also include other age group information (optional).

Values in the target column must be one of the following character strings:

  • "inc death"
  • "inc hosp"
  • "cum death"
  • "cum hosp"
  • "peak size hosp"
  • "peak time hosp"

inc death

This target is the national incident (weekly) number of deaths predicted by the model during the week that is N weeks after origin_date. There should not be any death projections at the state level.

A week-ahead scenario should represent the total number of new deaths reported during a given epiweek (from Sunday through Saturday, inclusive).

Predictions for this target will be evaluated compared to the number of new deaths derived from the CDC multiplier model, which inflates flu deaths reported in the hospital by a factor accounting for underreporting and out-of-hospital mortality.These estimates are published in real time starting a few weeks into the influenza season.

inc hosp

This target is the incident (weekly) number of hospitalized cases predicted by the model during the week that is N weeks after origin_date.

A week-ahead scenario should represent the total number of new hospitalized cases reported during a given epiweek (from Sunday through Saturday, inclusive).

Predictions for this target will be evaluated against the weekly number of new hospitalized cases, as recorded by the U.S. Department of Health & Human Services Flu and COVID-19 system (derived from the prior day influenza admissions variable).

cum death

This target is the national cumulative number of deaths predicted by the model up to and including N weeks after origin_date. There should be 0 cumulative deaths on week 0 of projection.

A week-ahead scenario should represent the cumulative number of deaths reported on the Saturday of a given epiweek.

Predictions for this target will be evaluated compared to the number of cumulative deaths derived from the CDC multiplier model, which inflates flu deaths reported in the hospital by a factor accounting for underreporting and out-of-hospital mortality.These estimates are published in real time starting a few weeks into the influenza season.

cum hosp

This target is the cumulative number of incident (weekly) number of hospitalized cases predicted by the model during the week that is N weeks after model_projection_date. There should be 0 cumulative hospitalization on week 0 of projection.

A week-ahead scenario should represent the cumulative number of hospitalized cases reported up to the Saturday of a given epiweek.

Predictions for this target will be evaluated against the cumulative number of hospitalized cases since the start of the projection period, as recorded by the U.S. Department of Health & Human Services Flu and COVID-19 system (derived from the prior day influenza admissions variable)

peak time hosp

This target is the cumulative probability of the incident hospitalization peak occurring before or during the week that is N weeks after orgin_date. For instance "peak time hosp" on the 22nd epiweek of projection is the probability that hospitalizations peak within the first 22 weeks of the projection period. This cumulative probability will be 1 on the last week of the projection period. A probability of 1 in the first week of the projection period could mean either future projections are not expected to exceed a prior peak or projections expect the peak will occur in the first week.

Predictions for this target will be evaluated against the week of the peak number of hospitalized cases, as recorded by the U.S. Department of Health & Human Services Flu and COVID-19 system (derived from the prior day influenza admissions variable).

peak size hosp

This target is the magnitude of the peak of weekly incident hospitalizations in the model, when considering the full projection period.

Further, we do not expect a full time series, the horizon column associated with this value should be set to NA.

Predictions for this target will be evaluated against the size of the peak number of weekly hospitalized cases, as recorded by the U.S. Department of Health & Human Services Flu and COVID-19 system (derived from the prior day influenza admissions variable).

horizon

Values in the horizon column must be a integer (N) between 1 and last week horion value representing the associated target value during the N weeks after origin_date.

For example, between 1 and 39 for Round 4 ("Simulation end date: June 1, 2024 (39-week horizon)") and in the following example table, the first row represent the number of incident death in the US, for the 1st epiweek (epiweek ending on 2023-09-09) after 2023-09-03 for the scenario A-2023-08-14.

origin_date scenario_id location target age_group horizon ...
2023-09-03 A-2023-08-14 US inc death 0-130 1 ...

location

Values in the location column must be one of the "locations" in this FIPS numeric code file which includes numeric FIPS codes for U.S. states, counties, territories, and districts as well as "US" for national scenarios.

Please note that when writing FIPS codes, they should be written in as a character string to preserve any leading zeroes.

output_type

Values in the output_type column are either

  • "sample" or
  • "quantile" (optional) or
  • "cdf" (optional)

This value indicates whether that row corresponds to a "sample" scenario or a quantile scenario, etc.

Scenarios must include "sample" scenario for every scenario-location-target-horizon-age_group group.

output_type_id

sample

For the optional simulation samples format only. Values in the output_type_id column are numeric between 1 and 100 indicating an id sample number. Each ID number represents one in 100 representative trajectories from the simulations.

All scenario-location-target-horizon-age_group group should have a unique associated sample.

For example:

origin_date scenario_id location target horizon age_group output_type output_type_id value
2023-09-03 A-2023-08-14 US inc death 1 0-130 sample 1
2023-09-03 A-2023-08-14 US inc death 2 0-130 sample 1
2023-09-03 A-2023-08-14 US inc death 3 0-130 sample 1
2023-09-03 A-2023-08-14 US inc death 1 0-130 sample 2
2023-09-03 A-2023-08-14 US inc death 2 0-130 sample 2

quantile

Values in the output_type_id column are quantiles in the format

0.###

For quantile scenarios, this value indicates the quantile for the value in this row.

Teams should provide the following 23 quantiles:

0.010 0.025 0.050 0.100 0.150 0.200 0.250 0.300 0.350 0.400 0.450 0.500
0.550 0.600 0.650 0.700 0.750, 0.800 0.850 0.900 0.950 0.975 0.990

An optional 0 and 1 value can also be provided.

For example:

origin_date scenario_id location target horizon age_group output_type output_type_id value
2023-09-03 A-2023-08-14 US inc death 1 0-130 quantile 0.010
2023-09-03 A-2023-08-14 US inc death 1 0-130 quantile 0.025

cdf

Values in the output_type_id column are the epiweek associated with cumulative probability of the incident hospitalization peak occurring before or during the week that is N weeks after origin_date in the format:

EWYYYYWW

For instance `"EW202337"`` is the probability that hospitalizations peak within the epiweek 2023-37 or before.

Teams should provide the complete time series associated with the round, and the horizon column should be set to NA value. The week information should be in 2 digits format, so if the epiweek is for example 2024-2, then it should be reported as "EW202402".

It can be calculated by applying:

  • origin_date + 7 * N - 1 (N being the number of week ahead projection in the associated target, e.g "1 wk ahead", "2 wk ahead" after the start of the projection), and transform the output in epiweek format.

For example:

# If `origin_date` is "2023-09-03"

# Week 1 will be:
week1_date = as.Date("2023-09-03") + 7 * 1 - 1
epiweek1 = MMWRweek::MMWRweek(week1_date) 
epiweek1

The output file with the cdf should follow this example (following round 4):

origin_date scenario_id location target horizon age_group output_type output_type_id value
2023-09-03 A-2023-08-14 US inc death NA 0-130 cdf EW202336
2023-09-03 A-2023-08-14 US inc death NA 0-130 cdf EW202337
2023-09-03 A-2023-08-14 US inc death NA 0-130 cdf EW202422

value

Values in the value column are non-negative numbers indicating the associated output_type prediction for this row.

Peak time hosp

For the peak time hosp target, the values in the value column are non-negative numbers between 0 and 1.

age_group

Values in the age_group column are:

  • "0-4"
  • "5-17"
  • "18-49"
  • "50-64"
  • "65-130" Or any aggregation of the previous list, for example: "0-17". If the submissions files contain projections for the overall population, please use 0-130 as age-group.

The age_group are optionals, however, the submission should contain at least one age group: 0-130, if multiples age_group are provided the overall population should still be provided with the age group 0-130.

For the peak targets, only the age-group 0-130 is required.

Scenario validation

To ensure proper data formatting, pull requests for new data or updates in data-processed/ will be automatically validated

When a pull request is submitted, the data are validated by running the scripts in validation.R. The intent for these tests are to validate the requirements above and all checks are specifically enumerated on the wiki.

Please let us know if the wiki is inaccurate.

Workflow

When a pull request is submitted, the validation will be automatically triggered.

  • If the pull request (PR) contains update on metadata and/or abstract file(s):

    • These files are manually validated, the automatic validation will only returns a message indicating it did not run any validation.

  • If the PR contains model output submission file(s). The validation automatically runs and output a message and a PDF file containing the projections of the requested targets at national and State level, plus some information on calibration.

    • The validation has 3 possible output:
      • "Error": the validation has failled and returned a message indicating the error(s). The error(s) should be fixed to have the PR accepted
      • "Warning": the PR can be accepted. However, it might be necessary for the submitting team to validate if the warning(s) is expected or not before merging the PR.
      • "Success": the validation did not found any issue and returns a message indicating that the validation is a success and the PR can be merged.

Run checks locally

To run these checks locally rather than waiting for the results from a pull request, follow these instructions (section File Checks Run Locally).