model-output

Data submission instructions

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

All projections should be submitted directly to the model-output/ 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.

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Example

The GitHub Repository contains multiple example files reproducing the required and optional targets for RSV round 1 and different sample format:

• Team 2 - Model B:
  • Example file: 2023-11-12-team2-modelb.parquet
• Team 3 - Model C
  • Example file: 2023-11-12-team3-modelc.parquet
• Team 4 - Model D:
  • Example file: 2023-11-12-team4-modeld.parquet
• Team 5 - Model E:
  • Example file: 2023-11-12-team5-modele.parquet

For more information on the different sample format, please consult the Sample Format Wiki page.

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Subdirectory

Each sub-directory within the model-output/ directory has the format:

team-model

where

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

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

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Metadadata

Each submission team should have an associated metadata file. The file should be submitted with the first projection in the model-metadata/ folder, in a file named: team-model.yaml.

For more information on the metadata file format, please consult the associated README

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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.

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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.

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Model results file format

The output file must contain eleven columns (in any order):

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

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.

Column format

Column Name	Accepted Format
origin_date	character, date (datetime not accepted)
scenario_id	character
target	character
horizon	numeric, integer
location	character
age_group	character
output_type	character
output_type_id	numeric, character, logical (if all NA)
value	numeric
run_grouping	numeric, integer
stochastic_run	numeric, interger

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 capitalized letter and date as YYYY-MM-DD, e.g., A-2023-10-27.

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 targets (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 hospitalizations

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

• quantile:
  • weekly cumulative hospitalizations
  • weekly incident hospitalizations
  • peak size hospitalizations
• cdf:
  • weekly peak timing hospitalizations

For the incident and cumulative targets, the age groups: "0-0.99", "1-4", "5-64", "65-130", and "0-130" are required, all the incident and cumulative targets can also include other age group information (optional).

For the peak targets, only the age groups "0-130" is accepted.

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

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

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 laboratory-confirmed RSV hospitalizations from the RSV-NET surveillance system.

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 tthe laboratory-confirmed RSV hospitalizations from the RSV-NET surveillance system.

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 the laboratory-confirmed RSV hospitalizations from the RSV-NET surveillance system.

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 laboratory-confirmed RSV hospitalizations from the RSV-NET surveillance system.

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. The information is noted in the scenario description on the main README, Submission Information), see "Simulation end date" information

For example, between 1 and 29, in the following example table, the first row represent the number of incident death in the US, for the 1st epiweek (epiweek ending on 2023-11-18, if start date is 2023-11-12)

origin_date	scenario_id	location	target	age_group	horizon	...
2023-11-12	A-2023-10-27	US	inc hosp	0-0.99	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.

For the round 1, only the location included in RSV-NET target data are expected: "US","06","08","09","13","24","26","27","35","36","41","47","49"

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 simulation samples format only. Value in the output_type_id column is NA

The id sample number is input via two columns:

• run_grouping: This column specifies any additional grouping if it controls for some factor driving the variance between trajectories (e.g., underlying parameters, baseline fit) that is shared across trajectories in different scenarios. I.e., if using this grouping will reduce overall variance compared to analyzing all trajectories as independent, this grouping should be recorded by giving all relevant rows the same number. If no such grouping exists, number each model run independently.
• stochastic_run : a unique id to differentiate multiple stochastic runs. If no stochasticity: the column will contain an unique value

Both columns should only contain integer number.

The submission file is expected to have 100 simulation samples (or trajectories) for each "group".

For round 1, it is required to have the trajectories grouped at least by "age_group" and "horizon", so it is required that the combination of the run_grouping and stochastic_run columns contains at least an unique identifier for each group containing all the possible value for "age_group" and "horizon".

Fore more information and examples, please consult the Sample Format Wiki page.

For example:

origin_date	scenario_id	location	target	horizon	age_group	output_type	output_type_id	run_grouping	stochastic_run	value
2023-11-12	A-2023-10-27	US	inc hosp	1	0-0.99	sample	NA	1	1
2023-11-12	A-2023-10-27	US	inc hosp	2	0-0.99	sample	NA	1	1
2023-11-12	A-2023-10-27	US	inc hosp	3	0-0.99	sample	NA	1	1
2023-11-12	A-2023-10-27	US	inc hosp	1	0-0.99	sample	NA	2	1
2023-11-12	A-2023-10-27	US	inc hosp	2	0-0.99	sample	NA	2	1

quantile

Values in the output_type_id column are quantiles in the format

0.###

and NA in the run_grouping and stochastic_run columns.

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	run_grouping	stochastic_run	value
2023-11-12	A-2023-10-27	US	inc hosp	1	0-0.99	quantile	0.010	NA	NA
2023-11-12	A-2023-10-27	US	inc hosp	1	0-0.99	quantile	0.025	NA	NA

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

and NA in the run_grouping and stochastic_run columns.

For instance `"EW202348"`` is the probability that hospitalizations peak within the epiweek 2023-48 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 "2023-11-12"

# Week 1 will be:
week1_date = as.Date("2023-11-12") + 7 * 1 - 1
epiweek1 = MMWRweek::MMWRweek(week1_date) 
epiweek1 

For example:

origin_date	scenario_id	location	target	horizon	age_group	output_type	output_type_id	run_grouping	stochastic_run	value
2023-11-12	A-2023-10-27	US	inc hosp	NA	0-0.99	cdf	EW202346	NA	NA
2023-11-12	A-2023-10-27	US	inc hosp	NA	0-0.99	cdf	EW202347	NA	NA

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

Accepted values in the age_group column are:

• "0-0.99" (required)
• "1-4" (required)
• "5-17"
• "18-49"
• "50-64"
• "65-130" (required)
• "5-64" (required)
• "0-130" (required)

Aggregation of the previous list, for example: "0-17" is NOT accepted.

Some of the age_group are optionals, however, the submission should contain at least the age group marked as required:"0-0.99", "1-4", "5-64", "65-130", and "0-130".

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

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Scenario validation

To ensure proper data formatting, pull requests for new data or updates in model-output/ 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 Validation wiki page.

Please let us know if the wiki is inaccurate or if any questions.

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 quantiles projections of the requested targets at national and State level (only if available in the submission file).

  • 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 Running Locally).