README.md

metadata

pretty_name: Predictions — ECDC ERVISS — ILI/ARI primary-care consultation rates
license: other
tags:
  - epi-eval
  - predictions
  - forecast-evaluation
  - companion-of-ecdc-erviss
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

Predictions for ECDC ERVISS — ILI/ARI primary-care consultation rates

Community-submitted forecasts targeting EPI-Eval/ecdc-erviss. Each row is one quantile (or point) forecast for one target date — see the schema below.

This repo accumulates accepted submissions from many forecasters. New predictions arrive as community pull requests opened from the EPI-Eval dashboard; a maintainer reviews each PR before merging.

Schema (v1)

column	type	notes
target_date	string (YYYY-MM-DD)	The date being forecast
target_dataset	string	Always ecdc-erviss
target_column	string	Truth column being forecast (see below)
submitter	string	Forecaster name or HF username
model_name	string	Identifier for the model run
description	string	Free-form notes on the model
quantile	float (nullable)	In [0, 1]. null = point estimate
value	float	Forecast value (in the truth column's units)
submitted_at	string (ISO 8601)	UTC submission timestamp
(pass-through dims)	string	Categorical dims from the source CSV

Long format: one row per (target_date, [dim values…], quantile). A forecaster providing the median plus 50%/80%/95% intervals emits 7 rows per date (one point + 6 quantiles). Multiple submissions from the same forecaster land as separate parquet files under data/.

Forecast targets

Truth columns from EPI-Eval/ecdc-erviss you can forecast:

• ili_rate (consultations per 100,000 population)
• ari_rate (consultations per 100,000 population)

Submitting

The dashboard at apart-forecasting-tool handles the full submission flow: drag-drop a CSV, pick this dataset as the "Compare to" target, review your scores against the truth, and click "Submit to HuggingFace." The dashboard serializes your CSV into the schema above and opens a community PR here.

Notes

• Predictions whose target_date falls outside the truth dataset's coverage (forecast horizon) are still accepted. Comparison metrics on the dashboard compute only on the dates where truth is available.
• A submission's submitter value is its only identity claim — there's no signed authentication. Reviewers should sanity-check unfamiliar submitters.

Initialized by upload_pipeline.core.bootstrap_predictions_repos.