This repository is designed to collect forecast data for the COVID-19 Forecast Hub run by the US CDC. The project collects forecast for two datasets:
- weekly new hospitalizations due to COVID-19, and
- weekly incident percentage of emergency department visits due to COVID-19 (optional, beginning June 18, 2025).
If you are interested in using these data for additional research or publications, please contact covidhub@cdc.gov for information regarding attribution of the source forecasts.
During the submission period, participating teams will be invited to submit national- and jurisdiction-specific (all 50 states, Washington DC, and Puerto Rico) probabilistic nowcasts and forecasts of the weekly number of confirmed COVID-19 hospital admissions during the preceding epidemiological week ("epiweek"), the current epiweek, and the following three epiweeks.
The weekly total COVID-19 admissions counts can be found in the totalconfc19newadm column of the National Healthcare Safety Network (NHSN) Hospital Respiratory Data (HRD) dataset.
NHSN provides a preliminary release of each week's HRD data on Wednesdays here. Official weekly data is released on Fridays here. For more details on this dataset, its release schedule, and its schema, see the NHSN Hospital Respiratory Data page.
Beginning June 18, 2025, the COVID-19 Forecast Hub will also accept probabilistic nowcasts and forecasts of the proportion of emergency department visits due to COVID-19. This new target represents COVID-19 as a proportion of emergency department (ED) visits, aggregated by epiweek (Sunday-Saturday) and jurisdiction (states, DC, United States). The numerator is the number of visits with a discharge diagnosis of COVID-19, and the denominator is total visits. This target is optional for any submitted location and forecast horizon.
The weekly percent of ED visits due to COVID-19 can be found in the percent_visits_covid column of the National Syndromic Surveillance Program (NSSP) Emergency Department Visits - COVID-19, Flu, RSV, Sub-state dataset. Although these numbers are reported in the percentage form, we will accept forecasts as decimal proportions (i.e., percent_visits_covid / 100). To obtain state-level data, we filter the dataset to include only the rows where the county column is equal to All.
We are working to make the Wednesday release of this dataset available on data.cdc.gov. Until then, we will update the dataset every Wednesday in the auxiliary-data/nssp-raw-data directory of our GitHub repository as a file named latest.parquet.
These Wednesday data updates contain the same data that are published on Fridays at NSSP Emergency Department Visit trajectories and underlie the percentage ED visit reported on the PRISM Data Channel's Respiratory Activity Levels page, which is also refreshed every Friday. The data represent the information available as of Wednesday morning through the previous Saturday. For example, the most recent data available on the 2025-06-11 release will be for the week ending 2025-06-07.
The Challenge Period is rolling.
Participants will be asked to submit nowcasts and forecasts by 11PM USA Eastern Time each Wednesday (the "Forecast Due Date"). If it becomes necessary to change the Forecast Due Date or time deadline, CovidHub will notify participants at least one week in advance.
Weekly submissions (including file names) will be specified in terms of a "reference date": the Saturday following the Forecast Due Date. This is the last day of the USA/CDC epiweek (Sunday to Saturday) that contains the Forecast Due Date.
Please note the following updated deadlines during the holiday period:
- Forecasts for reference date 2025-12-27 are due on 2025-12-29 (extended deadline), with expected data release on 2025-12-29 (holiday schedule).
- Forecasts for reference date 2026-01-03 are due on 2026-01-04 (extended deadline), with expected data release on 2025-12-31 (regular schedule).
Participating teams will be able to submit national- and jurisdiction-specific (all 50 states, Washington DC, and Puerto Rico) predictions for following targets.
- Quantile predictions for epiweekly total laboratory-confirmed COVID-19 hospital admissions.
- Individual forecast trajectories for epiweekly total laboratory-confirmed COVID-19 hospitalizations over time (i.e sampled trajectories).
- Quantile predictions for epiweekly percent of emergency department visits due to COVID-19.
- Individual forecast trajectories for epiweekly percent of emergency department visits due to COVID-19 over time (i.e sampled trajectories).
Targets 2, 3 and 4 are optional for any submitted location whereas target 1 (quantile predictions for epiweekly COVID-19 hospital admissions) is mandatory for any submitted location and forecast horizon. Teams are encouraged but not required to submit forecasts for all weekly horizons or for all locations.
Teams can submit nowcasts or forecasts for these targets for the following temporal "horizons":
horizon = -1: the epiweek preceding the reference datehorizon = 0: the current epiweekhorizon = 1, 2, 3: each of the three upcoming epiweeks
We use epiweeks as defined by the US CDC, which run Sunday through Saturday. The target_end_date for a prediction is the Saturday that ends the epiweek of interest. That is:
target_end_date = reference_date + (horizon * 7)Standard software packages for R and Python can help you convert from dates to epiweeks and vice versa:
Detailed guidelines for formatting and submitting forecasts are available in the model-output directory README. Detailed guidelines for formatting and submitting model metadata can be found in the model-metadata directory README.
Pull requests (PRs) into the Hub repository to register a new model or modify an existing model's metadata must always be reviewed and merged manually.
PR that submit forecasts for an existing model can be reviewed and merged automatically if the submission content passes automated validation checks and the submitting individual has been preregistered as an authorized submitter for the model.
To authorize one or more individuals to submit forecasts for a given model, add their github usernames to the designated_github_users field in the model's metadata.
To facilitate auto-merge of valid PRs, we suggest the following workflow:
-
Submit metadata first: Create a PR adding your model metadata file to the
model-metadatadirectory by 4 PM USA Eastern Time on the Wednesday you plan to submit your first forecast. -
Include
designated_github_users: In your metadata YAML file, include the GitHub usernames of all team members responsible for forecast submission in thedesignated_github_usersfield. We use this to ensure changes to model outputs are made by valid model contributors.
Once initial metadata PR is approved and merged, subsequent PRs that submit forecasts will be merged automatically, provided all checks pass.
Note
Please sync your PR branch with the main branch using the Update branch button if your PR falls behind the main branch. This ensures the automerge action runs smoothly.
We have made changes from previous versions of the COVID-19 Forecast Hub challenges to align COVID-19 forecasting challenges with influenza forecasting run via the Flusight Forecast Hub.
Both Hubs will require quantile-based forecasts of epiweekly incident hospital admissions reported into NHSN, with the same -1:3 week horizon span. Both will accept these forecasts via Github pull requests of files formatted according to the standard hubverse schema. The Hubs also plan to share a forecast deadline of 11pm USA/Eastern time on Wednesdays.
To ensure greater access to the data created by and submitted to this hub, real-time copies of files in the following directories are hosted on the Hubverse's Amazon Web Services (AWS) infrastructure, in a public S3 bucket: covid19-forecast-hub.
auxiliary-datahub-configmodel-metadatamodel-outputtarget-data
GitHub remains the primary interface for operating the COVID-19 Forecast Hub and collecting forecasts from modelers. However, the mirrors of hub files on S3 are the most convenient way to access hub data without using git/GitHub or cloning the entire hub to your local machine.
The sections below provide examples for accessing hub data on the cloud, depending on your goals and preferred tools. The options include:
| Access Method | Description |
|---|---|
| hubData (R) | Hubverse R client and R code for accessing hub data. |
| hub-data (Python) | Python package for working with hubverse data |
| AWS command line interface | Download data and use hubData, Pyarrow, or another tool for fast local access. |
In general, accessing the data directly from S3 (instead of downloading it first) is more convenient. However, if performance is critical (for example, you're building an interactive visualization), or if you need to work offline, we recommend downloading the data first.
hubData (R)
hubData, the Hubverse R client, can create an interactive session for accessing, filtering, and transforming hub model output data stored in S3.
hubData is a good choice if you:
- already use R for data analysis
- want to interactively explore hub data from the cloud without downloading it
- want to save a subset of the hub's data (e.g., forecasts for a specific date or target) to your local machine
- want to save hub data in a different file format (e.g.,
.parquetto.csv)
To install hubData and its dependencies (including the dplyr and arrow packages), follow the instructions in the hubData documentation.
hubData's connect_hub() function returns an Arrow multi-file dataset that represents a hub's model output data. The dataset can be filtered and transformed using dplyr and then materialized into a local data frame using the collect_hub() function.
Use hubData to connect to a hub on S3 and retrieve all model-output files into a local dataframe. (note: depending on the size of the hub, this operation will take a few minutes):
library(dplyr)
library(hubData)
bucket_name <- "covid19-forecast-hub"
hub_bucket <- s3_bucket(bucket_name)
hub_con <- hubData::connect_hub(hub_bucket, file_format = "parquet", skip_checks = TRUE)
model_output <- hub_con %>%
hubData::collect_hub()Use hubData to connect to a hub on S3 and filter model output data before "collecting" it into a local dataframe:
library(dplyr)
library(hubData)
bucket_name <- "covid19-forecast-hub"
hub_bucket <- s3_bucket(bucket_name)
hub_con <- hubData::connect_hub(hub_bucket, file_format = "parquet", skip_checks = TRUE)
hub_con %>%
dplyr::filter(target == "wk inc covid hosp", location == "25", output_type == "quantile") %>%
hubData::collect_hub() %>%
dplyr::select(reference_date, model_id, target_end_date, location, output_type_id, value)hub-data (Python)
The Hubverse team is developing a Python client which provides some initial tools for accessing Hubverse data. The repository is located at https://github.com/hubverse-org/hub-data.
Use pip to install hub-data (the pypi package is https://pypi.org/project/hubdata):
pip install hubdataPlease see the hub-data package documentation for examples of how to use the CLI, and the hubdata.connect_hub() and hubdata.create_hub_schema() functions.
AWS CLI
AWS provides a terminal-based command line interface (CLI) for exploring and downloading S3 files.
This option is ideal if you:
- plan to work with hub data offline but don't want to use git or GitHub
- want to download a subset of the data (instead of the entire hub)
- are using the data for an application that requires local storage or fast response times
- Install the AWS CLI using the instructions here
- You can skip the instructions for setting up security credentials, since Hubverse data is public
When using the AWS CLI, the --no-sign-request option is required, since it tells AWS to bypass a credential check
(i.e., --no-sign-request allows anonymous access to public S3 data).
[!NOTE]
Files in the bucket's
rawdirectory should not be used for analysis (they're for internal use only).
List all directories in the hub's S3 bucket:
aws s3 ls covid19-forecast-hub --no-sign-requestList all files in the hub's bucket:
aws s3 ls covid19-forecast-hub --recursive --no-sign-requestDownload all of target-data contents to your current working directory:
aws s3 cp s3://covid19-forecast-hub/target-data/ . --recursive --no-sign-requestDownload the model-output files for a specific model (e.g., the hub baseline):
aws s3 cp s3://covid19-forecast-hub/model-output/CovidHub-baseline/ . --recursive --no-sign-requestIf you are building a product (e.g., a dashboard, analysis pipeline, or evaluation) downstream of covid19-forecast-hub that uses data from this hub, please follow the guidance in this section.
We recommend accessing hub data through official hubverse tooling rather than by hard-coding paths into this repository's file tree. The hubverse R and Python packages (e.g., hubData and hub-data) provide interfaces to the COVIDHub model output, target data, and model metadata, which all follow the hubverse schema.
The specific version of the Hubverse schema currently used by the Hub is specified in the Hub's admin.json file. We notify users in advance of planned schema version update.
Warning
The layout of this repository is not a stable public API. Directories, file names, and schemas outside the hubverse-managed paths may change at any time, possibly without formal notice.
Specifically:
- Hubverse-managed directories (
model-output/,model-metadata/,target-data/,hub-config/) follow the hubverse schema. Changes here are guided by hubverse conventions; we will communicate planned changes in advance. auxiliary-data/is a catch-all for supporting files (e.g., location tables, raw NSSP snapshots, weekly submission summaries). Files within have no formal schema and no guarantee of consistency across time (e.g. they may be renamed, restructured, or removed). Please do not rely on specific filenames or columns inauxiliary-data/.
If you need a file only available through auxiliary-data/ for a downstream product, please open an issue with your use case so we can consider making its presence more stable.
If you maintain a downstream product and want to be notified of planned changes to hub data or structure, consider adding an entry to auxiliary-data/hub_developers.json.
To add or update product details, please open a pull request that edits this file.
Suggested fields for each entry include:
namedesignated_contactscontact_namecontact_email
organizationurldescription
Example of adding a new entry to the existing list of downstream products:
[
{
"name": "COVIDHub Reports",
"designated_contacts": [
{
"contact_name": "Subekshya Bidari",
"contact_email": "zib2@cdc.gov"
}
],
"organization": "CDC",
"url": "https://github.com/CDCgov/cfa-forecast-hub-reports",
"description": "Weekly summarized COVIDHub data"
},
{
"name": "My Downstream Product",
"designated_contacts": [
{
"contact_name" :"My Name",
"contact_email": "my_email@example.com"
}
],
"organization": "My Organization",
"url": "https://example.com/my-org-name/my-repo",
"description": "My product description"
}
]This repository follows the guidelines and standards outlined by the hubverse, which provides a set of data formats and open source tools for modeling hubs.
CDC GitHub Guidelines
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The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.
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