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covid19-forecast-hub

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A repository run by the Centers for Disease Control and Prevention (CDC) to collect forecasts of incident respiratory COVID-19 hospital admissions and emergency department visits.

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# COVID-19 Forecast Hub
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:

1. weekly new hospitalizations due to COVID-19, and
   2. 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](mailto:covidhub@cdc.gov) for information regarding attribution of the source forecasts.

## Nowcasts and Forecasts of Confirmed COVID-19 Hospital Admissions
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")](https://epiweeks.readthedocs.io/en/stable/background.html), 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](https://www.cdc.gov/nhsn/index.html) (NHSN) [Hospital Respiratory Data (HRD) dataset](https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html).

NHSN provides a preliminary release of each week's HRD data on Wednesdays [here](https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data). Official weekly data is released on Fridays [here](https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/ua7e-t2fy/about_data). For more details on this dataset, its release schedule, and its schema, see the [NHSN Hospital Respiratory Data page](https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html).

## Nowcasts and Forecasts of Covid-19 Emergency Department Visits
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](https://www.cdc.gov/nssp/index.html) (NSSP) [Emergency Department Visits - COVID-19, Flu, RSV, Sub-state](https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/about_data) 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`](auxiliary-data/nssp-raw-data) directory of our GitHub repository as a file named [`latest.parquet`](auxiliary-data/nssp-raw-data/latest.parquet).
These Wednesday data updates contain the same data that are published on Fridays at [NSSP Emergency Department Visit trajectories](https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/about_data) and underlie the percentage ED visit reported on the PRISM Data Channel's [Respiratory Activity Levels page](https://www.cdc.gov/respiratory-viruses/data/activity-levels.html), 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.

## Dates and Deadlines
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).

## Prediction Targets and Horizons

Participating teams will be able to submit national- and jurisdiction-specific (all 50 states, Washington DC, and Puerto Rico) predictions for following targets.

### Targets
   1. Quantile predictions for epiweekly total laboratory-confirmed COVID-19 hospital admissions.
   2. Individual forecast trajectories for epiweekly total laboratory-confirmed COVID-19 hospitalizations over time (i.e sampled trajectories).
   3. Quantile predictions for epiweekly percent of emergency department visits due to COVID-19.
   4. 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.

### Horizons

Teams can submit nowcasts or forecasts for these targets for the following temporal "horizons":

- `horizon = -1`: the epiweek preceding the reference date
- `horizon = 0`: the current epiweek
- `horizon = 1, 2, 3`: each of the three upcoming epiweeks

### Epiweeks

We use epiweeks as defined by the [US CDC](https://wwwn.cdc.gov/nndss/document/MMWR_Week_overview.pdf), which run Sunday through Saturday. The `target_end_date` for a prediction is the Saturday that ends the epiweek of interest. That is:

```python
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:
#### R
- [`lubridate`](https://lubridate.tidyverse.org/reference/week.html)
- [`MMWRweek`](https://cran.r-project.org/web/packages/MMWRweek/)
#### Python
- [`epiweeks`](https://pypi.org/project/epiweeks/)
- [`pymmwr`](https://pypi.org/project/pymmwr/)

## Further submission information

Detailed guidelines for formatting and submitting forecasts are available in the [`model-output` directory README](model-output/README.md). Detailed guidelines for formatting and submitting model metadata can be found in the [`model-metadata` directory README](model-metadata/README.md).

## Suggested workflow for first time submitters
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](model-metadata/README.md#designated_github_users) 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-metadata` directory](model-metadata) 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 the [`designated_github_users`](model-metadata/README.md#designated_github_users) field. 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.

## Alignment between CovidHub and FluSight

We have made changes from previous versions of the [COVID-19 Forecast Hub](https://github.com/reichlab/covid19-forecast-hub) challenges to align COVID-19 forecasting challenges with influenza forecasting run via the [Flusight Forecast Hub](https://github.com/cdcepi/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](https://hubverse.io/en/latest/user-guide/model-output.html#model-output). The Hubs also plan to share a forecast deadline of 11pm USA/Eastern time on Wednesdays.

## Accessing COVID-19 Data On The Cloud

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-data`
- `hub-config`
- `model-metadata`
- `model-output`
- `target-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.

<summary>hubData (R)</summary>

[hubData](https://hubverse-org.github.io/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.*, `.parquet` to `.csv`)

### Installing hubData

To install `hubData` and its dependencies (including the `dplyr` and `arrow` packages), follow the [instructions in the hubData documentation](https://hubverse-org.github.io/hubData/#installation).

### Using hubData

hubData's [`connect_hub()` function](https://hubverse-org.github.io/hubData/reference/connect_hub.html) returns an [Arrow multi-file dataset](https://arrow.apache.org/docs/r/reference/Dataset.html) 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](https://hubverse-org.github.io/hubData/reference/collect_hub.html).

#### Accessing Model Output Data

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):

```r
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:

```r
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)
```

- [Full hubData documentation](https://hubverse-org.github.io/hubData/)

</details>

<summary>hub-data (Python)</summary>

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>.

### Installing hub-data

Use `pip` to install `hub-data` (the `pypi` package is <https://pypi.org/project/hubdata>):

```sh
pip install hubdata
```

### Using hub-data

Please see the [hub-data package documentation](https://hubverse-org.github.io/hub-data) for examples of how to use the CLI, and the `hubdata.connect_hub()` and `hubdata.create_hub_schema()` functions.

</details>

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

### Installing AWS CLI

- Install the AWS CLI using the [instructions here](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)
- You can skip the instructions for setting up security credentials, since Hubverse data is public

### Using AWS CLI

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 `raw` directory should not be used for analysis (they're for internal use only).

List all directories in the hub's S3 bucket:

```sh
aws s3 ls covid19-forecast-hub --no-sign-request
```

List all files in the hub's bucket:

```sh
aws s3 ls covid19-forecast-hub --recursive --no-sign-request
```

Download all of target-data contents to your current working directory:

```sh
aws s3 cp s3://covid19-forecast-hub/target-data/ . --recursive --no-sign-request
```

Download the model-output files for a specific model (e.g., the hub baseline):

```sh
aws s3 cp s3://covid19-forecast-hub/model-output/CovidHub-baseline/ . --recursive --no-sign-request
```

- [Full documentation for `aws s3 ls`](https://docs.aws.amazon.com/cli/latest/reference/s3/ls.html)
- [Full documentation for `aws s3 cp`](https://docs.aws.amazon.com/cli/latest/reference/s3/cp.html)

</details>

## Using Hub Data In Downstream Products

If 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.

### Prefer Hubverse Tooling Over Direct File Paths

We recommend accessing hub data through official [hubverse](https://hubverse.io) tooling rather than by hard-coding paths into this repository's file tree. The hubverse R and Python packages (e.g., [`hubData`](https://hubverse-org.github.io/hubData/) and [`hub-data`](https://github.com/hubverse-org/hub-data)) provide interfaces to the COVIDHub model output, target data, and model metadata, which all follow the [hubverse schema](https://hubverse.io/en/latest/user-guide/model-output.html#model-output).

### Hubverse schema version
The specific version of the Hubverse schema currently used by the Hub is specified in the Hub's [`admin.json`](hub-config/admin.json) file. We notify users in advance of planned schema version update.

### File Structure And Guarantees

> [!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](https://hubverse.io/en/latest/user-guide/model-output.html#model-output). 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 in `auxiliary-data/`.

If you need a file only available through `auxiliary-data/` for a downstream product, please [open an issue](https://github.com/CDCgov/covid19-forecast-hub/issues) with your use case so we can consider making its presence more stable.

### Following Changes

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`](auxiliary-data/hub_developers.json).
To add or update product details, please open a [pull request](https://github.com/CDCgov/covid19-forecast-hub/pulls) that edits this file.

Suggested fields for each entry include:
- `name`
- `designated_contacts`
  - `contact_name`
  - `contact_email`
- `organization`
- `url`
- `description`

Example of adding a new entry to the existing list of downstream products:

```json
[
  {
    "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"
  }
]
```

## Acknowledgments

This repository follows the guidelines and standards outlined by the [hubverse](https://hubdocs.readthedocs.io/en/latest/), which provides a set of data formats and open source tools for modeling hubs.

<summary> CDC GitHub Guidelines </summary>

<br>

**General Disclaimer** This repository was created for use by CDC programs to collaborate on public health related projects in support of the [CDC mission](https://www.cdc.gov/about/cdc/#cdc_about_cio_mission-our-mission).  GitHub is not hosted by the CDC, but is a third party website used by CDC and its partners to share information and collaborate on software. CDC use of GitHub does not imply an endorsement of any one particular service, product, or enterprise.

## Related Documents

* [Open Practices](open_practices.md)
* [Rules of Behavior](rules_of_behavior.md)
* [Disclaimer](DISCLAIMER.md)
* [Contribution Notice](CONTRIBUTING.md)
* [Code of Conduct](code-of-conduct.md)

## Public Domain Standard Notice

This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the [CC0 1.0 Universal public domain dedication](https://creativecommons.org/publicdomain/zero/1.0/). All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.

## License Standard Notice

The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.

This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.

This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details.

The source code forked from other open source projects will inherit its license.

## Privacy Standard Notice

This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the [Disclaimer](DISCLAIMER.md) and [Code of Conduct](code-of-conduct.md). For more information about CDC's privacy policy, please visit [http://www.cdc.gov/other/privacy.html](https://www.cdc.gov/other/privacy.html).

## Contributing Standard Notice

Anyone is encouraged to contribute to the repository by [forking](https://help.github.com/articles/fork-a-repo) and submitting a pull request. (If you are new to GitHub, you might start with a [basic tutorial](https://help.github.com/articles/set-up-git).) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the [Apache Software License v2](http://www.apache.org/licenses/LICENSE-2.0.html) or later.

All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at [http://www.cdc.gov/other/privacy.html](http://www.cdc.gov/other/privacy.html).

## Records Management Standard Notice

This repository is not a source of government records, but is a copy to increase collaboration and collaborative potential. All government records will be published through the [CDC web site](http://www.cdc.gov).

</details>

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