[!IMPORTANT] This repository is currently under construction!

RSV Forecast Hub

This repository is designed to collect forecast data for the RSV Forecast Hub run by the US CDC. The project collects forecasts for two datasets:

1. Weekly new hospitalizations, due to RSV.
2. Weekly incident percentage of emergency department visits, due to RSV.

If you are interested in using these data for additional research or publications, please contact rsvhub@cdc.gov for information regarding attribution of the source forecasts.

Nowcasts And Forecasts Of Confirmed RSV 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 RSV hospital admissions during the preceding epidemiological week ("epiweek"), the current epiweek, and the following three epiweeks.

The weekly total RSV admissions counts can be found in the totalconfrsvnewadm 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.

Nowcasts And Forecasts Of RSV Emergency Department Visits

The RSV Forecast Hub also accepts probabilistic nowcasts and forecasts of the proportion of emergency department visits due to RSV. This target represents RSV 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 RSV, 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 RSV can be found in the percent_visits_rsv 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_rsv / 100). To obtain state-level data, we filter the dataset to include only the rows where the county column is equal to All.

This dataset will be updated on Wednesdays (preliminary release to facilitate forecasting) and Fridays (official weekly release) here.

The 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 (Wednesday) will be for the week ending 2025-06-07 (Saturday).

Dates And Deadlines

The Challenge Period will begin September 17, 2025, and will run until May 2026.

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, we 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.

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

Targets 2, 3 and 4 are optional for any submitted location whereas target 1 (quantile predictions for epiweekly RSV 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.

[!NOTE]

We are considering introducing samples-based ensembles for the hubs and would encourage teams to submit sample trajectories along with quantile forecasts.

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, 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
• MMWRweek

Python

• epiweeks
• pymmwr

Further Submission Information

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.

Suggested Workflow For First Time Submitters

First-time pull requests (PRs) into the Hub repository must be reviewed and merged manually; subsequent ones can be merged automatically if they pass appropriate checks.

We suggest that teams submitting for the first time make a PR adding their model metadata file to the model-metadata directory by 4 PM USA Eastern Time on the Wednesday they plan to submit their first forecast. This will allow subsequent PRs that submit forecasts to be merged automatically, provided checks pass. We also request that teams sync their PR branch with the main branch using the Update branch button if their PR is behind the main branch, to ensure the automerge action runs smoothly.

Alignment Between RSV Forecast Hub And Other Forecasting Hubs

We have made some changes from previous version of the RSV Forecast Hub to align RSV forecasting challenges with COVID-19 forecasting via COVID-19 Forecast Hub and influenza forecasting run via the Flusight Forecast Hub.

All Hubs will require quantile-based forecasts of epiweekly incident hospital admissions reported into NHSN, with the same -1:3 week horizon span. The COVIDhub and RSVhub optionally accept forecasts of proportion of Emergency department visits reported into NSSP. The Hubs also plan to share a forecast deadline of 11pm USA/Eastern time on Wednesdays.

Accessing RSV 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: [bucket pending].

• auxiliary-data
• hub-config
• model-metadata
• model-output
• target-data

GitHub remains the primary interface for operating the RSV 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.

Acknowledgments

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.