flusight: NHSN Weekly Hospital Respiratory Data from FluSight

FluSight's weekly hospital admission prediction targets based on the 'total number of new hospital admissions of patients with confirmed influenza captured during the reporting week' reported through CDC's NHSN (the dataset formerly known as HHS-Protect), Weekly Hospital Respiratory Data. Data was downloaded on November 12th 2025.

Usage

data(flusight)

Format

A data frame with 452,567 rows and 4 variables:

as_of

Date. The date this row's value was published – the report date. The same week appears many times, once per publication.

target_end_date

Date. The week being reported on – the event date.

location_name

character. US state or territory.

observation

numeric. Hospital admissions reported for that week as of that publication date.

Details

Data represents how many cases were considered influenza during the week of target_end_date given the information known until week as_of. Note that as_of is always one week ahead of target_end_date.

This is count data with 452,567 rows and 4 columns:

• as_of: The report date – the date the snapshot was taken, i.e. what was known as of that week.

• target_end_date: The event date – the week the admissions occurred.

• location_name: State, district or territory (53 levels).

• observation: Case counts for those dates. NA for 1,152 rows.

Together, as_of, target_end_date and location_name form a unique key.

Duplicate rows removed

The upstream FluSight time-series.csv ships exact duplicate rows – 39,139 of them in this snapshot. They were dropped with dplyr::distinct() before the dataset was saved (issue #25), taking it from 491,706 to 452,567 rows.

The removal is lossless: every repeated (as_of, target_end_date, location_name) key carried an identical observation, with no conflicting values anywhere in the file, so no information was discarded and the key became unique. If you download the upstream file yourself you will still see the duplicates and should distinct() them before use.

References

Target data from Flusight. Online: https://github.com/cdcepi/FluSight-forecast-hub/blob/main/target-data/time-series.csv

See also

tbl_now() to declare the date columns; summary() and diagnose() to inspect the result; the package's other datasets – denguedat, mpoxdat, flusight, covid_colombia, covid_us and hai_bucaramanga.

Examples

data(flusight)
head(flusight)
#> # A tibble: 6 × 4
#>   as_of      target_end_date location_name observation
#>   <date>     <date>          <chr>               <dbl>
#> 1 2023-09-23 2022-02-12      Alabama                10
#> 2 2023-09-23 2022-02-12      Alaska                  0
#> 3 2023-09-23 2022-02-12      Arizona                64
#> 4 2023-09-23 2022-02-12      Arkansas               29
#> 5 2023-09-23 2022-02-12      California             36
#> 6 2023-09-23 2022-02-12      Colorado               29

## This is count data: one row per (week, publication date, state).
nrow(flusight)
#> [1] 452567
length(unique(flusight$location_name))
#> [1] 53

# One state is enough to see the reporting process.
texas <- flusight[flusight$location_name == "Texas", ]
flu <- tbl_now(texas,
  event_date = target_end_date, report_date = as_of,
  case_count = observation, verbose = FALSE
)
#> Warning: 755 rows have a fractional `.delay`.
#> ℹ A fractional delay is what a converter chokes on: the two date columns are on
#>   different grids. `align_weeks()` is the fix for weekly data.
flu
#> # A tibble:  8,539 × 7
#> # Data type: "count-incidence"
#> # Frequency: Event: `weeks` | Report: `weeks`
#>    as_of        target_end_date location_name observation .event_num .report_num
#>    <date>       <date>          <chr>               <dbl>      <dbl>       <dbl>
#>    [report_dat… [event_date]    [...]             [cases]      [...]       [...]
#>  1 2023-09-23   2022-02-12      Texas                 164          1          85
#>  2 2023-09-23   2022-02-19      Texas                 266          2          85
#>  3 2023-09-23   2022-02-26      Texas                 350          3          85
#>  4 2023-09-23   2022-03-05      Texas                 334          4          85
#>  5 2023-09-23   2022-03-12      Texas                 438          5          85
#>  6 2023-09-23   2022-03-19      Texas                 639          6          85
#>  7 2023-09-23   2022-03-26      Texas                 637          7          85
#>  8 2023-09-23   2022-04-02      Texas                 538          8          85
#>  9 2023-09-23   2022-04-09      Texas                 364          9          85
#> 10 2023-09-23   2022-04-16      Texas                 355         10          85
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-11-12 | Event date: "target_end_date" | Report date: "as_of"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 8,529 more rows
#> # ℹ 1 more variable: .delay <dbl>

# `as_of` is not always the same weekday, so some delays are not whole weeks.
## `align_weeks()` fixes that.
mean(flu$.delay != round(flu$.delay))
#> [1] 0.08841785