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CDC FluSurv flu hospitalizations

Source: R/endpoints.R
pub_flusurv.Rd

[Deprecated]

This is a V3/V4 endpoint. As of September 22, 2026, the V3 and V4 APIs no longer receive new data. They still serve the historical data they already have, but current data is only added to the V5 API, accessed via the epidata_snapshot(), epidata_archive(), and epidata_meta() functions. For more details on the changes, refer to vignette("migration-guide"), and visit the V5 signals documentation to see which sources are currently available.

API docs: https://cmu-delphi.github.io/delphi-epidata/api/flusurv.html.

Obtain information on influenza hospitalization rates from the Center of Disease Control.

See also https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html.

Usage

pub_flusurv(
  locations,
  epiweeks = "*",
  ...,
  issues = NULL,
  lag = NULL,
  fetch_args = fetch_args_list()
)

Arguments

locations

character. List of locations to fetch. See geographic codes for details.

epiweeks

timeset. Epiweeks to fetch. Supports epirange() and defaults to all ("*") dates. Format as epirange(startweek, endweek), where startweek and endweek are of the form YYYYWW (string or numeric).

...

not used for values, forces later arguments to bind by name

issues

timeset. Optionally, the issue(s) of the data to fetch. See the "Data Versioning" section for details.

lag

integer. Optionally, the lag of the issues to fetch. See the "Data Versioning" section for details.

fetch_args

fetch_args_list(). Additional arguments to pass to fetch(). See fetch_args_list() for details.

Value

tibble::tibble

Details

The list of location argument can be found in https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/flusurv_locations.txt.

Data Versioning

Several endpoints support retrieving historical versions of the data. The following parameters control this and are mutually exclusive (only one can be provided at a time).

  • as_of: (Date) Retrieve the data as it was on this date.

  • issues: timeset Retrieve data from a specific issue date or range of dates.

  • lag: (integer) Retrieve data with a specific lag from its issue date.

If none of these is specified, the most recent version of the data is returned.

See vignette("versioned-data") for details and more ways to specify versioned data.

See also

For example queries showing how to discover signals and build calls, see vignette("signal-discovery", package = "epidatr").

Examples


pub_flusurv(locations = "ca", epiweeks = epirange(201701, 201801))
#> Warning: `pub_flusurv()` uses the V4 Epidata API.
#> ℹ As of September 22, 2026, V4 no longer receives new data. It still serves the
#>   historical data it already has, but for current data you must use the V5
#>   endpoints (`epidata_snapshot()`, `epidata_archive()`, `epidata_meta()`) with
#>   an up-to-date epidatr (and epiprocess, if you use it).
#> ℹ See `vignette("migration-guide")` (or
#>   <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html>) for the
#>   endpoint, argument, and column mapping.
#> This warning is displayed once every 8 hours.
#> # A tibble: 31 × 34
#>    release_date location season  issue      epiweek      lag rate_age_0
#>    <date>       <chr>    <chr>   <date>     <date>     <dbl>      <dbl>
#>  1 2025-11-03   CA       2016-17 2025-09-14 2017-01-01   454        3.4
#>  2 2025-11-03   CA       2016-17 2025-09-14 2017-01-08   453        2.9
#>  3 2025-11-03   CA       2016-17 2025-09-14 2017-01-15   452        3.4
#>  4 2025-11-03   CA       2016-17 2025-09-14 2017-01-22   451        2  
#>  5 2025-11-03   CA       2016-17 2025-09-14 2017-01-29   450        2  
#>  6 2025-11-03   CA       2016-17 2025-09-14 2017-02-05   449        2  
#>  7 2025-11-03   CA       2016-17 2025-09-14 2017-02-12   448        2  
#>  8 2025-11-03   CA       2016-17 2025-09-14 2017-02-19   447        0.5
#>  9 2025-11-03   CA       2016-17 2025-09-14 2017-02-26   446        2  
#> 10 2025-11-03   CA       2016-17 2025-09-14 2017-03-05   445        0.5
#> # ℹ 21 more rows
#> # ℹ 27 more variables: rate_age_1 <dbl>, rate_age_2 <dbl>, rate_age_3 <dbl>,
#> #   rate_age_4 <dbl>, rate_overall <dbl>, rate_age_5 <dbl>, rate_age_6 <dbl>,
#> #   rate_age_7 <dbl>, rate_age_18t29 <dbl>, rate_age_30t39 <dbl>,
#> #   rate_age_40t49 <dbl>, rate_age_5t11 <dbl>, rate_age_12t17 <dbl>,
#> #   rate_age_lt18 <dbl>, rate_age_gte18 <dbl>, rate_age_1t4 <dbl>,
#> #   rate_age_gte75 <dbl>, rate_age_0tlt1 <dbl>, rate_race_white <dbl>, …