The Epidata API includes numerous data streams – medical claims data, cases and deaths, mobility, and many others – covering different geographic regions. This can make it a challenge to find the data stream that you are most interested in.
Example queries with all the endpoint functions available in this package are given below.
The Epidata documentation lists all the data sources and signals available through the API for COVID-19 and for other diseases. The site also includes a search tool if you have a keyword (e.g. “Taiwan”) in mind.
Some endpoints have partner metadata available that provides information about the signals that are available, for example, what time ranges they are available for, and when they have been updated.
| Endpoint | Description |
|---|---|
| pub_covidcast_meta() | Metadata for the COVIDcast endpoint |
| pub_fluview_meta() | Metadata for the FluView endpoint |
| pub_meta() | Metadata for the Delphi Epidata API |
We provide a couple epidatr functions to help find data sources and signals.
The avail_endpoints() function lists endpoints, each of which, except for COVIDcast, corresponds to a single data source. avail_endpoints() outputs a tibble of endpoints and brief descriptions, which explicitly state when they cover non-US locations:
avail_endpoints()| Endpoint | Description |
|---|---|
| pub_covid_hosp_facility() | COVID hospitalizations by facility |
| pub_covid_hosp_facility_lookup() | Helper for finding COVID hospitalization facilities |
| pub_covid_hosp_state_timeseries() | COVID hospitalizations by state |
| pub_covidcast() | Various COVID and flu signals via the COVIDcast endpoint |
| pub_covidcast_meta() | Metadata for the COVIDcast endpoint |
| pub_delphi() | Delphi’s ILINet outpatient doctor visits forecasts |
| pub_dengue_nowcast() | Delphi’s PAHO dengue nowcasts (North and South America) |
| pub_ecdc_ili() | ECDC ILI incidence (Europe) |
| pub_flusurv() | CDC FluSurv flu hospitalizations |
| pub_fluview() | CDC FluView ILINet outpatient doctor visits |
| pub_fluview_clinical() | CDC FluView flu tests from clinical labs |
| pub_fluview_meta() | Metadata for the FluView endpoint |
| pub_gft() | Google Flu Trends flu search volume |
| pub_kcdc_ili() | KCDC ILI incidence (Korea) |
| pub_meta() | Metadata for the Delphi Epidata API |
| pub_nidss_dengue() | NIDSS dengue cases (Taiwan) |
| pub_nidss_flu() | NIDSS flu doctor visits (Taiwan) |
| pub_nowcast() | Delphi’s ILI Nearby nowcasts |
| pub_paho_dengue() | PAHO dengue data (North and South America) |
| pub_wiki() | Wikipedia webpage counts by article |
| pvt_cdc() | CDC total and by topic webpage visits |
| pvt_dengue_sensors() | PAHO dengue digital surveillance sensors (North and South America) |
| pvt_ght() | Google Health Trends health topics search volume |
| pvt_meta_norostat() | Metadata for the NoroSTAT endpoint |
| pvt_norostat() | CDC NoroSTAT norovirus outbreaks |
| pvt_quidel() | Quidel COVID-19 and influenza testing data |
| pvt_sensors() | Influenza and dengue digital surveillance sensors |
| pvt_twitter() | HealthTweets total and influenza-related tweets |
The covidcast_epidata() function lets you look more in-depth at the data sources available through the COVIDcast endpoint. The function describes all available data sources and signals:
covid_sources <- covidcast_epidata()
head(covid_sources$sources, n = 2)
#> $chng
#> [1] "Change Healthcare"
#> [1] "chng"
#> [1] "Change Healthcare is a healthcare technology company that aggregates medical claims data from many healthcare providers. This source includes aggregated counts of claims with confirmed COVID-19 or COVID-related symptoms. All claims data has been de-identified in accordance with HIPAA privacy regulations. "
#> # A tibble: 6 × 2
#> signal short_description
#> <chr> <chr>
#> 1 smoothed_outpatient_cli Estimated percentage of outpatient doctor visit…
#> 2 smoothed_adj_outpatient_cli Estimated percentage of outpatient doctor visit…
#> 3 smoothed_outpatient_covid COVID-Confirmed Doctor Visits
#> 4 smoothed_adj_outpatient_covid COVID-Confirmed Doctor Visits
#> # ℹ 2 more rows
#>
#> $`covid-act-now`
#> [1] "Covid Act Now (CAN)"
#> [1] "covid-act-now"
#> [1] "COVID Act Now (CAN) tracks COVID-19 testing statistics, such as positivity rates and total tests performed. This source only includes CAN data from the CDC's COVID-19 Integrated County View."
#> # A tibble: 2 × 2
#> signal short_description
#> <chr> <chr>
#> 1 pcr_specimen_positivity_rate Proportion of PCR specimens tested that have a p…
#> 2 pcr_specimen_total_tests Total number of PCR specimens testedEach source is included as an entry in the covid_sources$sources list, associated with a tibble describing included signals.
If you use an editor that supports tab completion, such as RStudio, type covid_sources$source$ and wait for the tab completion popup. You will be able to browse the list of data sources.
covid_sources$signals
#> # A tibble: 443 × 3
#> source signal short_description
#> <chr> <chr> <chr>
#> 1 chng smoothed_outpatient_cli Estimated percentage of outpatient docto…
#> 2 chng smoothed_adj_outpatient_cli Estimated percentage of outpatient docto…
#> 3 chng smoothed_outpatient_covid COVID-Confirmed Doctor Visits
#> 4 chng smoothed_adj_outpatient_covid COVID-Confirmed Doctor Visits
#> # ℹ 439 more rowsIf you use an editor that supports tab completion, type covid_sources$signals$ and wait for the tab completion popup. You will be able to type the name of signals and have the autocomplete feature select them from the list for you. In the tab-completion popup, signal names are prefixed with the name of the data source for filtering convenience.
Note that some signal names have dashes in them, so to access them we rely on the backtick operator:
covid_sources$signals$`fb-survey:smoothed_cli`
#> [1] "COVID-Like Symptoms (Unweighted 7-day average)"
#> [1] "fb-survey:smoothed_cli"
#> [1] "Estimated percentage of people with COVID-like illness "These signal objects can be used directly to fetch data, without requiring us to use the pub_covidcast() function. Simply use the $call attribute of the object:
epidata <- covid_sources$signals$`fb-survey:smoothed_cli`$call(
"state", "pa", epirange(20210405, 20210410)
)
knitr::kable(epidata)| geo_value | signal | source | geo_type | time_type | time_value | direction | issue | lag | missing_value | missing_stderr | missing_sample_size | value | stderr | sample_size |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pa | smoothed_cli | fb-survey | state | day | 2021-04-05 | NA | 2021-04-10 | 5 | 0 | 0 | 0 | 0.7157576 | 0.0729992 | 10894.01 |
| pa | smoothed_cli | fb-survey | state | day | 2021-04-06 | NA | 2021-04-11 | 5 | 0 | 0 | 0 | 0.6932097 | 0.0708692 | 10862.01 |
| pa | smoothed_cli | fb-survey | state | day | 2021-04-07 | NA | 2021-04-12 | 5 | 0 | 0 | 0 | 0.6859343 | 0.0706536 | 10790.01 |
| pa | smoothed_cli | fb-survey | state | day | 2021-04-08 | NA | 2021-04-13 | 5 | 0 | 0 | 0 | 0.6815110 | 0.0713939 | 10731.00 |
| pa | smoothed_cli | fb-survey | state | day | 2021-04-09 | NA | 2021-04-14 | 5 | 0 | 0 | 0 | 0.7094162 | 0.0721616 | 10590.00 |
| pa | smoothed_cli | fb-survey | state | day | 2021-04-10 | NA | 2021-04-15 | 5 | 0 | 0 | 0 | 0.7762399 | 0.0760370 | 10492.01 |
API docs: https://cmu-delphi.github.io/delphi-epidata/api/covidcast_signals.html
County geo_values are FIPS codes and are discussed in the API docs here. The example below is for Orange County, California.
pub_covidcast(
source = "fb-survey",
signals = "smoothed_accept_covid_vaccine",
geo_type = "county",
time_type = "day",
time_values = epirange(20201221, 20201225),
geo_values = "06059"
)
#> # A tibble: 5 × 15
#> geo_value signal source geo_type time_type time_value direction issue
#> <chr> <chr> <chr> <fct> <fct> <date> <dbl> <date>
#> 1 06059 smoothed_… fb-su… county day 2020-12-21 NA 2020-12-22
#> 2 06059 smoothed_… fb-su… county day 2020-12-22 NA 2020-12-23
#> 3 06059 smoothed_… fb-su… county day 2020-12-23 NA 2020-12-24
#> 4 06059 smoothed_… fb-su… county day 2020-12-24 NA 2020-12-25
#> # ℹ 1 more row
#> # ℹ 7 more variables: lag <dbl>, missing_value <dbl>, missing_stderr <dbl>,
#> # missing_sample_size <dbl>, value <dbl>, stderr <dbl>, sample_size <dbl>The covidcast endpoint supports * in its time and geo fields:
pub_covidcast(
source = "fb-survey",
signals = "smoothed_accept_covid_vaccine",
geo_type = "county",
time_type = "day",
time_values = epirange(20201221, 20201225),
geo_values = "*"
)
#> # A tibble: 2,025 × 15
#> geo_value signal source geo_type time_type time_value direction issue
#> <chr> <chr> <chr> <fct> <fct> <date> <dbl> <date>
#> 1 01000 smoothed_… fb-su… county day 2020-12-21 NA 2020-12-22
#> 2 01073 smoothed_… fb-su… county day 2020-12-21 NA 2020-12-22
#> 3 01089 smoothed_… fb-su… county day 2020-12-21 NA 2020-12-22
#> 4 01097 smoothed_… fb-su… county day 2020-12-21 NA 2020-12-22
#> # ℹ 2,021 more rows
#> # ℹ 7 more variables: lag <dbl>, missing_value <dbl>, missing_stderr <dbl>,
#> # missing_sample_size <dbl>, value <dbl>, stderr <dbl>, sample_size <dbl>API docs: https://cmu-delphi.github.io/delphi-epidata/api/covid_hosp_facility_lookup.html
pub_covid_hosp_facility_lookup(city = "southlake")
pub_covid_hosp_facility_lookup(state = "WY")
# A non-example (there is no city called New York in Wyoming)
pub_covid_hosp_facility_lookup(state = "WY", city = "New York")API docs: https://cmu-delphi.github.io/delphi-epidata/api/covid_hosp_facility.html
pub_covid_hosp_facility(
hospital_pks = "100075",
collection_weeks = epirange(20200101, 20200501)
)API docs: https://cmu-delphi.github.io/delphi-epidata/api/covid_hosp.html
pub_covid_hosp_state_timeseries(states = "MA", dates = "20200510")API docs: https://cmu-delphi.github.io/delphi-epidata/api/delphi.html
del <- pub_delphi(system = "ec", epiweek = 201501)
names(del[[1L]]$forecast)API docs: https://cmu-delphi.github.io/delphi-epidata/api/flusurv.html
pub_flusurv(locations = "ca", epiweeks = 202001)API docs: https://cmu-delphi.github.io/delphi-epidata/api/fluview.html
pub_fluview(regions = "nat", epiweeks = epirange(201201, 202001))API docs: https://cmu-delphi.github.io/delphi-epidata/api/fluview_clinical.html
pub_fluview_clinical(regions = "nat", epiweeks = epirange(201601, 201701))API docs: https://cmu-delphi.github.io/delphi-epidata/api/fluview_meta.html
pub_fluview_meta()API docs: https://cmu-delphi.github.io/delphi-epidata/api/gft.html
pub_gft(locations = "hhs1", epiweeks = epirange(201201, 202001))API docs: https://cmu-delphi.github.io/delphi-epidata/api/ecdc_ili.html
pub_ecdc_ili(regions = "Armenia", epiweeks = 201840)API docs: https://cmu-delphi.github.io/delphi-epidata/api/kcdc_ili.html
pub_kcdc_ili(regions = "ROK", epiweeks = 200436)API docs: https://cmu-delphi.github.io/delphi-epidata/api/nidss_flu.html
pub_nidss_flu(regions = "taipei", epiweeks = epirange(200901, 201301))API docs: https://cmu-delphi.github.io/delphi-epidata/api/nowcast.html
pub_nowcast(locations = "ca", epiweeks = epirange(202201, 202319))API docs: https://cmu-delphi.github.io/delphi-epidata/api/dengue_nowcast.html
pub_dengue_nowcast(locations = "pr", epiweeks = epirange(201401, 202301))API docs: https://cmu-delphi.github.io/delphi-epidata/api/nidss_dengue.html
pub_nidss_dengue(locations = "taipei", epiweeks = epirange(200301, 201301))API docs: https://cmu-delphi.github.io/delphi-epidata/api/paho_dengue.html
pub_paho_dengue(regions = "ca", epiweeks = epirange(200201, 202319))API docs: https://cmu-delphi.github.io/delphi-epidata/api/wiki.html
pub_wiki(
language = "en",
articles = "influenza",
time_type = "week",
time_values = epirange(202001, 202319)
)These require private access keys to use (separate from the Delphi Epidata API key). To actually run these locally, you will need to store these secrets in your .Reviron file, or set them as environmental variables.
API docs: https://cmu-delphi.github.io/delphi-epidata/api/cdc.html
pvt_cdc(auth = Sys.getenv("SECRET_API_AUTH_CDC"), epiweeks = epirange(202003, 202304), locations = "ma")API docs: https://cmu-delphi.github.io/delphi-epidata/api/dengue_sensors.html
pvt_dengue_sensors(
auth = Sys.getenv("SECRET_API_AUTH_SENSORS"),
names = "ght",
locations = "ag",
epiweeks = epirange(201404, 202004)
)API docs: https://cmu-delphi.github.io/delphi-epidata/api/ght.html
pvt_ght(
auth = Sys.getenv("SECRET_API_AUTH_GHT"),
epiweeks = epirange(199301, 202304),
locations = "ma",
query = "how to get over the flu"
)API docs: https://cmu-delphi.github.io/delphi-epidata/api/meta_norostat.html
pvt_meta_norostat(auth = Sys.getenv("SECRET_API_AUTH_NOROSTAT"))API docs: https://cmu-delphi.github.io/delphi-epidata/api/norostat.html
pvt_norostat(auth = Sys.getenv("SECRET_API_AUTH_NOROSTAT"), locations = "1", epiweeks = 201233)API docs: https://cmu-delphi.github.io/delphi-epidata/api/quidel.html
pvt_quidel(auth = Sys.getenv("SECRET_API_AUTH_QUIDEL"), locations = "hhs1", epiweeks = epirange(200301, 202105))API docs: https://cmu-delphi.github.io/delphi-epidata/api/sensors.html
pvt_sensors(
auth = Sys.getenv("SECRET_API_AUTH_SENSORS"),
names = "sar3",
locations = "nat",
epiweeks = epirange(200301, 202105)
)API docs: https://cmu-delphi.github.io/delphi-epidata/api/twitter.html
pvt_twitter(
auth = Sys.getenv("SECRET_API_AUTH_TWITTER"),
locations = "nat",
time_type = "week",
time_values = epirange(200301, 202105)
)