Finding data sources and signals of interest
Source:vignettes/signal-discovery.Rmd
signal-discovery.RmdThe Epidata API includes numerous data streams – medical claims data, cases and deaths, wastewater concentrations, and many others – covering different geographic regions. This can make it a challenge to find the data stream that you are most interested in.
Data streams fall into three categories: V5 sources, migrating endpoints, and historical endpoints (which include international sources and private endpoints requiring authentication).
V5 sources
The V5 API is the primary interface for active epidemiological surveillance data.
Online documentation and EpiPortal
The online documentation lists all data sources and signals available through the Delphi V5 API.
For an interactive visual exploration, the Delphi EpiPortal lets you filter sources and signals by disease, pathogen, geography, and date range, view live preview charts, and copy query code.
Exploring metadata with epidata_meta()
For sources on the V5 API (queried with
epidata_snapshot() and epidata_archive()),
epidata_meta() is the primary metadata lookup.
Called with no arguments, it lists all active V5 sources:
meta <- epidata_meta()
names(meta)
#> [1] "nssp" "nhsn" "pophive" "nwss"
#> [ reached 'max' / getOption("max.print") -- omitted 11 entries ]Called with source = ..., it reports that source’s
available signals, supported geographic levels, reference date ranges,
and version history:
nssp_meta <- epidata_meta(source = "nssp")
# all the fields available for this source
names(nssp_meta)
#> [1] "report_time_range" "reference_time_range" "signals"
#> [4] "geo_types"
#> [ reached 'max' / getOption("max.print") -- omitted 4 entries ]
nssp_meta$signals # available signal names
#> [1] "pct_ed_visits_ari" "pct_ed_visits_combined"
#> [3] "pct_ed_visits_covid" "pct_ed_visits_influenza"
#> [ reached 'max' / getOption("max.print") -- omitted 5 entries ]
nssp_meta$geo_types # supported geography levels
#> [1] "census_division" "census_region" "county" "hhs"
#> [ reached 'max' / getOption("max.print") -- omitted 5 entries ]
nssp_meta$reference_time_range # earliest/latest reference_time available
#> $latest
#> [1] "2026-09-12"
#>
#> $first
#> [1] "2022-10-01"
nssp_meta$report_time_range # earliest/latest report_time (publication date) available
#> $latest
#> [1] "2026-09-16T00:00:00"
#>
#> $first
#> [1] "2024-04-18T00:00:00"You can also convert the metadata into a tabular summary to search
across all sources using dplyr:
signals_df <- bind_rows(
lapply(names(meta), function(src) {
tibble(
source = src,
signals = meta[[src]]$signals,
geo_types = paste(meta[[src]]$geo_types, collapse = ", ")
)
})
)
# Search for signals related to influenza
signals_df %>%
filter(grepl("flu", signals, ignore.case = TRUE))
#> # A tibble: 31 × 3
#> source signals geo_types
#> <chr> <chr> <chr>
#> 1 nssp pct_ed_visits_influenza census_division, census_region, co…
#> 2 nssp smoothed_pct_ed_visits_influenza census_division, census_region, co…
#> 3 nhsn confirmed_admissions_flu_ew census_division, census_region, hh…
#> 4 nhsn hosprep_confirmed_admissions_flu_ew census_division, census_region, hh…
#> # ℹ 27 more rowsExample queries for V5 sources
The V5 API uses epidata_snapshot() to fetch data as of a
given moment (latest by default) and epidata_archive() to
fetch the full revision history. See vignette("epidatr")
for a general introduction to the package and its functions, and
vignette("versioned-data") for details on versioning.
Here are examples across several major V5 surveillance streams:
# NSSP: Influenza emergency department visits, across multiple states
epidata_snapshot(
source = "nssp",
signals = "pct_ed_visits_influenza",
geo_type = "state",
geo_values = c("pa", "ca"),
reference_time = epirange("2024-10-01", "2024-10-15")
)
#> # A tibble: 4 × 7
#> signal report_time geo_type geo_value fill_method reference_time value
#> <chr> <date> <chr> <chr> <chr> <date> <dbl>
#> 1 pct_ed_visit… 2026-06-26 state ca source 2024-10-05 0.140
#> 2 pct_ed_visit… 2026-06-26 state ca source 2024-10-12 0.140
#> 3 pct_ed_visit… 2026-06-26 state pa source 2024-10-05 0.0500
#> 4 pct_ed_visit… 2026-06-26 state pa source 2024-10-12 0.0700
# NHSN: Confirmed hospital admissions, for multiple signals at once
epidata_snapshot(
source = "nhsn",
signals = c("confirmed_admissions_flu_ew", "confirmed_admissions_covid_ew"),
geo_type = "state",
geo_values = "pa",
reference_time = epirange("2024-10-01", "2024-10-21")
)
#> # A tibble: 6 × 7
#> signal report_time geo_type geo_value fill_method reference_time value
#> <chr> <date> <chr> <chr> <chr> <date> <dbl>
#> 1 confirmed_adm… 2026-06-26 state pa source 2024-10-19 45
#> 2 confirmed_adm… 2026-06-26 state pa source 2024-10-12 15
#> 3 confirmed_adm… 2026-06-26 state pa source 2024-10-05 468
#> 4 confirmed_adm… 2026-06-26 state pa source 2024-10-19 6
#> # ℹ 2 more rows
# POPHIVE: Outpatient COVID-19 emergency visits, for a single exact reference date
epidata_snapshot(
source = "pophive",
signals = "covid_pct_ed",
geo_type = "state",
geo_values = "pa",
reference_time = "2024-10-05"
)
#> # A tibble: 7 × 8
#> signal report_time geo_type geo_value fill_method reference_time age_group
#> <chr> <date> <chr> <chr> <chr> <date> <chr>
#> 1 covid_pct… 2026-08-14 state pa source 2024-10-05 <1
#> 2 covid_pct… 2026-08-14 state pa source 2024-10-05 1-4
#> 3 covid_pct… 2026-08-14 state pa source 2024-10-05 18-49
#> 4 covid_pct… 2026-08-14 state pa source 2024-10-05 50-64
#> # ℹ 3 more rows
#> # ℹ 1 more variable: value <dbl>
# NWSS: Wastewater SARS-CoV-2 concentrations, for a set of specific dates rather than a range
epidata_snapshot(
source = "nwss",
signals = "covid_avg_conc",
geo_type = "sewershed",
geo_values = "128",
reference_time = c("2024-12-03", "2024-12-10")
)
#> # A tibble: 2 × 10
#> signal report_time geo_type geo_value fill_method reference_time nwss_source
#> <chr> <date> <chr> <chr> <chr> <date> <chr>
#> 1 covid_a… 2026-06-26 sewersh… 128 source 2024-12-03 CDC_Verily
#> 2 covid_a… 2026-06-26 sewersh… 128 source 2024-12-10 CDC_Verily
#> # ℹ 3 more variables: sample_index <chr>, pcr_target <chr>, value <dbl>
# Archive: Revision history for a single reference date, using a comparison
# operator on report_time instead of an epirange()
epidata_archive(
source = "nssp",
signals = "pct_ed_visits_influenza",
geo_type = "state",
geo_values = "pa",
reference_time = "2024-12-07",
report_time = "<2024-12-15"
)
#> # A tibble: 1 × 7
#> signal report_time geo_type geo_value fill_method reference_time value
#> <chr> <date> <chr> <chr> <chr> <date> <dbl>
#> 1 pct_ed_visits… 2024-12-13 state pa source 2024-12-07 0.550Migrating endpoints
Datasets that originated in the legacy API like
pub_covidcast(), pub_covidcast_meta(),
pub_fluview(), pub_fluview_clinical(),
pub_fluview_meta(), pub_flusurv(), and
pub_meta() are transitioning to V5. Starting in October
2026, these V4 functions are tentatively deprecated in favor of V5:
their historical data will remain available for at least a year, but new
ingestion will end. covidcast_epidata(), which describes
them, is not being retired outright, but will only keep describing
frozen historical data once a source’s V4 ingestion stops.
Exploring legacy COVIDcast sources with
covidcast_epidata()
For datasets still queried through the legacy
pub_covidcast() function, covidcast_epidata()
describes all available COVIDcast 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: 8 × 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 4 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. Without tab
completion, list them directly:
names(covid_sources$sources)
#> [1] "chng" "covid-act-now" "doctor-visits" "fb-survey"
#> [ reached 'max' / getOption("max.print") -- omitted 18 entries ]You can also look at all the signals available across sources:
covid_sources$signals
#> # A tibble: 520 × 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 516 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 "Example legacy query
Legacy endpoints remain accessible while their sources transition:
pub_covidcast(
source = "fb-survey",
signals = "smoothed_accept_covid_vaccine",
geo_type = "state",
time_type = "day",
time_values = epirange(20201221, 20201225),
geo_values = "pa"
)
#> # 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 pa smoothed_… fb-su… state day 2020-12-21 NA 2020-12-22
#> 2 pa smoothed_… fb-su… state day 2020-12-22 NA 2020-12-23
#> 3 pa smoothed_… fb-su… state day 2020-12-23 NA 2020-12-24
#> 4 pa smoothed_… fb-su… state 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>See vignette("migration-guide") for the argument mapping
from V4 to V5.
Historical endpoints
Some datasets are not moving to V5 because data collection has ended.
These endpoints remain available for historical reference using their
original pub_* and pvt_* functions. For more
information on the datasets that are and are not moving, please visit
the Endpoints
kept for historical reference section of the migration guide and the
Delphi
V5 Sources and Signals documentation.
Exploring package endpoints with avail_endpoints()
The avail_endpoints() function lists all endpoint
functions (i.e. functions that query particular data sources within the
API) in the package and provides brief descriptions, explicitly noting
which endpoints cover non-US locations:
| Endpoint | Description |
|---|---|
| cast_api_queries() | cast-API snapshot and archive queries |
| epidata_aux() | Fetch V5 auxiliary data |
| epidata_meta() | Get cast-API source metadata |
| 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 |
cast_api_queries() groups
epidata_snapshot(), epidata_archive(), and
epidata(), which share a single documentation page;
epidata_meta() and epidata_aux() each have
their own page, so they get their own row.
Examples
Some endpoints contain data within the United States only, some are international, and some are private and require additional access to query.
Domestic endpoints
# Google Flu Trends: Historical flu search volume
pub_gft(locations = "hhs1", epiweeks = epirange(201401, 201404))
#> `pub_gft()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 4 × 3
#> location epiweek num
#> <chr> <date> <dbl>
#> 1 hhs1 2013-12-29 1918
#> 2 hhs1 2014-01-05 2114
#> 3 hhs1 2014-01-12 2294
#> 4 hhs1 2014-01-19 1896
# Wikipedia: Article page view counts
pub_wiki(
articles = "influenza",
time_type = "day",
time_values = epirange(20200101, 20200105)
)
#> `pub_wiki()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 5 × 6
#> article date count total hour value
#> <chr> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 influenza 2020-01-01 676 82359844 -1 8.21
#> 2 influenza 2020-01-02 1171 105590677 -1 11.1
#> 3 influenza 2020-01-03 1127 106237989 -1 10.6
#> 4 influenza 2020-01-04 967 91328987 -1 10.6
#> # ℹ 1 more row
# COVID-19 hospitalizations: State-level timeseries as of a historical date
pub_covid_hosp_state_timeseries(
states = "pa",
dates = epirange(20210101, 20210105)
)
#> `pub_covid_hosp_state_timeseries()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 5 × 118
#> state geocoded_state issue date critical_staffing_shortage_today_…¹
#> <chr> <chr> <date> <date> <lgl>
#> 1 PA <NA> 2024-05-03 2021-01-01 TRUE
#> 2 PA <NA> 2024-05-03 2021-01-02 TRUE
#> 3 PA <NA> 2024-05-03 2021-01-03 TRUE
#> 4 PA <NA> 2024-05-03 2021-01-04 TRUE
#> # ℹ 1 more row
#> # ℹ abbreviated name: ¹critical_staffing_shortage_today_yes
#> # ℹ 113 more variables: critical_staffing_shortage_today_no <lgl>,
#> # critical_staffing_shortage_today_not_reported <lgl>,
#> # critical_staffing_shortage_anticipated_within_week_yes <lgl>,
#> # critical_staffing_shortage_anticipated_within_week_no <lgl>,
#> # critical_staffing_shortage_anticipated_within_week_not_reported <lgl>, …International endpoints
# PAHO Dengue: Surveillance in the Americas
pub_paho_dengue(regions = "ca", epiweeks = epirange(202001, 202004))
#> `pub_paho_dengue()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 4 × 11
#> release_date region serotype issue epiweek lag total_pop num_dengue
#> <date> <chr> <chr> <date> <date> <dbl> <dbl> <dbl>
#> 1 2020-08-07 CA " " 2020-08-02 2019-12-29 31 0 0
#> 2 2020-08-07 CA " " 2020-08-02 2020-01-05 30 0 0
#> 3 2020-08-07 CA " " 2020-08-02 2020-01-12 29 0 0
#> 4 2020-08-07 CA " " 2020-08-02 2020-01-19 28 0 0
#> # ℹ 3 more variables: num_severe <dbl>, num_deaths <dbl>, incidence_rate <dbl>
# ECDC ILI: Influenza-like illness in Europe
pub_ecdc_ili(regions = "austria", epiweeks = epirange(201901, 201904))
#> `pub_ecdc_ili()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 3 × 6
#> release_date region issue epiweek lag incidence_rate
#> <date> <chr> <date> <date> <dbl> <dbl>
#> 1 2020-03-26 Austria 2020-03-15 2019-01-06 62 787.
#> 2 2020-03-26 Austria 2020-03-15 2019-01-13 61 855.
#> 3 2020-03-26 Austria 2020-03-15 2019-01-20 60 1022.
# KCDC ILI: Influenza-like illness in South Korea
pub_kcdc_ili(regions = "ROK", epiweeks = epirange(201801, 201804))
#> `pub_kcdc_ili()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 4 × 6
#> release_date region issue epiweek lag ili
#> <date> <chr> <date> <date> <dbl> <dbl>
#> 1 2020-11-03 ROK 2020-11-01 2017-12-31 148 71.8
#> 2 2020-11-03 ROK 2020-11-01 2018-01-07 147 72.1
#> 3 2020-11-03 ROK 2020-11-01 2018-01-14 146 69
#> 4 2020-11-03 ROK 2020-11-01 2018-01-21 145 59.6
# Taiwan CDC NIDSS: Influenza outpatient visits
pub_nidss_flu(regions = "nationwide", epiweeks = epirange(201801, 201804))
#> `pub_nidss_flu()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 4 × 7
#> release_date region issue epiweek lag visits ili
#> <date> <chr> <date> <date> <dbl> <dbl> <dbl>
#> 1 2018-03-12 Nationwide 2018-03-04 2017-12-31 9 84227 1.71
#> 2 2018-03-12 Nationwide 2018-03-04 2018-01-07 8 86865 1.71
#> 3 2018-03-12 Nationwide 2018-03-04 2018-01-14 7 111976 2.03
#> 4 2018-03-12 Nationwide 2018-03-04 2018-01-21 6 117080 2.15
# Taiwan CDC NIDSS: Dengue cases
pub_nidss_dengue(locations = "nationwide", epiweeks = epirange(201801, 201804))
#> `pub_nidss_dengue()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> # A tibble: 4 × 3
#> location epiweek count
#> <chr> <date> <dbl>
#> 1 nationwide 2017-12-31 0
#> 2 nationwide 2018-01-07 2
#> 3 nationwide 2018-01-14 2
#> 4 nationwide 2018-01-21 0
# PAHO Dengue Nowcasts: Delphi nowcast estimates for the Americas
pub_dengue_nowcast(locations = "ca", epiweeks = epirange(202001, 202004))
#> `pub_dengue_nowcast()` covers a data source that is no longer updated.
#> ℹ Historical data remains available, but no new data is being ingested.
#> ℹ See the "Endpoints kept for historical reference" section of
#> `vignette("migration-guide")` (or
#> <https://cmu-delphi.github.io/epidatr/articles/migration-guide.html#endpoints-kept-for-historical-reference>)
#> for details.
#> This message is displayed once per session.
#> Warning: epidata warning: `no results`
#> # A tibble: 0 × 0Some private endpoints require a dedicated secret key passed via the
auth argument (separate from the standard Epidata API key).
Store these in your .Renviron file or environment
variables:
Private endpoints
# CDC Web Metrics: Website traffic for select topics
pvt_cdc(
auth = Sys.getenv("SECRET_API_AUTH_CDC"),
epiweeks = epirange(202003, 202304),
locations = "ma"
)
# Digital Surveillance Sensors: Delphi sensor estimates
pvt_sensors(
auth = Sys.getenv("SECRET_API_AUTH_SENSORS"),
names = "delphi",
locations = "nat",
epiweeks = epirange(202001, 202010)
)
# Twitter / HealthTweets: Influenza and total tweet counts
pvt_twitter(
auth = Sys.getenv("SECRET_API_AUTH_TWITTER"),
locations = "hhs1",
dates = epirange(20200101, 20200115)
)
# Google Health Trends: Search queries
pvt_ght(
auth = Sys.getenv("SECRET_API_AUTH_GHT"),
locations = "ca",
query = "cough",
dates = epirange(20200101, 20200115)
)
# CDC NoroSTAT: Norovirus outbreak data
pvt_norostat(
auth = Sys.getenv("SECRET_API_AUTH_NOROSTAT"),
location = "midatl",
epiweeks = epirange(202001, 202010)
)
# PAHO Dengue Sensors: Digital dengue surveillance
pvt_dengue_sensors(
auth = Sys.getenv("SECRET_API_AUTH_DENGUE_SENSORS"),
locations = "ca",
epiweeks = epirange(202001, 202010)
)