Finding data sources and signals of interest • epidatr

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.

Using the documentation

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.

Signal metadata

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
epidata_meta()	Get cast-API source metadata
pub_covidcast_meta()	Metadata for the COVIDcast endpoint
pub_fluview_meta()	Metadata for the FluView endpoint
pub_meta()	Metadata for the Delphi Epidata API

Interactive tooling

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
cast_api_queries()	cast-API snapshot and archive queries
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

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: 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 tested

Each 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: 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 rows

If 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	value	stderr	sample_size
pa	smoothed_cli	fb-survey	state	day	2021-04-05	NA	2021-04-10	5	0.7157576	0.0729992	10894.01
pa	smoothed_cli	fb-survey	state	day	2021-04-06	NA	2021-04-11	5	0.6932097	0.0708692	10862.01
pa	smoothed_cli	fb-survey	state	day	2021-04-07	NA	2021-04-12	5	0.6859343	0.0706536	10790.01
pa	smoothed_cli	fb-survey	state	day	2021-04-08	NA	2021-04-13	5	0.6815110	0.0713939	10731.00
pa	smoothed_cli	fb-survey	state	day	2021-04-09	NA	2021-04-14	5	0.7094162	0.0721616	10590.00
pa	smoothed_cli	fb-survey	state	day	2021-04-10	NA	2021-04-15	5	0.7762399	0.0760370	10492.01

Example Queries

COVIDcast Main Endpoint

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>

Other Covid Endpoints

COVID-19 Hospitalization: Facility Lookup

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

pub_covid_hosp_facility_lookup(city = "southlake")
#> # A tibble: 2 × 10
#>   hospital_pk state ccn    hospital_name    address city  zip   hospital_subtype
#>   <chr>       <chr> <chr>  <chr>            <chr>   <chr> <chr> <chr>           
#> 1 450888      TX    450888 TEXAS HEALTH HA… 1545 E… SOUT… 76092 Short Term      
#> 2 670132      TX    670132 METHODIST SOUTH… 421 E … SOUT… 76092 Short Term      
#> # ℹ 2 more variables: fips_code <chr>, is_metro_micro <dbl>
pub_covid_hosp_facility_lookup(state = "WY")
#> # A tibble: 34 × 10
#>   hospital_pk     state ccn   hospital_name address city  zip   hospital_subtype
#>   <chr>           <chr> <chr> <chr>         <chr>   <chr> <chr> <chr>           
#> 1 100 LANCASTER … WY    2020… 42091         NA      [C39… MAIN  390195          
#> 2 2333 BIDDLE AVE WY    2020… 26163         POINT … [C23… HENRY 230146          
#> 3 2333 BIDDLE AV… WY    2020… 26163         POINT … [C23… SELEC 232031          
#> 4 2752 CENTURY B… WY    2020… 42011         POINT … [C39… SURGI 390316          
#> # ℹ 30 more rows
#> # ℹ 2 more variables: fips_code <chr>, is_metro_micro <dbl>

# A non-example (there is no city called New York in Wyoming)
pub_covid_hosp_facility_lookup(state = "WY", city = "New York")
#> Error in `pub_covid_hosp_facility_lookup()`:
#> ! only one of `state`, `ccn`, `city`, `zip`, or `fips_code` can be specified

COVID-19 Hospitalization by Facility

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)
)
#> # A tibble: 12 × 113
#>   hospital_pk state ccn    hospital_name    address city  zip   hospital_subtype
#>   <chr>       <chr> <chr>  <chr>            <chr>   <chr> <chr> <chr>           
#> 1 100075      FL    100075 ST JOSEPHS HOSP… 3001 W… TAMPA 33677 Short Term      
#> 2 100075      FL    100075 ST JOSEPHS HOSP… 3001 W… TAMPA 33677 Short Term      
#> 3 100075      FL    100075 ST JOSEPHS HOSP… 3001 W… TAMPA 33677 Short Term      
#> 4 100075      FL    100075 ST JOSEPHS HOSP… 3001 W… TAMPA 33677 Short Term      
#> # ℹ 8 more rows
#> # ℹ 105 more variables: fips_code <chr>, geocoded_hospital_address <chr>,
#> #   hhs_ids <chr>, publication_date <date>, collection_week <date>,
#> #   is_metro_micro <lgl>, total_beds_7_day_sum <dbl>,
#> #   all_adult_hospital_beds_7_day_sum <dbl>,
#> #   all_adult_hospital_inpatient_beds_7_day_sum <dbl>,
#> #   inpatient_beds_used_7_day_sum <dbl>, …

COVID-19 Hospitalization by State

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

pub_covid_hosp_state_timeseries(states = "MA", dates = "20200510")
#> # A tibble: 1 × 118
#>   state geocoded_state issue      date       critical_staffing_shortage_today_…¹
#>   <chr> <lgl>          <date>     <date>     <lgl>                              
#> 1 MA    NA             2024-05-03 2020-05-10 FALSE                              
#> # 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>,
#> #   hospital_onset_covid <dbl>, hospital_onset_covid_coverage <dbl>, …

Flu Endpoints

Delphi’s ILINet forecasts

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

del <- pub_delphi(system = "ec", epiweek = 201501)
names(del[[1L]]$forecast)
#> [1] "_version"  "baselines" "data"      "epiweek"  
#>  [ reached 'max' / getOption("max.print") -- omitted 6 entries ]

FluSurv hospitalization data

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

pub_flusurv(locations = "ca", epiweeks = 202001)
#> # A tibble: 1 × 34
#>   release_date location season issue      epiweek      lag rate_age_0 rate_age_1
#>   <date>       <chr>    <chr>  <date>     <date>     <dbl>      <dbl>      <dbl>
#> 1 2025-11-03   CA       2019-… 2025-09-14 2019-12-29   298        8.6        0.8
#> # ℹ 26 more variables: 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>, rate_race_black <dbl>,
#> #   rate_race_hisp <dbl>, rate_race_asian <dbl>, rate_race_natamer <dbl>, …

Fluview data

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

pub_fluview(regions = "nat", epiweeks = epirange(201201, 202001))
#> # A tibble: 418 × 16
#>   release_date region issue      epiweek      lag num_ili num_patients
#>   <date>       <chr>  <date>     <date>     <dbl>   <dbl>        <dbl>
#> 1 2017-10-24   nat    2017-10-01 2012-01-01   300   11992       678631
#> 2 2017-10-24   nat    2017-10-01 2012-01-08   299   11543       747894
#> 3 2017-10-24   nat    2017-10-01 2012-01-15   298   11939       724623
#> 4 2017-10-24   nat    2017-10-01 2012-01-22   297   13209       784244
#> # ℹ 414 more rows
#> # ℹ 9 more variables: num_providers <dbl>, num_age_0 <dbl>, num_age_1 <dbl>,
#> #   num_age_2 <dbl>, num_age_3 <dbl>, num_age_4 <dbl>, num_age_5 <dbl>,
#> #   wili <dbl>, ili <dbl>

Fluview virological data from clinical labs

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

pub_fluview_clinical(regions = "nat", epiweeks = epirange(201601, 201701))
#> # A tibble: 14 × 11
#>   release_date region issue      epiweek      lag total_specimens total_a
#>   <date>       <chr>  <date>     <date>     <dbl>           <dbl>   <dbl>
#> 1 2018-10-08   nat    2018-09-23 2016-10-02   103           13380     120
#> 2 2018-10-08   nat    2018-09-23 2016-10-09   102           14053     108
#> 3 2018-10-08   nat    2018-09-23 2016-10-16   101           15110     115
#> 4 2018-10-08   nat    2018-09-23 2016-10-23   100           15312     143
#> # ℹ 10 more rows
#> # ℹ 4 more variables: total_b <dbl>, percent_positive <dbl>, percent_a <dbl>,
#> #   percent_b <dbl>

Fluview metadata

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

pub_fluview_meta()
#> # A tibble: 1 × 3
#>   latest_update latest_issue table_rows
#>   <date>        <date>            <dbl>
#> 1 2026-05-29    2026-05-17      2765829

Google Flu Trends data

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

pub_gft(locations = "hhs1", epiweeks = epirange(201201, 202001))
#> # A tibble: 189 × 3
#>   location epiweek      num
#>   <chr>    <date>     <dbl>
#> 1 hhs1     2012-01-01  1567
#> 2 hhs1     2012-01-08  1339
#> 3 hhs1     2012-01-15  1220
#> 4 hhs1     2012-01-22  1271
#> # ℹ 185 more rows

ECDC ILI

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

pub_ecdc_ili(regions = "Armenia", epiweeks = 201840)
#> # A tibble: 1 × 6
#>   release_date region  issue      epiweek      lag incidence_rate
#>   <date>       <chr>   <date>     <date>     <dbl>          <dbl>
#> 1 2020-03-26   Armenia 2020-03-15 2018-09-30    76              0

KCDC ILI

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

pub_kcdc_ili(regions = "ROK", epiweeks = 200436)
#> # A tibble: 1 × 6
#>   release_date region issue      epiweek      lag   ili
#>   <date>       <chr>  <date>     <date>     <dbl> <dbl>
#> 1 2020-11-03   ROK    2020-11-01 2004-09-05   843   0.6

NIDSS Flu

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

pub_nidss_flu(regions = "taipei", epiweeks = epirange(200901, 201301))
#> # A tibble: 209 × 7
#>   release_date region issue      epiweek      lag visits   ili
#>   <date>       <chr>  <date>     <date>     <dbl>  <dbl> <dbl>
#> 1 2015-08-04   Taipei 2015-07-26 2009-01-04   342  17021  1.14
#> 2 2015-08-04   Taipei 2015-07-26 2009-01-11   341  20250  1.28
#> 3 2015-08-04   Taipei 2015-07-26 2009-01-18   340  26337  1.36
#> 4 2015-08-04   Taipei 2015-07-26 2009-01-25   339   8508  1.68
#> # ℹ 205 more rows

ILI Nearby Nowcast

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

pub_nowcast(locations = "ca", epiweeks = epirange(202201, 202319))
#> # A tibble: 36 × 4
#>   location epiweek    value   std
#>   <chr>    <date>     <dbl> <dbl>
#> 1 ca       2022-01-02  3.63 0.276
#> 2 ca       2022-01-09  3.54 0.276
#> 3 ca       2022-01-16  3.51 0.276
#> 4 ca       2022-01-23  2.51 0.279
#> # ℹ 32 more rows

Dengue Endpoints

Delphi’s Dengue Nowcast

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

pub_dengue_nowcast(locations = "pr", epiweeks = epirange(201401, 202301))
#> # A tibble: 320 × 4
#>   location epiweek    value   std
#>   <chr>    <date>     <dbl> <dbl>
#> 1 pr       2014-02-23  92.7  547.
#> 2 pr       2014-03-02  96.8  601.
#> 3 pr       2014-03-09  93.5  655.
#> 4 pr       2014-03-16  87.8  707.
#> # ℹ 316 more rows

NIDSS dengue

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

pub_nidss_dengue(locations = "taipei", epiweeks = epirange(200301, 201301))
#> # A tibble: 523 × 3
#>   location epiweek    count
#>   <chr>    <date>     <dbl>
#> 1 Taipei   2002-12-29     0
#> 2 Taipei   2003-01-05     1
#> 3 Taipei   2003-01-12     0
#> 4 Taipei   2003-01-19     1
#> # ℹ 519 more rows

PAHO Dengue

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

pub_paho_dengue(regions = "ca", epiweeks = epirange(200201, 202319))
#> # A tibble: 342 × 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 2013-12-29   344         0          0
#> 2 2020-08-07   CA     "  "     2020-08-02 2014-01-05   343         0          0
#> 3 2020-08-07   CA     "  "     2020-08-02 2014-01-12   342         0          0
#> 4 2020-08-07   CA     "  "     2020-08-02 2014-01-19   341         0          0
#> # ℹ 338 more rows
#> # ℹ 3 more variables: num_severe <dbl>, num_deaths <dbl>, incidence_rate <dbl>

Other Endpoints

Wikipedia Access

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)
)
#> # A tibble: 64 × 6
#>   article   count     total  hour epiweek    value
#>   <chr>     <dbl>     <dbl> <dbl> <date>     <dbl>
#> 1 influenza  6516 663604044    -1 2019-12-29  9.82
#> 2 influenza 10244 789885521    -1 2020-01-05 13.0 
#> 3 influenza 10728 783760384    -1 2020-01-12 13.7 
#> 4 influenza 24843 785222292    -1 2020-01-19 31.6 
#> # ℹ 60 more rows

Private methods

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.

Usage of private endpoints

CDC

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")

Dengue Digital Surveillance Sensors

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)
)

Google Health Trends

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"
)

NoroSTAT metadata

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

pvt_meta_norostat(auth = Sys.getenv("SECRET_API_AUTH_NOROSTAT"))

NoroSTAT data

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)

Quidel Influenza testing

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))

Sensors

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)
)

Twitter

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)
)