Finding data of interest

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. This page will provide some advice on how to locate donate that may be useful to you.

Using the Delphi Epidata API documentation

The Delphi Epidata API documentation lists all the available data sources and signals for COVID-19 and for other diseases. The site also includes a search tool if you have a keyword (e.g. “Taiwan”) in mind. Generally, any endpoint listed in the Delphi Epidata API has an associated function in this client where its API endpoint name is prefixed with either pub_ or pvt_, e.g. pub_covidcast or pvt_twitter.

Epidata data sources

The parameters available for each source data are documented in each linked source-specific API page. The epidatpy client will also expect certain fields, depending on the endpoint, though the Delphi Epidata API documentation will contain more information about the accepted ranges of values for each field.

A dynamically generated list of all available data sources can be obtained by using the built-in available_endpoints():

from IPython.display import HTML

from epidatpy import available_endpoints

table = available_endpoints()
HTML(table.to_html(index=False))
Endpoint Description
pub_covid_hosp_facility Fetch COVID hospitalizations by facility.
pub_covid_hosp_facility_lookup Helper for finding COVID hospitalization facilities.
pub_covid_hosp_state_timeseries Fetch COVID hospitalizations by state.
pub_covidcast Fetch Delphi's COVID-19 Surveillance Streams.
pub_covidcast_meta Fetch COVIDcast surveillance stream metadata.
pub_delphi Fetch Delphi's ILINet outpatient doctor visits forecasts.
pub_dengue_nowcast Fetch Delphi's PAHO dengue nowcasts (North and South America).
pub_ecdc_ili Fetch ECDC ILI incidence (Europe).
pub_flusurv Fetch CDC FluSurv flu hospitalizations.
pub_fluview Fetch CDC FluView ILINet outpatient doctor visits.
pub_fluview_clinical Fetch CDC FluView flu tests from clinical labs.
pub_fluview_meta Fetch Metadata for the FluView endpoint.
pub_gft Fetch Google Flu Trends flu search volume.
pub_kcdc_ili Fetch KCDC ILI incidence (Korea).
pub_meta Fetch API metadata.
pub_nidss_dengue Fetch NIDSS dengue data (Taiwan).
pub_nidss_flu Fetch NIDSS flu data (Taiwan).
pub_nowcast Fetch Delphi's wILI nowcast.
pub_paho_dengue Fetch PAHO Dengue data.
pub_wiki Fetch Wikipedia access data.
pvt_cdc Fetch CDC total and by topic webpage visits.
pvt_dengue_sensors Fetch PAHO dengue digital surveillance sensors (North and South America).
pvt_ght Fetch Google Health Trends data.
pvt_meta_norostat Fetch NoroSTAT metadata.
pvt_norostat Fetch NoroSTAT data (point data, no min/max).
pvt_quidel Fetch Quidel data.
pvt_sensors Fetch Delphi's digital surveillance sensors.
pvt_twitter Fetch HealthTweets data.

Covidcast source and signal metadata

The CovidcastEpidata class provides a way to access information about the data in the pub_covidcast endpoint directly from within the client. The cell below demonstrates how to access this metadata by using source_df property, which returns a Pandas DataFrame of metadata describing all data streams publically accessible from the COVIDcast endpoint of the Delphi Epidata API. This mirrors the information found in the COVIDcast signals endpoint.

from epidatpy import CovidcastEpidata

epidata = CovidcastEpidata()
epidata.source_df
source name description reference_signal license dua signals
0 chng Change Healthcare Change Healthcare is a healthcare technology c... smoothed_outpatient_cli CC BY-NC https://cmu.box.com/s/cto4to822zecr3oyq1kkk9xm... smoothed_outpatient_cli,smoothed_adj_outpatien...
1 covid-act-now Covid Act Now (CAN) COVID Act Now (CAN) tracks COVID-19 testing st... pcr_specimen_total_tests CC BY-NC NaN pcr_specimen_positivity_rate,pcr_specimen_tota...
2 doctor-visits Doctor Visits From Claims Information about outpatient visits, provided ... smoothed_cli CC BY https://cmu.box.com/s/l2tz6kmiws6jyty2azwb43po... smoothed_cli,smoothed_adj_cli
3 fb-survey Delphi US COVID-19 Trends and Impact Survey We conduct the Delphi US COVID-19 Trends and I... smoothed_cli CC BY https://cmu.box.com/s/qfxplcdrcn9retfzx4zniyug... raw_wcli,raw_cli,smoothed_cli,smoothed_wcli,ra...
4 google-symptoms Google Symptoms Search Trends Google's [COVID-19 Search Trends symptoms data... s05_smoothed_search To download or use the data, you must agree to... NaN ageusia_raw_search,ageusia_smoothed_search,ano...
... ... ... ... ... ... ... ...
17 nssp National Syndromic Surveillance Program The National Syndromic Surveillance Program (N... pct_ed_visits_covid [Public Domain US Government](https://www.usa.... NaN pct_ed_visits_covid,pct_ed_visits_influenza,pc...
18 nhsn National Healthcare Safety Network The National Healthcare Safety Network (NHSN) ... confirmed_admissions_covid_ew [Public Domain US Government](https://www.usa.... NaN confirmed_admissions_covid_ew,hosprep_confirme...
19 beta_google_symptoms BETA Google Symptoms Search Trends BETA Google's [COVID-19 Search Trends symptoms... s05_smoothed_search To download or use the data, you must agree to... NaN s01_raw_search,s01_smoothed_search,s02_raw_sea...
20 beta_nssp BETA National Syndromic Surveillance Program BETA The National Syndromic Surveillance Progr... pct_ed_visits_covid [Public Domain US Government](https://www.usa.... NaN pct_ed_visits_covid,pct_ed_visits_influenza,pc...
21 beta_nssp_github BETA National Syndromic Surveillance Program (... BETA The National Syndromic Surveillance Progr... pct_ed_visits_covid [Public Domain US Government](https://www.usa.... NaN pct_ed_visits_covid,pct_ed_visits_influenza,pc...

22 rows × 7 columns

This DataFrame contains the following columns:

  • source - API-internal source name.

  • name - Human-readable source name.

  • description - Description of the signal.

  • reference_signal - Geographic level for which this signal is available, such as county, state, msa, hss, hrr, or nation. Most signals are available at multiple geographic levels and will hence be listed in multiple rows with their own metadata.

  • license - The license.

  • dua - Link to the Data Use Agreement.

  • signals - List of signals available from this data source.

The signal_df DataFrame can also be used to obtain information about the signals that are available - for example, what time range they are available for, and when they have been updated.

epidata.signal_df
source signal name active short_description description time_type time_label value_label format category high_values_are is_smoothed is_weighted is_cumulative has_stderr has_sample_size geo_types
0 chng smoothed_outpatient_cli COVID-Related Doctor Visits False Estimated percentage of outpatient doctor visi... Estimated percentage of outpatient doctor visi... day Date Value raw early bad True False False False False county,hhs,hrr,msa,nation,state
1 chng smoothed_adj_outpatient_cli COVID-Related Doctor Visits (Day-adjusted) False Estimated percentage of outpatient doctor visi... Estimated percentage of outpatient doctor visi... day Date Value raw early bad True False False False False county,hhs,hrr,msa,nation,state
2 chng smoothed_outpatient_covid COVID-Confirmed Doctor Visits False COVID-Confirmed Doctor Visits Estimated percentage of outpatient doctor visi... day Date Value raw early bad True False False False False county,hhs,hrr,msa,nation,state
3 chng smoothed_adj_outpatient_covid COVID-Confirmed Doctor Visits (Day-adjusted) False COVID-Confirmed Doctor Visits Estimated percentage of outpatient doctor visi... day Date Value raw early bad True False False False False county,hhs,hrr,msa,nation,state
4 chng smoothed_outpatient_flu Influenza-Confirmed Doctor Visits False Estimated percentage of outpatient doctor visi... Estimated percentage of outpatient doctor visi... day Day Value raw early bad True False False None None county,hhs,hrr,msa,nation,state
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
515 beta_nssp_github pct_ed_visits_ari_fa Emergency Department Visits for Acute Respirat... False Percent of ED visits that had a discharge diag... Percent of ED visits that had a discharge diag... week Week Percentage percent other bad False False False False False hhs,hrr,msa
516 beta_nssp_github pct_ed_visits_covid_fz COVID Emergency Department Visits (Percent of ... True Percent of ED visits that had a discharge diag... Percent of ED visits that had a discharge diag... week Week Percentage percent other bad False False False False False hhs,hrr,msa
517 beta_nssp_github pct_ed_visits_influenza_fz Influenza Emergency Department Visits (Percent... True Percent of ED visits that had a discharge diag... Percent of ED visits that had a discharge diag... week Week Percentage percent other bad False False False False False hhs,hrr,msa
518 beta_nssp_github pct_ed_visits_rsv_fz RSV Emergency Department Visits (Percent of to... True Percent of ED visits that had a discharge diag... Percent of ED visits that had a discharge diag... week Week Percentage percent other bad False False False False False hhs,hrr,msa
519 beta_nssp_github pct_ed_visits_ari_fz Emergency Department Visits for Acute Respirat... False Percent of ED visits that had a discharge diag... Percent of ED visits that had a discharge diag... week Week Percentage percent other bad False False False False False hhs,hrr,msa

520 rows × 18 columns

This DataFrame contains one row each available signal, with the following columns:

  • source - Data source name.

  • signal - API-internal signal name.

  • name - Human-readable signal name.

  • active - Whether the signal is currently not updated or not. Signals may be inactive because the sources have become unavailable, other sources have replaced them, or additional work is required for us to continue updating them.

  • short_description - Brief description of the signal.

  • description - Full description of the signal.

  • geo_types - Spatial resolution of the signal (e.g., county, hrr, msa, dma, state). More detail about all geo_types is given in the geographic coding documentation.

  • time_type - Temporal resolution of the signal (e.g., day, week; see date coding details).

  • time_label - The time label (“Date”, “Week”).

  • value_label - The value label (“Value”, “Percentage”, “Visits”, “Visits per 100,000 people”).

  • format - The value format (“per100k”, “percent”, “fraction”, “count”, “raw”).

  • category - The signal category (“early”, “public”, “late”, “other”).

  • high_values_are- What the higher value of signal indicates (“good”, “bad”, “neutral”).

  • is_smoothed - Whether the signal is smoothed.

  • is_weighted - Whether the signal is weighted.

  • is_cumulative - Whether the signal is cumulative.

  • has_stderr - Whether the signal has stderr statistic.

  • has_sample_size - Whether the signal has sample_size statistic.

  • geo_types - Geographical levels for which this signal is available.

Example Queries

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.

from epidatpy import EpiDataContext, EpiRange
# Reuse existing context if available, but creating new one for standalone clarity
epidata = EpiDataContext()

epidata.pub_covidcast(
  data_source="fb-survey",
  signals="smoothed_accept_covid_vaccine",
  geo_type="county",
  time_type="day",
  time_values=EpiRange(20201221, 20201225),
  geo_values="06059"
).df()
source signal geo_type geo_value time_type time_value issue lag value stderr sample_size direction missing_value missing_stderr missing_sample_size
0 fb-survey smoothed_accept_covid_vaccine county 06059 day 2020-12-21 2020-12-22 1 80.859972 2.083591 356.4932 <NA> 0 0 0
1 fb-survey smoothed_accept_covid_vaccine county 06059 day 2020-12-22 2020-12-23 1 78.891717 1.761983 536.39 <NA> 0 0 0
2 fb-survey smoothed_accept_covid_vaccine county 06059 day 2020-12-23 2020-12-24 1 80.031942 1.499166 711.0497 <NA> 0 0 0
3 fb-survey smoothed_accept_covid_vaccine county 06059 day 2020-12-24 2020-12-25 1 79.265789 1.350678 900.8858 <NA> 0 0 0
4 fb-survey smoothed_accept_covid_vaccine county 06059 day 2020-12-25 2020-12-26 1 80.32682 1.211601 1076.5045 <NA> 0 0 0

Other Covid Endpoints

COVID-19 Hospitalization: Facility Lookup

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

epidata.pub_covid_hosp_facility_lookup(city="southlake").df()
hospital_pk state ccn hospital_name address city zip hospital_subtype fips_code is_metro_micro
0 450888 TX 450888 TEXAS HEALTH HARRIS METHODIST HOSPITAL SOUTHLAKE 1545 E SOUTHLAKE BLVD SOUTHLAKE 76092 Short Term 48439 1
1 670132 TX 670132 METHODIST SOUTHLAKE MEDICAL CENTER 421 E STATE HIGHWAY 114 SOUTHLAKE 76092 Short Term 48439 1

COVID-19 Hospitalization by Facility

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

epidata.pub_covid_hosp_facility(
  hospital_pks="100075",
  collection_weeks=EpiRange(20200101, 20200501)
).df()
hospital_pk state ccn hospital_name address city zip hospital_subtype fips_code publication_date collection_week is_metro_micro total_beds_7_day_sum all_adult_hospital_beds_7_day_sum all_adult_hospital_inpatient_beds_7_day_sum inpatient_beds_used_7_day_sum all_adult_hospital_inpatient_bed_occupied_7_day_sum total_adult_patients_hosp_confirmed_suspected_covid_7d_sum total_adult_patients_hospitalized_confirmed_covid_7_day_sum total_pediatric_patients_hosp_confirmed_suspected_covid_7d_sum total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum inpatient_beds_7_day_sum total_icu_beds_7_day_sum total_staffed_adult_icu_beds_7_day_sum icu_beds_used_7_day_sum staffed_adult_icu_bed_occupancy_7_day_sum staffed_icu_adult_patients_confirmed_suspected_covid_7d_sum staffed_icu_adult_patients_confirmed_covid_7_day_sum total_patients_hospitalized_confirmed_influenza_7_day_sum icu_patients_confirmed_influenza_7_day_sum total_patients_hosp_confirmed_influenza_and_covid_7d_sum total_beds_7_day_coverage all_adult_hospital_beds_7_day_coverage all_adult_hospital_inpatient_beds_7_day_coverage inpatient_beds_used_7_day_coverage all_adult_hospital_inpatient_bed_occupied_7_day_coverage total_adult_patients_hosp_confirmed_suspected_covid_7d_cov total_adult_patients_hospitalized_confirmed_covid_7_day_coverage total_pediatric_patients_hosp_confirmed_suspected_covid_7d_cov total_pediatric_patients_hosp_confirmed_covid_7d_cov inpatient_beds_7_day_coverage total_icu_beds_7_day_coverage total_staffed_adult_icu_beds_7_day_coverage icu_beds_used_7_day_coverage staffed_adult_icu_bed_occupancy_7_day_coverage staffed_icu_adult_patients_confirmed_suspected_covid_7d_cov staffed_icu_adult_patients_confirmed_covid_7_day_coverage total_patients_hospitalized_confirmed_influenza_7_day_coverage icu_patients_confirmed_influenza_7_day_coverage total_patients_hosp_confirmed_influenza_and_covid_7d_cov previous_day_admission_adult_covid_confirmed_7_day_sum previous_day_admission_adult_covid_confirmed_18_19_7_day_sum previous_day_admission_adult_covid_confirmed_20_29_7_day_sum previous_day_admission_adult_covid_confirmed_30_39_7_day_sum previous_day_admission_adult_covid_confirmed_40_49_7_day_sum previous_day_admission_adult_covid_confirmed_50_59_7_day_sum previous_day_admission_adult_covid_confirmed_60_69_7_day_sum previous_day_admission_adult_covid_confirmed_70_79_7_day_sum previous_day_admission_adult_covid_confirmed_80plus_7_day_sum previous_day_admission_adult_covid_confirmed_unknown_7_day_sum previous_day_admission_pediatric_covid_confirmed_7_day_sum previous_day_covid_ed_visits_7_day_sum previous_day_admission_adult_covid_suspected_7_day_sum previous_day_admission_adult_covid_suspected_18_19_7_day_sum previous_day_admission_adult_covid_suspected_20_29_7_day_sum previous_day_admission_adult_covid_suspected_30_39_7_day_sum previous_day_admission_adult_covid_suspected_40_49_7_day_sum previous_day_admission_adult_covid_suspected_50_59_7_day_sum previous_day_admission_adult_covid_suspected_60_69_7_day_sum previous_day_admission_adult_covid_suspected_70_79_7_day_sum previous_day_admission_adult_covid_suspected_80plus_7_day_sum previous_day_admission_adult_covid_suspected_unknown_7_day_sum previous_day_admission_pediatric_covid_suspected_7_day_sum previous_day_total_ed_visits_7_day_sum previous_day_admission_influenza_confirmed_7_day_sum total_beds_7_day_avg all_adult_hospital_beds_7_day_avg all_adult_hospital_inpatient_beds_7_day_avg inpatient_beds_used_7_day_avg all_adult_hospital_inpatient_bed_occupied_7_day_avg total_adult_patients_hosp_confirmed_suspected_covid_7d_avg total_adult_patients_hospitalized_confirmed_covid_7_day_avg total_pediatric_patients_hosp_confirmed_suspected_covid_7d_avg total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg inpatient_beds_7_day_avg total_icu_beds_7_day_avg total_staffed_adult_icu_beds_7_day_avg icu_beds_used_7_day_avg staffed_adult_icu_bed_occupancy_7_day_avg staffed_icu_adult_patients_confirmed_suspected_covid_7d_avg staffed_icu_adult_patients_confirmed_covid_7_day_avg total_patients_hospitalized_confirmed_influenza_7_day_avg icu_patients_confirmed_influenza_7_day_avg total_patients_hosp_confirmed_influenza_and_covid_7d_avg
0 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2024-03-08 2020-03-22 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 160 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 80.0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
1 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2023-06-23 2020-03-27 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 615 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 87.9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
2 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2024-03-08 2020-03-29 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 654 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 93.4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
3 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2023-06-23 2020-04-03 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 527 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 87.8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
4 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2024-03-08 2020-04-05 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 521 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 86.8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2023-06-23 2020-04-17 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 666 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 95.1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
8 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2024-03-08 2020-04-19 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 665 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 95.0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
9 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2023-06-23 2020-04-24 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 661 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 94.4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
10 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2024-03-08 2020-04-26 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 660 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 94.3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
11 100075 FL 100075 ST JOSEPHS HOSPITAL 3001 W MARTIN LUTHER KING JR BLVD TAMPA 33677 Short Term 12057 2023-06-23 2020-05-01 True <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 661 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 94.4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>

12 rows × 94 columns

COVID-19 Hospitalization by State

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

epidata.pub_covid_hosp_state_timeseries(states="MA", dates="20200510").df()
state issue date critical_staffing_shortage_today_yes critical_staffing_shortage_today_no critical_staffing_shortage_today_not_reported critical_staffing_shortage_anticipated_within_week_yes critical_staffing_shortage_anticipated_within_week_no critical_staffing_shortage_anticipated_within_week_not_reported hospital_onset_covid hospital_onset_covid_coverage inpatient_beds inpatient_beds_coverage inpatient_beds_used inpatient_beds_used_coverage inpatient_beds_used_covid inpatient_beds_used_covid_coverage previous_day_admission_adult_covid_confirmed previous_day_admission_adult_covid_confirmed_coverage previous_day_admission_adult_covid_suspected previous_day_admission_adult_covid_suspected_coverage previous_day_admission_pediatric_covid_confirmed previous_day_admission_pediatric_covid_confirmed_coverage previous_day_admission_pediatric_covid_suspected previous_day_admission_pediatric_covid_suspected_coverage staffed_adult_icu_bed_occupancy staffed_adult_icu_bed_occupancy_coverage staffed_icu_adult_patients_confirmed_suspected_covid staffed_icu_adult_patients_confirmed_suspected_covid_coverage staffed_icu_adult_patients_confirmed_covid staffed_icu_adult_patients_confirmed_covid_coverage total_adult_patients_hosp_confirmed_suspected_covid total_adult_patients_hosp_confirmed_suspected_covid_coverage total_adult_patients_hosp_confirmed_covid total_adult_patients_hosp_confirmed_covid_coverage total_pediatric_patients_hosp_confirmed_suspected_covid total_pediatric_patients_hosp_confirmed_suspected_covid_coverage total_pediatric_patients_hosp_confirmed_covid total_pediatric_patients_hosp_confirmed_covid_coverage total_staffed_adult_icu_beds total_staffed_adult_icu_beds_coverage inpatient_beds_utilization_coverage inpatient_beds_utilization_numerator inpatient_beds_utilization_denominator percent_of_inpatients_with_covid_coverage percent_of_inpatients_with_covid_numerator percent_of_inpatients_with_covid_denominator inpatient_bed_covid_utilization_coverage inpatient_bed_covid_utilization_numerator inpatient_bed_covid_utilization_denominator adult_icu_bed_covid_utilization_coverage adult_icu_bed_covid_utilization_numerator adult_icu_bed_covid_utilization_denominator adult_icu_bed_utilization_coverage adult_icu_bed_utilization_numerator adult_icu_bed_utilization_denominator inpatient_beds_utilization percent_of_inpatients_with_covid inpatient_bed_covid_utilization adult_icu_bed_covid_utilization adult_icu_bed_utilization
0 MA 2024-05-03 2020-05-10 False False True False False True 53 84 15691 73 12427 83 3625 84 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 <NA> 0 72 10876 15585 83 3607 12427 73 3304 15691 <NA> <NA> <NA> <NA> <NA> <NA> 0.69785 0.290255 0.210567 <NA> <NA>

Flu Endpoints

FluSurv hospitalization data

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

epidata.pub_flusurv(locations="ca", epiweeks=202001).df()
release_date location issue epiweek lag rate_age_0 rate_age_1 rate_age_2 rate_age_3 rate_age_4 rate_overall rate_age_5 rate_age_6 rate_age_7 rate_age_18t29 rate_age_30t39 rate_age_40t49 rate_age_5t11 rate_age_12t17 rate_age_lt18 rate_age_gte18 rate_age_0tlt1 rate_age_1t4 rate_age_gte75 rate_race_white rate_race_black rate_race_hisp rate_race_asian rate_race_natamer rate_sex_male rate_sex_female rate_flu_a rate_flu_b season
0 2025-11-03 CA 2025-03-08 202001 298 8.6 0.8 1.5 5.7 15.3 4.7 11.5 20.4 20.8 1.2 1.6 1.8 1.1 0.4 2.9 5.1 12.6 7.6 20.5 4.1 8.1 4.1 3.4 0.0 4.7 4.7 3.6 1.0 2019-20

Fluview data

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

epidata.pub_fluview(regions="nat", epiweeks=EpiRange(201201, 202001)).df()
release_date region issue epiweek lag num_ili num_patients num_age_0 num_age_1 num_age_2 num_age_3 num_age_4 num_age_5 wili ili
0 2017-10-24 nat 201740 201201 300 11992 678631 4514 3393 <NA> 2342 1028 715 1.73625 1.76709
1 2017-10-24 nat 201740 201202 299 11543 747894 4059 3636 <NA> 2319 918 611 1.55066 1.5434
2 2017-10-24 nat 201740 201203 298 11939 724623 4364 4030 <NA> 2247 800 498 1.62891 1.64762
3 2017-10-24 nat 201740 201204 297 13209 784244 4766 4802 <NA> 2303 825 513 1.76038 1.6843
4 2017-10-24 nat 201740 201205 296 14448 775298 5218 5336 <NA> 2415 915 564 1.92728 1.86354
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
413 2021-10-08 nat 202139 201949 95 48904 1476208 13852 16397 <NA> 11947 3929 2779 3.2579 3.31281
414 2021-10-08 nat 202139 201950 94 56444 1458774 14728 21877 <NA> 12822 4114 2903 3.94308 3.86928
415 2021-10-08 nat 202139 201951 93 71594 1424436 18001 29182 <NA> 16227 4835 3349 5.06435 5.02613
416 2021-10-08 nat 202139 201952 92 95757 1338669 24097 34004 <NA> 24617 7638 5401 7.06161 7.15315
417 2021-10-08 nat 202139 202001 91 88731 1426691 21594 23392 <NA> 27655 9209 6881 5.90066 6.21936

418 rows × 15 columns

Delphi’s ILINet forecasts

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

delphi_forecast = epidata.pub_delphi(system="ec", epiweek=201501)
delphi_forecast()['epidata']

[{'epiweek': datetime.date(2015, 1, 4),
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   'baselines': {'hhs1': 1.2,
    'hhs10': 1.1,
    'hhs2': 2.3,
    'hhs3': 2.0,
    'hhs4': 1.9,
    'hhs5': 1.7,
    'hhs6': 3.3,
    'hhs7': 1.7,
    'hhs8': 1.3,
    'hhs9': 2.7,
    'nat': 2.0},
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       9.1e-05,
       9.1e-05,
       9.1e-05,
       9.1e-05,
       9.1e-05],
      'point': 3.864},
     'x3': {'dist': [9.2e-05,
       0.001238,
       0.097953,
       0.583159,
       0.304399,
       0.012673,
       0.000123,
       9.1e-05,
       9.1e-05,
       9.1e-05,
       9.1e-05],
      'point': 3.864},
     'x4': {'dist': [0.000862,
       0.022667,
       0.184564,
       0.432946,
       0.296359,
       0.058811,
       0.003376,
       0.000141,
       9.1e-05,
       9.1e-05,
       9.1e-05],
      'point': 3.476}}},
   'epiweek': 201501,
   'ili_bin_size': 1,
   'ili_bins': 11,
   'name': 'DELPHI-Epicast-(Carnegie-Mellon-University)',
   'season': 2014,
   'season_weeks': 34,
   'year_weeks': 53},
  'system': 'ec'}]

FluView Clinical

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

epidata.pub_fluview_clinical(regions="nat", epiweeks=EpiRange(201601, 201701)).df()
release_date region issue epiweek lag total_specimens total_a total_b percent_positive percent_a percent_b
0 2018-10-08 nat 201839 201640 103 13380 120 90 1.56951 0.896861 0.672646
1 2018-10-08 nat 201839 201641 102 14053 108 83 1.35914 0.768519 0.590621
2 2018-10-08 nat 201839 201642 101 15110 115 97 1.40304 0.761085 0.641959
3 2018-10-08 nat 201839 201643 100 15312 143 88 1.50862 0.933908 0.574713
4 2018-10-08 nat 201839 201644 99 16652 201 118 1.91569 1.20706 0.708624
... ... ... ... ... ... ... ... ... ... ... ...
9 2018-10-08 nat 201839 201649 94 20564 696 180 4.25987 3.38456 0.875316
10 2018-10-08 nat 201839 201650 93 23476 1359 210 6.68342 5.78889 0.894531
11 2018-10-08 nat 201839 201651 92 26775 2556 339 10.8123 9.54622 1.26611
12 2018-10-08 nat 201839 201652 91 33273 4270 388 13.9993 12.8332 1.16611
13 2018-10-08 nat 201839 201701 90 35028 4288 386 13.3436 12.2416 1.10198

14 rows × 11 columns

FluView Metadata

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

epidata.pub_fluview_meta().df()
latest_update latest_issue table_rows
0 2026-06-12 2026-02-02 2778211

ECDC ILI

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

epidata.pub_ecdc_ili(regions="Armenia", epiweeks=201840).df()
region release_date issue epiweek lag incidence_rate
0 Armenia 2020-03-26 202012 201840 76 0.0

KCDC ILI

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

epidata.pub_kcdc_ili(regions="ROK", epiweeks=200436).df()
release_date region issue epiweek lag ili
0 2020-11-03 ROK 202045 200436 843 0.6

NIDSS Flu

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

epidata.pub_nidss_flu(regions="taipei", epiweeks=EpiRange(200901, 201301)).df()
release_date region epiweek issue lag visits ili
0 2015-08-04 Taipei 200901 201530 342 17021 1.14
1 2015-08-04 Taipei 200902 201530 341 20250 1.28
2 2015-08-04 Taipei 200903 201530 340 26337 1.36
3 2015-08-04 Taipei 200904 201530 339 8508 1.68
4 2015-08-04 Taipei 200905 201530 338 22275 1.34
... ... ... ... ... ... ... ...
204 2015-08-04 Taipei 201249 201530 138 12115 0.79
205 2015-08-04 Taipei 201250 201530 137 12032 0.8
206 2015-08-04 Taipei 201251 201530 136 12810 0.83
207 2015-08-04 Taipei 201252 201530 135 13929 0.85
208 2015-08-04 Taipei 201301 201530 134 12715 0.93

209 rows × 7 columns

ILI Nearby Nowcast

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

epidata.pub_nowcast(locations="ca", epiweeks=EpiRange(202201, 202319)).df()
location epiweek value std
0 ca 202201 3.62907 0.275527
1 ca 202202 3.53796 0.276336
2 ca 202203 3.51095 0.276373
3 ca 202204 2.50756 0.279392
4 ca 202205 1.84246 0.279689
... ... ... ... ...
31 ca 202232 1.34375 0.300289
32 ca 202233 1.2412 0.299868
33 ca 202234 1.32822 0.299832
34 ca 202235 1.61345 0.299792
35 ca 202236 1.868 0.299878

36 rows × 4 columns

Dengue Endpoints

Delphi’s Dengue Nowcast

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

epidata.pub_dengue_nowcast(locations="pr", epiweeks=EpiRange(201401, 202301)).df()
location epiweek value std
0 pr 201409 92.7234 547.278
1 pr 201410 96.7706 601.445
2 pr 201411 93.4814 654.537
3 pr 201412 87.7994 706.574
4 pr 201413 93.9504 758.041
... ... ... ... ...
315 pr 202027 12.0741 44.6905
316 pr 202028 10.286 44.1914
317 pr 202029 0.480332 42.919
318 pr 202030 8.26231 46.6275
319 pr 202032 263.337 1897.88

320 rows × 4 columns

NIDSS Dengue

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

epidata.pub_nidss_dengue(locations="taipei", epiweeks=EpiRange(200301, 201301)).df()
location epiweek count
0 Taipei 200301 0
1 Taipei 200302 1
2 Taipei 200303 0
3 Taipei 200304 1
4 Taipei 200305 0
... ... ... ...
518 Taipei 201249 0
519 Taipei 201250 2
520 Taipei 201251 1
521 Taipei 201252 2
522 Taipei 201301 2

523 rows × 3 columns

PAHO Dengue

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

epidata.pub_paho_dengue(regions="ca", epiweeks=EpiRange(200201, 202319)).df()
release_date region serotype epiweek issue lag total_pop num_dengue num_severe num_deaths incidence_rate
0 2020-08-07 CA 201401 202032 344 0 0 0 0 0.0
1 2020-08-07 CA 201402 202032 343 0 0 0 0 0.0
2 2020-08-07 CA 201403 202032 342 0 0 0 0 0.0
3 2020-08-07 CA 201404 202032 341 0 0 0 0 0.0
4 2020-08-07 CA 201405 202032 340 0 0 0 0 0.0
... ... ... ... ... ... ... ... ... ... ... ...
337 2020-08-07 CA 202026 202032 6 0 0 0 0 0.0
338 2020-08-07 CA 202027 202032 5 0 0 0 0 0.0
339 2020-08-07 CA 202028 202032 4 0 0 0 0 0.0
340 2020-08-07 CA 202029 202032 3 0 0 0 0 0.0
341 2020-08-07 CA 202030 202032 2 0 0 0 0 0.0

342 rows × 11 columns

Other Endpoints

Wikipedia Access

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

epidata.pub_wiki(
  language="en",
  articles="influenza",
  time_type="week",
  time_values=EpiRange(202001, 202319)
).df()
article epiweek count total hour value
0 influenza 202001 6516 663604044 -1 9.819108
1 influenza 202002 10244 789885521 -1 12.968968
2 influenza 202003 10728 783760384 -1 13.687857
3 influenza 202004 24843 785222292 -1 31.638175
4 influenza 202005 62850 780291898 -1 80.54678
... ... ... ... ... ... ...
59 influenza 202107 7975 684093074 -1 11.65777
60 influenza 202108 9473 710145306 -1 13.339524
61 influenza 202109 9043 721180910 -1 12.539156
62 influenza 202110 8477 710483659 -1 11.931309
63 influenza 202111 6581 566080628 -1 11.625552

64 rows × 6 columns

Private methods

These require private access keys to use.

CDC

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

# epidata.pvt_cdc(auth="...", locations="ma", epiweeks=EpiRange(202003, 202304)).df()

Dengue Digital Surveillance Sensors

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

# epidata.pvt_dengue_sensors(
#   auth="...",
#   names="ght",
#   locations="ag",
#   epiweeks=EpiRange(201404, 202004)
# ).df()

NoroSTAT metadata

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

# epidata.pvt_meta_norostat(auth="...").df()

NoroSTAT data

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

# epidata.pvt_norostat(auth="...", locations="1", epiweeks=201233).df()

Quidel Influenza testing

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

# epidata.pvt_quidel(auth="...", locations="hhs1", epiweeks=EpiRange(200301, 202105)).df()

Sensors

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

# epidata.pvt_sensors(
#   auth="...",
#   names="sar3",
#   locations="nat",
#   epiweeks=EpiRange(200301, 202105)
# ).df()

Twitter

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

# epidata.pvt_twitter(
#   auth="...",
#   locations="nat",
#   time_type="week",
#   time_values=EpiRange(200301, 202105)
# ).df()