Finding data sources and signals of interest¶
The 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 returns a dict keyed by source name covering every active V5 source:
from epidatpy import EpiDataContext, EpiRange
epidata = EpiDataContext()
meta = epidata.epidata_meta()
sorted(meta)
['claims_inpatient',
'claims_outpatient',
'flusurv',
'fluview_ilinet',
'fluview_resp_lab_clinical',
'fluview_resp_lab_ph',
'nchs_mortality',
'nhsn',
'nickel_beta',
'nssp',
'nwss',
'pophive',
'rvdss',
'sleepcycle',
'va_respiratory']
Called with a source, it returns that source’s entry: its available signals,
supported geographic levels, reference date range, and version history (as UTC
timestamps).
nssp_meta = epidata.epidata_meta(source="nssp")
# all the fields available for this source
sorted(nssp_meta)
['column_types',
'extra_key_columns',
'geo_types',
'key_columns',
'reference_time_range',
'report_time_range',
'signals',
'value_columns']
print(nssp_meta["signals"]) # available signal names
print(nssp_meta["geo_types"]) # supported geography levels
print(nssp_meta["reference_time_range"]) # earliest/latest reference_time available
print(nssp_meta["report_time_range"]) # earliest/latest report_time (publication instant) available
['pct_ed_visits_ari', 'pct_ed_visits_combined', 'pct_ed_visits_covid', 'pct_ed_visits_influenza', 'pct_ed_visits_rsv', 'smoothed_pct_ed_visits_combined', 'smoothed_pct_ed_visits_covid', 'smoothed_pct_ed_visits_influenza', 'smoothed_pct_ed_visits_rsv']
['census_division', 'census_region', 'county', 'hhs', 'hrr', 'hsa_nci', 'msa', 'nation', 'state']
{'latest': '2026-09-19', 'first': '2022-10-01'}
{'latest': '2026-09-23T00:00:00Z', 'first': '2024-04-18T00:00:00Z'}
You can also flatten the metadata into a data frame to search across all
sources. (A source whose metadata is temporarily unavailable comes back as an
error stub without a signals key, so skip those.)
signals_df = pd.DataFrame(
[
{"source": src, "signal": sig, "geo_types": ", ".join(entry["geo_types"])}
for src, entry in meta.items()
if "signals" in entry
for sig in entry["signals"]
]
)
# Search for signals related to influenza
signals_df[signals_df["signal"].str.contains("flu", case=False)]
| source | signal | geo_types | |
|---|---|---|---|
| 3 | nssp | pct_ed_visits_influenza | census_division, census_region, county, hhs, h... |
| 7 | nssp | smoothed_pct_ed_visits_influenza | census_division, census_region, county, hhs, h... |
| 12 | nhsn | abs_chg_hosprep_pct_confirmed_admissions_flu_a... | census_division, census_region, hhs, nation, s... |
| 13 | nhsn | abs_chg_hosprep_pct_confirmed_admissions_flu_ew | census_division, census_region, hhs, nation, s... |
| 14 | nhsn | abs_chg_hosprep_pct_confirmed_admissions_flu_p... | census_division, census_region, hhs, nation, s... |
| ... | ... | ... | ... |
| 418 | nchs_mortality | deaths_pneumonia_notflu_incidence_prop | nation, state |
| 419 | nchs_mortality | deaths_pneumonia_or_flu_or_covid_incidence_num | nation, state |
| 420 | nchs_mortality | deaths_pneumonia_or_flu_or_covid_incidence_prop | nation, state |
| 459 | flusurv | rate_flu_a | misc, msa, nation, state |
| 460 | flusurv | rate_flu_b | misc, msa, nation, state |
121 rows × 3 columns
Example 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
Getting started for a general introduction to the
package and Accessing versioned data for details on
versioning.
Both methods take one or more signals and one or more geo_type values.
geo_values and reference_time are filtered locally after the request, so
they accept single values, lists, or an EpiRange. A report_time filter is
applied server-side and takes a comparison string (such as "<2024-12-15") or
an EpiRange; a bare date raises, since a single point in time is a
snapshot_date question.
Here are examples across several major V5 surveillance streams:
# NSSP: Influenza emergency department visits, across multiple states
epidata.epidata_snapshot(
source="nssp",
signals="pct_ed_visits_influenza",
geo_type="state",
geo_values=["pa", "ca"],
reference_time=EpiRange("2024-10-01", "2024-10-15"),
).df()
| signal | report_time | geo_type | geo_value | fill_method | reference_time | value | |
|---|---|---|---|---|---|---|---|
| 787 | pct_ed_visits_influenza | 2026-06-26 00:00:00+00:00 | state | ca | source | 2024-10-05 | 0.14 |
| 788 | pct_ed_visits_influenza | 2026-06-26 00:00:00+00:00 | state | ca | source | 2024-10-12 | 0.14 |
| 7017 | pct_ed_visits_influenza | 2026-06-26 00:00:00+00:00 | state | pa | source | 2024-10-05 | 0.05 |
| 7018 | pct_ed_visits_influenza | 2026-06-26 00:00:00+00:00 | state | pa | source | 2024-10-12 | 0.07 |
# NHSN: Confirmed hospital admissions, for multiple signals at once
epidata.epidata_snapshot(
source="nhsn",
signals=["confirmed_admissions_flu_ew", "confirmed_admissions_covid_ew"],
geo_type="state",
geo_values="pa",
reference_time=EpiRange("2024-10-01", "2024-10-21"),
).df()
| signal | report_time | geo_type | geo_value | fill_method | reference_time | value | |
|---|---|---|---|---|---|---|---|
| 6408 | confirmed_admissions_covid_ew | 2024-11-19 00:00:00+00:00 | state | pa | source | 2024-10-12 | 15.0 |
| 17432 | confirmed_admissions_flu_ew | 2024-11-19 00:00:00+00:00 | state | pa | source | 2024-10-05 | 61.0 |
| 21440 | confirmed_admissions_flu_ew | 2025-01-24 00:00:00+00:00 | state | pa | source | 2024-10-19 | 6.0 |
| 22373 | confirmed_admissions_covid_ew | 2025-02-05 00:00:00+00:00 | state | pa | source | 2024-10-19 | 45.0 |
| 22396 | confirmed_admissions_flu_ew | 2025-02-05 00:00:00+00:00 | state | pa | source | 2024-10-12 | 26.0 |
| 24003 | confirmed_admissions_covid_ew | 2025-05-21 00:00:00+00:00 | state | pa | source | 2024-10-05 | 468.0 |
# POPHIVE: Outpatient COVID-19 emergency visits, for a single exact reference date
epidata.epidata_snapshot(
source="pophive",
signals="covid_pct_ed",
geo_type="state",
geo_values="pa",
reference_time="2024-10-05",
).df()
| signal | report_time | geo_type | geo_value | fill_method | reference_time | value | age_group | |
|---|---|---|---|---|---|---|---|---|
| 111732 | covid_pct_ed | 2026-08-14 00:00:00+00:00 | state | pa | source | 2024-10-05 | 1.584507 | <1 |
| 111733 | covid_pct_ed | 2026-08-14 00:00:00+00:00 | state | pa | source | 2024-10-05 | 0.631439 | 1-4 |
| 111734 | covid_pct_ed | 2026-08-14 00:00:00+00:00 | state | pa | source | 2024-10-05 | 0.680811 | 18-49 |
| 111735 | covid_pct_ed | 2026-08-14 00:00:00+00:00 | state | pa | source | 2024-10-05 | 1.001541 | 50-64 |
| 111736 | covid_pct_ed | 2026-08-14 00:00:00+00:00 | state | pa | source | 2024-10-05 | 0.286689 | 5-17 |
| 111737 | covid_pct_ed | 2026-08-14 00:00:00+00:00 | state | pa | source | 2024-10-05 | 1.836271 | 65+ |
| 111738 | covid_pct_ed | 2026-08-14 00:00:00+00:00 | state | pa | source | 2024-10-05 | 1.015059 | all |
# NWSS: Wastewater SARS-CoV-2 concentrations, for a set of specific dates rather than a range
epidata.epidata_snapshot(
source="nwss",
signals="covid_avg_conc",
geo_type="sewershed",
geo_values="128",
reference_time=["2024-12-03", "2024-12-10"],
).df()
| signal | report_time | geo_type | geo_value | fill_method | reference_time | value | nwss_source | sample_index | pcr_target | |
|---|---|---|---|---|---|---|---|---|---|---|
| 8871 | covid_avg_conc | 2026-06-26 00:00:00+00:00 | sewershed | 128 | source | 2024-12-03 | 29207.79037 | CDC_Verily | 161629 | sars-cov-2 |
| 159055 | covid_avg_conc | 2026-06-26 00:00:00+00:00 | sewershed | 128 | source | 2024-12-10 | 32843.66936 | CDC_Verily | 111700 | sars-cov-2 |
# Archive: Revision history for a single reference date, using a comparison
# operator on report_time instead of an EpiRange
epidata.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",
).df()
| signal | report_time | geo_type | geo_value | fill_method | reference_time | value | |
|---|---|---|---|---|---|---|---|
| 104723 | pct_ed_visits_influenza | 2024-12-13 00:00:00+00:00 | state | pa | source | 2024-12-07 | 0.55 |
Migrating 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 methods are tentatively deprecated in
favor of V5: their historical data will remain available for at least a year,
but new ingestion will end. Calling them emits a UserWarning pointing to the
migration guide. CovidcastEpidata, 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 CovidcastEpidata¶
For datasets still queried through the legacy pub_covidcast() method,
CovidcastEpidata describes all available COVIDcast data sources and signals.
Its source_df property mirrors the
COVIDcast signals documentation:
from epidatpy import CovidcastEpidata
covid_sources = CovidcastEpidata()
covid_sources.source_df.head()
| 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... |
This data frame contains the following columns:
source- API-internal source name.name- Human-readable source name.description- Description of the source.reference_signal- The source’s headline signal.license- The license.dua- Link to the Data Use Agreement.signals- List of signals available from this data source.
source_names() lists the sources directly, and signal_df has one row per
signal with its geographic levels, temporal resolution, and descriptive flags
(is_smoothed, is_cumulative, has_stderr, …):
covid_sources.source_names()
['chng',
'covid-act-now',
'doctor-visits',
'fb-survey',
'google-symptoms',
'hhs',
'hospital-admissions',
'indicator-combination-cases-deaths',
'jhu-csse',
'safegraph-weekly',
'usa-facts',
'ght',
'google-survey',
'indicator-combination-nmf',
'safegraph-daily',
'nchs-mortality',
'dsew-cpr',
'nssp',
'nhsn',
'beta_google_symptoms',
'beta_nssp',
'beta_nssp_github']
covid_sources.signal_df.head()
| 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 |
Index the object with a source name, or a (source, signal) pair, to drill in:
covid_sources["nssp"].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 | nssp | pct_ed_visits_covid | 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 | county,hhs,hrr,hsa_nci,msa,nation,state |
| 1 | nssp | pct_ed_visits_influenza | 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 | county,hhs,hrr,hsa_nci,msa,nation,state |
| 2 | nssp | pct_ed_visits_rsv | 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 | county,hhs,hrr,hsa_nci,msa,nation,state |
| 3 | nssp | pct_ed_visits_combined | Emergency Department Visits for COVID, Influen... | 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 | county,hhs,hrr,msa,nation,state |
| 4 | nssp | smoothed_pct_ed_visits_covid | COVID Emergency Department Visits (Percent of ... | True | 3-week moving average of percent of ED visits ... | 3-week moving average of percent of ED visits ... | week | Week | Percentage | percent | other | bad | True | False | False | False | False | county,hhs,hrr,hsa_nci,msa,nation,state |
| 5 | nssp | smoothed_pct_ed_visits_influenza | Influenza Emergency Department Visits (Percent... | True | 3-week moving average of percent of ED visits ... | 3-week moving average of percent of ED visits ... | week | Week | Percentage | percent | other | bad | True | False | False | False | False | county,hhs,hrr,hsa_nci,msa,nation,state |
| 6 | nssp | smoothed_pct_ed_visits_rsv | RSV Emergency Department Visits (Percent of to... | True | 3-week moving average of percent of ED visits ... | 3-week moving average of percent of ED visits ... | week | Week | Percentage | percent | other | bad | True | False | False | False | False | county,hhs,hrr,hsa_nci,msa,nation,state |
| 7 | nssp | smoothed_pct_ed_visits_combined | Emergency Department Visits for COVID, Influen... | False | 3-week moving average of percent of ED visits ... | 3-week moving average of percent of ED visits ... | week | Week | Percentage | percent | other | bad | True | False | False | False | False | county,hhs,hrr,msa,nation,state |
covid_sources["fb-survey", "smoothed_cli"]
DataSignal(_create_call=<function CovidcastEpidata.<locals>.create_call at 0x7f6c70830720>, source='fb-survey', signal='smoothed_cli', signal_basename='raw_wcli', name='COVID-Like Symptoms (Unweighted 7-day average)', active=False, short_description='Estimated percentage of people with COVID-like illness ', description='Estimated percentage of people with COVID-like illness \n\nFor this signal, we estimate the percentage of people self-reporting COVID-like symptoms, defined here as fever along with either cough, shortness of breath, or difficulty breathing. While many other conditions can cause these symptoms, comparing the rates of COVID-like symptoms across the country can suggest where COVID is most active.', time_label='Date', value_label='Percentage', format='percent', category='early', high_values_are='bad', is_smoothed=True, is_weighted=False, is_cumulative=False, has_stderr=True, has_sample_size=True, link=[WebLink(alt='Survey details', href='https://delphi.cmu.edu/covidcast/surveys/')], compute_from_base=False, time_type='day', geo_types={'county': DataSignalGeoStatistics(min=0.0, max=13.0003763, mean=1.027805, stdev=1.0362304), 'hrr': DataSignalGeoStatistics(min=0.0, max=11.2962963, mean=1.2269157, stdev=1.0692117), 'msa': DataSignalGeoStatistics(min=0.0, max=12.5231652, mean=1.1602289, stdev=1.0960308), 'nation': DataSignalGeoStatistics(min=0.3647163, max=4.382599, mean=1.1685062, stdev=0.7841888), 'state': DataSignalGeoStatistics(min=0.0, max=7.4156739, mean=1.2031664, stdev=0.9198052)})
Example legacy query¶
Legacy endpoints remain accessible while their sources transition:
epidata.pub_covidcast(
data_source="fb-survey",
signals="smoothed_accept_covid_vaccine",
geo_type="state",
time_type="day",
time_values=EpiRange(20201221, 20201225),
geo_values="pa",
).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:786: UserWarning: `pub_covidcast` uses the V4 Epidata API. Starting in October 2026, V4 is tentatively deprecated in favor of the V5 API. See the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html) for the V5 endpoints and how to move to them.
_warn_v4_sunset("pub_covidcast")
| 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 | state | pa | day | 2020-12-21 | 2020-12-22 | 1 | 70.235389 | 0.770102 | 3525.0021 | <NA> | 0 | 0 | 0 |
| 1 | fb-survey | smoothed_accept_covid_vaccine | state | pa | day | 2020-12-22 | 2020-12-23 | 1 | 69.909543 | 0.636341 | 5195.0028 | <NA> | 0 | 0 | 0 |
| 2 | fb-survey | smoothed_accept_covid_vaccine | state | pa | day | 2020-12-23 | 2020-12-24 | 1 | 70.218495 | 0.551964 | 6864.0033 | <NA> | 0 | 0 | 0 |
| 3 | fb-survey | smoothed_accept_covid_vaccine | state | pa | day | 2020-12-24 | 2020-12-25 | 1 | 70.69477 | 0.496062 | 8419.0038 | <NA> | 0 | 0 | 0 |
| 4 | fb-survey | smoothed_accept_covid_vaccine | state | pa | day | 2020-12-25 | 2020-12-26 | 1 | 71.510794 | 0.455597 | 9815.0045 | <NA> | 0 | 0 | 0 |
See the 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_* methods, and calling one emits a note saying so. For more
information on the datasets that are and are not moving, see
Endpoints kept for historical reference
in the migration guide and the
Delphi V5 Sources and Signals
documentation.
Exploring package endpoints with available_endpoints()¶
available_endpoints() lists all pub_* and pvt_* endpoint methods in the
package with brief descriptions. Generally, any endpoint listed in the Delphi
Epidata API documentation has an associated method here, named after the API
endpoint with a pub_ or pvt_ prefix, e.g. pub_fluview or pvt_twitter.
from IPython.display import HTML
from epidatpy import available_endpoints
HTML(available_endpoints().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. |
Examples¶
Some endpoints contain data within the United States only, some are international, and some are private and require additional access to query. Each method’s docstring links to its API documentation page.
Domestic endpoints¶
# Google Flu Trends: Historical flu search volume
epidata.pub_gft(locations="hhs1", epiweeks=EpiRange(201401, 201404)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:1261: UserWarning: `pub_gft` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_gft")
| location | epiweek | num | |
|---|---|---|---|
| 0 | hhs1 | 201401 | 1918 |
| 1 | hhs1 | 201402 | 2114 |
| 2 | hhs1 | 201403 | 2294 |
| 3 | hhs1 | 201404 | 1896 |
# Wikipedia: Article page view counts
epidata.pub_wiki(
articles="influenza",
time_type="day",
time_values=EpiRange(20200101, 20200105),
).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:1801: UserWarning: `pub_wiki` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_wiki")
| article | date | count | total | hour | value | |
|---|---|---|---|---|---|---|
| 0 | influenza | 2020-01-01 | 676 | 82359844 | -1 | 8.207883 |
| 1 | influenza | 2020-01-02 | 1171 | 105590677 | -1 | 11.089994 |
| 2 | influenza | 2020-01-03 | 1127 | 106237989 | -1 | 10.608258 |
| 3 | influenza | 2020-01-04 | 967 | 91328987 | -1 | 10.588095 |
| 4 | influenza | 2020-01-05 | 880 | 98834572 | -1 | 8.903767 |
# COVID-19 hospitalizations: State-level timeseries
epidata.pub_covid_hosp_state_timeseries(states="pa", dates=EpiRange(20210101, 20210105)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:514: UserWarning: `pub_covid_hosp_state_timeseries` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_covid_hosp_state_timeseries")
| 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 | PA | 2024-05-03 | 2021-01-01 | True | True | True | True | True | True | 98 | 223 | 37255 | 229 | 27759 | 229 | 6844 | 223 | 620 | 229 | 528 | 223 | 5 | 222 | 35 | 222 | 3124 | 229 | 1261 | 223 | 1243 | 229 | 6764 | 223 | 5837 | 229 | 80 | 222 | 43 | 222 | 4008 | 229 | 229 | 27759 | 37255 | 223 | 6844 | 27567 | 223 | 6844 | 36862 | 223 | 1261 | 3891 | 229 | 3124 | 4008 | 0.745108 | 0.248268 | 0.185665 | 0.324081 | 0.779441 |
| 1 | PA | 2024-05-03 | 2021-01-02 | True | True | True | True | True | True | 59 | 223 | 37464 | 229 | 27509 | 229 | 6576 | 223 | 498 | 229 | 500 | 223 | 4 | 221 | 20 | 221 | 3029 | 229 | 1263 | 223 | 1239 | 229 | 6498 | 223 | 5767 | 229 | 78 | 221 | 40 | 221 | 3974 | 229 | 229 | 27509 | 37464 | 223 | 6576 | 27324 | 223 | 6576 | 37069 | 223 | 1263 | 3855 | 229 | 3029 | 3974 | 0.734278 | 0.240668 | 0.177399 | 0.327626 | 0.762204 |
| 2 | PA | 2024-05-03 | 2021-01-03 | True | True | True | True | True | True | 67 | 223 | 37144 | 229 | 27801 | 229 | 6489 | 223 | 618 | 229 | 505 | 223 | 7 | 221 | 27 | 221 | 3071 | 229 | 1273 | 223 | 1245 | 229 | 6419 | 223 | 5851 | 229 | 70 | 221 | 44 | 221 | 3945 | 229 | 229 | 27801 | 37144 | 223 | 6489 | 27608 | 223 | 6489 | 36751 | 223 | 1273 | 3826 | 229 | 3071 | 3945 | 0.748465 | 0.235041 | 0.176567 | 0.332723 | 0.778454 |
| 3 | PA | 2024-05-03 | 2021-01-04 | True | True | True | True | True | True | 62 | 199 | 35240 | 205 | 26836 | 205 | 6490 | 199 | 544 | 205 | 473 | 199 | 5 | 198 | 18 | 198 | 3087 | 205 | 1308 | 199 | 1267 | 205 | 6431 | 199 | 5940 | 205 | 59 | 198 | 42 | 198 | 3853 | 205 | 205 | 26836 | 35240 | 199 | 6490 | 26625 | 199 | 6490 | 34847 | 199 | 1308 | 3737 | 205 | 3087 | 3853 | 0.761521 | 0.243756 | 0.186243 | 0.350013 | 0.801194 |
| 4 | PA | 2024-05-03 | 2021-01-05 | True | True | True | True | True | True | 70 | 197 | 34959 | 204 | 27606 | 204 | 6673 | 198 | 673 | 204 | 661 | 198 | 7 | 196 | 34 | 196 | 3195 | 204 | 1269 | 197 | 1217 | 203 | 6597 | 198 | 5997 | 204 | 76 | 196 | 40 | 196 | 3903 | 204 | 204 | 27606 | 34959 | 198 | 6673 | 27372 | 198 | 6673 | 34562 | 197 | 1269 | 3784 | 204 | 3195 | 3903 | 0.789668 | 0.243789 | 0.193073 | 0.335359 | 0.818601 |
# COVID-19 hospitalizations: Facility lookup and by-facility timeseries
epidata.pub_covid_hosp_facility_lookup(city="southlake").df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:279: UserWarning: `pub_covid_hosp_facility_lookup` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_covid_hosp_facility_lookup")
| 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 |
epidata.pub_covid_hosp_facility(hospital_pks="100075", collection_weeks=EpiRange(20200101, 20200501)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:337: UserWarning: `pub_covid_hosp_facility` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_covid_hosp_facility")
| 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
# Delphi's ILINet forecasts (classic JSON format only)
epidata.pub_delphi(system="ec", epiweek=201501)()["epidata"]
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:828: UserWarning: `pub_delphi` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_delphi")
[{'epiweek': datetime.date(2015, 1, 4),
'forecast': {'_version': 1,
'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},
'data': {'hhs1': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 49},
'peak': {'dist': [9.1e-05,
9.1e-05,
0.606623,
0.389349,
0.003301,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.68261},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.141845,
0.166269,
0.17314,
0.160168,
0.131628,
0.096099,
0.062331,
0.03592,
0.018396,
0.008379,
0.003401,
0.001238,
0.000414,
0.000138,
5.6e-05,
3.5e-05,
3e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 2},
'x1': {'dist': [0.000299,
0.142162,
0.774656,
0.082188,
0.00015,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.57828},
'x2': {'dist': [0.008214,
0.284314,
0.611941,
0.094128,
0.000857,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.2778},
'x3': {'dist': [0.009499,
0.316014,
0.599539,
0.073896,
0.000508,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.2778},
'x4': {'dist': [0.012442,
0.28382,
0.583112,
0.118032,
0.002046,
9.3e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.26591}},
'hhs10': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 49},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
0.265469,
0.327005,
0.248962,
0.11714,
0.034076,
0.006176,
0.000762,
0.000138],
'point': 3.827},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.059974,
0.085468,
0.110244,
0.128706,
0.135998,
0.130064,
0.112583,
0.088203,
0.062546,
0.040146,
0.023328,
0.012275,
0.005855,
0.002537,
0.001006,
0.000373,
0.000139,
6e-05,
3.7e-05,
3.1e-05],
'none': 2.9e-05,
'point': 5},
'x1': {'dist': [0.000239,
0.022839,
0.330283,
0.539504,
0.10451,
0.002166,
9.5e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.09921},
'x2': {'dist': [0.003541,
0.052104,
0.255792,
0.417205,
0.227338,
0.041111,
0.0025,
0.000136,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.12097},
'x3': {'dist': [0.007661,
0.066554,
0.242833,
0.371571,
0.238944,
0.064435,
0.0073,
0.000424,
9.7e-05,
9.1e-05,
9.1e-05],
'point': 3.12097},
'x4': {'dist': [0.012907,
0.074889,
0.220362,
0.328373,
0.247992,
0.094864,
0.018384,
0.001867,
0.000177,
9.3e-05,
9.1e-05],
'point': 2.976}},
'hhs2': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 45},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.999091,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 5.836},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 52},
'x1': {'dist': [9.3e-05,
0.000729,
0.030231,
0.271193,
0.494915,
0.187903,
0.014368,
0.000294,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.471},
'x2': {'dist': [9.3e-05,
0.000968,
0.051853,
0.403065,
0.462591,
0.079156,
0.001905,
9.6e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.143},
'x3': {'dist': [0.000106,
0.004776,
0.149634,
0.55783,
0.270959,
0.01614,
0.000191,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.143},
'x4': {'dist': [0.00019,
0.012741,
0.215321,
0.542862,
0.215562,
0.01277,
0.00019,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.612}},
'hhs3': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 48},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.404385,
0.573085,
0.021793,
0.000101,
9.1e-05],
'point': 6.991},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.490594,
2.9e-05,
2.9e-05,
0.278137,
0.144772,
0.059761,
0.019573,
0.005098,
0.001071,
0.000198,
5.1e-05,
3.1e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 52},
'x1': {'dist': [9.1e-05,
9.1e-05,
0.000434,
0.081742,
0.647287,
0.265462,
0.004528,
9.3e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.59632},
'x2': {'dist': [0.000147,
0.004844,
0.089483,
0.389223,
0.407192,
0.102588,
0.006081,
0.000168,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.94511},
'x3': {'dist': [0.000578,
0.014584,
0.132912,
0.383475,
0.353809,
0.104275,
0.009727,
0.000364,
9.3e-05,
9.1e-05,
9.1e-05],
'point': 3.94511},
'x4': {'dist': [0.004678,
0.042022,
0.175175,
0.335853,
0.296666,
0.12068,
0.022576,
0.002002,
0.000164,
9.2e-05,
9.1e-05],
'point': 3.72394}},
'hhs4': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 47},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.999091,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 7.517},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 52},
'x1': {'dist': [0.011206,
0.077571,
0.245094,
0.353247,
0.232533,
0.069814,
0.009574,
0.000672,
0.000107,
9.1e-05,
9.1e-05],
'point': 3.069},
'x2': {'dist': [0.020822,
0.109403,
0.276134,
0.335043,
0.195526,
0.054814,
0.007413,
0.000557,
0.000105,
9.1e-05,
9.1e-05],
'point': 2.76},
'x3': {'dist': [0.029399,
0.129114,
0.285572,
0.318314,
0.178853,
0.05061,
0.007249,
0.000597,
0.000109,
9.1e-05,
9.1e-05],
'point': 2.76},
'x4': {'dist': [0.028264,
0.153558,
0.345858,
0.323968,
0.126162,
0.020365,
0.001425,
0.000126,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.516}},
'hhs5': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 48},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.999091,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 6.559},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 52},
'x1': {'dist': [0.000212,
0.023922,
0.363184,
0.532228,
0.078934,
0.001065,
9.2e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.93489},
'x2': {'dist': [0.000118,
0.023279,
0.499287,
0.459099,
0.017656,
0.000107,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.84025},
'x3': {'dist': [0.015845,
0.122716,
0.346468,
0.358614,
0.136122,
0.018838,
0.001016,
0.000107,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.84025},
'x4': {'dist': [0.039492,
0.154966,
0.305849,
0.303889,
0.152003,
0.038242,
0.004885,
0.000391,
0.0001,
9.1e-05,
9.1e-05],
'point': 2.48465}},
'hhs6': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 47},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.999091],
'point': 11.31},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 51},
'x1': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.4e-05,
0.001179,
0.049718,
0.365378,
0.471076,
0.107979,
0.004188,
0.000115],
'point': 6.96399},
'x2': {'dist': [9.1e-05,
0.000115,
0.00073,
0.008599,
0.056821,
0.190236,
0.32153,
0.274588,
0.118447,
0.025795,
0.003048],
'point': 6.481},
'x3': {'dist': [0.000843,
0.004469,
0.018511,
0.056139,
0.123467,
0.196609,
0.226625,
0.189082,
0.114196,
0.04994,
0.020118],
'point': 6.481},
'x4': {'dist': [0.003679,
0.012878,
0.035858,
0.078626,
0.135475,
0.18333,
0.194822,
0.162579,
0.106546,
0.054849,
0.031356],
'point': 5.83978}},
'hhs7': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 48},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.999091,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 6.538},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 52},
'x1': {'dist': [9.1e-05,
0.000104,
0.003659,
0.117105,
0.514204,
0.333217,
0.030895,
0.000451,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.86797},
'x2': {'dist': [0.000122,
0.002243,
0.042668,
0.247951,
0.435037,
0.232378,
0.037447,
0.001856,
0.000115,
9.1e-05,
9.1e-05],
'point': 4.71607},
'x3': {'dist': [0.000158,
0.003827,
0.060307,
0.289898,
0.425797,
0.192192,
0.02638,
0.001155,
0.000103,
9.1e-05,
9.1e-05],
'point': 4.71607},
'x4': {'dist': [0.000465,
0.013008,
0.129955,
0.390634,
0.357538,
0.099506,
0.008331,
0.000289,
9.2e-05,
9.1e-05,
9.1e-05],
'point': 3.80664}},
'hhs8': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 49},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.999091,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.449},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 53},
'x1': {'dist': [0.000122,
0.0125,
0.30124,
0.584134,
0.100241,
0.001306,
9.2e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.30456},
'x2': {'dist': [0.000444,
0.045118,
0.456115,
0.45321,
0.044226,
0.000432,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.06773},
'x3': {'dist': [0.00038,
0.06721,
0.60625,
0.317294,
0.008314,
9.8e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.06773},
'x4': {'dist': [0.001436,
0.149783,
0.674095,
0.172228,
0.001912,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 2.602}},
'hhs9': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 51},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.572685,
0.42446,
0.002128,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.702},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.251052,
2.9e-05,
0.289579,
0.22157,
0.135234,
0.065842,
0.025579,
0.007939,
0.001981,
0.000413,
8.9e-05,
3.6e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 53},
'x1': {'dist': [9.1e-05,
0.000139,
0.023828,
0.450986,
0.492724,
0.031697,
0.000171,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.958},
'x2': {'dist': [0.000119,
0.002981,
0.0662,
0.348026,
0.438913,
0.133656,
0.00959,
0.000241,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.055},
'x3': {'dist': [0.000205,
0.006095,
0.086272,
0.348737,
0.407087,
0.137609,
0.01328,
0.000439,
9.3e-05,
9.1e-05,
9.1e-05],
'point': 4.055},
'x4': {'dist': [0.000131,
0.004162,
0.085448,
0.392373,
0.412594,
0.099508,
0.005362,
0.000149,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 3.871}},
'nat': {'onset': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.999029,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 47},
'peak': {'dist': [9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05,
0.999091,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 6.08},
'peakweek': {'dist': [2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
2.9e-05,
0.266381,
2.9e-05,
2.9e-05,
0.222786,
0.177498,
0.130045,
0.087619,
0.05429,
0.030939,
0.016221,
0.007828,
0.003483,
0.001436,
0.000556,
0.00021,
8.6e-05,
4.5e-05,
3.3e-05,
3e-05,
2.9e-05,
2.9e-05,
2.9e-05],
'none': 2.9e-05,
'point': 52},
'x1': {'dist': [9.1e-05,
9.1e-05,
0.005016,
0.448752,
0.536121,
0.009474,
9.2e-05,
9.1e-05,
9.1e-05,
9.1e-05,
9.1e-05],
'point': 4.05598},
'x2': {'dist': [9.1e-05,
0.0001,
0.027235,
0.639516,
0.329868,
0.002735,
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'}]
# Delphi's ILI Nearby nowcasts
epidata.pub_nowcast(locations="ca", epiweeks=EpiRange(202201, 202210)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:1550: UserWarning: `pub_nowcast` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_nowcast")
| 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 |
| 5 | ca | 202206 | 1.4663 | 0.280395 |
| 6 | ca | 202207 | 1.31492 | 0.280542 |
| 7 | ca | 202208 | 1.12478 | 0.280985 |
| 8 | ca | 202209 | 1.14382 | 0.280941 |
| 9 | ca | 202210 | 1.14812 | 0.280612 |
International endpoints¶
# PAHO Dengue: Surveillance in the Americas
epidata.pub_paho_dengue(regions="ca", epiweeks=EpiRange(202001, 202004)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:1594: UserWarning: `pub_paho_dengue` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_paho_dengue")
| release_date | region | serotype | epiweek | issue | lag | total_pop | num_dengue | num_severe | num_deaths | incidence_rate | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020-08-07 | CA | 202001 | 202032 | 31 | 0 | 0 | 0 | 0 | 0.0 | |
| 1 | 2020-08-07 | CA | 202002 | 202032 | 30 | 0 | 0 | 0 | 0 | 0.0 | |
| 2 | 2020-08-07 | CA | 202003 | 202032 | 29 | 0 | 0 | 0 | 0 | 0.0 | |
| 3 | 2020-08-07 | CA | 202004 | 202032 | 28 | 0 | 0 | 0 | 0 | 0.0 |
# ECDC ILI: Influenza-like illness in Europe
epidata.pub_ecdc_ili(regions="austria", epiweeks=EpiRange(201901, 201904)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:951: UserWarning: `pub_ecdc_ili` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_ecdc_ili")
| region | release_date | issue | epiweek | lag | incidence_rate | |
|---|---|---|---|---|---|---|
| 0 | Austria | 2020-03-26 | 202012 | 201902 | 62 | 786.877545 |
| 1 | Austria | 2020-03-26 | 202012 | 201903 | 61 | 855.386784 |
| 2 | Austria | 2020-03-26 | 202012 | 201904 | 60 | 1022.364217 |
# KCDC ILI: Influenza-like illness in South Korea
epidata.pub_kcdc_ili(regions="ROK", epiweeks=EpiRange(201801, 201804)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:1358: UserWarning: `pub_kcdc_ili` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_kcdc_ili")
| release_date | region | issue | epiweek | lag | ili | |
|---|---|---|---|---|---|---|
| 0 | 2020-11-03 | ROK | 202045 | 201801 | 148 | 71.8 |
| 1 | 2020-11-03 | ROK | 202045 | 201802 | 147 | 72.1 |
| 2 | 2020-11-03 | ROK | 202045 | 201803 | 146 | 69.0 |
| 3 | 2020-11-03 | ROK | 202045 | 201804 | 145 | 59.6 |
# Taiwan CDC NIDSS: Influenza outpatient visits
epidata.pub_nidss_flu(regions="nationwide", epiweeks=EpiRange(201801, 201804)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:1479: UserWarning: `pub_nidss_flu` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_nidss_flu")
| release_date | region | epiweek | issue | lag | visits | ili | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-03-12 | Nationwide | 201801 | 201810 | 9 | 84227 | 1.71 |
| 1 | 2018-03-12 | Nationwide | 201802 | 201810 | 8 | 86865 | 1.71 |
| 2 | 2018-03-12 | Nationwide | 201803 | 201810 | 7 | 111976 | 2.03 |
| 3 | 2018-03-12 | Nationwide | 201804 | 201810 | 6 | 117080 | 2.15 |
# Taiwan CDC NIDSS: Dengue cases
epidata.pub_nidss_dengue(locations="nationwide", epiweeks=EpiRange(201801, 201804)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:1437: UserWarning: `pub_nidss_dengue` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_nidss_dengue")
| location | epiweek | count | |
|---|---|---|---|
| 0 | nationwide | 201801 | 0 |
| 1 | nationwide | 201802 | 2 |
| 2 | nationwide | 201803 | 2 |
| 3 | nationwide | 201804 | 0 |
# PAHO Dengue Nowcasts: Delphi nowcast estimates for the Americas
epidata.pub_dengue_nowcast(locations="ca", epiweeks=EpiRange(201501, 201504)).df()
/home/runner/work/epidatpy/epidatpy/epidatpy/_endpoints.py:857: UserWarning: `pub_dengue_nowcast` covers a data source that is no longer updated. Historical data remains available, but no new data is being ingested. See the "Endpoints kept for historical reference" section of the migration guide (https://cmu-delphi.github.io/epidatpy/migration_guide.html#endpoints-kept-for-historical-reference) for details.
_note_frozen_endpoint("pub_dengue_nowcast")
| location | epiweek | value | std | |
|---|---|---|---|---|
| 0 | ca | 201501 | 0.0 | 1.0 |
| 1 | ca | 201502 | 0.0 | 1.0 |
| 2 | ca | 201503 | 0.0 | 1.0 |
| 3 | ca | 201504 | 0.0 | 1.0 |
Private endpoints¶
Some private endpoints require a dedicated secret key passed via the auth
argument (separate from the standard Epidata API key). Store these in
environment variables or a .env file rather than in code. These examples are
not executed here:
import os
# CDC Web Metrics: Website traffic for select topics
epidata.pvt_cdc(
auth=os.environ["SECRET_API_AUTH_CDC"],
locations="ma",
epiweeks=EpiRange(202003, 202304),
).df()
# Digital Surveillance Sensors: Delphi sensor estimates
epidata.pvt_sensors(
auth=os.environ["SECRET_API_AUTH_SENSORS"],
names="delphi",
locations="nat",
epiweeks=EpiRange(202001, 202010),
).df()
# Twitter / HealthTweets: Influenza and total tweet counts
epidata.pvt_twitter(
auth=os.environ["SECRET_API_AUTH_TWITTER"],
locations="hhs1",
time_type="day",
time_values=EpiRange(20200101, 20200115),
).df()
# Google Health Trends: Search queries
epidata.pvt_ght(
auth=os.environ["SECRET_API_AUTH_GHT"],
locations="ca",
query="cough",
epiweeks=EpiRange(202001, 202003),
).df()
# CDC NoroSTAT: Norovirus outbreak data
epidata.pvt_norostat(
auth=os.environ["SECRET_API_AUTH_NOROSTAT"],
location="midatl",
epiweeks=EpiRange(202001, 202010),
).df()
# NoroSTAT metadata
epidata.pvt_meta_norostat(auth=os.environ["SECRET_API_AUTH_NOROSTAT"]).df()
# PAHO Dengue Sensors: Digital dengue surveillance
epidata.pvt_dengue_sensors(
auth=os.environ["SECRET_API_AUTH_DENGUE_SENSORS"],
names="ght",
locations="ca",
epiweeks=EpiRange(202001, 202010),
).df()
# Quidel: Influenza testing
epidata.pvt_quidel(
auth=os.environ["SECRET_API_AUTH_QUIDEL"],
locations="hhs1",
epiweeks=EpiRange(202001, 202010),
).df()